KR20220096234A - Cafe table recommadation apparatus and method using the same - Google Patents

Abstract

A cafe table recommendation apparatus is disclosed. The cafe table recommendation apparatus includes: a user UI part for providing a user UI for a user to set cafe preference information for a table; a cafe analysis part for comparing the cafe preference information with pre-stored table information about cafes and deriving a recommendation score based on a degree of agreement between the cafe preference information and the table information; a cafe recommendation part for providing the user with cafe information including a table corresponding to the cafe preference information based on the recommendation score; and a table reservation part for reserving a table and a food/drink menu according to a selection of the user.

Description

CAFE TABLE RECOMMADATION APPARATUS AND METHOD USING THE SAME

The present invention relates to a cafe table recommendation apparatus and a cafe table recommendation method.

In the case of a restaurant recommendation service including existing restaurants, it only provides approximate information about the store, for example, available days, times, and total number of tables.

For example, in the case of a cafe, a technology for providing emotion-based recommendation information by applying a weight to the weather exists, but it does not deviate from the existing service provision range.

With current technology, when a user searches for and selects a store, it is possible to automatically select a menu and make a payment based on the local area and the Internet.

On the other hand, unlike general restaurants, in the case of cafes, the purpose of a user's visit to the cafe can be subdivided, and because the stay time is longer than that of a restaurant, there is a need to obtain information about the cafes that users prefer and the tables they need. do. In addition, there is a need to search for and reserve a table in advance because there are many cases of wandering in search of an empty seat in a cafe, and each user has a different preferred table within a cafe.

Therefore, there is a need for a cafe table recommendation service that can reflect the detailed taste of an individual.

Korean Patent Publication No. 10-212562

SUMMARY OF THE INVENTION An object of the present invention is to provide a cafe table recommendation apparatus and a cafe table recommendation method capable of recommending a cafe table by reflecting the detailed taste of an individual.

SUMMARY OF THE INVENTION An object of the present invention is to provide a cafe table recommendation apparatus and a cafe table recommendation method capable of providing microscopic spatial unit information.

SUMMARY OF THE INVENTION An object of the present invention is to provide a cafe table recommendation apparatus and a cafe table recommendation method capable of subdividing taste categories for each cafe and recommending a cafe based on a degree of suitability with user's taste.

According to an embodiment of the present invention, a cafe table recommendation apparatus is provided. The cafe table recommendation apparatus includes: a user UI unit that provides a user UI for a user to set cafe taste information for a table; a cafe analysis unit that compares the cafe taste information with pre-stored table information on cafes and derives a recommendation score based on a degree of matching between the cafe taste information and the table information; a cafe recommendation unit providing cafe information including a table corresponding to the cafe taste information to a user based on the recommendation score; It includes a table reservation unit for reserving a table and food and beverage menu according to a user's selection.

The cafe taste information may include essential item information and optional item information.

The essential item information may include at least one of the presence or absence of an outlet, whether a toilet is provided, whether or not a smoking room exists, and an interior concept.

The selection item information may include at least one of illuminance, noise, operating hours, congestion, table shape, and operating hours.

The user UI may include a weight item for assigning a weight to the selection item information.

The cafe analyzer may derive a sum of values obtained by multiplying a value obtained by multiplying a value obtained by digitizing the degree of matching for each item included in the selection item information by the weight as the recommendation score.

The cafe recommendation unit may provide the cafe information having a different color or size to the user based on the recommendation score.

The user UI unit may provide the user with a UI for viewing the cafe taste information set by another user based on the recommendation score.

The table information may be collected in real time based on indoor positioning technology and a sensor attached to the table.

According to another embodiment of the present invention, a cafe table recommendation method is provided. The cafe table recommendation method includes: a user UI providing step of providing a user UI for a user to set cafe taste information for a table; a cafe analysis step of comparing the cafe taste information with pre-stored table information on cafes and deriving a recommendation score based on a degree of matching between the cafe taste information and the table information; a cafe recommendation step of providing cafe information including a table corresponding to the cafe taste information to a user based on the recommendation score; It includes a table reservation step of reserving a table and food menu according to the user's selection.

The cafe taste information may include essential item information and optional item information.

The essential item information may include at least one of the presence or absence of an outlet, whether a toilet is provided, whether or not a smoking room exists, and an interior concept.

The selection item information may include at least one of illuminance, noise, operating hours, congestion, table shape, and operating hours.

The user UI may include a weight item for assigning a weight to the selection item information.

