JP4374016B2 - Visit spot extraction program and interest evaluation program - Google Patents

Description

  The present invention relates to a visit spot extraction program used when a specific visit spot is extracted from a collection of visit spots. At the same time, the present invention relates to an interest level evaluation program for evaluating personal preference and other interest levels, and in particular, an interest level evaluation program useful in selecting a specific target corresponding to the personal level of interest from a collection of selection targets. About.

For example, as disclosed in Japanese Patent Application Laid-Open No. 2001-184393, there is known a travel plan creation method for presenting a visit spot of a travel destination on an information processing terminal such as a computer. In this travel plan creation method, preferences of individual users are referred to when selecting a visit spot. It is expected that travel plans will be formulated appropriately according to individual user preferences.
JP 09-218643 A JP 2001-184393 A JP 2001-290727 A

  User preferences are determined by keywords. For example, if keywords such as “hot spring” and “Japanese-style room” are set for a specific individual, “hot spring inn” can be presented to the specific individual. Keywords such as “hot spring” and “Japanese-style room” are associated with the hot spring inn in advance.

  For example, an individual who prefers a “gorgeous” hot spring inn would like to be presented with a “gorgeous” hot spring inn. However, the determination of whether or not it is “gorgeous” largely depends on the individual sensibility, that is, the individual subjectivity. In this case, if the individual sensibilities that set keywords for the hot spring inn do not completely match the individual sensibilities that receive the presentation of the visiting spot, it is sure to be a “gorgeous” hot spring for individuals who prefer “gorgeous” hot spring inns. The inn cannot be presented. In the above-described travel plan creation method, preference depending on individual sensibility is not considered at all in selecting a visit spot.

  The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a visit spot extraction program that is very useful when extracting visit spots that match personal preference, that is, satisfaction with the highest possible accuracy. At the same time, an object of the present invention is to provide an interest level evaluation program that can evaluate an individual's preference, that is, an interest level with as high accuracy as possible.

  In order to achieve the above object, according to the first invention, a process for obtaining personal evaluation data for specifying satisfaction of a specific individual in at least two stages with respect to the specific visit spot; For each evaluation standard, the standard evaluation data specifies the process of obtaining standard evaluation data for identifying evaluation values formed based on an unspecified number of evaluations, and the satisfaction of specific individuals specified by the personal evaluation data. And a step of causing the processor to execute a step of associating the evaluation values with each other and a step of searching for a visit spot corresponding to the satisfaction degree of the specific individual from a set of visit spots based on the evaluation value associated with the satisfaction degree of the specific individual. A visit spot extraction program is provided.

  According to such a visit spot extraction program, the satisfaction degree of a specific individual can be evaluated based on an evaluation value formed based on an unspecified number of evaluations. That is, the sensibility of a specific individual can be evaluated by a “scale” that is constructed based on an unspecified number of sensibilities. In this way, the sensitivity of a specific individual is objectively re-evaluated. As a result, the satisfaction level of a specific individual can be determined with high accuracy. If a visit spot is extracted based on such determination, it is considered that the extracted evaluation object surely reflects the satisfaction of a specific individual. The evaluation value may be composed of, for example, an unspecified number of “average values of evaluation”.

  The visit spot extraction program may cause the processor to further execute a step of acquiring map information data for specifying a map and a step of associating the found visit spot with the map. For example, the image information signal for specifying the map may be sent to the Internet communication network. Thus, for example, in a personal computer (personal computer) or a mobile terminal (PDA or mobile phone), the extracted visit spot can be presented on the map.

  In forming the reference evaluation data, the processor may obtain an evaluation information signal that specifies an individual evaluation according to the evaluation reference for each individual visit spot. In this way, reference evaluation data can be established based on an unspecified number of votes. Such evaluation information signals may be collected from, for example, the Internet communication network.

  In addition, according to the second invention, the process of obtaining personal evaluation data for specifying the degree of satisfaction of a specific individual for each visit spot in at least two stages, and the satisfaction of the specific individual from a set of visit spots based on the personal evaluation data A step of extracting a visit spot suitable for each degree, a step of obtaining evaluation information data for identifying an average value of satisfaction formed based on an unspecified number of evaluations for at least the extracted visit spot, and a visit spot A visit spot extraction program is provided that causes a processor to execute a step of associating an average value of satisfaction with satisfaction of a specific individual for each.

  According to such a visit spot extraction program, a specific visit spot can be evaluated based on an “average value of satisfaction” formed based on an unspecified number of evaluations. With reference to these evaluations, a specific individual can select a visit spot. Such an image information signal that specifies the average value of satisfaction may be sent to an Internet communication network, for example. Thus, for example, in a personal computer or a portable terminal, the “average value of satisfaction” can be presented for each visited spot.

  In forming the reference evaluation data, the processor may acquire an evaluation information signal that specifies individual satisfaction for each visit spot. In this way, reference evaluation data can be established based on an unspecified number of votes. Such evaluation information signals may be collected from, for example, the Internet communication network.

  Furthermore, according to the third invention, a process for obtaining personal evaluation data for specifying the degree of satisfaction of a specific individual in at least two stages for each visited spot, and the satisfaction of the specific individual from a set of visited spots based on the personal evaluation data A step of extracting a visit spot suitable for each time, a step of displaying a visit spot in the aggregate on an arbitrary display screen based on an option presentation information signal for identifying the aggregate of the extracted visit spots, and from the aggregate There is provided a visit spot extraction program characterized by causing a processor to execute a step of receiving a selection result notification signal for specifying a selected visit spot and a step of recording a visit spot specified by the selection result notification signal. The

  According to such a visit spot extraction program, a specific individual can individually select a visit spot from a collection of visit spots that are extracted in advance based on the degree of satisfaction of the individual. When making a selection, a specific individual can confirm whether the visit spot is good or bad. In this way, the optimum visiting spot can be selected. What is necessary is just to construct what is called a server client computer system for such selection. The server computer and the client computer may be connected to each other via the Internet communication network. At this time, the processor of the server computer only has to send out an option presentation information signal toward the Internet communication network, and may acquire a selection result notification signal from the Internet communication network.

    Furthermore, according to the fourth invention, for a specific visit spot, a step of acquiring post information data for specifying a sentence expression included in a post from an unspecified number of persons, and a predetermined reference from the sentence expression included in the post A visit spot extraction program is provided, which causes a processor to execute a step of extracting a keyword based on a keyword and a step of displaying a keyword on an arbitrary display screen based on a keyword information signal for specifying the extracted keyword The These keywords can be used greatly in the selection of visiting spots.

  Furthermore, according to the fifth invention, the step of acquiring process information data for identifying a specific visit spot selected from a set of visit spots in relation to the specific individual, and for each specific visit spot, A feature of causing a processor to execute a process of acquiring post management data for specifying presence / absence of a post and a step of generating a reminder notification signal for prompting a post to a specific visit spot when an unposted post is confirmed. A visit spot extraction program is provided. According to such a reminder notification signal, it is possible to reliably collect posts from an unspecified number.

  The dunning notification signal may include input form data for specifying an input form for posting in relation to a specific visit spot. The posting may include personal evaluation data that specifies the satisfaction degree of the specific individual in at least two stages with respect to the specific visit spot.

