JP2015072637A - Program, information processing device, and information processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a program or the like capable of determining data attributes with high accuracy.SOLUTION: A computer extracts object data to be an analysis object from a data group. The computer determines attributes to be a candidate for the object data and evaluation values of the attributes on the basis of contents of the extracted data, data adjacent to the object data and a position. Further, the computer extracts a position at which the object data is arranged, refers to a storage part storing the attributes and the evaluation values in association with a position at which data is arranged, reads the attributes and the evaluation values of the object data, and determines attributes to be a candidate for the object data and evaluation values of the attributes on the basis of the read attributes and evaluation values.

Description

  The present invention relates to a program for causing a computer to execute various processes, an information processing apparatus, and an information processing method.

  Conventionally, when a table is described in a document or various forms, a table processing method that arranges and reads necessary data from image data of the table has been proposed (see, for example, Patent Document 1).

Japanese Patent Laid-Open No. 10-116314

  However, the conventional technique cannot accurately determine the data attribute.

  In one aspect, an object of the present invention is to provide a program or the like that can accurately determine data attributes.

  The program disclosed in the present application extracts target data to be analyzed from a data group to a computer, and based on the content of the extracted target data, data adjacent to the target data, and the attribute that is a candidate for the target data And processing for determining the evaluation value of the attribute.

  In one aspect, data attributes can be determined with high accuracy.

It is a block diagram which shows the hardware group of information processing apparatus. It is explanatory drawing which shows the image of form data. It is explanatory drawing which shows a part of form data. It is explanatory drawing which shows the record layout of a position table. It is explanatory drawing which shows the record layout of an adjacent table. It is explanatory drawing which shows the record layout of a numerical value table. It is explanatory drawing which shows the record layout of a keyword table. It is explanatory drawing which shows the record layout of a target data table. It is a flowchart which shows the extraction process procedure of object data. It is a flowchart which shows the extraction process procedure of object data. It is a flowchart which shows the procedure of the attribute based on a position, and evaluation value determination processing. It is a flowchart which shows the procedure of the attribute and evaluation value determination process based on the presence or absence of adjacent object data. It is a flowchart which shows the procedure of the attribute and evaluation value determination process based on the character and number content of object data. It is a flowchart which shows the procedure of the attribute and evaluation value determination process based on the character and number content of object data. It is a flowchart which shows the calculation process procedure of a total evaluation value. It is a flowchart which shows the determination processing procedure of object data name. It is a flowchart which shows the determination processing procedure of object data name. It is explanatory drawing which shows the image of form data. It is explanatory drawing which shows the record layout of the object data table which concerns on Embodiment 2. FIG. It is a flowchart which shows the display processing procedure of a setting tab. It is a flowchart which shows the correction process procedure of an evaluation value. It is a flowchart which shows the correction process procedure of an evaluation value. It is a functional block diagram which shows operation | movement of the computer of the form mentioned above. FIG. 10 is a block diagram illustrating a hardware group of a computer according to a third embodiment.

Embodiment 1
Hereinafter, embodiments will be described with reference to the drawings. FIG. 1 is a block diagram illustrating a hardware group of the information processing apparatus 1. The information processing apparatus 1 is, for example, a personal computer, a server computer, a mobile phone, a smartphone, a game machine, or a PDA (Personal Digital Assistant). Hereinafter, an example in which the information processing apparatus 1 is applied to a personal computer 1 (hereinafter referred to as a computer 1) will be described. The computer 1 includes a central processing unit (CPU) 11 as a control unit, a random access memory (RAM) 12, an input unit 13, a display unit 14, a clock unit 18, a storage unit 15, a communication unit 16, and the like. The CPU 11 is connected to each part of the hardware via the bus 17. The CPU 11 controls each part of the hardware according to the control program 15P stored in the storage unit 15. The RAM 12 is, for example, SRAM (Static RAM), DRAM (Dynamic RAM), flash memory, or the like. The RAM 12 also functions as a storage unit, and temporarily stores various data generated when the CPU 11 executes various programs.

  The input unit 13 is an input device such as a mouse or a keyboard, a mouse or a touch panel, and outputs received operation information to the CPU 11. The display unit 14 is a liquid crystal display, an organic EL (electroluminescence) display, or the like, and displays various information according to instructions from the CPU 11. The communication unit 16 is a communication module, and transmits / receives information to / from another computer (not shown) via a communication network such as the Internet or a public line network.

  The clock unit 18 outputs the date and time to the CPU 11. The storage unit 15 is a hard disk or a large-capacity memory, and includes a position table 151, an adjacent table 152, a numerical table 153, a keyword table 154, a target data table 155, and the like in addition to the control program 15P described above. In the present embodiment, an example in which various tables are stored in the storage unit 15 of the computer 1 is described, but the present invention is not limited to this. For example, the various tables described above may be stored as appropriate in a database server (not shown) connected via a communication network. In this case, the CPU 11 accesses the database server as necessary, and writes and reads data. The CPU 11 reads a data group from the storage unit 15 and analyzes each data according to the control program 15P. Details will be described below.

  FIG. 2 is an explanatory diagram showing an image of the form data. The form data is, for example, a data group displayed by spreadsheet software such as Excel (registered trademark), document input software, or file browsing software such as PDF (Portable Document Format: registered trademark). Various data groups such as titles, items, and numerical data are described in a text format in the table. The example of FIG. 2 shows the individual employment performance of employees in the company. The “Personal Employment Record” displayed in the upper center is the title of the book data. In the lower left corner of the title is the staff name “Nippon Hanako” and unique identification information for identifying “Nippon Hanako” ID)).

