KR20210057338A - Method and computer program for providing travel schedules - Google Patents

Abstract

According to some embodiments of the present disclosure, provided is a computer program stored in a computer-readable storage medium. The computer program includes instructions for causing a processor of a server to perform the steps of: receiving a signal for requesting a travel schedule from a user terminal; transmitting a signal requesting user information for generating the travel schedule to the user terminal in response to receiving the signal for requesting the travel schedule; receiving the user information corresponding to a signal for requesting the user information from the user terminal; deriving travel preference information of the user by analyzing the user's social networking service (SNS) account information included in the user information; generating travel schedule information by processing the user's travel period and the user's travel preference information included in the user information with an AI-based travel schedule generation model; and transmitting the travel schedule information including a schedule for each date included in the travel period and a moving route to the user terminal.

Description

METHOD AND COMPUTER PROGRAM FOR PROVIDING TRAVEL SCHEDULES}

The present disclosure relates to a method of providing a travel schedule and a computer program stored on a computer readable storage medium.

Due to factors such as the development of transportation and the increase in leisure time, interest and demand for travel are greatly increasing. In accordance with this demand, various and extensive travel information is provided to travelers. In particular, with the development of the Internet and mobile terminals, travelers can easily access travel information anytime, anywhere.

In order to more effectively deliver such vast travel information to travelers, a travel information guidance method that categorizes and provides various travel information according to various criteria is considered, and is developed as a method of recommending travel destinations that meet the needs and conditions of travelers. have.

However, in general, the method of recommending travel destinations categorizes and provides information on each destination according to the subject or type, or provides information on destinations corresponding to the traveler's choice. It does not comprehensively reflect various factors.

Accordingly, there is a demand in the art for a method of providing a travel schedule by comprehensively reflecting various factors that may be considered in a traveler's travel destination selection.

Korean Patent Publication No. 10-1094484

The present disclosure has been devised in response to the above-described background technology, and an object thereof is to provide a method for providing a travel schedule and a computer program stored in a computer-readable storage medium.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems that are not mentioned will be clearly understood by those skilled in the art from the following description.

According to some embodiments of the present disclosure for solving the above-described problem, a computer program stored in a computer-readable storage medium is disclosed. The computer program includes instructions for causing a processor of the server to perform the following steps, the steps comprising: receiving a signal requesting a travel schedule from a user terminal; Upon receiving the signal requesting the travel schedule, transmitting a signal requesting user information for generating the travel schedule to the user terminal; Receiving the user information corresponding to a signal for requesting the user information from the user terminal; Deriving travel preference information of the user by analyzing the user's SNS (Social Networking Service) account information included in the user information; Generating travel schedule information by processing the user's travel period and the user's travel preference information included in the user information with an artificial intelligence-based travel schedule generation model; And transmitting the travel schedule information including a schedule for each date and a movement route included in the travel period to the user terminal.

In addition, the step of deriving the user's travel preference information based on the user's SNS account information included in the user information includes at least one of a profile, a comment, album information, and image information. Analyzing the user's SNS account information using at least one of natural language processing and image analysis; And deriving travel preference information of the user based on the analyzed result.

In addition, the step of analyzing the user's SNS account information in at least one of natural language processing and image analysis may include: deriving representative words, nouns, and adjectives for text included in the SNS account information; And analyzing the SNS account information of the user using the natural language processing method for deriving a first travel tendency of the user based on the representative word, the noun, and the adjective.

In addition, analyzing the user's SNS account information by using at least one of natural language processing and image analysis may include performing location recognition, face recognition, and facial expression recognition on images included in the SNS account information; And location analysis through the location recognition, number counting of the person included in the image through the face recognition, and SNS account information of the user through the image analysis method to derive the user's second travel tendency through the facial expression recognition. Analyzing the; may include.

In addition, the step of deriving the travel preference information of the user based on the analysis result may include, based on the analysis result, a first preference related to the weather and natural environment of the travel destination, and a first preference related to the auxiliary facilities of the travel destination. 2) deriving travel preference information of the user including at least one of a preference, a third preference related to the service of the travel destination, a fourth preference related to the transportation of the travel destination, and a fifth preference related to the price of the travel destination; including; can do.

In addition, the step of generating travel schedule information by processing the user's travel period and the user's travel preference information included in the user information with an artificial intelligence-based travel schedule generation model includes: weather and nature of each of a plurality of travel destinations. First information related to the environment, second information related to auxiliary facilities of each of the plurality of travel destinations, third information related to services of each of the plurality of travel destinations, fourth information related to traffic of each of the plurality of travel destinations, and the plurality of travel destinations Acquiring travel destination information including at least one of fifth information related to each price; Inputting the travel destination information corresponding to the travel period and travel preference information of the user into the travel schedule generation model; And obtaining travel schedule information output from the travel schedule generation model.

In addition, the travel schedule generation model may be configured with an artificial neural network and may be learned through a supervised learning method.

In addition, the supervised learning method may include obtaining travel satisfaction information received from each of a plurality of user terminals that have received the travel schedule information corresponding to each of a plurality of users; Labeling the travel satisfaction information on travel schedule information for learning; Comparing the travel schedule information output from the artificial neural network with the training travel schedule information to derive an error; And updating the weight of the artificial neural network by backpropagating the error to the artificial neural network.

In addition, the travel satisfaction information may target each of a plurality of users who have been provided with the travel schedule information, and may be information responding to a questionnaire including question text for deriving a satisfaction level of the travel schedule information.

In addition, the step of generating travel schedule information by processing the user's travel period and the user's travel preference information included in the user information with an artificial intelligence-based travel schedule generation model includes: the user's travel period and the user Inputting the travel preference information of the travel schedule into the travel schedule generation model; Acquiring travel places for each date output from the travel schedule generation model; And determining that the travel schedule information includes the travel places for each date, wherein the movement path may include a movement path for moving each of the travel locations for each date.