  Furthermore, according to the sixth invention, the process of acquiring the process information data for specifying the visit spot according to the visit order, the process of acquiring the visited information data for specifying the visited visit spot, and the visited information data Based on the process information data, identifying unvisited visit spots, obtaining additional selection information data identifying newly selected detour visit spots from the collection of visit spots, and unvisited visit spots and There is provided a visit spot extraction program that causes a processor to execute a step of setting a visit order of detour visit spots. Such a visit spot extraction program can surely cope with a detour during travel.

  Furthermore, according to the seventh invention, the process for obtaining personal evaluation data for specifying the degree of interest of a specific individual in at least two stages with respect to the specific evaluation object, and for each evaluation criterion for the specific evaluation object , Obtaining reference evaluation data for specifying evaluation values formed based on an unspecified number of evaluations, and associating the evaluation value specified in the reference evaluation data with the interest level of the specific individual specified in the personal evaluation data An interest degree evaluation program characterized by causing a processor to execute a process is provided. Such an interest degree evaluation program can be executed by a processor in a server computer connected to the Internet, for example.

  According to such an interest level evaluation program, the interest level of a specific individual can be evaluated based on an evaluation value formed based on an unspecified number of evaluations. That is, the sensibility of a specific individual can be evaluated by a “scale” that is constructed based on an unspecified number of sensibilities. In this way, the sensitivity of a specific individual is objectively re-evaluated. As a result, the degree of interest of a specific individual can be determined with high accuracy. The evaluation value may be composed of, for example, an unspecified number of “average values of evaluation”.

  Such a degree-of-interest evaluation program may cause the processor to further execute a process of searching for an evaluation object corresponding to the degree of interest of the specific individual from the set of evaluation objects based on the evaluation value associated with the degree of interest of the specific individual. Good. It is considered that the evaluation object thus found surely reflects the degree of interest of a specific individual.

  As described above, according to the present invention, there is provided a visit spot extraction program that is very useful in extracting visit spots that meet personal preference, that is, satisfaction with the highest possible accuracy. Moreover, in the visit spot extraction program, individual preferences can be understood not only in the visit spot extraction process but also in various stages such as during and after the visit. At the same time, according to the present invention, there is provided an interest level evaluation program capable of evaluating an individual's preference, that is, an interest level with the highest possible accuracy.

  Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings.

  FIG. 1 shows a computer network system 11. The computer network system 11 includes a server computer 13 connected to a network such as the Internet 12, and an information processing terminal connected to the Internet 12, that is, a personal computer (personal computer) 14. Data exchange between the personal computer 14 and the server computer 13 is realized according to a so-called TCP / IP protocol.

  The server computer 13 includes a processor 16 such as a CPU (Central Processing Unit) and a memory 17 connected to the processor 16. The processor 16 can execute a program stored in a hard disk drive (HDD) 18, for example. In executing the program, the processor 16 uses a work area in the memory 17. The server computer 13 functions as a web server that provides a homepage “travel site”. Details of the “travel site” will be described later.

  For example, a disk array device 19 is connected to the server computer 13. In this disk array device 19, a basic personal information database 21, an authentication information database 22, a map information database 23, a spot information database 24, an evaluation information database 25, a personal preference information database 26 and a process information database 27 are constructed. Details of these databases 21 to 27 will be described later. The processor 16 can send data to the databases 21 to 27 and read data from the databases 21 to 27. The processor 16 can grasp the relationship between the databases 21 to 27.

  Similarly, the personal computer 14 includes a processor 31 such as a CPU (Central Processing Unit) and a memory 32 connected to the processor 31. The processor 31 can execute a program stored in a hard disk drive (HDD) 33, for example. In executing the program, the processor 31 uses a work area in the memory 32. An input device such as a keyboard 34 and a mouse 35 and an output device such as a display 36 are connected to the personal computer 14. The processor 31 can receive various commands and data from the keyboard 34 and the mouse 35. Similarly, the processor 31 can display various images (for example, graphics and text) on the screen of the display 36.

  The processor 16 of the server computer 13 can cooperate with the processor 42 of the server computer 41 connected to the Internet 12 in executing a program for realizing a “travel site”, for example. The processor 42 may be a CPU, for example. The processor 42 can execute a program stored in a hard disk drive (HDD) 43, for example. In executing the program, the processor 42 uses a work area in the memory 44. The server computer 41 functions as a web server that provides a homepage “route information site”. In realizing such a route information site, a route information database 46 and a timetable information database 47 are constructed in the disk array device 45 connected to the server computer 41. In the route information database 46, route information of transportation means such as railways and buses is registered. Based on such route information, the processor 42 can search for moving means and moving time between any two points. The processor 42 refers to the timetable information database 47 when searching for the travel time. In the timetable information database 47, the latest timetable is registered for each railway station and each bus stop. The processor 42 can accurately calculate the travel time between two points based on the timetable.

  In addition, for example, a portable terminal 51 can be connected to the Internet 12. The portable terminal 51 includes a processor 52 such as a CPU (Central Processing Unit) and a RAM memory 53 connected to the processor 52. The processor 52 can execute a program stored in the nonvolatile memory 54, for example. In executing the program, the processor 52 uses a work area in the RAM memory 53. The processor 52 can be connected to the Internet 12 wirelessly, for example. Thus, data exchange between the processor 52 and the server computer 13 can be realized.

  The portable terminal 51 incorporates a GPS (Global Positioning System) sensor 55. Based on the output signal of the GPS sensor 55, the position of the portable terminal 51 can be specified with high accuracy. For example, a keyboard 56 and a display 57 are incorporated in the casing of the portable terminal 51. The processor 52 can receive various commands and data from the keyboard 56. Similarly, the processor 52 can display various images (for example, graphics and text) on the screen of the display 57. Such a portable terminal 51 may be a so-called PDA (personal digital assistant) or a cellular phone.

  As shown in FIG. 2, the personal basic information database 21 stores personal ID data 61 for specifying a personal ID for each user. Individual IDs are individually assigned to individual users. Users are identified from each other based on the personal ID. Such personal ID data includes name data 62 that specifies the name of the user for each user, age data 63 that specifies the date of birth of the user for each user, and a user for each user. The gender data 64 that specifies the gender of the user and the family data 65 that specifies the presence or absence of the user's family for each user are commonly associated. In the name data 62, for example, the name may be described using double-byte characters. In the age data 63, the date of birth may be described in, for example, a numerical sequence (yyymmdd). In the sex data 64, a “0” value may be described for a male, and a “1” value may be described for a female. The family data 65 may be described with a “0” value if unmarried and a “1” value if married.

  As shown in FIG. 3, password data 66 for specifying a password for each individual user is registered in the authentication information database 22. The password data 66 is encrypted and associated with the personal ID data 61, for example. Based on the personal ID data 61, the password data 66 can be retrieved.

  As shown in FIG. 4, the map information database 23 stores, for example, map information data 67 that represents a map of Japan. The map information data 67 is associated with spot position information data 68 that specifies the position of each visited spot such as a restaurant, accommodation facility, or tourist attraction. Each spot position information data 68 is mutually identified based on the spot ID. According to the map information data 67, for example, the personal computer 14 can display a map image on the screen of the display 36. The location of each visited spot can be specified on the map image.