  The “second development department” in the upper right corner indicates the department to which the staff belongs. The table below the staff name shows items such as attendance, leaving work, scheduled work (hereinafter referred to as designated work), overtime, holiday work, etc. Numerical data such as predetermined hours, overtime hours, and holiday work hours are described. On the lower side, numerical data relating to the total of predetermined hours, overtime hours, holiday work hours, etc. are described in association with the item “total”. The CPU 11 analyzes the form data stored in the storage unit 15 or received via the communication unit 16. In the present embodiment, the form data is targeted, but the present invention is not limited to this. It may be medical chart data, financial data, or various statistical data.

  The CPU 11 reads form data for one line. In the case of the first line, “individual employment record” and “second development department” are read. The CPU 11 sequentially extracts words in one line. Hereinafter, as an example, a process in which the CPU 11 extracts “individual employment performance” and determines the attribute and evaluation value of “individual employment performance” will be described. In addition, what is necessary is just to judge that the extraction of a word is a word division | segmentation, when the distance between characters is more than the predetermined distance memorize | stored in the memory | storage part 15, or when a ruled line exists between characters. The distance between words may be an appropriate length (the number of coordinates or the number of pixels) according to the font size. Hereinafter, data to be analyzed among titles, items, numerical data, and the like divided between words by character recognition by the CPU 11 is referred to as target data.

  FIG. 3 is an explanatory diagram showing a part of the form data. For the items “leaving work”, “within a predetermined time”, and “overtime”, numerical data of the work time, the predetermined time, and the overtime time are stored. For example, in the numerical data in the first column, it can be understood that the working time is 17:15, the predetermined time is 8 hours, and the overtime time is 0 hours. The second target data will be described by taking the overtime “0.00” in the first column of numerical data as an example. The CPU 11 determines the attribute and evaluation value of the numerical data “0.00”. The CPU 11 sequentially extracts words for one line. The CPU 11 detects 17:15, 8.00, and 0.00, and extracts and analyzes 0.00. It is assumed that the attributes and evaluation values have already been analyzed for the target data “within predetermined” on the upper left, the target data “8.00” adjacent on the left, and the target data “overtime” adjacent on the upper side.

  FIG. 4 is an explanatory diagram showing a record layout of the position table 151. The position table 151 includes a coordinate area field, an attribute field, an evaluation value field, and the like. The coordinate area field stores the coordinates of the area in which the target data in the form is described. In this embodiment, the upper left corner of the form file is the origin, the rightward direction is the X-axis positive direction, and the downward direction is the Y-axis positive direction. The coordinate area may store the upper left coordinates and lower right coordinates of the area where the target data exists.

  In the attribute field, attributes are stored in association with coordinate areas. The attribute indicates the property or characteristic of the target data. In this embodiment, titles, items, numerical data, affiliations, and the like are used as examples. As will be described later, numerical data further includes time, telephone number, price, staff ID, time, number of days, date, numerical value, year, etc. as lower attributes. The evaluation value field stores an evaluation value that is a value for evaluating the likelihood of the attribute of the target data in association with the coordinate area. In the present embodiment, the description will be made assuming that the greater the value, the more accurate the attribute.

  The contents of the position table 151 can be appropriately set from the input unit 13. For example, a title is often written in a coordinate area near the upper middle stage. Therefore, when the target data exists in the area, the attribute is determined as “title” and the evaluation value is determined as “3”. CPU11 calculates | requires the center point or area | region of object data. The CPU 11 refers to the position table 151 and extracts a coordinate area to which the center point belongs. The CPU 11 reads the attribute and evaluation value corresponding to the coordinate area. For example, in the case of the above-mentioned “individual work performance”, the CPU 11 reads the attributes “title” and “evaluation value” from the center point and the coordinate area. In the case of the target data “0.00”, the corresponding attribute “numerical data” and evaluation value “1” are read from the center point and the coordinate area. In the present embodiment, an example in which the center point of the target data is used has been described. However, the present invention is not limited to this, and arbitrary coordinates of the target data may be used. The CPU 11 counts the number of overlapping coordinates between the coordinate area of the target data and the coordinate area stored in the coordinate area field of the position table, and selects an attribute and an evaluation value corresponding to the coordinate area having the largest count value. You may do it.

  FIG. 5 is an explanatory diagram showing a record layout of the adjacent table 152. The adjacent table 152 includes a left adjacent target data field, an upper left adjacent target data field, an upper adjacent target data field, an attribute evaluation value field, and the like. Attributes and evaluation values are stored in association with the presence / absence of adjacent target data (hereinafter abbreviated as adjacent target data in some cases). For example, when there is no adjacent target data anywhere on the left, upper left, and above, there is a high possibility of being a title, and therefore an evaluation value “1.0” is stored in the attribute “title”. In the present embodiment, the form using the presence / absence of adjacent target data on the left, upper left and upper is described, but the present invention is not limited to this. In addition to using the adjacent target data in all directions, any data that uses at least one adjacent target data such as the upper right adjacent target data may be used.

  In the case of the above-mentioned “individual work record”, the CPU 11 reads the attribute “title” and the evaluation value “1.0” because there is no adjacent target data. In the case of “0.00”, the left, upper left, and upper adjacent target data exist, so the CPU 11 reads the attribute “numerical data” and the evaluation value “1.0”. Note that the contents of the adjacent table 152 can be changed as appropriate from the input unit 13.