In addition, acquiring event information corresponding to each travel place for each date; Determining a travel schedule for each time corresponding to each travel location for each date, based on the event information; And determining that the travel schedule information includes the travel schedule for each time; the schedule for each date may include a travel schedule for each time corresponding to each of the travel locations for each date.

Further, it may further include determining that the travel schedule information includes the event information so that the travel schedule for each time can be changed based on the event information.

In addition, the travel schedule information may be shared with the user's SNS account or a specific website.

According to some embodiments of the present disclosure for solving the problems as described above, a method is disclosed in which a server provides a travel schedule. The method includes: receiving a signal requesting a travel schedule from a user terminal; Upon receiving the signal requesting the travel schedule, transmitting a signal requesting user information for generating the travel schedule to the user terminal; Receiving the user information corresponding to a signal for requesting the user information from the user terminal; Deriving travel preference information of the user by analyzing the user's SNS (Social Networking Service) account information included in the user information; Generating travel schedule information based on the user's travel period included in the user information and the user's travel preference information; And transmitting the travel schedule information including a schedule for each date and a moving route included in the travel period to the user terminal.

The technical solutions obtained in the present disclosure are not limited to the above-mentioned solutions, and other solutions that are not mentioned are clearly to those of ordinary skill in the technical field to which the present disclosure belongs from the following description. It will be understandable.

The present disclosure provides a method and a computer program for providing a travel schedule based on a user's travel preferences, thereby enhancing a user's travel satisfaction.

The effects obtainable in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art from the following description. .

Various aspects are now described with reference to the drawings, wherein like reference numbers are used collectively to refer to like elements. In the examples that follow, for illustrative purposes, a number of specific details are presented to provide a comprehensive understanding of one or more aspects. However, it will be apparent that such aspect(s) may be practiced without these specific details.
1 illustrates a system for providing a travel schedule according to some embodiments of the present disclosure.
2 is a flowchart illustrating an example of a method for a server to provide a travel schedule according to some embodiments of the present disclosure.
3 is a diagram for describing travel schedule information according to some embodiments of the present disclosure.
4 is a block diagram of an artificial neural network configuring a travel schedule generation model according to some embodiments of the present disclosure.
5 shows a simplified and general schematic diagram of an exemplary computing environment in which some embodiments of the present disclosure may be implemented.

Various embodiments and/or aspects are now disclosed with reference to the drawings. In the following description, for illustrative purposes, a number of specific details are disclosed to aid in an overall understanding of one or more aspects. However, it will also be appreciated by those of ordinary skill in the art that this aspect(s) may be practiced without these specific details. The following description and the annexed drawings set forth in detail certain illustrative aspects of the one or more aspects. However, these aspects are illustrative and some of the various methods in the principles of the various aspects may be used, and the descriptions described are intended to include all such aspects and their equivalents. Specifically, as used herein, “an embodiment”, “example”, “aspect”, “example”, and the like are not construed as having any aspect or design being better or advantageous than other aspects or designs. May not.

Further, various aspects and features will be presented by a system that may include one or more apparatuses, terminals, servers, devices, components and/or modules, and the like. The devices, terminals, servers discussed in connection with the drawings, that various systems may include additional devices, terminals, servers, devices, components and/or modules, etc. It should also be understood and appreciated that it may not include all of devices, components, modules, etc.

The terms "computer program" "component", "module", "system" and the like as used herein may be used interchangeably, and computer-related entities, hardware, firmware, software, a combination of software and hardware, or Refers to the execution of software. For example, a component may be, but is not limited to, a process executed on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component can be distributed between two or more computers.

In addition, these components can execute from a variety of computer readable media having various data structures stored therein. Components can be, for example, through a signal with one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system and/or a signal through another system and a network such as the Internet. Depending on the data being transmitted), it may communicate via local and/or remote processes.

Hereinafter, the same or similar components are assigned the same reference numerals regardless of the reference numerals, and redundant descriptions thereof are omitted. In addition, in describing the embodiments disclosed in the present specification, when it is determined that a detailed description of related known technologies may obscure the subject matter of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed in the present specification is not limited by the accompanying drawings.

The terms used in the present specification are for describing exemplary embodiments and are not intended to limit the present disclosure. In this specification, the singular form also includes the plural form unless specifically stated in the phrase. As used herein, “comprises” and/or “comprising” do not exclude the presence or addition of one or more other elements other than the mentioned elements.

Although the first, second, and the like are used to describe various devices or components, it goes without saying that these devices or components are not limited by these terms. These terms are only used to distinguish one device or component from another device or component. Therefore, it goes without saying that the first device or component mentioned below may be a second device or component within the spirit of the present disclosure.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used with meanings that can be commonly understood by those of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in a commonly used dictionary are not interpreted ideally or excessively unless explicitly defined specifically.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise or is not clear from the context, "X employs A or B" is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or when X uses both A and B, "X uses A or B" can be applied to either of these cases. In addition, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the listed related items.

In addition, the terms "information" and "data" as used herein may often be used interchangeably with each other.

The suffixes “module” and “unit” for constituent elements used in the following description are given or used interchangeably in consideration of only the ease of preparation of the specification, and do not have distinct meanings or roles by themselves.

Objects and effects of the present disclosure, and technical configurations for achieving them will become apparent with reference to the embodiments described later in detail together with the accompanying drawings. In describing the present disclosure, if it is determined that a detailed description of a known function or configuration may unnecessarily obscure the subject matter of the present disclosure, a detailed description thereof will be omitted. In addition, terms to be described later are terms defined in consideration of functions in the present disclosure, which may vary according to the intention or custom of users or operators.

However, the present disclosure is not limited to the embodiments disclosed below, and may be implemented in various different forms. The present embodiments are provided only to make the present disclosure complete, and to fully inform the scope of the disclosure to those of ordinary skill in the art to which the present disclosure belongs, and the present disclosure is only defined by the scope of the claims. . Therefore, the definition should be made based on the contents throughout the present specification.