In the cafe analysis step, a sum of values obtained by multiplying a value obtained by multiplying a value obtained by quantifying the degree of matching for each item included in the selection item information by the weight may be derived as the recommendation score.

According to an embodiment of the present invention, there are provided a cafe table recommendation apparatus and a cafe table recommendation method capable of recommending a cafe table by reflecting a detailed personal taste.

According to an embodiment of the present invention, a cafe table recommendation apparatus and a cafe table recommendation method capable of providing microscopic spatial unit information are provided.

According to an embodiment of the present invention, a cafe table recommendation apparatus and a cafe table recommendation method are provided, which can recommend a cafe based on a degree of suitability with a user's taste by subdividing the taste categories for each cafe.

In addition, users can more accurately recognize their tastes by reflecting personal preferences for cafe use and recommending another space that suits them. can

1 is a control block diagram of a cafe table recommendation apparatus according to an embodiment of the present invention.
2 is a diagram illustrating a user UI for setting cafe taste information according to an embodiment of the present invention.
3 is a diagram illustrating a UI showing a cafe search result according to an example of the present invention.
4 is a diagram illustrating a UI for guiding table information according to an embodiment of the present invention.
5 is a diagram illustrating a UI for utilizing a user community according to an embodiment of the present invention.
6 is a control flowchart of a method for recommending a cafe table according to an embodiment of the present invention.
7 is a diagram illustrating a computing device according to an embodiment of the present invention.

Hereinafter, with reference to the accompanying drawings, the embodiments of the present invention will be described in detail so that those of ordinary skill in the art to which the present invention pertains can easily implement them. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

In the present specification, duplicate descriptions of the same components will be omitted.

Also, in this specification, when it is said that a certain element is 'connected' or 'connected' to another element, it may be directly connected or connected to the other element, but other elements in the middle It should be understood that there may be On the other hand, in this specification, when it is mentioned that a certain element is 'directly connected' or 'directly connected' to another element, it should be understood that the other element does not exist in the middle.

In addition, the terms used herein are used only to describe specific embodiments, and are not intended to limit the present invention.

Also, in this specification, the singular expression may include the plural expression unless the context clearly dictates otherwise.

Also, in this specification, terms such as 'include' or 'have' are only intended to designate that the features, numbers, steps, operations, components, parts, or combinations thereof described in the specification exist, and one or more It is to be understood that the existence or addition of other features, numbers, steps, operations, components, parts, or combinations thereof, is not precluded in advance.

Also in this specification, the term 'and/or' includes a combination of a plurality of described items or any item of a plurality of described items. In this specification, 'A or B' may include 'A', 'B', or 'both A and B'.

Also, in this specification, detailed descriptions of well-known functions and configurations that may obscure the gist of the present invention will be omitted.

1 is a control block diagram of a cafe table recommendation apparatus according to an embodiment of the present invention.

As shown, the cafe table recommendation apparatus according to the present embodiment includes a user UI unit 10 , a database unit 20 , a cafe analysis unit 30 , a cafe recommendation unit 40 , and a table reservation unit 50 . include

The cafe table recommendation apparatus may be implemented as a user mobile terminal such as a mobile phone, or may be implemented as a server that provides a cafe table recommendation service while communicating with the user terminal.

When the cafe table recommendation apparatus is a user terminal, the database unit 20 may be implemented as a memory within the user terminal, or may be implemented as an external separate database server. That is, the cafe table recommendation apparatus may be implemented as any electronic device capable of performing the functions of the components described below, and is not limited to its physical form.

Also, a cafe table recommendation method to be described later may be performed by an algorithm that can be installed and executed in an electronic device or a server, that is, an application.

In addition, the cafe table recommendation apparatus may further include a display unit that receives a user's input and selection not shown in FIG. 1 and displays a GUI.

In addition, each component of FIG. 1 is functionally separated for convenience of description, and the components may be implemented as a single chipset or module, or may be implemented as an electronic module including a GUI chip.

The user UI unit 10 provides a user UI for a user to set cafe taste information for a table.

The cafe table recommendation apparatus according to the present embodiment does not simply guide the location of the cafe, but focuses on the purpose of the user's cafe visit, and aims to match the various table environments requested by the user with each other. Therefore, it is necessary to understand the user's preference for detailed tables.

2 is a diagram illustrating a user UI for setting cafe taste information according to an embodiment of the present invention. 2 and the following drawings show a display screen when the cafe table recommendation device is implemented as a mobile mobile phone.