  As shown in FIG. 5, spot information data 69 is stored in the spot information database 24 for each visit spot specified by the spot position information data 68. Each spot information data 69 is mutually identified based on the spot ID. The spot information data 69 includes spot name data 71 for specifying the name of the visited spot, telephone number data 72 for specifying the telephone number of the visited spot, address data 73 for specifying the location of the visited spot, and features of the visited spot. Characteristic data 74 describing the business hours, business hours data 75 for identifying the business hours of the visit spot, regular holiday data 76 for identifying the regular holiday of the visit spot, and coordinate value information for identifying the coordinate value of the visit spot on the earth The data 77, the nearest station data 78 for specifying the nearest station (or bus stop) of the visit spot, the walking time data 79 for specifying the walking time from the nearest station (or stop), and the visit spot are averaged. The stay time data 81 specifying the expected stay time is written. These data 71 to 79, 81 may be described by text data or numerical data. In the coordinate value information data 77, the location of the visit spot may be described based on the latitude and longitude with the accuracy of being measured by GPS. However, the location of the visit spot may be specified by other methods. Each spot information data 69 is associated with each spot position information data 68 in the map information database 23 based on the spot ID.

  As shown in FIG. 6, individual visited spots are classified according to predetermined criteria. Here, each visit spot is classified by a large classification such as a restaurant, an accommodation facility, and a tourist attraction. The major category “restaurants” is subdivided into small categories such as Japanese, Chinese, European (for example, French and Italian), and ethnic. Similarly, the large classification “accommodation facilities” is subdivided into small classifications such as hotels, inns, and hot spring inns, and the large classification “tourist attraction” is subdivided into small classifications such as historic sites, shrines and temples, and theme parks. Such classification may be reflected in the spot ID, for example. That is, as apparent from FIG. 6, a specific spot ID is given to each visited spot according to a predetermined rule. Visiting spots can be classified into a large classification and a small classification relatively easily based on the spot ID. In addition, other methods may be used for classifying visit spots.

  As shown in FIG. 7, the evaluation information database 25 stores evaluation information data 82 for each visited spot. As will be described later, this evaluation information data 82 is formed based on votes and postings of an unspecified number of users. The evaluation information data 82 includes reference evaluation data 83. The reference evaluation data 83 specifies an “evaluation average value” for a specific visit spot. Here, several options are set for each evaluation criterion (for example, “atmosphere”, “budget”, “taste”) in forming the “average value of evaluation”. One option of the maximum number of votes is extracted for each evaluation criterion based on an unspecified number of votes. However, other evaluation criteria for forming the “average value of evaluation” may be used. Such evaluation criteria may be set individually for each major classification, for example.

  The evaluation information data 82 further includes recommendation degree data 84. The recommendation level data 84 specifies a recommendation level, that is, a recommendation level for a specific visit spot. Such a recommendation degree is evaluated in, for example, five levels according to the sensitivity of individual users. The recommendation level of “5” means “visit spot that you want to visit”. On the other hand, a recommendation level of “1” means “visiting spots that cannot be recommended”. In the recommendation level data 84, an average value of recommendation level may be specified based on the maximum number of votes, as described above.

  The evaluation information data 82 further includes detailed statistical data 85. The detailed statistical data 85 specifies a rough person image of the user who voted for the recommendation degree “5”. Here, based on the three conditions of “age”, “gender”, and “single or married”, the person images are classified into 44 ways, for example. For each person image, the number of users who voted for the recommendation degree “5” is specified.

  In addition, photo data 86 and diary data 87 are incorporated in the evaluation information data 82. The photograph data 86 only needs to include image data that identifies the image of the specific visit spot. Such image data is provided by an unspecified number of users. According to the photograph data, a photograph of the specific visit spot can be displayed on the screen of the display 36, for example. The diary data 87 only needs to include text data for specifying the sentence expression regarding the specific visit spot. Text data is posted by an unspecified number of users. According to the diary data 87, the text representation of the specific visit spot can be displayed on the screen of the display 36, for example.

  As shown in FIG. 8, the personal preference information database 26 stores preference information data 88 for each individual user. The preference information data 88 is described for each visit spot. The preference information data 88 includes reference evaluation data 89. The reference evaluation data 89 specifies the aforementioned “average value of evaluation” for each evaluation criterion (for example, “atmosphere”, “budget”, and “taste”) for a specific evaluation object, that is, a specific visit spot. The reference evaluation data 89 may be created based on the reference evaluation data 83 included in the evaluation information data 82.

  The preference information data 88 further includes personal evaluation data 91. The personal evaluation data 91 specifies the degree of interest, that is, the degree of preference of the specific individual specified by the personal ID data 61 described above with respect to the specific visit spot. The personal evaluation data 91 may be expressed by a recommendation level, that is, a recommendation level for a specific visit spot, like the recommendation level data 84 described above. Such a recommendation degree may be evaluated in, for example, five levels according to individual sensibility, as described above.

  In addition, photograph data 92 and diary data 93 may be incorporated in the preference information data 88. The photograph data 92 only needs to include image data that identifies the image of the specific visit spot. Such image data is provided from a specific individual specified by the personal ID data 61 described above. The diary data 93 only needs to include text data for specifying the sentence expression regarding the specific visit spot. The sentence expression may be composed of, for example, a diary provided by a specific individual.

  As shown in FIG. 9, the process information database 27 stores process information data 95 for each individual user. The process information data 95 includes time data 96 for specifying time, visit spot data 97 for specifying a visit spot identifier, that is, a spot ID, transfer means data 98 for specifying a transfer means between visit spots, and each visit spot. The estimated staying time data 99 for specifying the expected staying time is described. These data 96 to 99 may be bundled for each date, for example. Thus, the daily schedule is specified. In addition, an input flag 101 is set in the process information data 95 for each visit spot. Details of the input flag 101 will be described later.

  Now, assume that a “travel site” is viewed from the personal computer 14. The user accesses the server computer 13 from the personal computer 14. For example, a web browser may operate on the personal computer 14. As is apparent from the flowchart of FIG. 10, the processor 16 of the server computer 13 sends out an HTML document of “travel site” (step S1). The sent HTML document is taken into the personal computer 14 from the Internet 12. In this way, the top page of the “travel site” is displayed on the screen of the display 36 as shown in FIG. 11, for example.

  In the subsequent step S2, the processor 16 of the server computer 13 checks whether it is used for the first time or after the second time. Such confirmation may be performed based on the above-mentioned top page. For example, if “here” is clicked on the top page, the processor 16 recognizes the first use. When the first use is recognized, the processing operation of the processor 16 proceeds to step S3. The processor 16 creates the process information data 95 based on the dialogue with the user. Details of the process of “creation of stroke information 1)” will be described later.

  When the second and subsequent uses are recognized in step S2, the processor 16 accepts a personal ID and password in step S4. As is clear from FIG. 11, the user may click the “Send” button 104 after entering the personal ID and password in the blank fields 102 and 103 on the screen. The personal ID data specifying the input personal ID and the password data specifying the input password are sent to the server computer 13. In accepting such personal ID and password, for example, CGI technology or ASP technology may be used.