The scope of rights to the steps (methods) in the claims of the present disclosure is generated by the functions and features described in each step, and unless the precedence of the order is specified in each step, the claim It is not affected by the order of description of each step in the range. For example, in the claims described as steps including steps A and B, even if step A is described before step B, the scope of rights is not limited to that step A must precede step B.

1 illustrates a system for providing a travel schedule according to some embodiments of the present disclosure.

Referring to FIG. 1, a system for providing a travel schedule may include a server 100, a user terminal 200, and a network 300 providing a travel schedule. However, the components shown in FIG. 1 are not essential in implementing the travel schedule providing system, so the travel schedule providing system described in this specification may have more or fewer components than the components listed above. have.

The server 100 of the travel scheduling system may include any type of computer system or computer device, such as, for example, a microprocessor, a mainframe computer, a digital processor, a portable device and a device controller, and the like. In addition, the server 100 may be a server related to a travel schedule service that provides travel schedule information.

Meanwhile, the user terminal 200 may receive travel schedule information provided by the server 100 according to some embodiments of the present disclosure through the network 300.

In the present specification, the travel schedule information (or travel schedule) may mean customized travel schedule information generated based on user information. Specifically, the travel schedule information may be information generated based on the user's travel period included in the user information and the user's travel preference information.

The user terminal 200 of the travel schedule providing system includes, for example, a personal computer (PC), a notebook (note book), a mobile terminal, a smart phone, a tablet PC, and a wearable device. (wearable device), etc. may be included, and all types of terminals capable of accessing a wired/wireless network may be included. In addition, the user terminal 200 may be a terminal related to a user who wants to be provided with a travel schedule.

The network 300 of the travel schedule providing system is a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and Very High Speed DSL (VDSL). ), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and local area network (LAN).

In addition, the network 300 presented here is CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier- FDMA) and other systems.

The network 300 according to the embodiments of the present disclosure may be configured regardless of its communication mode such as wired and wireless, and various types of short-range communication networks (PAN: Personal Area Network), local area networks (WAN: Wide Area Network), etc. It can be configured as a communication network. In addition, the network 300 may be a known World Wide Web (WWW), and may use a wireless transmission technology used for short-distance communication such as infrared (IrDA) or Bluetooth. have.

The techniques described herein may be used not only in the networks mentioned above, but also in other networks.

According to some embodiments of the present disclosure, the server 100 may include a processor 110, a communication unit 120, and a memory 130. However, since the above-described components are not essential to implement the server 100, the server 100 described in the present specification may have a structure that is more or less than the components listed above.

The communication unit 120 of the server 100 may include one or more modules that enable communication between the server 100 and a communication system or between the server 100 and the user terminal 200. In addition, the communication unit 120 may include one or more modules that connect the server 100 to one or more networks.

According to some embodiments of the present disclosure, the communication unit 120 of the server 100 may receive user information from the user terminal 200. In addition, the communication unit 120 may transmit travel schedule information to the user terminal 200.

Hereinafter, a description of information (eg, user degree and travel schedule information, etc.) transmitted and received by the communication unit 120 with the user terminal 200 will be described later with reference to FIG. 2.

The memory 130 of the server 100 stores data supporting various functions of the server 100. In addition, the memory 130 may store a program for the operation of the processor 110 and/or data and instructions for the operation of the server 100, and may temporarily or permanently store input/output data.

The memory 130 is a flash memory type, a hard disk type, a solid state disk type, an SDD type, a multimedia card micro type. ), card-type memory (for example, SD or XD memory), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read) -Only Memory), Programmable Read-Only Memory (PROM), magnetic memory, magnetic disk, and optical disk may include at least one type of storage medium. The memory 130 may be operated under control of the processor 110.

According to some embodiments of the present disclosure, the memory 130 of the server 100 may store an artificial neural network constituting a travel schedule generation model for generating travel schedule information.

Hereinafter, a description of the artificial neural network will be described later with reference to FIG. 4.

The processor 110 of the server 100 typically controls the overall operation of the server 100. The processor 110 may provide or process appropriate information or functions to a user by processing signals, data, information, etc. input or output through the above-described components or by driving an application program stored in the memory 130.

The processor 110 may control at least some of the components described with reference to FIG. 1 in order to drive the application program stored in the memory 130. Further, in order to drive the application program, the processor 110 may operate by combining at least two or more of the components included in the server 100 with each other.

According to some embodiments of the present disclosure, the processor 110 of the server 100 may generate travel schedule information based on user information received from the user terminal 200 through the communication unit 120. In addition, the processor 110 may control the communication unit 120 to transmit the generated travel schedule information to the user terminal 200.

Hereinafter, a description of a method for generating travel schedule information by the processor 110 and transmitting the generated travel schedule information to the user terminal 200 will be described later with reference to FIG. 2.

Various embodiments described herein may be implemented in a recording medium and a storage medium that can be read by a computer or a similar device using, for example, software, hardware, or a combination thereof.

According to hardware implementation, the embodiments described herein include application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), It may be implemented using at least one of processors, controllers, micro-controllers, microprocessors, and electrical units for performing other functions. The described embodiments may be implemented by the processor 110 itself.

According to software implementation, embodiments such as procedures and functions described herein may be implemented as separate software modules. Each of the software modules may perform one or more functions and operations described herein. The software code can be implemented with a software application written in an appropriate programming language. The software code may be stored in the memory 130 and executed by the processor 110.

2 is a flowchart illustrating an example of a method for a server to provide a travel schedule according to some embodiments of the present disclosure.

Referring to FIG. 2, the processor 110 of the server 100 may receive a signal requesting a travel schedule from the user terminal 200 through the communication unit 120 (S110). In addition, as the processor 110 receives a signal for requesting a travel schedule, the processor 110 may control the communication unit 120 to transmit a signal for requesting user information for generating a travel schedule to the user terminal 200 (S120). ). In addition, the processor 110 may receive user information corresponding to a signal for requesting user information from the user terminal 200 through the communication unit 120 (S130).

