JP2007164374A - Method and device for creating ml document - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and device for creating an ML document for quickly and simply facilitating countermeasures to a new type of terminal to be newly added without changing a program for ML document creation itself. <P>SOLUTION: An original document in which specification information adapted to a terminal is preliminarily included as adaptation condition information with an identifier in a tag is created, and when a request for an ML document is accepted from the terminal, the adaptation condition information is retrieved by a filing means 4, and when it is determined that the adaptation condition information is adapted to the request terminal, the identifier and the adaptation condition information are erased, and in the other case, an ML document from which the tag or attributes including the identifier and the adaptation condition information is erased is prepared, and provided to the request terminal. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a server that creates and provides a page made up of an ML (Markup Language) document to be displayed on a terminal in response to a request from an end user terminal. In particular, the server side stores the ML document. The present invention relates to an ML document generation method and an ML document generation apparatus for creation.

  The present invention can be applied to various terminals such as an ordinary personal computer (PC) and a portable information terminal as the end user terminal, and a mobile phone is a most preferable example. Hereinafter, this mobile phone will be described as a preferred example of a terminal.

  In the case where the terminal is a mobile phone, in order to appropriately display a page made up of an ML document on the terminal, the server side that provides this page must comply with the specifications of each terminal requesting the page. A matching ML document must be created. In this case, if the terminal is the personal computer (PC) or the personal digital assistant (PDA), there is not much trouble in the “creation of an ML document that meets the specifications of each terminal”.

  However, if the terminal is the mobile phone that is currently targeted, there will be a problem in the above-mentioned “creation of an ML document that meets the specifications of each terminal”.

  In recent years, mobile phones have exploded explosively on a global scale, and a large number of so-called carriers have started to appear along with this. For this reason, the types of terminals (mobile phones) provided by each carrier are increasing day by day. In other words, new models are appearing one after another.

  Then, on the server side, it is necessary to create the page by using various ML documents for each model. That is, since the contents that can be displayed differ depending on the terminal model, it is necessary to recreate a page creation program corresponding to each model on the server side.

In addition, as a well-known technique relevant to this invention, there exist the following [patent document 1]-[patent document 3], for example. However, these patent documents do not suggest any technical idea such as addition of “adaptation condition information” in the tag, which is a feature of the present invention described in detail later.
By the way, [Patent Document 1] states that “terminal information used by a user is registered in advance, and terminal information is acquired from user information at the time of actual communication, and then converted into ML suitable for each model by a conversion program. [Patent Document 2] is a method of “extracting terminal constraint information from information transmitted from a terminal and converting it into ML suitable for each model based on this information”. Yes, [Patent Document 3] is a method of “referring to resource information of a terminal stored in advance on the server side and converting it into an ML suitable for the model by a conversion program”.

JP 2004-78354 A JP 2001-222487 A Japanese Patent Laid-Open No. 11-250009

  As described above, when the terminal models are added one after another, the server side has to take measures according to each model, but there are two conventional methods for dealing with this.

  First, the first method is that a branch process corresponding to each model is installed in the program for creating the page so that an ML document suitable for the target model can be created. Is the method.

  The second method is to once create an ML document in a predetermined fixed format, and then convert the ML document into an ML document suitable for each model using the ML conversion program. Is the method.

  However, the first and second methods have the following problems. First, in the first method, since the program creator must re-create a very complicated program for each page, the man-hour for developing the program becomes enormous and the quality assurance of the program is sufficient. It is a problem that is not done.

  On the other hand, in the second method, when a new model terminal appears, the ML conversion program must be rewritten, so that it takes a long time to cope with the new model. Since there is a problem, and the ML conversion program itself that should be used in common must also be changed, it is necessary to re-examine the quality assurance of the existing site (WEB site) that has already been constructed. There is also a second problem.

  Eventually, regardless of which of the first and second methods described above, conventionally, it is indispensable to “change the program itself for ML document creation every time a model is added”. It caused various problems.

  Therefore, in view of the above-mentioned problems, the present invention provides an ML document generation method and apparatus that can quickly and simply cope with a newly added terminal without changing the ML document creation program itself. It is intended to provide.