  In subsequent step S5, the processor 16 performs user authentication based on the acquired personal ID data and password data. The processor 16 reads the password data 66 specific to the personal ID data from the authentication information database 22. If the password data do not match, the processor 16 denies the user access. At this time, the processing operation of the processor 16 may move to step S3. Thus, the user is treated as the “first user”. If the password data match, the processor 16 of the server computer 13 transmits a new HTML document to the personal computer 14. Based on the sent HTML document, a menu screen is displayed on the screen of the display 36 as shown in FIG. 12, for example.

  In step S <b> 6, the processor 16 determines whether or not the “new travel plan” button 105 has been clicked. When this button 105 is clicked, the processing operation of the processor 16 proceeds to step S7. The processor 16 creates the process information data 95 based on the dialogue with the user. Details of the process of “creation of stroke information data 2)” will be described later. The stroke information data 95 created after the second time is distinguished from the stroke information data 95 created for the first time.

  In step S8, the processor 16 determines whether or not the “download” button 106 has been clicked. When any download button 106 is clicked on the menu screen, the processing operation of the processor 16 proceeds to step S9. The processor 16 sends download data of the process information data 95 to the Internet 12, for example. Such a click on the download button 106 may be performed by the mobile terminal 51, for example. If the download data of the travel information data 95 is thus taken into the mobile terminal 51, the user can check the travel schedule, that is, the travel based on the download data at the mobile terminal 51 during the travel. Details of the “download process information data” process will be described later.

  In step S <b> 10, the processor 16 determines whether or not the “registration of evaluation information and diary” button 107 has been clicked. When this button 107 is clicked, the processing operation of the processor 16 proceeds to step S11. For each visit spot specified based on the above-described process information data 95, the user registers the evaluation according to the individual evaluation criteria, registers the personal evaluation data 91, and stores the photo data 92 and the diary data 93. Or register. The above-described evaluation information data 82 and preference information data 88 can be created based on the evaluation thus registered, the personal evaluation data 91, the photograph data 92, and the diary data 93. Details of the process of “voting and posting” will be described later.

  In step S <b> 12, the processor 16 determines whether or not the “detour” button 108 has been clicked. When any detour button 108 is clicked on the menu screen, the processing operation of the processor 16 proceeds to step S13. The processor 16 recreates the process information data 95 based on the dialogue with the user. Such a click on the detour button 108 may be performed by the mobile terminal 51, for example. The user can recreate the travel information data 95 even while traveling. Thus, the stroke information data 95 can easily correspond to the user's side trip. Details of the process of “re-creation of process information data” will be described later.

  Here, with reference to the flowchart of FIG. 13, the processing steps of “creation of stroke information data 1)” (step S3 of FIG. 10) will be described in detail. When the processor 16 recognizes the first use as described above, it executes a predetermined program module. The processor 16 acquires personal basic information data in step T1. In this acquisition, for example, as shown in FIG. 14, the processor 16 displays a predetermined image on the screen of the display 36. Based on this image, the user can input “name”, “date of birth”, “sex”, “single / married”. For example, a keyboard 34 or a mouse 35 may be used for input. When the “Register” button 109 is clicked, for example, name data for specifying the name by text data, age data for specifying the date of birth by an 8-digit number, and “0” value or “1” as described above. The “sex” data specifying the sex by the “value” and the family data specifying “single / married” by the “0” value or the “1” value as described above are sent to the server computer 13. Thus, the processor 16 acquires name data, age data, gender data, and family data.

  In subsequent step T <b> 2, the processor 16 newly creates one personal ID data 61 in the personal basic information database 21. The personal ID data 61 may be composed of random text data, for example. The personal basic information data such as name data, age data, sex data, and family data described above is stored in the personal basic information database 21 in association with the newly created personal ID data 61.

  In the subsequent step T3, the processor 16 sets a search range of visited spots, that is, a region range of visited sites. In this setting, the processor 16 acquires moving means data for specifying the moving means and area name data for specifying the visited area name. At this time, the processor 16 displays a predetermined image on the screen of the display 36 as shown in FIG. 15, for example. Based on this image, the user can input a moving means such as “car” or “walk”, or the name of the area to be visited, for example, in units of municipalities. For example, a keyboard 34 or a mouse 35 may be used for input. When the “OK” button 110 is clicked, the movement means data specifying the input movement means and the input area name data specifying the area name are sent to the server computer 13. Thus, the processor 16 obtains the moving means data and the area name data.

  The processor 16 specifies a visited base and a moving range based on the moving means specified by the moving means data. For example, when “walking” is set as the moving means, the processor 16 sets a regional range having a radius of about 5 km from the railway station. The processor 16 acquires the map information data 67 from the map information database 23 based on the set area range. For example, when “car” is set as the moving means, the processor 16 sets a regional range having a radius of about 30 km from the interchange of the highway. Similarly, the processor 16 acquires the map information data 67 from the map information database 23 based on the set area range.

  In the subsequent step T4, the processor 16 specifies a specific area from a set of all the visiting spots in the set area range based on the age data 63, the gender data 64 and the family data 65 registered in the personal basic information database 21. Extract visited spots. In this extraction, the processor 16 refers to the evaluation information data 82 in the evaluation information database 25. Details of the extraction method will be described later.

  In subsequent step T5, the processor 16 accepts a selection of a visit spot from the aggregate of the extracted visit spots. For example, as shown in FIG. 16, the processor 16 presents the extracted visited spot to the user on the screen of the display 36. In such presentation, a flag 111 is displayed for each visited spot. The flag 111 is distinguished for each small classification of the visit spot based on, for example, the color and shape. The user can select a specific visit spot from the presented visit spots. Such a selection is accepted by the processor 16. Details of the receiving method will be described later.

  In subsequent step T6, the processor 16 creates a travel itinerary based on the selected visited spots. Details of the creation method will be described later. The created travel schedule is embedded in, for example, a predetermined HTML document. The created HTML document is sent out to the personal computer 14. As a result, for example, as shown in FIG. 17, the itinerary is displayed on the screen of the display 36. The itinerary is thus presented to the user.

  In subsequent step T7, the processor 16 issues a personal ID and a password to the user. The processor 16 acquires personal ID data 61 from the personal basic information database 21. At the same time, the processor 16 automatically creates a password. The password data specifying the password thus created and the personal ID data 61 are embedded in, for example, a predetermined HTML document. The created HTML document is sent out to the personal computer 14. As a result, for example, as shown in FIG. 18, the personal ID and password are displayed on the screen of the display 36. Thus, the personal ID and password are presented to the user. At the same time, the processor 16 registers the personal ID data 61 and the password data 66 in the authentication information database 22.

  Here, the processing operation of “visit spot extraction 1)” (step T4 in FIG. 13) will be described in detail with reference to the flowchart in FIG. When the processor 16 receives the moving means data and the area name data as described above, the processor 16 executes a predetermined program module. In step U1, the processor 16 creates a list of all the visit spots included in the visited area range set as described above. In the creation, the processor 16 reads the spot position information data 68 from the map information database 23. In the list, each visited spot may be specified by, for example, a spot ID.