Meanwhile, the processor 110 may derive travel preference information of the user by analyzing the user's social networking service (SNS) account information included in the user information (S140). Here, the user's SNS account information may include at least one of profile, feed, comment, friends, like, follow, album information, and image information. have.

Here, the profile may mean personal information of the user such as the user's name, age, and gender. That is, the processor 110 may recognize the user's name, age, and gender, using the profile. A feed is a new post that you see on your social media account. That is, the processor 110 may recognize a post exposed to the user by using the feed. The comment may refer to a text that a user has written in another post through an SNS account. That is, when the processor 110 analyzes SNS account information in a natural language processing method to be described later, it may analyze text included in the comment. Friends may refer to other users registered in the user's SNS account. That is, the processor 110 may recognize how many users and posts are shared in the user's SNS account. Like may mean information on posts that the user has expressed interest in through an SNS account. That is, when analyzing SNS account information in a natural language processing method to be described later, the processor 110 may analyze the text of the post that the user has displayed like. Follow may mean information on posts that a user subscribes to through an SNS account. The processor 110 may recognize a post exposed (subscribed) to a user by using a follow. Album information and image information may refer to a collection of images uploaded by a user to an SNS account and an image exposed to the user. That is, when analyzing SNS account information in an image analysis method to be described later, the processor 110 may analyze an image uploaded by the user or exposed to the user.

As described above, the processor 110 may analyze at least one of profile, feed, comment, friend, like, follow, album information, and image information included in the user's SNS account information or recognize specific information. In addition, the processor 110 may derive travel preference information of the user by using at least one of the analyzed information and the recognized specific information. In addition, the processor 110 may generate travel schedule information based on the derived travel preference information of the user.

According to some embodiments of the present disclosure, when analyzing the user's SNS account information, the processor 110 may analyze at least one of natural language processing and image analysis. In addition, the processor 110 may derive travel preference information of the user based on the analysis result.

For example, the processor 110 may analyze text included in the user's SNS account information in a natural language processing method.

Specifically, the processor 110 may derive representative words, nouns, and adjectives for text included in SNS account information. Further, the processor 110 may derive the user's first travel tendency based on the representative word, the noun, and the adjective.

More specifically, the processor 110 may compare representative words, nouns, and adjectives derived from SNS account information with a representative word package, a noun package, and an adjective package corresponding to each of a plurality of pre-stored inclinations in the memory 130. Further, the processor 110 may derive the user's first travel tendency based on the comparison result.

On the other hand, the memory 130 of the server 100 is a representative word corresponding to each of the travel tendency related to the weather and natural environment, travel tendency related to auxiliary facilities, travel tendency related to service, travel tendency related to transportation, and travel tendency related to prices. It may contain packages, noun packages, and adjective packages. Additionally, the memory 130 may store a representative word package, a noun package, and an adjective package corresponding to each of the active travel disposition and the static travel disposition.

That is, the processor 110 includes representative words included in the representative word package corresponding to each travel disposition, nouns included in the noun package corresponding to each travel disposition, and adjectives included in the adjective package corresponding to each travel disposition According to the number of matching representative words, nouns, and adjectives for each and a plurality of preferences derived from SNS account information, the user's first travel preference may be derived.

For example, the processor 110 is a representative word package corresponding to a travel tendency related to weather and natural environment, representative words included in each of the noun package, and adjective package, nouns and adjectives, and a representative derived from SNS account information. When the number of words, nouns, and adjectives coincide the most, the user's first travel propensity to focus on weather and natural environment may be derived.

As described above, the processor 110 may derive the user's travel preference information based on a result of analyzing the user's SNS account information. Specifically, the processor 110 based on the first travel tendency which is a result of analyzing the user's SNS account information, the first preference related to the weather and natural environment of the travel destination, the second preference related to the auxiliary facilities of the travel destination, and the Travel preference information of a user including at least one of a third preference related to a service of a travel destination, a fourth preference related to the transportation of the travel destination, and a fifth preference related to the price of the travel destination may be derived. However, the travel preference is not limited to the above-described travel preference, and the travel preference may further include, for example, a travel preference related to sports, a travel preference related to shopping, and a travel preference related to history.

As another example, the processor 110 may analyze an image included in the user's SNS account information in an image analysis method.

Specifically, the processor 110 may perform location recognition, face recognition, and facial expression recognition on an image included in the SNS account information. In addition, the processor 110 may derive a second travel tendency of the user through location analysis through the location recognition, number counting of a person included in the image through the face recognition, and facial expression recognition.

More specifically, the processor 110 includes a location package, a location package corresponding to each of a plurality of orientations, pre-stored in the memory 130, a location recognized from an image included in the SNS account information, a face (a number of people) and an expression, and the number of people. Packages and facial expression packages can be compared. In addition, the processor 110 may derive the user's second travel tendency based on the comparison result.

Meanwhile, the memory 130 of the server 100 may store a place package corresponding to each of an active travel tendency and a static travel tendency, a number package of a person, and an expression package. Additionally, the memory 130 includes a travel tendency related to weather and natural environment, a travel tendency related to ancillary facilities, a travel tendency related to a service, a travel tendency related to transportation, a place package corresponding to each of the travel tendency related to prices, and a number package of people. And an expression package may be stored.

For example, the processor 110 may include places and faces (characters) recognized in images included in the package and the number of places and persons included in the package corresponding to the active travel tendency. If the number) matches the largest number, a second travel tendency of a user with an active travel tendency may be derived.

As described above, the processor 110 may derive the user's travel preference information based on a result of analyzing the user's SNS account information. Specifically, the processor 110 is based on a second travel tendency that is a result of analyzing the user's SNS account information, a first preference related to the weather and natural environment of the travel destination, a second preference related to the auxiliary facilities of the travel destination, and the Including at least one of a third preference related to service of a travel destination, a fourth preference related to transportation of the travel destination, a fifth preference related to prices of the travel destination, a sixth preference related to active travel, and a seventh preference related to static travel. It is possible to derive information on the travel preferences of the user.

