WO2014115198A1 - 入力支援システム、入力支援方法および入力支援プログラム - Google Patents
入力支援システム、入力支援方法および入力支援プログラム Download PDFInfo
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- WO2014115198A1 WO2014115198A1 PCT/JP2013/005267 JP2013005267W WO2014115198A1 WO 2014115198 A1 WO2014115198 A1 WO 2014115198A1 JP 2013005267 W JP2013005267 W JP 2013005267W WO 2014115198 A1 WO2014115198 A1 WO 2014115198A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0487—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
- G06F3/0489—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using dedicated keyboard keys or combinations thereof
- G06F3/04895—Guidance during keyboard input operation, e.g. prompting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
- G06F3/04842—Selection of displayed objects or displayed text elements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/166—Editing, e.g. inserting or deleting
- G06F40/174—Form filling; Merging
Definitions
- the present invention relates to an input support system, an input support method, and an input support program that support input of information into a predetermined input field performed by a user.
- Patent Document 1 describes a technique for inputting a facility name into a text box and converting it into an address.
- the present invention provides an input support system, an input support method, and an input support that can support the user to input information in various input fields without specifying the type of data that can be input in advance.
- the purpose is to provide a program.
- the input support system includes an input log storage means for storing information input in the past as a target input field as an input log, and a correct answer storage for each type for storing information indicating correct input for each type of information.
- Type of information to be entered in the input field based on the means, the input log stored in the input log storage means, and the information indicating the correct input for each type stored in the correct answer input storage means for each type Is provided with type estimation means for estimating which type of field is a type-specific field stored in the type-specific correct input storage means.
- the input log storage means stores information input in the past in the target input field as an input log, and the correct answer input storage means for each type inputs the correct input for each type of information.
- the information processing apparatus stores the information to be displayed on the basis of the input log stored in the input log storage unit and the information indicating the correct input for each type stored in the correct input storage unit for each type. It is estimated that the type of information to be input to the field corresponds to one of the type-specific fields stored in the type-specific correct input storage means.
- the input support program also includes an input log storage means for storing information input in the past as a target input field as an input log, and a correct answer for each type for storing information indicating correct input for each type of information.
- FIG. 1 is a block diagram illustrating a configuration example of the input support system according to the first embodiment.
- the input support system shown in FIG. 1 includes an input log storage unit 101, a type-specific correct input storage unit 102, and a type estimation unit 103.
- the input log storage means 101 stores information input in the past in the input field associated with it as an input log. Note that the input log storage unit 101 may store, as an input log, information that has been converted into correct information as a result of performing input support in the associated input field.
- the type-specific correct input storage means 102 (hereinafter referred to as type-specific correct input DB 102) stores information indicating the correct input for each type of information.
- the information indicating the correct input for each type includes, for example, examples of input information corresponding to the type, a candidate list, an input format indicating a correct expression format, and the like.
- each data in the data set is homogeneous data in the type expression method. That is, each data in the data set is preferably data in which the same expression method is taken for the corresponding type.
- the content and number of types to be held in the type-specific correct input DB 102 are arbitrary, but it is preferable that the type of information desired to be input in the input field targeted by the system is included.
- information indicating the correct input may be registered in advance for the types that are likely to be input in the input field, or information that has been input to the corresponding input field in a test or the like. Can be registered as an example.
- a database used in other systems such as a database of personal information belonging to an organization and a database of product information of a company can also be used as the correct answer input DB 102 by type. It is also possible to use an input log acquired in another system as information indicating a certain type of correct input.
- the type estimation unit 103 stores the input log stored in the input log storage unit 101 and the target input field based on the information indicating the correct input for each type stored in the correct answer input DB 102 for each type. Estimate the type of information to be entered. More specifically, the type estimation unit 103 determines whether the type of information to be input in the input field is a type-specific field (hereinafter referred to as a type-specific field) stored in the type-specific correct input DB 102. Estimate if applicable.
- the field refers to a collection of information stored with a specific label attached to the storage means or a storage area storing the information.
- the type estimation unit 103 does not have to specify what kind of content the type is specifically as an estimation result of the type of information to be input to the target input field. Further, when the type estimation unit 103 determines that the result does not correspond to any of the type fields stored in the type-specific correct input DB 102 as a result of the estimation, the type estimation unit 103 may make the type of estimation unknown.
- the type estimation unit 103 calculates the degree of coincidence with the input log stored in the input log storage unit 101, that is, past input information, for each type field stored in the type-specific correct input DB 102, A type field in which the degree of match is a predetermined threshold or more or a maximum value may be estimated as the type of information to be input in the input field.
- the degree of match for example, for each type field stored in the type-specific correct input DB 102, each past input information stored as an input log matches the input format registered in the type field. It may be determined based on the result by determining whether or not it matches any of the information included in the example of the input information or the candidate list.
- the type estimation unit 103 may obtain the number of input logs matched for each type-specific field, and may use the number of records (hereinafter referred to as the number of matched logs) as the degree of matching. For example, the type estimation unit 103 may use the ratio of the number of matched logs in the total number of input logs as the matching degree.
- the input log storage unit 101 and the type-specific correct input DB 102 are realized by a storage device such as a database. Moreover, the kind estimation means 103 is implement
- the input log storage unit 101 and the type-specific correct input DB 102 are not necessarily provided in the input support system itself as long as the type estimation unit 103 is accessible.
- FIG. 2 is a flowchart showing an example of the operation of the present embodiment.
- FIG. 2 is a flowchart showing an example of the processing flow of the input information type estimation processing by the type estimation means 103 in the operation of this embodiment.
- the type estimation unit 103 stores the input log stored in the input log storage unit 101 in the input field to be estimated. Based on the information indicating the correct input for each type stored in the correct input DB 102 for each type, the degree of coincidence with the input log is calculated for each type field of the correct input DB 102 for each type (step S101).
- the type estimation unit 103 specifies a type field as an estimation result based on the calculated matching degree of each type field (step S102).
- the type of input information estimation process may be performed, for example, as an initialization process at the time of system introduction, or may be performed periodically during system operation.
- FIG. 3 is an explanatory diagram showing an example of information stored in the type-specific correct input DB 102. As illustrated in FIG. 3, information indicating the correct input may be registered for only one type in the correct answer input DB 102 by type.
- FIG. 3 shows an example of the type-specific correct input DB 102 having one type-specific field to which the field name (identifier) “field A” is given.
- FIG. 3 shows an example in which a candidate list of input information corresponding to the type is registered as information indicating correct input.
- the “field A” in this example is an example of a type-specific field in which the type of input information is “address”.