FIG. 1 is a flowchart showing an ML document generation method according to the present invention. First, the premise of the method according to the present invention is an ML document generation method for creating an ML document related to a request in the server device for the request terminal in response to a request from each terminal of a plurality of end users. And here are the steps characteristic of that method:
Step S11: In the server device, an original document in which various specification information suitable for the terminal is included in the tag as adaptation condition information in advance with an identifier is created.
Step S12: The server device side has filtering means, and when the request is received from the terminal, the filtering means searches the adaptation condition information in the tag,
Step S13: It is determined for each tag or attribute whether or not the requesting terminal meets the adaptation condition.

Step S14: When it is determined in step S13 that it is compatible, the identifier in the tag and the adaptation condition information are deleted,
Step S15: If it is determined in step S13 that it does not conform, the tag or attribute including the identifier and the adaptation condition information is deleted,
Step S16: Completing the ML document,
Step S17: The completed ML document is provided to the requesting terminal.

According to the present invention, the branch processing that requires a great number of man-hours is excluded from the program, and the compatibility with the new model is solved by introducing the above-described concept of “filtering” processing. By introducing this filtering process, define the characteristics of each terminal, that is, its specific specifications and attributes without changing anything that affects all sites, that is, the part corresponding to the above-mentioned ML conversion program. By using only the “adaptation condition information” described above as a change parameter, it is possible to easily cope with a new model on the server device side. In other words, the server device can quickly do so only by changing the additional information (adaptation condition information) to be newly added to the original document.
When paying particular attention to the above-mentioned [Patent Document 1] to [Patent Document 3], there is a difference in how these known technologies acquire restriction information unique to the terminal, but ML suitable for the terminal is used. All of the portions that generate ”depend on the“ conversion program ”. In other words, when a new terminal appears with a new function, the ML conversion program must be rewritten, and it takes time to cope with the new model, and the commonly used ML conversion program is changed. Accordingly, there is a problem that the quality assurance of the existing site needs to be performed again. However, according to the present invention, “rapid response to new models” and “localization of influence on existing sites” can be achieved.

  In addition, when such extra additional information is included in the ML itself, the browser on the terminal side cannot be decrypted, so the idea of inserting additional information into the ML could not exist.

  Thus, according to the present invention, it is possible to solve the quality assurance problem of the existing site (WEB site) described above, and to easily and quickly cope with new terminals that appear one after another in a short period of time. .

  FIG. 2 is a diagram showing a basic configuration of an ML document generation apparatus based on the present invention. Throughout the drawings, similar components are denoted by the same reference numerals or symbols. In addition, definitions of main terms used in the present invention are summarized in paragraphs [0053] to [0055] with reference to FIG.

  An apparatus 1 shown in FIG. 1 is an ML document generation apparatus for creating an ML document related to a request for the request terminal in response to a request from each terminal of a plurality of end users. Means 2, model identifying means 3, filtering means 4, and reply means 5 are provided. The filtering unit 4 includes a determination unit 41 and a selection unit 42, and the determination unit 41 includes a correspondence table 43. Each of the above means has the following functions.

The original document creation means 2 creates an original document in which various specification information suitable for the terminal side is included in the tag together with an identifier as adaptation condition information.
The model identification unit 3 identifies the model of the terminal that requests provision of the ML document.
The filtering unit 4 determines whether or not the identified terminal model is compatible with the adaptation condition information by the determination unit 41, and based on the determination result, the selection unit 42 Delete the identifier and adaptation condition information in the tag, and if it does not match, delete the tag or attribute from the original document,
The reply unit 5 returns the filtered original document as an ML document to the requesting terminal.

  As described above, in the server device including the ML document generation device 1, various specification information suitable for the terminal is included in advance in the tag as adaptation condition information together with the identifier.

FIG. 3 is a diagram (part 1) for explaining the adaptation condition information following the identifier (@).
FIG. 4 is the same figure (the 2).

  First, referring to FIG. 3, there is shown an example of adaptation condition information (3GPP, JPG, etc.) and corresponding terminal model specifications. In Example 1, it is shown that the terminals of the types called G802i and M890i are compatible with 3GPP (3rd Generation Partnership Project). That is, it represents a model that can play a 3GPP format movie.

  Example 2 shows JPG as adaptation condition information, and further shows a model capable of displaying a JPG image. Here, a terminal of model B5000K and a terminal of each model belonging to 3GPP are shown.

  Contents such as Example 1 and Example 2 are stored in advance in the correspondence table 43 of FIG. The contents of the correspondence table 43 can be freely added (or deleted). For example, when a new terminal corresponding to scalable vector graphics (SVG) appears, the model name is added. This is shown in FIG. According to Example 3 shown here, there are G880i, M800i, and G802i as SVG compatible models.