However, the present invention is not limited thereto, and the processor 110 is based on at least one of the first travel disposition and the second travel disposition information, which is a result of analyzing the user’s SNS account information, Preference, a second preference related to auxiliary facilities of the travel destination, a third preference related to the service of the travel destination, a fourth preference related to the transportation of the travel destination, a fifth preference related to the price of the travel destination, a sixth preference related to the active travel And travel preference information of the user including at least one of the seventh preference related to the static travel.

In the present specification, the user's travel tendency may correspond to the user's travel preference. That is, the processor 110 may derive the user's travel preference, and may derive the user's travel preference information based on the travel preference.

Specifically, the processor 110 may derive the user's first travel inclination and second travel inclination using the above-described method. Here, each of the first travel inclination and the second travel inclination may be associated with one or more travel preferences. In addition, the processor 110 may derive, as travel preference information of the user, overlapping travel preferences among the travel preferences associated with each of the first travel preference and the second travel preference each derived by a different method.

Accordingly, the processor 110 of the server 100 may derive the user's travel preference in various ways to derive more accurate travel preference information of the user.

According to some embodiments of the present disclosure, the processor 110 may generate travel schedule information based on the user's travel period included in the user information and the user's travel preference information (S150).

Accordingly, the processor 110 of the server 100 may generate user-customized travel schedule information based on the user's travel preference. That is, the processor 110 may provide user-customized travel schedule information to the user, thereby increasing the user's travel satisfaction.

Specifically, the processor 110 may generate travel schedule information by processing the user's travel period and the user's travel preference information with an artificial intelligence-based travel schedule generation model.

Meanwhile, the processor 110 includes first information related to the weather and natural environment of each of the plurality of travel destinations, second information related to auxiliary facilities of each of the plurality of travel destinations, third information related to services of each of the plurality of travel destinations, and the It is possible to obtain travel destination information including at least one of fourth information related to traffic of each of the plurality of travel destinations and fifth information related to water prices of each of the plurality of travel destinations.

In addition, the processor 110 may input the travel destination information corresponding to the travel period and the travel preference information of the user into the travel schedule generation model. In this case, the processor 110 may obtain travel schedule information output from the travel schedule generation model.

Here, the travel schedule generation model may derive a travel destination suitable for the user's travel preference based on the travel destination information and the user's travel preference information. In addition, the travel schedule generation model may be composed of an artificial neural network. Specifically, a travel schedule generation model composed of an artificial neural network may be learned through a supervised learning method.

Specifically, the processor 110 may supervise the travel schedule generation model in the following manner.

First, the processor 110 may obtain travel satisfaction information received from each of a plurality of user terminals that have received travel schedule information corresponding to each of a plurality of users. Here, the travel satisfaction information may target each of a plurality of users who have been provided with travel schedule information, and may be information responding to a questionnaire consisting of question text for deriving the satisfaction of the travel schedule information.

In addition, the processor 110 may label the travel satisfaction information on the travel schedule information for learning. In addition, the processor 110 may derive an error by comparing the travel schedule information output from the artificial neural network with the training travel schedule information. In addition, the processor 110 may update the weight of the artificial neural network by backpropagating the error to the artificial neural network.

According to some embodiments of the present disclosure, when the processor 110 generates travel schedule information, the user's travel period and user's travel preference information may be input to the travel schedule generation model.

In addition, the processor 110 may obtain travel places for each date output from the travel schedule generation model. In this case, the processor 110 may determine that the travel schedule information includes travel places for each date. Here, the moving path included in the travel schedule information may include a moving path for moving each travel place for each date.

That is, the processor 110 of the server 100 may provide the user with travel schedule information including a moving route, thereby causing the user to conveniently move the travel place.

Additionally, the processor 110 may obtain event information corresponding to each of the travel places for each date. Further, the processor 110 may determine a travel schedule for each time corresponding to each travel location for each date, based on the event information. In this case, the processor 110 may determine that the travel schedule information includes the travel schedule for each time.

Specifically, the processor 110 may classify event information corresponding to each travel place for each date based on a preset travel preference. Further, the processor 110 may determine an event to be added to the user's time-specific travel schedule among the event information based on the user's travel preference.

Here, the schedule for each date included in the travel schedule information may include a travel schedule for each time corresponding to each of the travel locations for each date. Here, event information corresponding to each travel place for each date may mean information on an event (eg, a festival or an event, etc.) occurring at each of the travel places.

Additionally, the processor 110 may determine that the travel schedule information includes the event information so that the travel schedule for each time can be changed based on the event information.

In the present specification, there may be a plurality of travel schedules for each time provided to the user. That is, each of the time-based travel schedules may be ordered. In addition, the order of travel schedules by time may be provided to users who match travel preferences and other conditions (eg, age and number of travel companions).

Meanwhile, the processor 110 may receive weights for the order of travel schedules by time from user terminals of users whose travel preferences and other conditions match (i.e., users provided with the same travel order by time). have. In addition, the processor 110 may update the order of the travel schedule by time based on the weight of the order of the travel schedule by time.

In the following description related to the order of the time-based travel itinerary, a user (including a plurality of users, a first user, and a second user) may mean a user having the same time-based travel itinerary travel preferences and other conditions.

The weight for the order of the travel schedule by time may be added as the order of the travel schedule by time is changed by the user. That is, whenever a plurality of users who have been provided with a plurality of time-based travel schedules change the order of the time-based travel schedule, the first order of the previously provided time-based travel schedule and the second of the time-based travel schedule changed by the user A weight may be added for each order.

For example, when there is a first user who uses the travel schedule for each time provided in the first order as it is, the weight for the first order may be additionally assigned 2 points to 10 points, which are basic points. In this case, the weight for the first order may be 12 points. In addition, when there is a second user using the time-based travel schedule by changing the first order to the second order, the weight for the second order may be additionally 5 points to 10 points, which are basic points. In this case, the weight for the second order may be 15 points. In addition, if there are two users using the time-based travel schedule by changing to the second order, the weight for the second order is 10 points (i.e., 5 * 2 points) added to the basic 10 points. Can be. In this case, the weight for the second order may be 20 points.