- FIG. 4 is an explanatory diagram illustrating an example of an input log stored in the input log storage unit 101.
- FIG. 5 is an explanatory diagram showing an example of the estimation result of the type of input information.
- the example shown in FIG. 5 is an example in which the type of input information estimation processing is performed based on the type-specific correct input DB 102 shown in FIG. 3 and the input log example shown in FIG.
- the type estimation unit 103 determines the contents of each record of the input log (past input information) and the type. The contents of each candidate included in the field may be compared to obtain the number of matching logs for each type field, and the degree of matching may be calculated based on the number.
- the type estimation unit 103 specifies which of the candidates included in the type-specific field the content of each record of the input log.
- the type estimation means 103 may count the number of input logs that match each type-specific field, and calculate the degree of match based on the result.
- the type estimation unit 103 may determine whether or not the content of each record of the input log corresponds to each candidate included in the type field using the following method. For example, the type estimation unit 103 may determine whether or not both formats match. Further, the type estimation unit 103 may treat each piece of information as character string information and determine whether or not the two match completely. Further, the type estimation unit 103 may determine whether or not a candidate character string that is a candidate content matches forward with a past input character string that is a log content. Further, when there is a forward match, the type estimation unit 103 may determine whether or not the ratio of the number of characters that matched the past input character string to the number of characters in the candidate character string is equal to or greater than a predetermined value.
- the type estimation unit 103 when an example of input information is registered as information indicating correct input information in the correct input DB 102 by type, the type estimation unit 103 includes the contents of each record (past input information) of the input log, The contents of each example included in the type field may be compared. And if the similarity of both character strings is more than a predetermined value, the kind estimation means 103 may include both in the number of matching logs as matching. Note that the similarity between character strings may be calculated using an edit distance, a distance of information vectorized by n-gram, and the like. Further, the type estimation unit 103 may use a weighted distance that changes the importance depending on the character position, such as emphasizing that the first character strings match.
- the type estimation unit 103 may count the number of matches in each field. In addition, when the forward matching method is used, the type estimation unit 103 counts the number of matched logs only for a field having a larger percentage of matched characters or a field having a closer distance indicating similarity of character strings. May be counted as
- FIG. 5A shows a result of comparison between the type-specific correct input DB 102 shown in FIG. 3 and the input log example shown in FIG. It is shown that there are 700 records of input logs that did not match “other”, that is, any type field candidate.
- the type estimation unit 103 may use the number of matching logs for each type field as the matching degree of the type field.
- the type estimation means 103 may be used to determine whether or not the type is unknown.
- the type estimation unit 103 may calculate a matching ratio in the total number of input logs (1000) based on the number of matching logs, and may use it as the matching degree. That is, the type estimation unit 103 may use a value obtained by dividing the number of matched input logs by the total number of input logs used for determination.
- FIG. 5B shows an example of the calculation result of the matching degree when the matching ratio is the matching degree.
- the type estimation means 103 estimates the type of information to be input to the input field where the input log is collected based on the degree of match of each type field obtained in this way. As shown in FIG. 5B, in this example, the degree of matching with “field A” is 0.7, and the degree of matching with “others” is 0.3. Therefore, the type estimation unit 103 may identify “field A” as an estimation result as a type field corresponding to the type of information to be input in the input field (see FIG. 5C). Here, if the matching ratio of “others” takes the maximum value, the type estimation unit 103 may make the estimation result without the corresponding field, that is, the type unknown.
- FIG. 6 is an explanatory diagram showing another example of information stored in the type-specific correct input DB 102.
- the type-specific correct input DB 102 may store a plurality of description formats for the same entry as different type fields.
- FIG. 6 shows an example of a type-specific correct input DB 102 having a type field given a field name “Field A” and a type field given a field name “Field B”. Yes.
- an information group (candidate list) representing “address” is registered in both “field A” and “field B”. More specifically, a list of information candidates “address starting from the prefecture name” is registered in “field A”, and information “address excluding the prefecture name” is registered in “field B”.
- Candidate list of is registered.
- the information registered in the same record for “field A” and “field B” indicates the same address. That is, the records in the type field are associated with each other. Each record in the type field does not necessarily have to be associated.
- FIG. 7 is an explanatory diagram showing another example of input information type estimation processing.
- the example illustrated in FIG. 7 is an example in which the type estimation process is performed based on the type-specific correct input DB 102 illustrated in FIG. 6 and the input log example illustrated in FIG.
- FIG. 7A as a result of comparing the information in the type field with the input log, “Field A” has 700 matching log records, “Field B” has 200 matching log records, “ Assume that the number of logs that did not match any of the other fields, that is, any type field candidate, was 100. In such a case, as shown in FIG.
- the type estimation unit 103 calculates the matching ratio in the entire number of input logs (1000) based on the number of matching logs, and “field A”. May be 0.7, “Field B” may be 0.2, and “Other” may be 0.1.
- FIG. 7C shows that “field A” is identified as a type-specific field corresponding to the type of the input field to be estimated from the above results.
- the type estimation unit 103 performs input for each record in the type field identified as corresponding to the type of the input field to be estimated, as a result of the process of estimating the type of the input field.
- a score based on the number of matches with the log may be calculated.
- the type estimation unit 103 may hold the calculated score in the type-specific correct input DB 102 or may output it together with the estimation result.
- the score of each record is not particularly limited as long as it is a value based on the degree of coincidence with the input log.
- the score of each record may be, for example, the number of matching logs as it is, or a matching ratio calculated based on the number of matching logs.
- the score of each record may be newly calculated for each estimation process, or may be a cumulative value from scores calculated in the past.
- FIG. 9 is an explanatory diagram showing another example of information stored in the type-specific correct input DB 102.
- a plurality of type fields associated with different entry items may be registered in the type-specific correct input DB 102.
- FIG. 9 shows an example of the type-specific correct input DB 102 having three types of fields of “field A”, “field B”, and “field C”.
- an information group (in this example, a candidate list) representing “affiliation” is registered in “field A”.
- Field B an information group (in this example, a candidate list) representing “Last name” is registered.
- Field C an information group (in this example, a candidate list) representing “Mail address” is registered.
- the type-specific correct input DB 102 may hold a plurality of type-specific fields associated with different entry items (eg, “affiliation”, “first name”, “mail address”, etc.). Further, in the example shown in FIG. 9, each element of each type field is associated with each other between the records. That is, information related to the same object is registered in records located in the same column. Note that there is no need to associate such records.
- FIG. 10 is an explanatory diagram showing an example in which the type of input information is estimated using the information in the type-specific correct input DB 102 shown in FIG.