  FIG. 6 is an explanatory diagram showing a record layout of the numerical value table 153. The numerical value table 153 includes a display format field, an attribute field, an evaluation value field, and the like. In the display format field, numerical values, symbols, conditions, and the like are stored. The attribute field stores time, telephone number, price, staff ID, time, number of days, and the like, which are lower attributes of the attribute “numerical data”, in association with the display format. An evaluation value is stored in the evaluation value field in association with the display format. For example, in the case of target data including the display format “:”, since the possibility of time is high, the attribute is stored as “time” and the evaluation value is stored as “2”.

  Further, the display condition “: numeric value to the right of“: 60 or less ”may be, for example, 2:56 when“ 56 ”of numeric data“ 2:56 ”satisfies the condition of 60 or less. Is high, the attribute is “time”, and the evaluation value is “1”. In addition, when the target data includes “am” and “pm”, the attribute is determined to be “time”. When the display format “-” is included, since it is highly likely that the number is a telephone number, the attribute is stored as “telephone number” and the evaluation value is stored as “1”. In addition, the condition “first number is 0” related to the display format, for example, in the case of numerical data “090-XXX-XXX”, there is a high possibility of indicating a telephone number because the condition is satisfied, and the attribute is “phone number”, The evaluation value is stored as “1”.

  When the target data includes the display format “$” or “¥”, since the possibility of the price is high, the attribute is stored as “price” and the evaluation value is stored as “3”. When the target data includes the display format “ID”, the attribute “staff ID” and the evaluation value “3” are stored because there is a high possibility of the staff ID. When the target data includes the display format “.”, The attribute is stored as “hour” or “days” because the possibility of being the time or the number of days is high, and the evaluation values are stored as “2” and “1”, respectively. ing.

  The CPU 11 determines whether the target data includes a numerical value. The CPU 11 divides the extracted target data for each character. The CPU 11 counts the total number of divided characters and the number of numerical values. When the ratio of the numerical value to the total number is 60% or more, the CPU 11 determines that the target data is numerical data. For example, “2:50” is numeric data because the number is 75% and the symbol is 25%. On the other hand, the “2013 plan table” is not determined as numerical data because the numerical ratio is as low as 25%. An example of determining whether the target data is numerical data based on a numerical ratio will be described. However, the present invention is not limited to this. When a predetermined number of numerical values exist, the target data may be determined as numerical data.

  In the case of the above “individual work performance”, the CPU 11 does not determine that the data is numerical data. In the case of “0.00” described above, the CPU 11 determines that the numerical value data because the ratio of numerical values is higher than a predetermined value. Then, the CPU 11 refers to the numerical value table 153 and determines whether there is data that matches the display format. Since the example of FIG. 6 includes “.”, The attribute is “time” and the evaluation value is “2”. The attribute is “days” and the evaluation value is “1”. That is, the first candidate of the attribute of the target data “0.00” is the time, and the second candidate is the number of days. Note that the storage contents of the numerical value table 153 can be set to appropriate values from the input unit 13.

  FIG. 7 is an explanatory diagram showing a record layout of the keyword table 154. The keyword table 154 includes a keyword field, an attribute field, an evaluation value field, and the like. In the keyword field, keywords included in the target data are stored. For example, working results, business divisions, overtime, Yamada, etc. In the attribute field, attributes such as title, affiliation, item, and name are stored in association with the keyword. In the evaluation value field, an evaluation value is stored in association with the keyword. When the CPU 11 determines that the target data is not numerical data, the CPU 11 refers to the keyword table 154 and determines whether or not a keyword is included in the target data.

  When the CPU 11 determines that the keyword is included, the CPU 11 reads the attribute and evaluation value corresponding to the keyword. For example, in the case of the above “individual work record”, the CPU 11 reads the attribute “title” and the evaluation value “3” corresponding to the keyword “work record”.

  FIG. 8 is an explanatory diagram showing a record layout of the target data table 155. In the target data ID field, unique identification information (hereinafter referred to as target data ID) for specifying the target data is stored. In the extracted data field, target data extracted by character recognition is stored in association with the target data ID. In the target data name field, the name of the target data (hereinafter referred to as the target data name) is stored in association with the tag ID. In the attribute field, candidate attributes are stored in association with the target data ID. In the total evaluation value field, the total value of the evaluation values determined by the above-described processing is stored in association with the target data ID and the attribute. In other words, the CPU 11 calculates the total value of the attribute evaluation values based on the data contents, the position of the target data, and the attributes and evaluation values determined according to the presence or absence of target data to be laid. In the above-described example of “individual work performance”, the CPU 11 stores the total value of evaluation values when the attribute “title” is determined by the above-described processing in the total evaluation value field. Similarly, the CPU 11 stores the total value of the evaluation values when “item” is determined by the above-described processing in the total evaluation value field.

  In the example of “0.00” described above, the CPU 11 stores the total value of the evaluation values when the attribute “time” is determined by the above-described processing in the total evaluation value field. Similarly, the CPU 11 stores the total value of the evaluation values when the attribute “days” is determined by the above-described processing in the total evaluation value field. Similarly, the CPU 11 stores the total value of the evaluation values when the “money amount” is determined by the above-described processing in the total evaluation value field. When the CPU 11 determines that the attribute is “numerical data”, the attribute is assigned to the evaluation values of the subordinate attributes “time”, “phone number”, “price”, “staff ID”, “time”, and “days”. Add the attribute value determined to be “numeric data”. In addition, CPU11 should just sort and memorize | store in order with the highest total evaluation value, when a some attribute exists.