That is, when there is a first order and a second order of time-specific schedules weighted from users who match travel preferences and other conditions, the processor 110 has a weight of the first order and the second order. It is possible to determine (update) the time-specific travel schedule applying a higher order. In this case, other users with matching travel preferences and other conditions (specifically, users who are provided with a time-based travel schedule after the time-based travel schedule is updated) have a higher weight among the first order and the second order. You may be provided with a travel itinerary for each time applied to.

Meanwhile, the processor 110 may control the communication unit 120 to transmit travel schedule information including a schedule for each date and a moving route included in the travel period to the user terminal 200 (S160).

Accordingly, a user (traveler) can conveniently receive a user-customized travel schedule provided by the server 100 through the user terminal 200.

According to some embodiments of the present disclosure, travel schedule information may be shareable in a user's SNS account or a specific website.

Specifically, the user terminal 200 may upload the travel schedule information received from the server 100 to the user's SNS account or a specific website.

Accordingly, the user (traveler) can easily share the travel schedule provided from the server 100 through the user terminal 200 to nearby users.

Hereinafter, information on a form in which travel schedule information is provided to a user will be described later with reference to FIG. 3.

3 is a diagram for describing travel schedule information according to some embodiments of the present disclosure.

Referring to FIG. 3, an example of a screen that can be displayed on the display unit of the user terminal 200 that has received travel schedule information provided by the server 100 is illustrated. That is, the user may be provided with a travel schedule through the travel schedule information screen 10 as illustrated in FIG. 3.

According to some embodiments of the present disclosure, the travel schedule information screen 10 includes a first area 11 displaying a travel map, a second area 12 displaying a schedule for each date, a moving route, and other information. A third area 13 and a share button 14 for sharing travel schedule information may be included. However, the above-described elements are not essential for implementing the travel schedule information screen 10, so the travel schedule information screen 10 described in this specification has a structure that is more or less than the elements listed above. I can have it.

First, as shown in the first area 11 of the travel schedule information screen 10, a travel map indicating travel areas for each date may be displayed.

Accordingly, the user can intuitively recognize the travel area through the travel map in which the travel area for each date is displayed.

As illustrated, in the second area 12 of the travel schedule information screen 10, a travel schedule for each date may be displayed. Specifically, a travel schedule for each time corresponding to each travel location for each date may be displayed in the second area 12. Here, the time-specific travel schedule may include a meal-related schedule, an activity-related schedule, and an event participation-related schedule.

Accordingly, the user can eliminate the hassle of finding an event, a tourist destination, or a place to eat at each travel place for each date.

As described above in the description of FIG. 2, the processor 110 of the server 100 may generate a travel schedule for each date based on the user's travel preference. Accordingly, the user can be provided with a travel place suitable for his or her travel preference.

The third area 13 of the travel schedule information screen 10 may include a movement path for moving each travel place for each date.

Accordingly, the user can eliminate the hassle of finding a means of transportation or the like when moving to a travel location.

In addition, the third area 13 of the travel schedule information screen 10 further includes information recommending a restaurant or cafe located at each travel location by date and information recommending participation in an event corresponding to each travel location by date. can do.

That is, the user may receive meal-related schedules, activity-related schedules, and event participation schedules provided in the travel schedule for each date, as well as additional restaurants or cafes. In addition, the user may be provided with additional event participation.

Accordingly, the user may be provided with a restaurant or cafe to visit while traveling, and a wide range of options related to participation in events.

As described above in the description of FIG. 2, the travel schedule information may be shared with a user's SNS account or a specific website. Specifically, the user terminal 200 may upload the travel schedule information received from the server 100 to the user's SNS account or a specific website. That is, the user may upload to the user's SNS account or a specific website through input to the share button 14 included in the travel schedule screen 10.

Accordingly, the user (traveler) can easily share the travel schedule provided from the server 100 through the user terminal 200 to nearby users.

4 is a block diagram of an artificial neural network configuring a travel schedule generation model according to some embodiments of the present disclosure.

In this specification, a neural network, a network function, and a neural network may be used with the same meaning. A neural network can be made up of a set of interconnected computational units, which can generally be referred to as “nodes”. These “nodes” may also be referred to as “neurouns”. The neural network includes at least one or more nodes. The nodes (or neurons) that make up neural networks can be interconnected by one or more “links”.

In a neural network, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may have an input node relationship in a relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable and may be changed by a user or an algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node includes values input to input nodes connected to the output node and a link corresponding to each input node. The output node value may be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and an output node relationship in the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the association relationship between the nodes and the links, and a weight value assigned to each of the links. For example, when there are the same number of nodes and links, and two neural networks having different weight values between the links exist, the two neural networks may be recognized as being different from each other.

As shown in FIG. 4, a neural network may include one or more nodes, and may be configured. Some of the nodes constituting the neural network may configure one layer based on the distances from the initial input node. For example, a set of nodes with a distance n from the initial input node, n layers can be configured. The distance from the first input node may be defined by the minimum number of links that must be traversed to reach the node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of the layer in the neural network may be defined in a different manner from that described above. For example, the layer of nodes may be defined by the distance from the last output node.

The first input node may mean one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may mean one or more nodes that do not have an output node in a relationship with other nodes among nodes in the neural network. In addition, the hidden node may refer to nodes constituting a neural network other than the first input node and the last output node. The neural network according to an embodiment of the present disclosure may be a neural network in which the number of nodes of the input layer may be greater than the nodes of the hidden layer close to the output layer, and the number of nodes decreases as the input layer proceeds to the hidden layer.

In some embodiments of the present disclosure, the neural network may include a multilayer perceptron (MLP), a recurrent neural network (RNN), a convolutional neural network (CNN), and the like to enable data processing. In addition, in an embodiment of the present disclosure, the neural network of the present disclosure has a long short-term memory (LSTM) to prevent performance degradation due to a long range dependency vanishing gradient that may occur as the length of an event increases. ) Can be included. In addition, in an embodiment of the present disclosure, as an optimization technique of the artificial neural network method, a stochastic gradient descent (SGD), momentum, Adam, AdaGrad, RMSprop, and the like may be used. In addition, if the training data D can be learned only once, parameters that minimize the error function can be obtained through multiple iterative epochs, and after checking whether sufficient optimization has been made, it is possible to decide whether to complete the learning step. have.