  In the following step U2, the processor 16 classifies the visited spots in the list into the aforementioned minor classifications. The processor 16 forms a group for each sub-category such as “Japanese food at a restaurant”, “Chinese food at a restaurant”, or “Hotel at an accommodation facility”. However, such grouping may be performed simultaneously with the creation of the list.

  In the following step U3, the processor 16 extracts a specific visit spot from the collection of visit spots based on the detailed statistical data 85 of each visit spot. At this time, the processor 16 assumes a person image of the user based on the above-mentioned age data 63, gender data 64, and family data 65. The processor 16 searches the detailed statistical data 85 for each visited spot based on the age group and sex of the user, whether single or married. A visit spot supported by an unspecified number of users who match these conditions is extracted. The processor 16 selects a prescribed number of visiting spots in order from visiting spots supported by a large number of people. In specifying the age group, the processor 16 derives the age of the user from the age data 63 of the user.

  In a subsequent step U4, the processor 16 records the extracted visited spots. The visit spot may be temporarily written in the HDD 18 of the server computer 13, for example. In step U5, the processor 16 confirms whether or not the visit spot extraction has been completed for all of the small classifications. If not completed, the processing operation of the processor 16 returns to step U3. In this way, a specific number of visit spots are extracted and recorded for each small category. An option presentation information signal that identifies a collection of extracted visit spots is generated. When the visit spot extraction is completed for all the small categories, the processing operation of the processor 16 proceeds to “accepting spot selection” (step T5 in FIG. 13).

  Next, the processing operation of “accepting spot selection” (step T5 in FIG. 13) will be described in detail with reference to the flowchart in FIG. When the processor 16 generates the option presentation information signal as described above, the processor 16 executes a predetermined program module. The processor 16 acquires the map information data 67 of the display range from the map information database 23, for example, at step V1. A predetermined HTML document is created based on the acquired map information data 67. In the subsequent step V2, the processor 16 identifies a collection of visited spots based on the option presentation information signal. In specifying, the processor 16 may acquire a spot ID for each visited spot. In subsequent step V3, the processor 16 acquires flag image data for each spot ID. The flag image data is associated with the map information data 67. Thereafter, in step V4, the created HTML document, that is, an image information signal is sent to the Internet 12. As a result, the visited spot after extraction can be presented on the screen of the display 36 based on the flag 111 on the map as shown in FIG. 16, for example.

  In step V5, the processor 16 monitors a click operation on the “OK” button. As long as the “OK” button is not clicked, in step V 6, the processor 16 monitors the movement of the pointer 112 of the mouse 35 on the screen of the display 36. For example, as illustrated in FIG. 21, when the pointer 112 is overlaid on a specific flag 111, the processor 16 acquires simple information image data of a visit spot specified by the flag 111. This simple information image data includes, for example, image data that displays the spot name and spot color related to the visited spot, the “average value of evaluation”, and the average value of recommendation. The spot name and spot color may be acquired from the spot information data 69. The “evaluation average value” and the recommendation average value may be acquired from the evaluation information data 82. At this time, the processor 16 may record the number of times and the time over which the pointer 112 is overlaid for each visited spot. Such log data can be used, for example, to determine user preferences.

  In the subsequent step V7, the processor 16 sends out simple information image data, that is, an image information signal, to the Internet 12. Therefore, when the pointer 112 of the mouse 35 is superimposed on a specific flag 111 on the screen of the display 36, a predetermined image is displayed on the screen of the display 36 based on the simple information image data, for example, as shown in FIG. Is displayed. Based on this display, the user can confirm the simple information of the visit spot. In addition, the simple information image data may be embedded in the HTML document in advance together with the above-described flag image data.

  In step V8, the processor 16 monitors the click operation of the mouse 35. When the click operation is performed after the pointer 112 is overlaid on the specific flag 111, the processor 16 acquires detailed information image data of the visited spot specified by the flag 111. In this detailed information image data, for example, in addition to the above-mentioned spot name, the telephone number specified by the telephone number data 72, the regular holiday of the store specified by the regular holiday data 76, and the business hours data 75 are specified. It is only necessary to include business hours of the store, an image of the visit spot specified by the photo data 86, and image data that displays a text representation of the visit spot specified by the diary data 87. Photo data 86 and diary data 87 may be used for a plurality of people.

  When the diary data 87, that is, post information data is included in the detailed information image data, a specific keyword may be extracted from the diary data 87 according to a predetermined standard. The keyword information signal for specifying the keyword may be sent to the Internet 12 together with the above-described simple information image data, for example. Thus, an image including the keyword can be displayed on the display 36 based on the keyword information signal and the simple information image data. The keyword may be simply composed of a subject and a predicate, for example.

  In subsequent step V <b> 9, the processor 16 sends out detailed information image data to the personal computer 14. Therefore, when a click operation is performed on a specific flag 111 on the screen of the display 36, a predetermined image is displayed on the screen of the display 36 based on the detailed information image data, for example, as shown in FIG. Is done. Based on this display, the user can confirm the detailed information of the visited spot. At this time, the processor 16 may record the number of click operations and the image display time for each visited spot. Such log data can be used, for example, to determine user preferences.

  In subsequent step V10, the processor 16 confirms whether or not the visit spot has been selected based on the displayed image. In this confirmation, the processor 16 receives a selection result notification signal from the Internet 12 or the personal computer 14. In the selection result notification signal, the visit spot selected from the aggregate of the extracted visit spots is specified. In the subsequent step V11, the processor 16 temporarily registers the visit spot for each designated date based on the selection result notification signal. The visited spot may be specified by, for example, a spot ID. The selected visit spot may be temporarily stored in the HDD 18 of the server computer 13, for example. When it is finally confirmed in step V5 that the “OK” button has been clicked, the processing operation of the processor 16 proceeds to “creation of a travel table” (step T6 in FIG. 13).

  Next, referring to the flowchart of FIG. 23, the processing operation of “creation of the travel table” (step T6 of FIG. 13) will be described in detail. When the processor 16 confirms the click of the “OK” button as described above, the processor 16 executes a predetermined program module. In step W1, the processor 16 identifies the visit spot temporarily registered for each date. In step W2, the processor 16 sets a specific starting point and final point for each date. For example, if it is the first day of travel, a station or an interchange may be set as a starting point, and an accommodation facility may be set as a final point. If it is a middle day of travel, accommodation facilities may be set at the starting point and the final point. If it is the last day of the trip, an accommodation facility may be set as the starting point, and a station or an interchange may be set as the final point. In setting the starting point and the final point, for example, the user's intention may be reflected. In such a case, for example, CGI technology or APS technology may be used.