- input information for example, 1000 records
- FIG. 10B shows the contents of each record in the input log (past input information) and the contents (inputs) of each record included in each type field in the type-specific correct input DB 102 shown in FIG.
- the number of matching logs, matching ratios and estimation results for each type of field obtained as a result of comparison with the information candidate list) are shown. Specifically, FIG.
- FIG. 10B shows that the number of matching logs in “Field A” is 50, the number of matching logs in “Field B” is 150, the number of matching logs in “Field C” is 700, It is shown that the number of matching logs of “others” is 100. Further, FIG. 10B shows that the degree of matching calculated based on the number of matching logs is 0.05 for “Field A”, 0.15 for “Field B”, and 0 for “Field C”. 7. “Others” is 0.1, and “Field C” having the highest degree of match is taken as the estimation result under the condition.
- FIG. 11 is an explanatory diagram illustrating an example of information stored in the type-specific correct input DB 102 and an example of input information type estimation processing.
- FIG. 11A is stored in the type-specific correct input DB 102. It is explanatory drawing which shows the other example of the information which is.
- the type-specific correct input DB 102 includes a plurality of type-specific fields, such as concatenating and registering a type-specific field in which information groups having different granularities are registered as one type-specific field. One concatenated type field may be registered.
- information obtained by dividing predetermined information is registered in “field A” and “field B”. In this example, it is assumed that “field A” has a larger information granularity.
- the type estimation unit 103 may treat the concatenated field as one type-specific field and perform a match determination with the input log.
- the type estimation unit 103 may use the type field classification based on “ID1” instead of the type field classification based on “ID2”.
- the type estimation unit 103 may handle information obtained by concatenating information of each record in the fields A and B as information of each record in the concatenated field.
- “Osaka City”, which is a candidate registered in the first record of field A, and “Kita Ward”, a candidate registered in the first record of field B, are connected.
- the type estimating unit 103 may compare the “Osaka City Kita Ward” with each record of the input log, assuming that the connected “Kita Ward in Osaka” is a candidate registered in the first record of the connected field.
- FIG. 11 (c) compares the contents of each record in such an input log with the contents of each record in the type field according to the classification of “ID1” in the type-specific correct input DB 102 shown in FIG. 11 (a).
- the number of matching logs, matching ratios, and estimation results for each type of field obtained as a result of the above are shown.
- FIG. 11A shows an example in which only the “concatenated field AB” is provided as the type-specific field by the classification of “ID1”, but other types-specific fields (including the concatenated field) are included. Also good.
- the type estimation means 103 may calculate the degree of match for other types of fields and use the type field with the highest degree of match as the estimation result.
- the type estimation unit 103 uses, as an estimation result, a concatenated field obtained by concatenating the type-specific fields having the magnitude relation of the information granularity as in this example, the type estimation unit 103 further sets the type-specific field constituting the concatenated field.
- Some candidates (elements) specifically defined from among the included records may be output as priority elements.
- the candidate as the priority element may be a candidate that has been used effectively in the past in the type field from the tendency of the input log, or a candidate that is particularly likely to be used for input information.
- the type estimation unit 103 records that match the input log in the type-specific field with the larger granularity among the type-specific fields that form the concatenated field. May be acquired and the union of the contents may be output as a priority element.
- FIG. 11D is an explanatory diagram illustrating an example of acquiring priority elements.
- a certain Sakai City was acquired and made a priority element.
- FIG. 12 is an explanatory diagram illustrating an example of acquiring priority elements using clustering based on the similarity of record contents.
- clustering may be performed based on the distance between character strings if the record content is a character string, for example. Specifically, an edit distance, a distance of information vectorized by n-gram, and the like are used. It should be noted that a weighted distance that changes the degree of importance depending on the character position may be used for clustering, such as placing importance on matching the first character string.
- FIG. 12C shows a result obtained by comparing the contents of each record of such an input log with the contents of each record included in each type field of the type-specific correct input DB 102 shown in FIG.
- the number of matched logs, the percentage of matches, and the estimation results for each type of field are shown.
- FIG. 12B and FIG. 12C show that the number of matching logs in “Field A” is 6 and the number of matching logs in “Other” is 0, and such matching
- the degree of match calculated based on the number of logs is that “Field A” is 1.0 and “Others” is 0. Under the conditions, “Field A” having the highest degree of match is the estimated result. It has been shown.
- the type estimation unit 103 holds a result of specifying which record in the type field corresponds to the input log in such an estimation process. Then, when the type field as the estimation result is determined, the type estimation unit 103 acquires a cluster including a record that matches the input log in the type field, and uses the union element as a priority element. Good.
- the shaded square marks indicate the positions that are the union of clusters.
- a range ⁇ indicates a record range acquired as a priority element.
- FIG. 12D shows an example of priority elements acquired in this example.
- the type estimation unit 103 may perform an estimation process using an input log with validity.
- FIG. 13A is an explanatory diagram illustrating an example of an input log with validity.
- FIG. 13B is an explanatory diagram illustrating an example of a result of estimation processing using an input log with validity.
- FIG. 13A shows an example of an input log in which the validity is set to 1 when the input is approved by the input content of the corresponding record, and 0 is set when the approval is not approved.
- Such a log with validity is obtained, for example, by waiting for the result of error determination for the input when registering the log and registering it together with the result.
- the type estimation unit 103 may digitize the degree of matching by treating the effectiveness attached to each record of the input log as a weight.
- the type estimation unit 103 treats this effectiveness as a weight, and when calculating the number of matching logs, instead of adding 1 for each log, the result of adding the weight is used as the number of weighted matching logs. Also good.
- the match ratio is used as the match level, the type estimation unit 103 uses the value obtained in this way (the number of weighted match logs, that is, the total value of the effectiveness) as the effectiveness of each record included in the input log. Divide by the sum.
- FIG. 13B shows the result of comparing the contents of each record in the input log shown in FIG. 13A with the contents of each record in the type-specific correct input DB 102 shown in FIG.
- the number of matching logs, the number of weighted matching logs, the matching ratio, and the estimation result are shown for each type of field.
- the number of matching logs for “field A” and “field B” is both 50, but the validity level attached to the corresponding log record is 0.
- An example in which the number of weighted matching logs is 0 is shown.
- the type estimation unit 103 performs input for each record in the type field identified as corresponding to the type of the input field to be estimated as a result of the type information input estimation process.
- a score using a log with information indicating the person may be calculated.
- the type estimation unit 103 may hold the calculated score in the type-specific correct input DB 102 or may output it together with the estimation result.