  3 and 4 described above relate to the above-described determination unit 41, particularly the correspondence table 43 provided therein. That is, the discriminating unit 41 in the filtering unit 4 shown in FIG. 2 groups, for each specification, one or a plurality of terminal models that conform to the specification, and the group model name and the specification name The correspondence table 43 is provided, and with reference to the correspondence table 43, it is determined whether or not it matches the requesting terminal described above.

Here, a specific example of a tag including all the adaptation condition information based on the present invention will be shown. FIG. 5 is a diagram showing an example of an original document, which is created by the original document (test page) creation means 2 in FIG. In FIG. 5, tags starting with <object are represented. The at sign @ following this <object is an identifier for the filtering means 4 to identify in the tag that the portion representing the adaptation condition information follows, and is attached immediately before the adaptation condition information. In this figure, there are two identifiers. Adaptation condition information indicating a specification (3GPP in this example) is described in the first identifier @, and conformance indicating another specification (SVG in this example) is described in the second identifier @. Information is described. In general, it is as follows.
<Tag name @ Adaptation condition Attribute name @ Adaptation condition = Attribute value ...>

  The at sign @ as the identifier is merely an example, and any symbol may be used as long as the filtering means 4 can recognize that the adaptation condition information is described. Regardless of the symbol used, the original document creation means 2 shown in FIG. 2 uses an identifier for identifying, for example, @ in the tag, that the filtering means 4 identifies the part representing the above-mentioned adaptation condition information. And additional information adding means 21 for adding to the adaptation condition information.

  FIG. 6 shows a first example and a second example of the tag after filtering as (a) and (b), respectively. This figure and FIG. 5 described above best illustrate the features of the present invention.

  FIG. 6A shows a case where the model of the requesting terminal that requests the ML document is M890i. If this M890i terminal accesses the server apparatus 1 side, the filtering unit 4 in FIG. 2 refers to the contents shown in FIG. 4 in the correspondence table 43 by the discriminating unit 41. First, the M890i terminal has the specification “3GPP ”And that it does not have another specification“ SVG ”. Therefore, the scale attribute is deleted and does not remain in the object tag in the ML document finally returned to the M890i terminal. What remains is attribute name width = “150”. This is the role of the selection unit 42 in the filtering means 4 shown in FIG. What should be noted at this time is that the additional information including the identifier @ and the adaptation condition information is deleted. For this additional information, it is sufficient to inform the filtering means 4 only of the existence of the adaptation condition information. After that, if the additional information remains, the browser of the M890i terminal cannot decode the tag. Therefore, it is deleted from the tag.

  Further, in the second example shown in FIG. 6B, a case where the model of the requesting terminal that requests the ML document is G802i is shown. If this G802i terminal accesses the server device 1 side, the filtering means 4 in FIG. 2 refers to the contents shown in FIG. 4 in the correspondence table 43 by the discriminating unit 41, and first the G802i terminal has the specification “3GPP ”And“ SVG ”. For this reason, the scale attribute is also left in the object tag. As described above, this is the role of the selection unit 42 in the filtering means 4 shown in FIG. Also in this case, it should be noted that the additional information composed of the identifier and the adaptation condition information is deleted. As described above, it is sufficient for the additional information to notify the filtering unit 4 only of the existence of the adaptation condition information. After that, if the additional information remains, the tag in the browser of the G802i terminal is sufficient. Since it is impossible to decipher, the tag is deleted.

  Note that when the adaptation condition information (= additional information) attached to the identifier is not described, the filtering unit 4 determines that the tag or attribute is compatible with the request terminals of all models.

  Among the adaptation condition information specified by the identifier @, the adaptation condition information related to the determination of the specification is expressed by a logical expression that combines a plurality of specification names. The selection process can be easily performed. This is illustrated in the figure.

FIG. 7 is a diagram showing a logical expression when a plurality of specification names are combined. As shown in the lower part of the figure, for example, when a terminal model having specification a but not specification b or c is designated as adaptation condition information, the following is performed.
<Object @ a *! (B + c) ...>

Here, to say a word about the above-described deletion processing, it is possible to specify whether or not all the subordinate tags are to be deleted when the adaptation conditions are not met. An example of this description is shown below. If (all) is specified, everything under the tag is deleted.
<Object @ (all) ...>
A
B
C
</ Object> (1)
<Object @ a ...>
A
B
C
</ Object> (2)
(However, A, B, and C are tags or text)

In the above (1), in the case of a terminal that does not have the specification a, everything including ABC is deleted,
In (2) above, in the case of a terminal that does not have the specification a, the object tag is deleted and only ABC is output.