  In the subsequent step W3, the processor 16 sets a fixed time spot. With this setting, the processor 16 assigns a specific time zone to, for example, a restaurant selected for lunch or a visit spot that is restricted by a specific business hours. In subsequent step W4, the processor 16 sets a travel table. In setting the travel table, the processor 16 cooperates with the processor 42 of the server computer 41, for example. Based on the distance from the starting point and the final point to the visiting spot, the distance between the visiting spots, the moving means between the visiting spots, the timetable of the moving means, the estimated staying time for each visiting spot, etc. The order of visit will be decided. The planned stay time may be specified based on the stay time data 81, for example. The processor 16 registers the stroke information data 95 in the stroke information database 27 in step W5 based on the stroke table thus created. Based on the stroke information data 95 thus created, the processor 16 generates an HTML document, that is, an image information signal as described above. When the HTML document is sent to the personal computer 14 in step W6, the itinerary can be displayed on the screen of the display 36 as shown in FIG. In addition, a map image may be displayed on the screen of the display 36 according to the itinerary. On such a map image, the movement route may be described according to the itinerary.

  Here, with reference to the flowchart of FIG. 24, the processing steps of “creation of stroke information data 2)” (step S7 of FIG. 10) will be described in detail. When the processor 16 recognizes the click of the “new travel plan” button 105 as described above, the processor 16 executes a predetermined program module. In step X1, the processor 16 sets the search range of the visited spot, that is, the area range of the visited site, as in step T3 (FIG. 13) described above. In the subsequent step X2, the processor 16 extracts a specific visit spot from the aggregate of all visit spots existing in the set area range based on the preference information data 88 accumulated so far. Details of the extraction method will be described later.

  In the subsequent step X3, the processor 16 accepts the selection of the visit spot from the aggregate of the extracted visit spots, similarly to the above-described step T5 (flowchart in FIG. 20). In subsequent step X4, the processor 16 creates a stroke table in the same manner as in the above-described step T6 (flowchart in FIG. 23). The created travel schedule is embedded in, for example, a predetermined HTML document. Thus, the stroke table is displayed on the screen of the display 36 as described above (see FIG. 17). The itinerary is presented to the user.

  Next, the processing operation of “visit spot extraction 2)” (step X2 in FIG. 24) will be described in detail with reference to the flowchart in FIG. When the processor 16 receives the moving means data and the area name data as described above, the processor 16 executes a predetermined program module. In step Y1, the processor 16 creates a list of all the visiting spots included in the visited area range in the same manner as described above. In the following step Y2, as described above, the processor 16 forms a group of visited spots for each minor classification. In the following step Y3 and subsequent steps, the processor 16 extracts a prescribed number of visiting spots for each small classification based on the preference information data 88 stored in the personal preference information database 26.

  First, in step Y3, the processor 16 resets the total number N. The total number N indicates the number of visit spots extracted in the subsequent processing operation. At the same time, the processor 16 sets “5” as the recommended degree V. In subsequent step Y4, the processor 16 checks whether or not the recommended degree is “3” or less. In this case, since the recommended degree is “5”, the processing operation of the processor 16 proceeds to Step Y5.

  In step Y5, the processor 16 searches the recommendation degree “5” in the personal evaluation data 91 of the preference information data 88. In this search, for example, the processor 16 searches the recommendation level “5” in order from the new personal evaluation data 91 to the old personal evaluation data 91. If the recommendation level “5” is found in the one-person evaluation data 91 in the subsequent step Y6, the processing operation of the processor 16 proceeds to step Y7.

  In step Y7, the processor 16 reads the reference evaluation data 89 from the preference information data 88 specified by the found personal evaluation data 91. In this way, the processor 16 acquires the “average evaluation value” corresponding to the recommendation level “5” of the user. The “average value of evaluation” is specified for each evaluation criterion (for example, “atmosphere”, “budget”, “taste”). In the subsequent step Y8, the processor 16 extracts a specific visit spot from the group of visit spots grouped based on the acquired “average value of evaluation”. That is, reference evaluation data 83 that simultaneously satisfies “atmosphere”, “budget”, and “taste” included in the reference evaluation data 89 of the preference information data 88 is searched from the evaluation information data 82 of the evaluation information database 25. In this way, a specific visit spot is extracted from the group of visit spots grouped based on a condition worth the recommendation degree “5” of the specific user. These visit spots are considered to reflect the user's preference greatly.

  In subsequent step Y9, the processor 16 counts the number A of the extracted visited spots. In the subsequent step Y10, the processor 16 calculates the total number N of visited spots extracted so far based on the counted number A. In steps Y11 and Y12, the processor 16 compares the total number N of visited spots with a predetermined number. If the total number N matches the specified number, the processor 16 records the extracted visiting spot in step Y13. The spot ID of each visit spot may be temporarily written in the HDD 18 of the server computer 13, for example.

  In step Y14, the processor 16 confirms whether or not the visit spot extraction has been completed for all the small classifications. If not completed, the processing operation of the processor 16 returns to step Y3. In this way, a prescribed number of visit spots are extracted and recorded for each small category. An option presentation information signal that identifies a collection of extracted visit spots is generated. When the visit spot extraction is completed for all the small categories, the processing operation of the processor 16 proceeds to “accepting spot selection” (step X3 in FIG. 24).

  If the total number N does not reach the specified number in step Y11, the processing operation of the processor 16 returns to step Y6. At this time, the number A is reset in step Y15. In step Y6, the processor 16 searches for the next occurrence of the recommendation level “5”. When the personal evaluation data 91 including the next recommendation level “5” is found, the processing operation of the processor 16 proceeds to step Y7. After step Y7, the processing operation of the processor 16 proceeds as described above. With respect to the extracted visit spots, a prescribed number of visit spots are preferentially extracted from the new personal evaluation data 91. In this way, a prescribed number of visiting spots are secured. In addition, the extracted visit spot can surely reflect the change in the user's preference.

  For example, if no recommendation level “5” is found in the personal evaluation data 91 before the total number N reaches the specified number in step Y11, the processing operation of the processor 16 proceeds from step Y6 to step Y4. At this time, the recommended degree is reduced by one step in step Y16. Accordingly, in step Y5, the processor 16 searches the recommendation level “4” in the personal evaluation data 91 of the preference information data 88. In the search, the processor 16 searches the recommendation level “4” in order from the new personal evaluation data 91 to the old personal evaluation data 91 in the same manner as described above. When the recommendation level “4” is found in the one-person evaluation data 91 in the subsequent step Y6, the processing operation of the processor 16 proceeds to step Y7. After step Y7, the processing operation of the processor 16 proceeds as described above. Thus, the prescribed number of visiting spots are extracted while the degree of recommendation is lowered.

  On the other hand, when the recommendation level “3” is confirmed in step Y4, the processing operation of the processor 16 proceeds to step Y13. Even if the extracted number of visited spots is less than the specified number, the processing operation of the processor 16 is interrupted after the recommendation degree “3”. Therefore, only the visiting spots that match the user's preference can be reliably extracted.

  In addition, when the total number N exceeds the specified number in step Y12, the processing operation of the processor 16 proceeds to step Y17. In step Y <b> 17, the processor 16 narrows down the visited spots after extraction based on the detailed statistical data 85 included in each evaluation information data 82. Similarly to step U3 (FIG. 19) described above, the processor 16 may search the detailed statistical data 85 for each visit spot based on conditions such as the age group, gender, single or married of the user. Visit spots supported by an unspecified number of users who meet these conditions are extracted.