- the score of each record is not particularly limited as long as it is a value based on the degree of matching with the input log of the designated user.
- FIG. 14A is an explanatory diagram illustrating an example in which a score is calculated in an estimation process with a score of each record in the correct answer input DB 102 by type.
- FIG.14 (b) is explanatory drawing which shows the example of the effectiveness matched with the input log with input person information, and the input log.
- such an input log with information indicating an input person registers the log by using an authentication system such as inputting an ID for identifying a user who is an input person to the input field at the time of system login, for example. At this time, it is obtained by registering the ID entered at the time of login together with the log.
- the type estimation unit 103 holds a result of specifying which record in the type field corresponds to the input log in the estimation process.
- the type estimation unit 103 may calculate a score for each designated user (for example, a currently logged-in user) in the type field. For example, the type estimation unit 103 may calculate a score by adding 1 to a record that matches an input log to which a designated user ID is attached, or the log is given a validity level. In such a case, the score may be calculated by adding the effectiveness. At that time, the type estimation unit 103 may adjust the score so that the calculated score becomes larger if there is a record that has been input by the designated user with a correct content in the past.
- the processing means at the later stage can recommend candidates suitable for the user when recommending input candidates. Input support is possible.
- the input support system determines the type of information to be input to the input field based on the log of information previously input to the target input field and the information in the correct input DB 102 by type.
- Information indicating a correct input such as a matched information group and input format can be specified and provided as an estimation result. And using this estimation result, input support, such as performing error determination or performing predictive conversion, can be implemented for various input fields.
- any type of information can be obtained by combining the type-specific correct input DB 102 and a log when a correct input is made, without performing detailed designation in advance in the input field. Can be dynamically derived as an estimation result. Therefore, a detailed input support system can be easily introduced.
- the present embodiment it is possible to determine whether the address in Tokyo is easy to enter even in the same address or whether a general address is easy to enter by controlling the classification of types registered in the correct answer input DB 102 by type. . Therefore, the accuracy of prediction conversion and error determination can be increased by this determination. Further, according to the present embodiment, it is possible to change the type and granularity of information to be input in the input field depending on the setting of the input log even when the system is operating.
- FIG. 15 is a block diagram illustrating a configuration example of the input support system according to the second embodiment.
- the input support system shown in FIG. 15 is different from the first embodiment shown in FIG. 1 in that an error detection unit 104 is newly provided.
- the error detection unit 104 is newly input to the target input field based on the estimation result output from the type estimation unit 103 and other information (for example, priority element information, score for each record, etc.). Error determination is performed on the information and an error is detected. Further, when an error is detected as a result of the error determination, the error detection unit 104 outputs a message indicating that fact.
- other information for example, priority element information, score for each record, etc.
- the error detection unit 104 determines, for example, whether or not the information newly input in the target input field matches the information indicating the correct input included in the type field obtained as the estimation result, If they do not match, an error may be determined.
- the match determination here may be basically the same as the match determination performed when the type estimation unit 103 calculates the number of matched logs. That is, when an input format is registered as information indicating correct input in the correct answer input DB 102 for each type, the error detection unit 104 determines whether the information newly input in the target input field matches the input format. The error may be determined by.
- the error detection unit 104 uses the type field obtained as the estimation result as a search field, and the input field to be targeted from among the search fields Search for candidates that match the newly entered information. Then, the error detection unit 104 may determine an error if there is no matching candidate. For example, it is assumed that an example of input information is registered as information indicating correct input. In this case, the error detection unit 104 may determine an error depending on whether or not the two formats match each other, treat each piece of information as character string information, and the similarity between the two character strings is a predetermined value or more. An error may be determined depending on whether or not there is.
- the error detection unit 104 is realized by an information processing device that operates according to a program such as a CPU.
- FIG. 16 is a flowchart showing an example of the operation of the input support system of this embodiment.
- the input log storage unit 101 stores information input in the past in the target input field as an input log (step S201).
- the type estimation unit 103 indicates the input log of the input field stored in the input log storage unit 101, the information stored in the type-specific correct input DB 102, more specifically, the correct input by type.
- the type of input information in the input field is estimated (step S202).
- the type estimation means 103 obtains at least information indicating a type field corresponding to the type of input information in the input field as an estimation result.
- step S203 when new information is input to the target input field (Yes in step S203), the error detection unit 104 performs error determination on the input information (step S204). If an error is detected (Yes in step S205), an error message is displayed (step S206).
- FIG. 17 is an explanatory diagram showing a display example of an error message.
- the error detection unit 104 receives an input “yamamoto@sl.aaa.com” as new input information to the target input field.
- information indicating “field A” of the correct answer input DB 102 by type illustrated in FIG. 3 is obtained as an estimation result of the type of information to be input to the input field.
- the type-specific correct input DB 102 shown in FIG. 3 has one type-specific field “field A”, and an information group (candidate list) representing “address” is registered in “field A”. Yes.
- the error detection means 104 may set the type-specific field as the estimation result in the search field, and if an entry that matches the input information is found from the search field candidate list, there may be no error. On the other hand, if a match with the input information is not found, it may be notified that there is a possibility of an input error. For example, as shown in FIG. 17A, the error detection means 104 may output a message (OUT1-1) such as “Is the content to be written correct?”. In addition, when a name (for example, “address”) is attached to the type field determined as the estimation result, the error detection unit 104 uses the name as shown in FIG. A message (OUT1-2) such as “Please write your address” here may be output.
- the error detection unit 104 uses the description to indicate “here” (for example, city "Please write the address starting with the prefecture name)".
- the error detection unit 104 does not set only the type-specific fields that are estimated results as search fields. Other type fields may be added to the search field. If a matching field is not found as a result of searching the type field once determined as an estimation result, another type field can be added to the search field and the search can be performed again.
- 18 to 21 are explanatory diagrams showing examples of error determination and error messages when the type-specific correct input DB 102 has type-specific fields corresponding to a plurality of description formats.
- the error detection unit 104 has obtained information indicating “field A” of the correct answer input DB 102 by type shown in FIG. 6 as an estimation result of the type of information to be input to the target input field.
- the type-specific correct input DB 102 shown in FIG. 6 has two types of fields, “Field A” and “Field B”, and “Field A” and “Field B” both have “Address”. It is assumed that an information group (candidate list) representing “is registered. In addition, it is assumed that the type-specific correct input DB 102 holds in advance information indicating the relationship between type-specific fields such that “field A” has a more detailed description format.