  In the upper field, the left field, and the upper left field, the presence or absence of target data adjacent to the target data on the left or upper left, and the corresponding target data ID, if present, are stored in association with the target data ID. Yes. In the “individual work record” of the target data ID “1”, since there is no adjacent target data, “none” is stored. On the other hand, when the target data ID “18” is “0.00”, there is adjacent target data, so the upper field is the target data ID “10”, the left field is the target data ID “17”, and the upper left field is the target data ID “18”. Is stored as the target data ID “9”. In the coordinate area field, the coordinate area occupied by the target data on the file is stored in association with the target data ID.

  Next, the target data name determination process will be described. CPU11 produces | generates the target data name containing the said target data, when the attribute with the highest total evaluation value is a title, an item, or a name among the attributes of the extracted target data. For example, the attribute of the target data ID “01” is “title”. In this case, the extracted target data “individual employment performance” is stored in the target data table 155 as the target data name “individual employment performance”. Further, since the attribute of the target data ID “9” is an item, the extracted target data “within a predetermined” is stored as the target data name.

  On the other hand, when the attribute with the highest total evaluation value is the time, the number of days, or the amount of money among the extracted attributes of the target data, the CPU 11 determines the target data name based on the target data name of adjacent data. For example, the attribute of the target data ID “18” is “time”. In this case, the target data name “overtime” including the target data name “overtime” of the adjacent target data (target data ID “10”) is used.

  The CPU 11 uses one of the target data names when the target data name exists above or to the left of the target data names of the adjacent target data. For example, in FIG. 2, the object data “0.00” below the object data ID “18” has the attribute “time” and the object data name exists above the CPU 11. Overtime ”. In addition, when the attribute “time” is “119.00” on the right side of the target data “total” existing in the lower part of FIG. 2, the same target data name “total” as the target data name “total” on the left is stored. Note that, when the target data names exist on both the upper and left sides, the CPU 11 may use any one of the predetermined target data names. If there is no target data in all of the top, left, and top left, it may be set as unknown. In this embodiment, an example using the above target data name will be described.

  Various software processing procedures in the above hardware group will be described with reference to flowcharts. 9 and 10 are flowcharts showing the target data extraction processing procedure. The CPU 11 scans from the upper left row and extracts target data (step S91). CPU11 produces | generates object data ID, when new object data is extracted (step S92). The CPU 11 stores the generated target data ID in the target data table 155 (step S93). The CPU 11 stores the extracted target data in the target data table 155 (step S94).

  The CPU 11 stores the coordinate area of the target data in the target data table 155 (step S95). The CPU 11 determines whether or not there is target data adjacent above (step S96). Specifically, the CPU 11 refers to the coordinate area of the target data table 155 and extracts a target data ID that includes a predetermined range in the negative Y-axis direction in the coordinate area with respect to the coordinate area of the target data. In addition, target data IDs adjacent to each other in the coordinate area may be extracted, and target data IDs adjacent in the X-axis direction may be finally extracted as target data IDs adjacent on the upper side. If the CPU 11 determines that there is target data adjacent above (YES in step S96), the process proceeds to step S97. The CPU 11 stores the target data ID of the target data adjacent above in the target data table 155 (step S97). On the other hand, if it is determined that there is no target data adjacent above (NO in step S96), step S97 is skipped and the process proceeds to step S98.

  The CPU 11 determines whether there is target data adjacent to the left (step S98). Specifically, the CPU 11 refers to the coordinate area of the target data table 155, and extracts a target data ID that includes a predetermined range in the negative X-axis direction in the coordinate area of the target target data coordinate area. If the CPU 11 determines that there is target data adjacent to the left (YES in step S98), the process proceeds to step S99. The CPU 11 stores the target data ID of the target data adjacent to the left in the target data table 155 (step S99). On the other hand, if it is determined that there is no target data adjacent to the left (NO in step S98), step S99 is skipped, and the process proceeds to step S101.

  The CPU 11 determines whether there is adjacent target data on the upper left (step S101). Specifically, the CPU 11 refers to the coordinate area of the target data table 155, and the target data ID includes a predetermined range in the negative X-axis direction and negative Y-axis direction in the coordinate area with respect to the target coordinate area of the target data. To extract. If the CPU 11 determines that there is target data adjacent to the upper left (YES in step S101), the process proceeds to step S102. The CPU 11 stores the target data ID of the target data adjacent to the upper left in the target data table 155 (step S102). On the other hand, if it is determined that there is no target data adjacent to the upper left (NO in step S101), step S102 is skipped, and the series of processes is terminated. CPU11 performs the above process with respect to all the extracted object data.

  FIG. 11 is a flowchart showing a procedure of position-based attribute and evaluation value determination processing. The CPU 11 reads the coordinate area from the target data table 155 (step S111). The CPU 11 calculates the center point of the coordinate area (step S112). Specifically, the CPU 11 sets the average value of the upper left X coordinate and the lower right X coordinate as the X coordinate of the center point, and sets the average value of the upper left Y coordinate and the lower right Y coordinate as the Y coordinate of the center point. That's fine. The CPU 11 refers to the position table 151 and extracts a coordinate area to which the center point belongs (step S113).

  CPU11 reads the attribute and evaluation value corresponding to the extracted coordinate area (step S114). The CPU 11 stores the attribute and evaluation value in the RAM 12 in association with the target data ID (step S115). The CPU 11 determines whether or not the processing has been completed for all the target data IDs stored in the target data table 155 in the above-described processing (step S116). If the CPU 11 determines that all the processes have not been completed (NO in step S116), the process proceeds to step S117. The CPU 11 reads the coordinate area of the next target data ID from the target data table 155 (step S117). CPU11 returns a process to step S112 after that. By repeating the above processing, attributes and evaluation values for all target data IDs are determined. If the CPU 11 determines that the process has been completed for all target data IDs (YES in step S116), the series of processes is terminated.