Unlike a general feed-forward neural network, an RNN can have the output of the hidden layer again become the input of the same hidden layer. The RNN is a neural network that has memory capability by considering the input data currently input and the data received in the past at the same time and having a feedback structure. Therefore, the RNN can be trained to interpret the current data according to the meaning of the previous data in the data. LSTM, one of the RNNs, is also called a long short term memory network and can learn long-term dependence. In an embodiment of the present disclosure, the neural network may include an arbitrary neural network capable of processing data, such as a depth gated RNN, a clockwork RNN, as well as an LSTM, which is one of RNNs. have.

The artificial neural network 400 shown in FIG. 4 may include one or more hidden layers. The hidden node of the hidden layer can take the output of the previous layer and the output of the neighboring hidden node as inputs. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes of the input layer may be determined based on the number of data fields of the input data, and may be the same as or different from the number of hidden nodes. Input data input to the input layer may be calculated by a hidden node of the hidden layer and may be output by a fully connected layer (FCL) that is an output layer. For a description of the specific configuration of the RNN among artificial neural networks, US patent applications US15/233083 (filing date: August 10, 2016) and US14/810368 (application date: July 27, 2015) are incorporated by reference in their entirety in this application. ) In more detail.

5 shows a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

While the present disclosure has generally been described above with respect to computer-executable instructions that can be executed on one or more computers, those skilled in the art will appreciate that the present disclosure may be implemented in combination with other program modules and/or as a combination of hardware and software. will be.

In general, modules herein include routines, procedures, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, to those skilled in the art, the method of the present disclosure is not limited to single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable household appliances, etc. (each of which is It will be appreciated that other computer system configurations may be implemented, including one that may operate in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in a distributed computing environment where certain tasks are performed by remote processing devices that are connected through a communication network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any computer-readable media, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media.

Computer-readable storage media include volatile and nonvolatile media, temporary and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data. Includes the medium. Computer-readable storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage, or other magnetic storage. Devices, or any other medium that can be accessed by a computer and used to store desired information.

Computer-readable transmission media typically implement computer-readable instructions, data structures, program modules or other data on a modulated data signal such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal is set or changed so as to encode information in the signal. By way of example and not limitation, computer-readable transmission media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above-described media are also intended to be included within the scope of computer-readable transmission media.

An exemplary environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, which includes a processing device 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to, system memory 1106 to processing device 1104. The processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.

The system bus 1108 may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read-only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input/output system (BIOS) is stored in non-volatile memory 1110 such as ROM, EPROM, EEPROM, etc. This BIOS is a basic input/output system that helps transfer information between components in the computer 1102, such as during startup. Includes routines. RAM 1112 may also include high speed RAM such as static RAM for caching data.

The computer 1102 also includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)-this internal hard disk drive 1114 can also be configured for external use within a suitable chassis (not shown). Yes--, magnetic floppy disk drive (FDD) 1116 (for example, to read from or write to removable diskette 1118), and optical disk drive 1120 (e.g., CD-ROM For reading the disk 1122 or for reading from or writing to other high-capacity optical media such as DVD). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are each connected to the system bus 1108 by a hard disk drive interface 1124, a magnetic disk drive interface 1126, and an optical drive interface 1128. ) Can be connected. The interface 1124 for implementing an external drive includes, for example, at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of the computer-readable storage medium above refers to a removable optical medium such as a HDD, a removable magnetic disk, and a CD or DVD, those skilled in the art may include a zip drive, a magnetic cassette, a flash memory card, a cartridge, and the like. It will be appreciated that other types of computer-readable storage media, such as etc., may also be used in the exemplary operating environment and that any such media may contain computer-executable instructions for performing the methods of the present disclosure. .

A number of program modules, including the operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136, may be stored in the drive and RAM 1112. All or part of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented on several commercially available operating systems or combinations of operating systems.

A user may input commands and information to the computer 1102 through one or more wired/wireless input devices, for example, a pointing device such as a keyboard 1138 and a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. These and other input devices are often connected to the processing unit 1104 through the input device interface 1142, which is connected to the system bus 1108, but the parallel port, IEEE 1394 serial port, game port, USB port, IR interface, It can be connected by other interfaces such as etc.

A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, the computer generally includes other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communication. The remote computer(s) 1148 may be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other common network node, and is generally referred to as computer 1102. Although including many or all of the described components, only memory storage device 1150 is shown for simplicity. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or to a larger network, eg, a wide area network (WAN) 1154. Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks such as intranets, all of which can be connected to worldwide computer networks, for example the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 via a wired and/or wireless communication network interface or adapter 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed therein to communicate with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158, connected to a communication server on the WAN 1154, or other Have means. The modem 1158, which may be an internal or external and a wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program modules described for computer 1102 or portions thereof may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing communication links between computers may be used.

Computer 1102 is associated with any wireless device or entity deployed and operated in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communication satellite, wireless detectable tag. It operates to communicate with any device or place, and a phone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) allows you to connect to the Internet or the like without wires. Wi-Fi is a wireless technology such as a cell phone that allows such devices, for example computers, to transmit and receive data indoors and outdoors, that is, anywhere within the coverage area of a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide a secure, reliable and high-speed wireless connection. Wi-Fi can be used to connect computers to each other, to the Internet, and to a wired network (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz radio bands, for example, at a data rate of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). have.