- the error detection means 104 may display the following error message in addition to the error message as shown in FIG. In other words, if the error detection means 104 knows the relationship between the type-specific field that is the estimation result and the type-specific field that matches the input information (for example, which is more detailed), the error detection means 104 determines the error based on such relationship.
- a message may be created. For example, as shown in FIG. 20A, it is assumed that the input information matches a type field that is different from the type field that is the estimation result and that is not detailed. In this case, the error detection means 104 may output a message (OUT2-1) such as “please write detailed information”. Further, when a field name is given to the type-specific field, the error detection means 104 further uses it to output a message (OUT2-2) such as “Please write a detailed address”. Good.
- the error detection means 104 compares the record contents of the two, detects the difference, and uses the detected difference, so that “here“ XX ”(in this example, Osaka Prefecture) is unnecessary. May be output. "
- CASE 3 in FIG. 21 is an example of an error determination result when a matching item is not found even if “Field B”, which is a field of the same type, is added to the search field.
- the error determination is “error present”.
- the error detection means 104 may output an error message as shown in FIG.
- FIG. 22 to FIG. 25 are explanatory diagrams showing an example of error determination and an example of an error message when the correct answer DB 102 for each type has a plurality of type fields including those that do not have the same entry.
- the error detection unit 104 has obtained information indicating “field C” of the type-specific correct input DB 102 illustrated in FIG. 9 as an estimation result of the type of information to be input to the target input field.
- the type-specific correct input DB 102 shown in FIG. 9 has “field A” and “field B” in addition to “field C”. Records are associated with each other.
- the example shown as CASE2 in FIG. 23 is based on the premise that a field that matches the input information was not found only by using “field C”, which is the type-specific field as the estimation result, as a search field.
- the type-specific field different from the type-specific field that is the estimation result as a result of searching by adding the remaining type-specific fields “field B” and “field C”. This is an example of an error determination result when data matching “Field B” is found. In this case, the error determination result is “with error”.
- the error detection means 104 may display the following error message in addition to the error message as shown in FIG. That is, for example, as shown in FIG. 24 (a), the error detection unit 104 may indicate a message “OUT3--confirm the input information” that can implicitly indicate that the type of input information is different. 1) may be output. Further, for example, when a field name is given to the type-specific field, the error detection unit 104 further uses the message to write a message such as “Please write“ mail address ”instead of“ name ”” (OUT3 -2) may be output.
- the name of “field B”, which is a type-specific field in which a match is found, is “name”, and the type-specific field “field C”, which is the estimation result.
- the example shown as CASE 3 in FIG. 25 is an example of an error determination result when no match is found even if the remaining type-specific fields are added to the search field.
- the error determination is “error present”.
- the error detection means 104 may output an error message as shown in FIG.
- the error detection means 104 may perform error determination using them.
- 26 to 27 are explanatory diagrams illustrating an example of error determination when a priority element is given in addition to the estimation result.
- the error detection unit 104 has obtained information indicating “concatenated field AB” in the type-specific correct input DB 102 illustrated in FIG. 11A as an estimation result of the type of information to be input to the target input field.
- the error detection unit 104 has also obtained information on priority elements shown in FIG.
- the “concatenated field AB” in the type-specific correct input DB 102 shown in FIG. 11A is a concatenation of “field A” and “field B”.
- a group of information representing “address” is registered.
- the type-specific correct input DB 102 holds in advance information indicating the relationship between the type-specific fields such that “field A” has a larger granularity of information.
- the priority element information shown in FIG. 11D is information indicating that the priority element field is “field A” and “Kashiwa city” is the priority element in the priority element field.
- those that match the current input information for example, forward match
- FIG. 26 shows that a search is made that matches “Field B”.
- the error detection unit 104 displays a determination result indicating that there is a possibility of an input error and alerts the user.
- the content may not be a priority element or may not include a priority element.
- a message for confirming that the contents are input for the first time may be output.
- this is the first time data relating to “Osaka City” has been entered.
- An example of outputting an error message (OUT4) "Please check if it is correct just in case” is shown.
- the error detection unit 104 may perform error determination using the score. For example, when the input information matches a candidate whose score is smaller than a predetermined value, the error detection unit 104 outputs a similar confirmation message such as notifying that the message has not been input so much in the past. May be. Even when the score for each record is not calculated in the input information type estimation process, the error detection unit 104 includes a number of input logs that match the content of the acquired record. You may calculate a score, for example by counting. Then, the error detection unit 104 may perform error determination using the calculated score.
- the error detection unit 104 performs error determination using the estimation result by the type estimation unit 103 without specifying the type of data that can be input in advance. Therefore, accurate input information can be obtained.
- FIG. 28 is a block diagram illustrating a configuration example of the input support system according to the third embodiment.
- the input support system shown in FIG. 28 is different from the first embodiment shown in FIG. 1 in that an input information prediction unit 105 is newly provided.
- the input information prediction unit 105 Based on the estimation result output from the type estimation unit 103 and other information (for example, information on priority elements and scores for each record), the input information prediction unit 105 newly adds a target input field. Information to be input to the input field is predicted from the input information and presented to the user.
- the input information prediction unit 105 is realized by an information processing apparatus that operates according to a program such as a CPU, for example.
- FIG. 29 is a flowchart showing an example of the operation of the input support system of this embodiment.
- steps S201 to S203 in FIG. 29 are the same as those in the second embodiment shown in FIG.
- the input information prediction unit 105 when a new input is made in the target input field (Yes in step S203), the input information prediction unit 105 includes the input information and information indicating at least the type field that is the estimation result. Based on the above, correct input information to be input to the input field is predicted (step S301). Then, the input information prediction unit 105 outputs the result as a prediction conversion candidate (step S302).
- FIG. 30 is an explanatory diagram illustrating an example of a prediction process by the input information prediction unit 105.
- the input information prediction unit 105 has obtained information indicating the “field A” of the type-specific correct input DB 102 illustrated in FIG. 3 as the estimation result of the type of information to be input to the target input field.
- the type-specific correct input DB 102 shown in FIG. 3 has one type-specific field “field A”, and an information group (candidate list) representing “address” is registered in “field A”. Yes.
- the input information predicting means 105 sets “Field A”, which is the type-specific field as the estimation result, to the search field, and obtains information including the input information from the search field candidate list. It is good also as a prediction conversion candidate.
- the input information prediction unit 105 acquires a score based on the input log for each acquired record, ranks the prediction conversion candidates based on the acquired score, and presents the acquired records. Also good.
- the input information prediction unit 105 includes how many input logs that match the content of the acquired record. It is good also as a score.