  FIG. 12 is a flowchart showing a procedure of attribute and evaluation value determination processing based on the presence / absence of adjacent target data. The CPU 11 reads the target data ID from the target data table 155 (step S121). The CPU 11 reads the adjacent target data ID corresponding to the read target data ID from the target data table 155 (step S122). The CPU 11 refers to the adjacent table 152 and reads the corresponding attribute and evaluation value (step S123). Specifically, the CPU 11 reads the corresponding attribute and evaluation value from the adjacency table 152 based on the presence / absence of adjacency target data.

  The CPU 11 stores the attribute and the evaluation value in the RAM 12 in association with the target data ID (step S124). When there are a plurality of attribute candidates, each attribute and evaluation value may be stored. For example, if the left adjacent target data and the upper left adjacent target data do not exist, but only the upper adjacent data exists, “attribute numeric data, evaluation value 0.7” and “attribute are associated with the target data ID. Item, evaluation value 0.3 "is stored.

  The CPU 11 determines whether or not the processing has been completed for all the target data IDs stored in the target data table 155 in the above-described processing (step S125). If the CPU 11 determines that all the processes have not been completed (NO in step S125), the process proceeds to step S126. The CPU 11 reads the next target data ID from the target data table 155 (step S126). CPU11 returns a process to step S122 after that. By repeating the above processing, attributes and evaluation values for all target data IDs are determined. If the CPU 11 determines that the processing has been completed for all the target data IDs (YES in step S125), the series of processing ends.

  FIG. 13 and FIG. 14 are flowcharts showing the procedure of attribute and evaluation value determination processing based on the character and number contents of the target data. The CPU 11 reads out the target data ID and the target data from the target data table 155 (step S131). CPU11 calculates the ratio which the number of object data occupies (step S132). The CPU 11 reads out the threshold value stored in the storage unit 15 (step S133). The CPU 11 determines whether or not the calculated ratio is greater than or equal to a threshold value (step S134).

  If the CPU 11 determines that it is equal to or greater than the threshold (YES in step S134), the CPU 11 recognizes that the target data is numerical data, and proceeds to step S135. The CPU 11 refers to the numerical value table 153, and extracts attributes and evaluation values that satisfy the target data (step S135). Specifically, the CPU 11 determines whether numbers, symbols, or characters constituting the target data are included in the display format field of the numerical value table 153. Further, the CPU 11 determines whether or not the numbers, symbols, or characters constituting the target data satisfy the conditions described in the display format field. When the CPU 11 determines that it is included in the display format field or determines that the condition is satisfied, the CPU 11 extracts the corresponding attribute and evaluation value.

  The CPU 11 stores the attribute and the evaluation value in the RAM 12 in association with the target data ID read in step S131 (step S136). If the CPU 11 determines that it is not equal to or greater than the threshold value (NO in step S134), it determines that the target data is character data, and the process proceeds to step S137. The CPU 11 refers to the keyword table 154 and extracts the corresponding attribute and evaluation value (step S137). Specifically, the CPU 11 determines whether the keyword stored in the keyword field is included in the target data or matches. When the CPU 11 determines that the keyword matches or is included, the CPU 11 reads the attribute and evaluation value corresponding to the keyword.

  The CPU 11 stores the attribute and the evaluation value in the RAM 12 in association with the target data ID (step S138). After the process of step S138 or step S136, the CPU 11 determines whether or not the process has been completed for all the target data IDs stored in the target data table 155 (step S139). If the CPU 11 determines that all the processes have not been completed (NO in step S139), the process proceeds to step S141. The CPU 11 reads the next target data ID and target data from the target data table 155 (step S141). CPU11 returns a process to step S132 after that. By repeating the above processing, attributes and evaluation values for all target data IDs are determined. If the CPU 11 determines that the processing has been completed for all the target data IDs (YES in step S139), the series of processing ends.

  FIG. 15 is a flowchart showing the procedure for calculating the total evaluation value. The CPU 11 reads the attribute and evaluation value corresponding to the target data ID stored in steps S115, S124, S136, and S138 from the RAM 12 (step S151). The CPU 11 adds an evaluation value for each candidate attribute (step S152). The CPU 11 adds the evaluation value of the attribute “numerical data” to the evaluation values of the attributes “time”, “phone number”, “price”, “staff ID”, “time”, and “days” (step S153). . The CPU 11 stores the attribute and the total evaluation value in the target data table 155 (step S154). Note that the value obtained by multiplying the evaluation value of the attribute “numerical data” by a coefficient may be added. Further, the CPU 11 stores the attributes in the target data table 155 in descending order of the total evaluation value.

  The CPU 11 determines whether or not the process has been completed for all the target data IDs stored in the RAM 12 (step S155). If the CPU 11 determines that all the processes have not been completed (NO in step S155), the process proceeds to step S156. The CPU 11 reads the next target data ID, the attribute corresponding to the target data ID, and the evaluation value from the target data table 155 (step S156). CPU11 returns a process to step S152 after that. By repeating the above processing, attributes and total evaluation values for all target data IDs are determined. If the CPU 11 determines that the processing has been completed for all the target data IDs (YES in step S155), the series of processing ends. Note that the CPU 11 may determine the attribute and the evaluation value based on the presence or absence of ruled lines. When the CPU 11 determines that the ruled line exists on both sides or above and below the target data, the CPU 11 may set the attribute “numerical data” and the evaluation value “1”.