  The above description can be expressed as an overall system as shown in FIG. However, it is drawn conceptually. Conceptually, the ML document generation device 1 includes a page (ML) generation program unit 35, a server device 34, an ML filtering device (described above 4) 33, and a correspondence table 36 (described above 43). .

  The ML document request access 37 from the mobile phone 31 reaches the ML document generation device 1 via the network 32, where information related to the model of the request source mobile phone 31 is extracted, and the ML document conforming to this model is extracted. Is returned (38) through the process described above. Note that the ML document is actually a document described in HTML, and the server device 34 is a WEB server.

  Finally, detailed specific examples according to the present invention will be shown.

  FIG. 9 is a functional block diagram showing a specific example based on the present invention. In this figure, a page creation function unit 52, an ML filtering function unit 53, and a group identification function unit 55 are functional units that particularly represent the features of the present invention. A request reception function unit 51, a terminal identification function unit 54, The response reply function unit 56 is a normal function unit that is also used in a general ML document generation apparatus.

  In the page creation process in the page creation function unit 52, all contents to be output are output according to the specifications of the terminals defined in the definition of “terminal group”. Conventionally, since it has been necessary to control whether to output or not depending on the terminal, the output program is complicated and difficult to create.

  FIG. 10 is a diagram illustrating an output example from the page creation function unit 52. (A) of this figure is an output example 1 and shows tags when it is desired to change the size of the logo image displayed on the terminal depending on the model (type) of the terminal 31, and is type 1 (large), type 2 (medium) And three types of type 3 (small) are illustrated.

  FIG. 10B shows three examples when it is desired to change the wording and image displayed on the terminal 31 depending on the model (type) of the terminal 31.

Returning to FIG. 9, in the terminal identification function unit 54, information for identifying the model of the terminal 31 is included in the request from the terminal, and this is extracted to identify the terminal type. For example, the ID indicating the terminal type is described in the User-Agent header of the request as follows.
User-Agent: Manufacturer name / Version number / S900i
Here, the part of S900i is an ID indicating the terminal model.

Further, the group identification function unit 55 extracts a group including the identified terminal from the group definition information, and creates a list. For example, when the IDs of all groups including S900i as an element are extracted as the “applicable group list”, the following is obtained.
List of applicable groups = {G1, G8, G29, G52, type2, ...}

  Furthermore, the ML filtering function unit 53 filters the original document with the matching condition information created by the page creation function unit 52 based on the “matching group list” extracted above. In other words, if it is included in the list of matching groups, it is evaluated as true, and if it is not included, it is evaluated as false, thereby performing a logical operation of the matching condition portion following @. Three specific examples are shown below.

Example 1: div @ G1 is true. In other words, output target to ML document Example 2: div @ G2 * G8 is false and true = false. That is, the object to be deleted from the original document Example 3: div @ G2 + type2 is false or true = true. In other words, output target to ML document

  Thus, the ML filtering function unit 53 performs filtering in the form shown in FIG.

  FIG. 11A shows the document after filtering in FIG. 10A, and FIG. 11B shows the document after filtering in FIG. 10B. FIG. 11A shows an example where only “medium.gif” extracted based on the table 43 is extracted from FIG. 10A shown in the upper part, and FIG. 11B shows the upper part. 10B shows an example in which all the following div tags that match based on the table 43 are extracted.

  Various terms have been described above, but in order to eliminate confusion in the interpretation of these terms, the definitions are summarized at the end. 12A and 12B are diagrams for explaining definitions of main terms used in the present invention.

Referring to FIG. 12,
"Tag": A generic term for "tag name" and "attribute name = ...". In the example of "tag" shown in FIG. 12A, "<" to ">", that is, "<...>" A clump is a tag.
Next, looking at “additional information”, “identifier”, and “adaptation condition information”, if there is a “tag” as shown in FIG.
"Additional information": Information added in the tag, that is, "@ JPG * SVG"
·"identifier":"@"
・ "Adaptation condition information": "JPG * SVG"
It is.