- the predictive conversion candidate is not generated from the input log, but the input log is used only for estimating the type of input information and for ranking the candidates.
- the content candidates are acquired by using the information in the correct answer input DB 102 for each type, so that even if there is no input “Sakai City” in the past, correct input information can be predicted and a conversion candidate can be presented.
- FIG. 31 is an explanatory diagram showing another example of the prediction process performed by the input information prediction unit 105.
- the input information prediction unit 105 has obtained information indicating the “field A” of the type-specific correct input DB 102 illustrated in FIG. 6 as the estimation result of the type of information to be input to the target input field.
- the type-specific correct input DB 102 shown in FIG. 6 has two types of fields, “Field A” and “Field B”, and “Field A” and “Field B” both have “Address”. "Is registered.
- the type-specific correct input DB 102 holds in advance information indicating the relationship between type-specific fields such that “field A” has a more detailed description format.
- it is assumed that at least the records in the type fields are associated with each other.
- the input information predicting means 105 first uses “field A”, which is the type-specific field as the estimation result, and “field B” having the same entry as the search field, and then searches the search field. Of these candidates, a match with the input information (forward match) may be searched. And if there exists a thing applicable, the input information prediction means 105 may acquire the record content in the said record position of the field according to the type made into the estimation result as a prediction conversion candidate.
- the input information prediction unit 105 acquires a score based on the input log for the acquired record, and ranks and presents the prediction conversion candidates based on the obtained score. Note that when the score for each record is not calculated in the input information type estimation process, the input information prediction unit 105 may calculate the score. In the example shown in FIG. 31, “Osaka City Sakai City Naka Ward” having the highest score as the first predictive conversion candidate and “Osaka City Sakai City Kita Ward” having the next highest score as the second predictive conversion candidate. Is presented.
- the input information can be converted into such type of information.
- simply entering “Sakai City” will present candidates that contain input characters and have a description format in the input field, so that not only can the user input information in the correct description format.
- such a predictive conversion function can be performed even in a state where there is no input log of “Sakai City”.
- the user can obtain a predictive conversion candidate without being conscious of the type. .
- the information stored in the type-specific correct input DB 102 may not be used as conversion knowledge, but an input format or type description registered in the type-specific correct input DB 102 may be used.
- the input information predicting means 105 converts the input information into information having a description format using the conversion processing of another system based on the description of the input format and type, and predictively converts the input information. It may be presented as a candidate.
- FIG. 32 is an explanatory diagram showing another example of the prediction process by the input information prediction unit 105.
- the input information prediction unit 105 has obtained information indicating “field C” of the type-specific correct input DB 102 shown in FIG. 9 as an estimation result of the type of information to be input to the target input field.
- the type-specific correct input DB 102 shown in FIG. 9 has “field A” and “field B” in addition to “field C”. Are associated with each other.
- the input information predicting means 105 may search for the one containing the input information from each candidate in the search field after using the type field as a search field. Then, if there is a corresponding information, the input information prediction means 105 may acquire the record contents at the record position of the type field determined as the estimation result as a prediction conversion candidate.
- the input information prediction unit 105 acquires a score based on the input log for the acquired record, and ranks and presents the prediction conversion candidates based on the obtained score. Note that when the score for each record is not calculated in the input information type estimation process, the input information prediction unit 105 may calculate the score. In the example shown in FIG. 32, “yamamoto@sl.aaa.com” having the highest score as the first predictive conversion candidate and “yamamoto@dev.aaa.com” having the next highest score as the second predictive conversion candidate. com "is presented.
- FIG. 33 is an explanatory diagram showing another example of the prediction process performed by the input information prediction unit 105.
- information indicating “concatenated field AB” of the correct input DB 102 by type shown in FIG. 11A is obtained as an estimation result of the type of information to be input to the target input field.
- the priority element information shown in FIG. Note that the “concatenated field AB” in the type-specific correct input DB 102 shown in FIG. 11A is a concatenation of “field A” and “field B”. A group of information representing “address” is registered.
- the type-specific correct input DB 102 holds in advance information indicating the relationship between the type-specific fields such that “field A” has a larger granularity of information.
- the priority element information shown in FIG. 11D is information indicating that the priority element field is “field A” and “Kashiwa city” is the priority element in the priority element field.
- the input information predicting means 105 uses the “concatenated field AB”, which is the type-specific field as the estimation result, or the concatenated “field A” and “field B” as search fields. You may search what contains input information from each candidate in a search field. Then, if there is a corresponding information, the input information prediction unit 105 may acquire the record contents at the record position in the type field determined as the estimation result as a prediction conversion candidate.
- the input information prediction unit 105 acquires a score based on the input log for the acquired record, and ranks and presents the prediction conversion candidates based on the obtained score. Note that when the score for each record is not calculated in the input information type estimation process, the input information prediction unit 105 may calculate the score. In such a case, a score that gives priority to a search record that is included in the input log many times and whose priority element field content is a priority element is given. The input information prediction unit 105 may rank and output the prediction conversion candidates based on the score thus obtained. In the example shown in FIG. 33, “Sakai City Kita Ward” having the highest score as the first predictive conversion candidate and “Osaka City Kita Ward” having the next highest score are presented as the second predictive conversion candidate. .
- FIG. 34 is an explanatory diagram showing another example of the prediction process performed by the input information prediction unit 105.
- the input information prediction unit 105 uses the priority element in the same manner when the priority element is acquired by clustering or the like even when the type-specific field that is the estimation result is not a connected field. Then, the ranking of prediction conversion candidates may be performed.
- information indicating the “field A” of the type-specific correct input DB 102 shown in FIG. 12A is obtained as an estimation result of the type of information to be input to the target input field.
- An example of prediction processing when information on priority elements shown in 12 (d) is obtained is shown.
- FIG. 35 is an explanatory diagram showing another example of the prediction process performed by the input information prediction unit 105.
- the input information prediction unit 105 has obtained “field C” of the correct answer input DB 102 by type shown in FIG. 9 as an estimation result of the type of information to be input to the target input field.
- the type-specific correct input DB 102 shown in FIG. 9 has “field A” and “field B” in addition to “field C”. Are associated with each other.
- the information shown in FIG. 13A is registered as the input log. That is, it is assumed that an input log with validity is registered.
- the input information predicting means 105 may search for the one containing the input information from each candidate in the search field after using the type field as a search field. Then, if there is a corresponding information, the input information prediction unit 105 may acquire the record contents at the record position in the type field determined as the estimation result as a prediction conversion candidate.