  16 and 17 are flowcharts showing the procedure for determining the target data name. The CPU 11 reads the target data ID and the attribute having the highest total evaluation value from the target data table 155 (step S161). The CPU 11 determines whether the attribute is a title, an item, or a name (step S162). If the CPU 11 determines that the attribute is a title, an item, or a name (YES in step S162), the CPU 11 proceeds to step S163. The CPU 11 copies the data in the extracted data field, that is, the target data to the target data name field (step S163).

  If the CPU 11 determines that the attribute is not a title, item, or name (NO in step S162), the CPU 11 proceeds to step S164. The CPU 11 refers to the target data table 155 and determines whether there is target data adjacent above (step S164). If the CPU 11 determines that it exists (YES in step S164), the process proceeds to step S165. The CPU 11 reads out the target data ID adjacent above (step S165). The CPU 11 reads a target data name corresponding to the read target data ID (step S166). The CPU 11 copies the read target data name to the target data name field (step S167). That is, the name is the same as the name of the target data adjacent above.

  If the CPU 11 determines that there is no adjacent target data (NO in step S164), the process proceeds to step S168. The CPU 11 determines whether there is target data adjacent on the left (step S168). If the CPU 11 determines that it exists (YES in step S168), the process proceeds to step S169. The CPU 11 reads the target data ID adjacent on the left (step S169). The CPU 11 reads a target data name corresponding to the read target data ID (step S171). The CPU 11 copies the read target data name to the target data name field (step S172). That is, the same name as the name of the target data adjacent on the left. In the present embodiment, the processing of steps S164 to S167 is performed before the processing of steps S168 to S172, or the processing of steps S168 to S172 is executed first in order to prioritize the left adjacent target data. good.

  If the CPU 11 determines that there is no target data adjacent on the left (NO in step S168), the process proceeds to step S173. The CPU 11 stores “unknown” in the target data name field (step S173). The CPU 11 determines whether or not the process has been completed for all the target data IDs stored in the RAM 12 (step S174). If the CPU 11 determines that all the processes have not been completed (NO in step S174), the process proceeds to step S175. Further, the CPU 11 shifts the processing to step S174 even after steps S163, S167, and S172.

  The CPU 11 reads the next target data ID and the attribute having the highest total evaluation value from the target data table 155 (step S175). The CPU 11 then returns the process to step S162. By repeating the above processing, target data names for all target data IDs are determined. If the CPU 11 determines that the process has been completed for all target data IDs (YES in step S174), the series of processes is terminated. Through the above processing, it is possible to specify the attribute of the extracted target data with high accuracy.

Embodiment 2
The second embodiment relates to a form in which an evaluation value is corrected according to a user's usage history. FIG. 18 is an explanatory diagram showing an image of form data. The CPU 11 receives selection of target data from the input unit 13. In the example of FIG. 18, a plurality of overtime hours for the item “overtime” are selected. In the present embodiment, an example of selecting a plurality of target data will be described. However, one target data may be selected. The CPU 11 receives a property display request for the selected target data from the input unit 13. When receiving a property display request, the CPU 11 reads the setting tab 191 from the storage unit 15 and displays it on the display unit 14 as shown in FIG.

  FIG. 19 is an explanatory diagram showing a record layout of the target data table 155 according to the second embodiment. The CPU 11 refers to the target data table 155 and reads the target data ID of the selected target data. The CPU 11 reads the target data name corresponding to the target data ID. The CPU 11 describes the target data name in the target data name box 194 on the setting tab 191. In the example of FIG. 18, “Overtime” is displayed. The CPU 11 reads the attribute corresponding to the target data ID. The CPU 11 describes the attributes read out in descending order of the total evaluation value in the attribute pull-down box 192. As shown in FIG. 18, the attribute pull-down box 192 describes “money amount” and “staff ID” starting with the attribute “time” having the highest total evaluation value. In the present embodiment, description will be made on the assumption that the target data names, attributes, and ranks of attribute total evaluation values of a plurality of selected target data match. The attribute pull-down box 192 may also display other attributes such as titles and items whose total evaluation value is zero.

  The user selects an attribute displayed in the attribute pull-down box 192 from the input unit 13. For example, the attribute “time” was initially defined, but when the attribute “amount” is desired to be changed, the attribute “amount” is selected. After selecting the attribute, the user inputs an OK button 193. The CPU 11 accepts input of the OK button 193 and input of attributes. The CPU 11 refers to the target data ID and the input attribute, and increments the number of selection times field of the target data table 155.

  The CPU 11 performs a process of reflecting the number of selections in the total evaluation value when the number of selections is updated or after a predetermined time has elapsed. The CPU 11 adds the increased number to the total evaluation value every time the number of attribute selections is updated. For example, when the attribute “time” is selected once, the total evaluation value of the attribute “time” is increased by one. The CPU 11 may multiply the coefficient and add the value after multiplication to the total evaluation value. In addition, the addition value may be calculated based on the number of selections of a plurality of attributes after a predetermined time has elapsed.