  Here, the processing operation of “downloading of stroke information data” (step S9 in FIG. 10) will be described in detail with reference to the flowchart in FIG. When the processor 16 confirms the click on the download button 106 as described above, the processor 16 executes a predetermined program module. The processor 16 acquires the stroke information data 95 from the stroke information database 27 in step Z1. In obtaining the process information data 95, the processor 16 extracts specific process information data 95 based on the personal ID and date. In subsequent step Z2, the processor 16 acquires spot name data 71 and coordinate value information data 77 from the spot information database 24 based on the spot ID included in the process information data 95.

  In the subsequent step Z3, the processor 16 creates download data for each date as shown in FIG. 27, for example. This download data includes time data 96, spot name data 71, coordinate value information data 77, moving means data 98, planned stay time data 99, and a visit flag 113. Details of the visit flag 113 will be described later. Download data is sent to the Internet 12. Thus, for example, the download data of the stroke information data 95 can be received by the portable terminal 51.

  Here, the processing operation of “voting and posting” (step S11 in FIG. 10) will be described in detail with reference to the flowchart in FIG. When the processor 16 confirms that the “evaluation information and diary registration” button 107 is clicked as described above, the processor 16 executes a predetermined program module. In step P1, the processor 16 creates an input form based on, for example, a predetermined HTML document. The HTML document thus created, that is, input form data, is sent out to the Internet 12. The sent input form data may be received by the personal computer 14 or the mobile terminal 51. On the screens of the displays 36 and 57, for example, as shown in FIG. 29, the input form is displayed. Such an input form may be prepared for each major classification such as “restaurant”, “accommodation facility”, and “tourist attraction”.

  The user can input photograph data, visit date and time, evaluation according to individual evaluation criteria (for example, “atmosphere”, “budget”, “taste”) and diary according to the input form. For such input, the keyboards 34 and 56 and the mouse 35 may be used. The photograph may be taken in the form of image data from a digital camera, for example. When the “Send” button is clicked, the input photo data, the input date / time data specifying the visit date / time, the evaluation data specifying one option for each evaluation criterion, and the input recommendation level are displayed. The recommended degree data to be specified and the diary data for specifying the diary by text data, for example, are sent to the server computer 13. Thus, the processor 16 receives the evaluation information signal and the posting information signal in step P2. The evaluation information signal may include at least evaluation data and recommendation level data, for example. The post information signal may include, for example, photo data, date / time data, diary data, and the like. In subsequent step P3, the processor 16 updates the evaluation information data 82 for each visited spot in the evaluation information database 25 based on the acquired various data. Details of the update method will be described later. Thereafter, in step P4, the processor 16 registers the preference information data 88 in the personal preference information database 26 based on the acquired various data. Details of the registration method will be described later.

  Such “voting and posting” processing operations may be automatically performed after password authentication (step S5 in FIG. 10). In this case, the processor 16 may refer to the input flag 101 with the process information data 95 of the past date. The input flag 101 is set to a “1” value, that is, “not input” when the process information data 95 is created. When voting and posting are completed as described above, the input flag 101 is rewritten to a “0” value, that is, “entered”. Thus, voting and posting can be managed based on the process information data 95. Based on such management, posting reminders can be sent. For example, the input form data described above may be incorporated in the dunning notification signal.

  Next, the processing operation of “update of evaluation information data” (step P3 in FIG. 28) will be described in detail with reference to the flowchart in FIG. When the processor 16 receives the photo data, date / time data, evaluation data, recommendation degree data, and diary data, the processor 16 executes a predetermined program module. In step Q1, the processor 16 identifies the spot ID of the visited spot where the input form has been created as described above. In subsequent step Q2, the processor 16 retrieves the evaluation information data 82 from the evaluation information database 25 based on the identified spot ID. In subsequent step Q3, the processor 16 updates the number of votes obtained for each evaluation criterion (for example, “atmosphere”, “budget”, and “taste”) in the reference evaluation data 83 included in the evaluation information data 82. In subsequent step Q4, the processor 16 confirms the “average value of evaluation” for each evaluation criterion based on the updated number of votes. If there is a change in the “average value of evaluation”, the “average value of evaluation” after the change is registered again.

  In subsequent step Q5, the processor 16 confirms the degree of recommendation based on the recommendation degree data. If the recommendation level is “5”, the processor 16 searches an item “may be disclosed” from the photo data, the evaluation data, and the diary data based on the input form in step Q6. The item “may be disclosed” is similarly registered in the evaluation information data 82. In the evaluation information data 82, the new data may be overwritten with the oldest data. In the subsequent step Q7, the processor 16 updates the detailed statistical data 85. Thereafter, the processing operation of the processor 16 proceeds to Step P4 (FIG. 28).

  Next, the processing operation of “registration information data registration” (step P4 in FIG. 28) will be described in detail with reference to the flowchart in FIG. When the update of the evaluation information data 82 is completed, the processor 16 subsequently specifies the spot ID of the visited spot where the input form has been created as described above in step R1. In subsequent step R2, the processor 16 retrieves the evaluation information data 82 from the evaluation information database 25 based on the identified spot ID. In subsequent step R <b> 3, the processor 16 registers the reference evaluation data 83 in the preference information data 88 of the personal preference information database 26. The reference evaluation data 83 is acquired from the reference evaluation data 83 of the searched evaluation information data 82.

  In subsequent step R4, the processor 16 registers the personal evaluation data 91 in the personal preference information database 26 based on the recommendation degree data. As a result, regarding the specific visit spot, the “average value of evaluation” accumulated in the evaluation information database 25 and the interest level of the specific user, that is, evaluation are correlated with each other. That is, the degree of interest, that is, the preference of the specific user is evaluated by “a ruler” constructed by an unspecified number of votes. For example, if “2) Casual” is specified as the “average value of evaluation” in the evaluation standard “Atmosphere”, even if a specific user selects “1) Luxurious”, this user's evaluation is “Evaluation” Average value ”, that is,“ 2) casual ”. This user's sensibility “1) luxury” is re-evaluated by an unspecified number of sensibilities “2) casual”. Thus, the sensitivity of the user can be objectively evaluated. If the preference information data 88 already exists, the old preference information data 88 may be overwritten with the new preference information data 88. In subsequent step R5, the processor 16 registers the remaining photo data 92 and diary data 93 in the personal preference information database 26. In this way, registration of the preference information data 88 is completed.

  Here, the processing operation of “re-creation of stroke information data” (step S13 in FIG. 10) will be described in detail with reference to the flowchart in FIG. When the processor 16 confirms the click of the “detour” button 108 as described above, the processor 16 executes a predetermined program module. Here, it is assumed that the “detour” button 108 is clicked on the portable terminal 51. When the processor 52 of the portable terminal 51 recognizes the click of the detour button 108, it executes a predetermined program module. The portable terminal 51 confirms the current date and time and the current position. The processor 52 searches for download data specified by the confirmed date and time. The retrieved download data is sent out to the Internet 12, for example. The download data sent out in this way is received by the processor 16 of the server computer 13. For example, current position information data for specifying the current position of the mobile terminal 51, that is, the user may be added to the download data. The current position of the user may be specified based on the GPS sensor 55 in the mobile terminal 51.