- the input information prediction unit 105 acquires a score based on the input log for the acquired record, and ranks and presents the prediction conversion candidates based on the obtained score. Note that when the score for each record is not calculated in the input information type estimation process, the input information prediction unit 105 may calculate the score. In the case of this example, the input information prediction unit 105 may count the number of valid logs that are input logs that match the contents of the acquired records and use them as a score. . Alternatively, the input information prediction unit 105 may use a score obtained by adding the validity of the input log that matches the content of the record for the acquired record. Then, the input information prediction unit 105 may rank the prediction conversion candidates based on the score based on the degree of coincidence with the input log taking the effectiveness into account. In the example shown in FIG. 35, “yamamoto@sl.aaa.com” having the highest score as the first predictive conversion candidate and “yamamoto @ dev.” Having the second highest score as the second predictive conversion candidate. aaa.com "is presented.
- candidates are ranked and presented based on the matching score with the input log considering the effectiveness of the log, so that information that has been input incorrectly in the past is presented in a high order. Conversion candidates can be presented in a more optimized order.
- FIG. 36 is an explanatory diagram showing another example of the prediction process performed by the input information prediction unit 105.
- the input information prediction unit 105 has obtained “field C” of the correct answer input DB 102 by type shown in FIG. 9 as an estimation result of the type of information to be input to the target input field.
- the type-specific correct input DB 102 shown in FIG. 9 has “field A” and “field B” in addition to “field C”. Are associated with each other.
- information shown in FIG. 14B is registered as an input log. That is, it is assumed that an input log with input user information is registered.
- the input information predicting means 105 may search for the one containing the input information from each candidate in the search field after using the type field as a search field. Then, if there is a corresponding information, the input information prediction unit 105 may acquire the record contents at the record position in the type field determined as the estimation result as a prediction conversion candidate.
- the input information prediction unit 105 acquires a score based on the input log for the acquired record, and ranks and presents the prediction conversion candidates based on the obtained score. Note that when the score for each record is not calculated in the input information type estimation process, the input information prediction unit 105 may calculate the score. In the case of this example, the input information prediction unit 105 includes an input log that matches the content of the acquired record, and includes a number of logs of the same user as the input user of the current input information. It is good also as a score. The input information prediction unit 105 may rank the prediction conversion candidates based on the score based on the degree of coincidence with the input log of the same user. 36, “yamamoto@sl.aaa.com” having the highest score as the first predictive conversion candidate and “yamamoto @ dev.” Having the second highest score as the second predictive conversion candidate. aaa.com "is presented.
- the input information prediction unit 105 rewrites the input content to the highest-ranked prediction conversion candidate during the description by the user, such as IME (Input Method Editor), in addition to displaying the list of prediction conversion candidates. It may be processed as a conversion candidate waiting for determination. Further, the input information prediction unit 105 may generate and output an alert message such as “Would you like to input XX?” Using a conversion candidate with a high score after the user input.
- IME Input Method Editor
- the input information prediction unit 105 when the input information prediction unit 105 is operated as an IME, the input information prediction unit 105 may be a Web IME that reacts in an input field, or may be an IME that is installed and operates in a client terminal. . In such a case, the input information prediction unit 105 may recommend a prediction conversion candidate in consideration of both the user-specific IME history and the input field history. As a recommendation method, both AND and OR may be taken. Also, for example, the user-specific IME history may be given priority, the input field history may be given priority, or one of the priority orders may be increased. Further, the input log may be stored on the system side (server side) or may be stored on the user side (client side).
- the input information prediction unit 105 predicts correct input using the estimation result by the type estimation unit 103 without specifying the type of data that can be input in advance. To do. Therefore, since the estimation result can be presented as a candidate, can be automatically rewritten, or an alert message can be output, accurate input information can be obtained.