  Specifically, the CPU 11 reads the number of times of selecting the attribute having the highest total evaluation value and the number of times of selecting other attributes. CPU11 calculates | requires the value which remove | divided the selection frequency of the other attribute by the selection frequency of the attribute with the highest total evaluation value as an addition value of the said other attribute. The CPU 11 adds the obtained addition value to the total evaluation value of the other attributes. The CPU 11 calculates the total value of the number of selections of other attributes. The CPU 11 subtracts the total value from the number of selections of the attribute having the highest total evaluation value, divides the subtracted value by the number of selections of the attribute having the highest total evaluation value, and the divided value has the highest total evaluation value. The added value of the attribute. The CPU 11 adds the added value to the total evaluation value of the attribute having the highest total evaluation value. The CPU 11 rearranges the attributes in descending order of the total evaluation value after obtaining the total table value considering the number of selections.

  FIG. 20 is a flowchart showing the display processing procedure of the setting tab 191. The CPU 11 reads the personal work performance file shown in FIG. CPU11 receives selection of object data from input part 13 (Step S201). The CPU 11 determines whether a property display request for the selected target data has been received (step S202). If the CPU 11 determines that it has not been accepted (NO in step S202), it waits until it is accepted. If the CPU 11 determines that it has been received (YES in step S202), the process proceeds to step S203.

  The CPU 11 reads out the target data ID of the selected target data (step S203). The CPU 11 reads the target data name corresponding to the read target data ID (step S204). The CPU 11 reads the setting tab 191 template from the storage unit 15. The CPU 11 describes the target data name in the target data name box 194 of the setting tab 191 (step S205). The CPU 11 reads all attributes and total evaluation values corresponding to the target data ID (step S206). The CPU 11 describes the attributes in the attribute pull-down box 192 of the setting tab 191 in descending order of the total evaluation value (step S207).

  The CPU 11 displays the setting tab 191 whose description has been completed on the display unit 14 (step S208). CPU11 judges whether selection of an attribute was received from input part 13 (Step S209). If the CPU 11 determines that no attribute selection has been received (NO in step S209), the CPU 11 waits until an attribute is received. If the CPU 11 determines that the selection of the attribute has been accepted (YES in step S209), the CPU 11 increments the number of selections corresponding to the target data ID read in step S203 and the accepted attribute on the condition that the OK button 193 is input (step S209). S2010). The CPU 11 deletes the display of the setting tab 191 (step S2011).

  21 and 22 are flowcharts showing the evaluation value correction processing procedure. Hereinafter, an example in which the total evaluation value is corrected periodically (for example, every three days) will be described. The CPU 11 reads the target data ID with reference to the target data table 155 (step S211). The CPU 11 reads the attribute, total evaluation value, and number of selections corresponding to the target data ID (step S212). The CPU 11 stores the number of selections of the attribute having the highest total evaluation value (hereinafter referred to as the maximum selection number) in the storage unit 15 (step S213). The CPU 11 calculates the total value of the selection counts of other attributes excluding the maximum selection count (step S214).

  The CPU 11 subtracts the total value from the maximum number of selections (step S215). The CPU 11 divides the subtraction value by the maximum number of selections (step S216). The CPU 11 adds the divided value to the total evaluation value (step S217). Note that the divided value may be further multiplied by a coefficient. The CPU 11 overwrites the added value in the total evaluation value field in association with the attribute (step S218). The CPU 11 divides the number of selections of the attribute having the next highest total evaluation value by the maximum number of selections (step S219). The CPU 11 adds the divided value to the total evaluation value corresponding to the attribute in step S219 (step S221).

  The CPU 11 overwrites the added value in the total evaluation value field in association with the attribute (step S222). CPU11 judges whether the process of step S219-S222 was complete | finished about the total evaluation value of all the attributes (step S223). If the CPU 11 determines that the process has not ended (NO in step S223), it returns the process to step S219, and further performs a process of dividing the number of times of selection of the attribute having the next highest total value by the maximum number of times of selection. If the CPU 11 determines that the process has been completed for the total evaluation value of all attributes (YES in step S223), the process proceeds to step S224.

  The CPU 11 rearranges the attributes and the total evaluation values of the target data table 155 in descending order of the total evaluation values (step S224). The CPU 11 deletes the value in the selection number field (step S225). The CPU 11 determines whether the processing has been completed for all target IDs (step S226). If the CPU 11 determines that the process has not been completed (NO in step S226), the process proceeds to step S227. The CPU 11 reads the next unprocessed target ID (step S227). Thereafter, the CPU 11 returns the process to step S212. If the CPU 11 determines that the process has been completed for all target IDs (YES in step S226), the series of processes is terminated. As a result, in addition to the attributes that can be specified from the form data, it is possible to accurately specify the attributes of the target data reflecting the user's usage record. In addition, for example, there are multiple types of attributes where the data of the attribute “Overtime” is a time such as “0.00,8.00 ...” and the amount of money such as “7,500, 15,000 ...”. In the form, the attribute can be specified with high accuracy based on the content of the target data and the selection record.

  The second embodiment is as described above, and the other parts are the same as those of the first embodiment. Therefore, the corresponding parts are denoted by the same reference numerals, and detailed description thereof is omitted.

Embodiment 3
FIG. 23 is a functional block diagram showing the operation of the computer 1 of the above-described form. When the CPU 11 executes the control program 15P, the computer 1 operates as follows. The extraction unit 231 extracts target data to be analyzed from the data group. The determining unit 232 determines attributes and attribute evaluation values that are candidates for the target data based on the content of the extracted target data, data adjacent to the target data, and position.