  At this time, in the download data, “visited” or “not visited” is specified based on the visit flag 113. A “1” value may be written in the download data in advance. When a specific visit spot is visited, the visit flag 113 of the visit spot is rewritten to “0” value. Such a “visit” fact may be monitored based on the output of the GPS sensor 55, for example. In such monitoring, the coordinate value of the current position calculated based on the output of the GPS sensor 55 and the coordinate value of the visited spot specified by the coordinate value information data 77 of the download data may be compared with each other.

  For example, when the download data is received in step L1, the processor 16 specifies the visited area range based on, for example, the download data in step L2. In specifying the area range, the spot ID included in the download data may be used, or the coordinate value of the current position included in the current position information data may be used. The processor 16 can specify the area range from the spot ID based on the map information database 23.

  In the subsequent step L3, the processor 16 extracts a specific visit spot from the aggregate of all the visit spots existing in the set area range, similar to the above-described step X2 (flowchart in FIG. 25). In the subsequent step L4, the processor 16 accepts the selection of the visit spot from the aggregate of the extracted visit spots, similarly to the above-described steps T5 and X3 (flowchart in FIG. 20). At this time, on the screen of the display 57, it is desired that the visit spot included in the preset travel information data and the visit spot not included in the travel information data are identified. The user can newly select a detour visit spot in addition to the visit spot included in the process information data. Additional selection information data specifying the detour visit spot is acquired by the processor 16. When the detour visit spot is thus selected, the processing of the processor 16 proceeds to step L5. In this step L5, the processor 16 creates a itinerary based on the unvisited visit spot and the newly selected detour visit spot that are included in the itinerary information data. Details of the creation method will be described later. Whether or not the visit has been made may be determined based on the visit flag 113 included in the download data.

  Next, the processing operation of “re-creation of the travel table” (step L5 in FIG. 32) will be described in detail with reference to the flowchart in FIG. When the processor 16 confirms the click of the “OK” button as described above, the processor 16 executes a predetermined program module. In step M1, the processor 16 determines a starting point based on the mobile terminal 51, that is, the current position of the user. A visiting spot that is closest to the current position may be set as the starting point. In the following step M2, the processor 16 sets a travel table. In setting the itinerary, the processor 16 cooperates with the processor 42 of the server computer 41, for example, as described above. The processor 16 determines the visit spot based on the distance from the starting point and the final point to the visited spot, the distance between the visited spots, the moving means between the visited spots, the timetable of the moving means, the estimated stay time for each visited spot, and the like. The order of visit will be decided. At this time, the visit schedule of the detour visit spot is set after the starting point. Following the detour visit spots set in this way, the visit spots that are included in the travel information data in advance and that have not been visited are sequentially incorporated into the visit schedule. Based on the stroke table thus created, the processor 16 re-registers the stroke information data 95 in the stroke information database 27 in step M3. Thereafter, in step M4, download data is created based on the recreated process information data 95. The created download data is sent out to the Internet 12. Thus, the portable terminal 51 can receive new download data. New download data may be overwritten with old download data.

1 is a conceptual diagram schematically showing the configuration of a computer network system. It is a conceptual diagram which shows the structure of the personal basic information database constructed | assembled in a disk array apparatus. It is a conceptual diagram which shows the structure of the authentication information database constructed | assembled in a disk array apparatus. It is a conceptual diagram which shows the structure of the map information database constructed | assembled in a disk array apparatus. It is a conceptual diagram which shows the structure of the spot information data registered into a spot information database. It is a conceptual diagram which shows the structure of spot ID roughly. It is a conceptual diagram which shows the structure of the evaluation information data registered into an evaluation information database. It is a conceptual diagram which shows the structure of the preference information data registered into a personal preference information database. It is a conceptual diagram which shows the structure of the process information data registered into process information database. It is a flowchart which shows roughly the processing operation of a homepage "travel site". It is a conceptual diagram which shows the top page of a "travel site" roughly. It is a conceptual diagram which shows roughly the menu screen after password authentication. 6 is a flowchart schematically showing a processing operation of “creation of stroke information data 1)”. It is a conceptual diagram which shows a specific example of the image utilized when acquiring personal basic information data. It is a conceptual diagram which shows one specific example of the image utilized when acquiring the area name of a moving means and a visited place. It is a conceptual diagram of the image of the map which shows the visit spot proposed by "travel site". It is a conceptual diagram of the image which shows the itinerary proposed by "travel site". It is a conceptual diagram which shows a specific example of the image utilized in presentation of personal ID and a password. 12 is a flowchart schematically showing a processing operation of “visit spot extraction 1)”. 10 is a flowchart schematically showing a processing operation of “accepting spot selection”. It is a conceptual diagram which shows one specific example of the image displayed based on simple image information data. It is a conceptual diagram which shows one specific example of the image displayed based on detailed image information data. It is a flowchart which shows roughly the processing operation of "creation of a process table". 10 is a flowchart schematically showing a processing operation of “creation of stroke information data 2)”. 10 is a flowchart schematically showing a processing operation of “visit spot extraction 2)”. It is a flowchart which shows roughly the processing operation of "download of process information data." It is a conceptual diagram which shows the structure of the download data of process information data. It is a flowchart which shows roughly the processing operation of "voting and posting." It is a conceptual diagram which shows a specific example of the input form utilized in voting and posting. It is a flowchart which shows roughly the processing operation of "update of evaluation information data". It is a flowchart which shows roughly the processing operation of "update of preference information data". It is a flowchart which shows roughly the processing operation of "re-creation of process information data". It is a flowchart which shows roughly the processing operation of "re-creation of a process table".

Explanation of symbols

  12 Internet, 13 Server computer providing “travel site”, 16 Processor for executing visit spot extraction program, 18 Storage medium for storing program, that is, hard disk drive (HDD), 67 Map information data, 82 Evaluation information data, 83 Reference evaluation data, 89 reference evaluation data, 91 personal evaluation data, 95 process information data, 101 input flag as post management data, 113 visit flag as visited information data.

Claims (3)

In conjunction with the authentication of a specific individual, a step of acquiring the travel information data identifying a particular visit spots selected from a collection of visiting the spot in relation to the specific individual, at least for each individual of the specific visited spot a step of the presence or absence of post including the specific individual to individual evaluation data specifying the satisfaction of the specific individual in two stages to obtain the post management flag specifying for each of the specific visited spot, non post the post management flag When There is identified, and generating a reminder notification signal containing the input form data for specifying an input form for the posts into each of the specific visit spots associated with the post management flag of the non post processor A visit spot extraction program characterized by being executed. The visit spot extraction program according to claim 1, wherein the visit spot is classified according to a predetermined criterion, and the input form data is set for each visit spot classification . In visiting the spot extraction program according to claim 1 or 2, with respect to the specific visiting spot, a step of acquiring post information data identifying a sentence representation contained in the post from an unspecified number, contained in those posts Extracting a keyword from a sentence expression based on a predetermined criterion, obtaining spot position information data for specifying a position of the specific visit spot on a map, a keyword information signal for specifying the extracted keyword, and the -out based on the spot position information data, visiting the spot extraction program, characterized in that to execute a step of displaying the keywords in association with the specific visit spots on the map on the front示画surface processor.

Images