- the input information prediction unit 105 is added to the configuration of the first embodiment.
- the input information prediction unit 105 is added to the configuration of the second embodiment. May be.
- the input support system may simultaneously perform error detection and prediction conversion candidate presentation, or may selectively perform only one of the functions.
- the present invention can be suitably applied to a system having various input fields in the user interface.
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Abstract
Description
次に、本発明の第2の実施形態を説明する。図15は、第2の実施形態の入力支援システムの構成例を示すブロック図である。図15に示す入力支援システムは、図1に示す第1の実施形態と比べて、新たにエラー検出手段104を備えている点が異なる。
次に、本発明の第3の実施形態を説明する。図28は、第3の実施形態の入力支援システムの構成例を示すブロック図である。図28に示す入力支援システムは、図1に示す第1の実施形態と比べて、新たに入力情報予測手段105を備えている点が異なる。
102 種類別正解入力記憶手段(種類別正解入力DB)
103 種類推定手段
104 エラー検出手段
105 入力情報予測手段
Claims (23)
- 対象とする入力欄に過去に入力された情報を入力ログとして記憶する入力ログ記憶手段と、
情報の種類別に、正しい入力を示す情報を記憶する種類別正解入力記憶手段と、
前記入力ログ記憶手段に記憶されている入力ログと、前記種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、前記入力欄に入力されるべき情報の種類が、前記種類別正解入力記憶手段に記憶されている種類別のフィールドである種類別フィールドのいずれに該当するかを推定する種類推定手段とを備えた
ことを特徴とする入力支援システム。 - 前記種類別正解入力記憶手段は、記入項目が同一であって記載形式が異なる情報を異なる種類の情報とした2以上の種類別フィールドを有する
請求項1に記載の入力支援システム。 - 前記種類別正解入力記憶手段は、記入項目が異なる情報を異なる種類の情報とした2以上の種類別フィールドを有する
請求項1または請求項2に記載の入力支援システム。 - 前記種類推定手段は、前記種類別正解入力記憶手段に記憶されている種類別フィールドごとに、入力ログに含まれる過去の各入力情報が当該種類別フィールドにおいて示されている正しい入力に合致するか否かを判定して、合致した入力情報の数である合致ログ件数を求め、求めた合致ログ件数に基づく入力ログとの合致度が所定の閾値以上または最大値をとった種類別フィールドを、対象とする入力欄に入力されるべき情報の種類と推定する
請求項1から請求項3のうちのいずれか1項に記載の入力支援システム。 - 前記入力ログ記憶手段は、過去に入力された各情報に対して有効度が付与された入力ログを記憶し、
前記種類推定手段は、前記有効度をログ1件あたりの重みとして用いて、種類別フィールドごとに入力ログとの合致度を算出する
請求項4に記載の入力支援システム。 - 前記種類別正解入力記憶手段には、粒度の異なる情報を異なる種類の情報とした2以上の種類別フィールドが連結フィールドとして登録されており、
前記種類推定手段は、連結フィールドとして登録されている種類別フィールドは連結された状態を1つの種類別フィールドとして扱って、入力ログとの合致度を算出する
請求項4または請求項5に記載の入力支援システム。 - 前記種類別正解入力記憶手段は、正しい入力を示す情報として当該情報の候補一覧を含み、
前記種類推定手段は、入力ログに含まれる過去の入力情報がいずれの候補に該当するかを特定し、前記結果に基づいて、推定結果とした種類別フィールドの各候補に対してスコアを付与する
請求項1から請求項6のうちのいずれか1項に記載の入力支援システム。 - 前記種類別正解入力記憶手段は、正しい入力を示す情報として当該情報の候補一覧を含み、
入力ログ記憶手段は、過去に入力された各情報に対して該情報を入力したユーザを示す情報が付与された入力ログを記憶し、
前記種類推定手段は、入力ログに含まれる過去の入力情報のうち指定されたユーザが入力した入力情報がいずれの候補に該当するかを特定し、前記結果に基づいて、推定結果とした種類別フィールドの各候補に対してスコアを付与する
請求項1から請求項7のうちのいずれか1項に記載の入力支援システム。 - 前記種類別正解入力記憶手段は、正しい入力を示す情報として当該情報の候補一覧を含み、
前記種類推定手段は、入力ログに含まれる過去の入力情報がいずれの候補に該当するかを特定し、前記結果に基づいて、推定結果とした種類別フィールドの候補一覧の中から優先要素を決定する
請求項1から請求項8のうちのいずれか1項に記載の入力支援システム。 - 前記種類推定手段による推定結果と、種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、対象とする入力欄に新たに入力された情報に対してエラー判定を行い、エラーを検出するエラー検出手段を備えた
請求項1から請求項9のうちのいずれか1項に記載の入力支援システム。 - 前記エラー検出手段は、対象とする入力欄に新たに入力された情報が、種類推定手段によって推定結果とされた種類別フィールドにおいて示されている正しい入力に合致するか否かを判定し、合致しない場合にエラーを検出する
請求項10に記載の入力支援システム。 - 前記エラー検出手段は、対象とする入力欄に新たに入力された情報が、種類推定手段によって推定結果とされた種類別フィールドにおいて示されている正しい入力に合致しない場合であっても、他の種類別フィールドにおいて示されている正しい入力に合致した場合には、エラーを検出するとともに、入力情報の種類が異なる旨を示すメッセージを出力する
請求項10または請求項11に記載の入力支援システム。 - 前記種類別正解入力記憶手段は、正しい入力を示す情報として当該情報の候補一覧を含み、
前記エラー検出手段は、推定結果とされた種類別フィールドに含まれる候補に対して優先要素が定められている場合であって、対象とする入力欄に新たに入力された情報が、前記優先要素を含まない場合には、入力ミスの可能性があるとして注意喚起のためのメッセージを出力する
請求項10から請求項12のうちのいずれか1項に記載の入力支援システム。 - 前記種類推定手段による推定結果と、前記種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、対象とする入力欄に新たに入力された情報から前記入力欄に入力されるべき情報を予測して出力する入力情報予測手段を備えた
請求項1から請求項13のうちのいずれか1項に記載の入力支援システム。 - 前記種類別正解入力記憶手段は、正しい入力を示す情報として当該情報の候補一覧を含み、
前記入力情報予測手段は、種類推定手段によって推定結果とされた種類別フィールドにおいて示されている候補一覧の中から、対象とする入力欄に新たに入力された情報を含む候補を取得し、取得された候補を予測結果として出力する
請求項14に記載の入力支援システム。 - 前記種類別正解入力記憶手段は、正しい入力を示す情報として当該情報の候補一覧を含み、
前記入力情報予測手段は、推定結果とされた種類別フィールドの各候補に対してスコアが付与されている場合には、スコアに基づいて取得された候補を順位付けた上で出力する
請求項15に記載の入力支援システム。 - 前記種類別正解入力記憶手段は、正しい入力を示す情報として当該情報の候補一覧を含み、各種類別フィールドの各候補はレコード間において互いに対応づけられており、
前記入力情報予測手段は、種類推定手段によって推定結果とされた種類別フィールド以外の種類別フィールドにおいて示されている候補一覧の中に、対象とする入力欄に新たに入力された情報と合致する候補が含まれている場合には、当該候補のレコード位置における前記推定結果とされた種類別フィールドの要素を取得し、取得された候補を予測結果として出力する
請求項15または請求項16に記載の入力支援システム。 - 入力ログ記憶手段が、対象とする入力欄に過去に入力された情報を入力ログとして記憶し、
種類別正解入力記憶手段が、情報の種類別に正しい入力を示す情報を記憶し、
情報処理装置が、前記入力ログ記憶手段に記憶されている入力ログと、前記種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、前記入力欄に入力されるべき情報の種類が、前記種類別正解入力記憶手段に記憶されている種類別のフィールドである種類別フィールドのいずれに該当するかを推定する
ことを特徴とする入力支援方法。 - 前記情報処理装置が、前記推定結果と、前記種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、対象とする入力欄に新たに入力された情報に対してエラー判定を行い、エラーを検出する
請求項18に記載の入力支援方法。 - 前記情報処理装置が、前記推定結果と、前記種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、対象とする入力欄に新たに入力された情報から前記入力欄に入力されるべき情報を予測して出力する
請求項18または請求項19に記載の入力支援方法。 - 対象とする入力欄に過去に入力された情報を入力ログとして記憶する入力ログ記憶手段と、情報の種類別に正しい入力を示す情報を記憶する種類別正解入力記憶手段とにアクセス可能な情報処理装置に適用される入力支援プログラムであって、
コンピュータに、
前記入力ログ記憶手段に記憶されている入力ログと、前記種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、前記入力欄に入力されるべき情報の種類が、前記種類別正解入力記憶手段に記憶されている種類別のフィールドである種類別フィールドのいずれに該当するかを推定する処理を実行させる
ための入力支援プログラム。 - 前記コンピュータに、
前記推定結果と、前記種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、対象とする入力欄に新たに入力された情報に対してエラー判定を行わせて、エラーを検出させる
請求項21に記載の入力支援プログラム。 - 前記コンピュータに、
前記推定結果と、前記種類別正解入力記憶手段に記憶されている種類別の正しい入力を示す情報とに基づいて、対象とする入力欄に新たに入力された情報から前記入力欄に入力されるべき情報を予測して出力させる
請求項21または請求項22に記載の入力支援プログラム。
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