  FIG. 24 is a block diagram illustrating a hardware group of the computer 1 according to the third embodiment. A program for operating the computer 1 reads a portable recording medium 1A such as a CD-ROM, a DVD (Digital Versatile Disc) disk, a memory card, or a USB (Universal Serial Bus) memory into a reading unit 10A such as a disk drive. It may be stored in the storage unit 15. Further, a semiconductor memory 1B such as a flash memory storing the program may be mounted in the computer 1. Further, the program can be downloaded from another server computer (not shown) connected via a communication network such as the Internet. The contents will be described below.

  The computer 1 shown in FIG. 24 reads a program for executing the above-described various software processes from the portable recording medium 1A or the semiconductor memory 1B or downloads it from another server computer (not shown) via a communication network. . The program is installed as the control program 15P, loaded into the RAM 12, and executed. Thereby, it functions as the computer 1 described above.

  The third embodiment is as described above, and the other parts are the same as those of the first embodiment. Therefore, the corresponding parts are denoted by the same reference numerals, and detailed description thereof is omitted.

  With respect to the embodiments including the above first to third embodiments, the following additional notes are further disclosed.

(Appendix 1)
On the computer,
Extract the target data to be analyzed from the data group,
A program for executing a process of determining attributes and attribute evaluation values as candidates for the target data based on the contents of the extracted target data, data and positions adjacent to the target data.

(Appendix 2)
Extract the location where the target data is placed,
Refer to the storage unit that stores the attribute and the evaluation value in association with the position where the data is arranged, and read the attribute and the evaluation value of the target data,
The program according to appendix 1, wherein attributes and evaluation values that are candidates for the target data are determined based on the read attributes and evaluation values.

(Appendix 3)
Determine the presence or absence of adjacent data,
Refer to the storage unit storing the attribute and evaluation value according to the presence or absence of adjacent data, read the attribute and evaluation value of the target data,
The program according to claim 1 or 2, wherein attributes and evaluation values that are candidates for the target data are determined based on the read attributes and evaluation values.

(Appendix 4)
If adjacent data exists, obtain the attribute of the adjacent data,
The program according to any one of appendices 1 to 3, wherein an attribute that is a candidate for the target data and an evaluation value of the attribute are determined based on the acquired attribute.

(Appendix 5)
Determine whether the target data is numeric data including numeric values,
If it is determined that the data is numerical data, refer to the storage unit storing the attribute and evaluation value including the time, the number of days or the amount in association with the display format of the target data, and read the attribute and the evaluation value of the target data,
The program according to any one of appendices 1 to 4, wherein attributes and evaluation values that are candidates for the target data are determined based on the read attributes and evaluation values.

(Appendix 6)
Refer to the storage unit storing the attribute and evaluation value including the title, item or name in association with the keyword, and read the attribute and evaluation value of the target data,
The program according to any one of appendices 1 to 5, wherein attributes and evaluation values that are candidates for the target data are determined based on the read attributes and evaluation values.

(Appendix 7)
The program according to any one of appendices 1 to 6, wherein when the determined attribute is a title, an item, or a name, a target data name including the target data is generated.

(Appendix 8)
The program according to any one of appendices 1 to 7, wherein when the determined attribute is time, number of days, or amount of money, the target data name of the target data is generated based on the target data name of adjacent data.

(Appendix 9)
Output the candidate attributes that have been determined,
Count the number of selections for each attribute,
The program according to any one of appendices 1 to 8, wherein the evaluation value of each attribute is corrected according to the number of times each attribute is selected.

(Appendix 10)
An extraction unit for extracting target data to be analyzed from the data group;
An information processing apparatus comprising: a determination unit that determines an attribute that is a candidate for the target data and an evaluation value of the attribute based on the content of the extracted target data, data adjacent to the target data, and a position.

(Appendix 11)
In an information processing method using an information processing device,
Extract the target data to be analyzed from the data group,
An information processing method for determining an attribute that is a candidate for the target data and an evaluation value of the attribute based on the content of the extracted target data, data adjacent to the target data, and a position.

DESCRIPTION OF SYMBOLS 1 Computer 1A Portable recording medium 1B Semiconductor memory 10A Reading part 11 CPU
12 RAM
13 Input unit 14 Display unit 15 Storage unit 15P Control program 16 Communication unit 18 Clock unit 151 Position table 152 Adjacent table 153 Numerical table 154 Keyword table 155 Target data table 191 Setting tab 192 Attribute pull-down box 193 OK button 194 Target data name box

Claims (5)

On the computer,
Extract the target data to be analyzed from the data group,
A program for executing a process of determining attributes and attribute evaluation values as candidates for the target data based on the contents of the extracted target data, data and positions adjacent to the target data. Extract the location where the target data is placed,
Refer to the storage unit that stores the attribute and the evaluation value in association with the position where the data is arranged, and read the attribute and the evaluation value of the target data,
The program according to claim 1, wherein the attribute and the evaluation value of the attribute that are candidates for the target data are determined based on the read attribute and the evaluation value. Determine the presence or absence of adjacent data,
Refer to the storage unit storing the attribute and evaluation value according to the presence or absence of adjacent data, read the attribute and evaluation value of the target data,
The program according to claim 1 or 2, wherein attributes and evaluation values that are candidates for the target data are determined based on the read attributes and evaluation values. An extraction unit for extracting target data to be analyzed from the data group;
An information processing apparatus comprising: a determination unit that determines an attribute that is a candidate for the target data and an evaluation value of the attribute based on the content of the extracted target data, data adjacent to the target data, and a position. In an information processing method using an information processing device,
Extract the target data to be analyzed from the data group,
An information processing method for determining an attribute that is a candidate for the target data and an evaluation value of the attribute based on the content of the extracted target data, data adjacent to the target data, and a position.

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