Furthermore, if you look at "specification name" and "model name", if you explain along the "tag" above,
・ "Specification name": "JPG" or "SVG"
The “specification name” includes a group of “specification name” and “model name”, and is, for example, the above-mentioned JPG: B5000K, (3GPP).
・ "Model name": "B5000K"

  As described above, according to the present invention, a new function of a new model can be easily and quickly performed by changing only additional information (particularly adaptation condition information) added to HTML without requiring a change of the program itself. It becomes possible to cope with.

It is a flowchart showing the ML document production | generation method based on this invention. It is a figure which shows the basic composition of the ML document production | generation apparatus based on this invention. It is FIG. (1) for demonstrating adaptation condition information. It is FIG. (2) for demonstrating adaptation condition information. It is a figure which shows an example of an original document. It is a figure which shows the 1st example of the tag after (a) filtering, and the 2nd example of the tag after (b) filtering. It is a figure which shows the logical expression in the case of combining a some specification name. It is a figure which shows the outline | summary of the whole system. It is a functional block diagram which shows the specific example based on this invention. (A) is a diagram showing an output example 1, (b) is a diagram showing an output example 2. (A) is a figure which shows the document after filtering about FIG. 10 (a) and (b) about FIG.10 (b) at each lower stage. (A) And (b) is a figure for demonstrating the definition of the main terms used by this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ML document production | generation apparatus 2 Original document creation means 3 Model identification means 4 Filtering means 5 Reply means 21 Additional information provision means 41 Discriminating part 42 Selection part 43 Correspondence table 52 Page creation function part 53 ML filtering function part 55 Group identification function part

Claims (11)

In accordance with a request from each terminal of a plurality of end users, an ML document generation method for creating an ML document related to the request in the server device for the request terminal,
(I) In the server device, an original document in which various specification information suitable for the terminal is preliminarily included in the tag as adaptation condition information together with an identifier is created,
(Ii) The server device side includes filtering means, and when the request is received from the terminal, the filtering means searches the adaptation condition information in the tag,
When it is determined that the request terminal is matched, the identifier and the adaptation condition information in the tag are deleted. On the other hand, when it is determined that the request terminal is not matched, the tag or attribute including the identifier and the adaptation condition information is deleted. Create an ML document with
(Iii) providing the created ML document to the requesting terminal;
An ML document generation method characterized by the above.   2. The ML document generation according to claim 1, wherein an identifier for identifying in the tag that the filtering means is a part representing the adaptation condition information is attached to the adaptation condition information. Method.   The ML document generation method according to claim 1, wherein the adaptation condition information includes a specification name of a terminal for determining whether or not the request terminal is adapted.   The filtering means groups, for each specification name, a model of one or a plurality of terminals conforming to the specification, and includes a correspondence table between the group of model names and the specification names, and refers to the correspondence table. The ML document generation method according to claim 2, wherein the determination as to whether or not the request terminal is suitable is performed.   The ML document generation method according to claim 2, wherein the adaptation condition information regarding the specification is expressed by a logical expression combining a plurality of the specification names.   The ML document generation method according to claim 1, wherein each terminal of the plurality of end users is a mobile phone.   The ML document generation method according to claim 1, wherein the ML document is a document described in HTML.   The ML document generation method according to claim 1, wherein the server device is a WEB server. In response to a request from each terminal of a plurality of end users, an ML document generation device for creating an ML document related to the request for the request terminal,
Original document creation means for creating an original document in which various specification information suitable for the terminal is included in the tag together with an identifier as adaptation condition information;
A model identifying means for identifying a model of a terminal that requests provision of the ML document;
A discriminating unit for discriminating whether or not the identified model of the terminal conforms to the adaptation condition information, and based on the discrimination result, deletes the identifier and the adaptation condition information in the tag when conforming, A selection unit that performs a process of deleting the tag or attribute from the original document when it does not fit,
A reply means for returning the filtered original document as the ML document to the requesting terminal;
An ML document generation device comprising:   The original document creation means adds additional information that adds adaptation condition information and an identifier for identifying in the tag that the filtering means is a part representing the adaptation condition information. The ML document generation device according to claim 9, further comprising: means.   The discriminator in the filtering means groups, for each specification, one or a plurality of terminal models that conform to the specification, and includes a correspondence table between the group of model names and the specification names. 10. The ML document generation apparatus according to claim 9, wherein the determination is made as to whether or not the requesting terminal is matched by referring to a table.

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