A person of ordinary skill in the art of the present disclosure includes various exemplary logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein, electronic hardware, (convenience). For the sake of clarity, it will be appreciated that it may be implemented by various forms of program or design code or a combination of both (referred to herein as "software"). To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. A person of ordinary skill in the art of the present disclosure may implement the described functions in various ways for each specific application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” includes a computer program, or media, accessible from any computer-readable device. For example, computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CD, DVD, etc.), smart cards, and flash Memory devices (eg, EEPROM, cards, sticks, key drives, etc.). The term “machine-readable medium” includes, but is not limited to, wireless channels and various other media capable of storing, holding, and/or transmitting instruction(s) and/or data.

It is to be understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. Based on the design priorities, it is to be understood that within the scope of this disclosure a specific order or hierarchy of steps in processes may be rearranged. The appended method claims provide elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

A description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those of ordinary skill in the art, and general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (14)

A computer program stored on a computer-readable storage medium, comprising:
The computer program includes instructions for causing a processor of the server to perform the following steps, the steps:
Receiving a signal requesting a travel schedule from a user terminal;
Upon receiving the signal requesting the travel schedule, transmitting a signal requesting user information for generating the travel schedule to the user terminal;
Receiving the user information corresponding to a signal for requesting the user information from the user terminal;
Deriving travel preference information of the user by analyzing the user's SNS (Social Networking Service) account information included in the user information;
Generating travel schedule information by processing the user's travel period and the user's travel preference information included in the user information with an artificial intelligence-based travel schedule generation model; And
Transmitting the travel schedule information including a travel route and a schedule for each date included in the travel period to the user terminal;
Containing,
A computer program stored on a computer readable storage medium.

The method of claim 1,
The step of deriving travel preference information of the user based on the user's SNS account information included in the user information,
Analyzing the SNS account information of the user including at least one of profile, comment, album information, and image information in at least one of natural language processing and image analysis; And
Deriving travel preference information of the user based on the analyzed result;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 2,
Analyzing the user's SNS account information in at least one of natural language processing and image analysis,
Deriving representative words, nouns, and adjectives for the text included in the SNS account information; And
Analyzing the SNS account information of the user using the natural language processing method for deriving a first travel tendency of the user based on the representative word, the noun and the adjective;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 2,
Analyzing the user's SNS account information in at least one of natural language processing and image analysis,
Performing location recognition, face recognition, and facial expression recognition on the image included in the SNS account information; And
Location analysis through the location recognition, number counting of the person included in the image through the face recognition, and the SNS account information of the user through the image analysis method to derive the user's second travel tendency through the facial expression recognition. Analyzing;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 2,
Based on the analysis result, the step of deriving travel preference information of the user,
Based on the analysis result, a first preference related to the weather and natural environment of the travel destination, a second preference related to the auxiliary facilities of the travel destination, a third preference related to the service of the travel destination, a fourth preference related to the transportation of the travel destination, and Deriving travel preference information of the user including at least one of fifth preferences related to prices of the travel destination;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 1,
The step of generating travel schedule information by processing the user's travel period and the user's travel preference information included in the user information with an artificial intelligence-based travel schedule generation model,
First information related to the weather and natural environment of each of the plurality of travel destinations, second information related to the auxiliary facilities of each of the plurality of travel destinations, third information related to the service of each of the plurality of travel destinations, and related to the transportation of each of the plurality of travel destinations Acquiring travel destination information including at least one of fourth information and fifth information related to water prices of each of the plurality of travel destinations;
Inputting the travel destination information corresponding to the travel period and travel preference information of the user into the travel schedule generation model; And
Acquiring travel schedule information output from the travel schedule generation model;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 1,
The travel schedule generation model,
It is composed of an artificial neural network,
Characterized in that it is learned through a supervised learning method,
A computer program stored on a computer readable storage medium.
The method of claim 7,
The supervised learning method,
Acquiring travel satisfaction information received from each of a plurality of user terminals that have received the travel schedule information corresponding to each of a plurality of users;
Labeling the travel satisfaction information on travel schedule information for learning;
Comparing the travel schedule information output from the artificial neural network with the training travel schedule information to derive an error; And
Updating the weight of the artificial neural network by backpropagating the error to the artificial neural network;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 8,
The travel satisfaction information,
Targeting each of a plurality of users who have been provided with the travel schedule information, information responding to a questionnaire consisting of question text for deriving the satisfaction of the travel schedule information
A computer program stored on a computer readable storage medium.
The method of claim 1,
The step of generating travel schedule information by processing the user's travel period and the user's travel preference information included in the user information with an artificial intelligence-based travel schedule generation model,
Inputting the user's travel period and the user's travel preference information into the travel schedule generation model;
Acquiring travel places for each date output from the travel schedule generation model; And
Determining that the travel schedule information includes travel places for each date;
Including,
The movement path is,
Including a movement path for moving each of the travel places for each date,
A computer program stored on a computer readable storage medium.
The method of claim 10,
Acquiring event information corresponding to each travel location for each date;
Determining a travel schedule for each time corresponding to each travel location for each date, based on the event information; And
Determining that the travel schedule information includes the travel schedule for each time;
Including more,
The schedule for each date above is:
Including a travel schedule for each time corresponding to each of the travel locations for each date,
A computer program stored on a computer readable storage medium.
The method of claim 11,
Determining that the travel schedule information includes the event information so that the travel schedule for each time can be changed based on the event information;
Further comprising,
A computer program stored on a computer readable storage medium.
The method of claim 1,
The travel schedule information,
It characterized in that it can be shared to the user's SNS account or a specific website,
A computer program stored on a computer readable storage medium.
In a method for a server to provide a travel schedule,
Receiving a signal requesting a travel schedule from a user terminal;
Upon receiving the signal requesting the travel schedule, transmitting a signal requesting user information for generating the travel schedule to the user terminal;
Receiving the user information corresponding to a signal for requesting the user information from the user terminal;
Deriving travel preference information of the user by analyzing the user's SNS (Social Networking Service) account information included in the user information;
Generating travel schedule information based on the user's travel period included in the user information and the user's travel preference information; And
Transmitting the travel schedule information including a travel route and a schedule for each date included in the travel period to the user terminal;
Containing,
How to provide a travel schedule.

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