As shown, the cafe taste information may include essential item information (I) and optional item information (II), and essential item information (I) is whether there is an outlet, whether there is an indoor toilet, whether there is a smoking room, among the interior concepts. It may include at least one, and the selection item information (II) may include at least one of illuminance, noise, operating hours, congestion, table shape, and operating hours.

The “taste filter search” of FIG. 2 indicates essential item information (I), and the user can check whether the cafe should include the items when recommended. Data retrieval and accuracy can be improved by removing indicators irrelevant to the user's cafe taste and excluding them from the table recommendation consideration. For example, if you select “Smoking rooms available”, cafes without smoking rooms are not recommended on the map.

The “detailed preference setting” of FIG. 2 corresponds to the selection item information (II), and the user can check which items he would like to receive a recommendation based on when receiving a recommendation for a cafe table.

In addition, the user may select a degree of preference for individual selection items. For example, you can choose a specific illuminance value, or you can choose your preferred taste for noise levels or operating hours. When a cafe table is recommended, it is to search for a table having a value similar to the value selected by the user, and the preference degree value selected by the user is converted into a specific value, for example, a value between 1 and 100 and reflected in the recommendation score. can

The selection item information (II) may also be excluded from consideration by the user removing the indicators for indicators that are irrelevant to the user's taste.

In addition, both the mandatory item information (I) and the optional item information (II) may be added with indicators. For example, if the user is sensitive to the height of the table, the indicator can be added to the selection item information (II) by finding and adding 'table height' in Add Indicator.

As illustrated, when selecting the selection item information (II), the user may select a weight for the selection item information (II). That is, the user UI for the cafe taste information may include a weight item (III) to which a weight can be given to the selection item information (II). The weight set by the user is then reflected in the table recommendation score in the form of a 'multiple'.

In addition, the user can refer to the 'taste bundle' previously created by other users through the item called taste recall (IV) before setting their own cafe taste information.

According to an example, when a taste bundle of another user is referred to, a taste bundle having similarity to the user's taste may be searched based on previously input or set user's cafe taste information.

The user may add and store a name such as a name and a file name for his/her cafe taste information.

The cafe taste information set by the user in this way is utilized for table recommendation and may be stored in the database unit 20 . The cafe taste information may be stored as a history whenever the user changes the cafe taste information, and may be modified and added.

The database unit 20 is a storage unit or server in which cafe taste information set by users and table information provided by business owners or other service providers who want to inform cafes are stored.

Table information in the cafe can be collected in real time based on indoor positioning technology and sensors attached to the table.

In addition, the database unit 20 may store reviews, experiences, etc. according to cafe use provided by users.

Cafe taste information and table information by the user may be updated in real time.

The cafe analysis unit 30 may compare the cafe taste information with pre-stored table information on cafes to derive a recommendation score based on the degree of matching between the cafe taste information and the table information. Specifically, the cafe analysis unit 30 may derive the recommendation score as the sum of values obtained by multiplying the weight by the value obtained by quantifying the degree of matching for each item included in the selection item information (II).

The cafe analyzer 30 may compare the taste information input by the user stored in the database unit 20 with the table information, and the user may derive a score and a side recommendation score from a nearby cafe table.

For example, the cafe analysis unit 30 sets a value of 100 when the value of a specific item is completely identical (or completely included) when comparing the data of the taste information and the data of the table information, and occurs in the two data. Decreases the value from 100 in such a way that it is reduced by a number proportional to the error. That is, the cafe analyzer 30 digitizes the degree of agreement with a value between a maximum of 100 and a minimum of 0, and may derive a final score by summing values obtained by multiplying this value by a weight set by the user. For example, when the degree of agreement for illumination is (100-a), the degree of agreement to noise is (100-b), and the operating time matches the value desired by the user, when the degree of agreement is 100, the recommended score is ( 100-a)*0.35+(100-b)*0.3+(100-0)*0.35.

The cafe recommendation unit 40 may provide the user with cafe information including a table corresponding to the cafe taste information based on the recommendation score.

3 is a diagram illustrating a UI indicating a cafe search result according to an example of the present invention.

As shown in FIG. 3 , the user may set the score of the recommendation table to be displayed on the map, that is, the range of the recommendation score. In other words, the user can directly set how many tables to find.

On the map, a cafe including a table with more than a lottery score set by a user may be displayed. According to an example, when there is a table with a recommended score or higher in the cafe, the cafe may be displayed in a highly saturated color, and the number of tables may also be displayed on the upper right corner of the cafe icon. Cafes that do not contain tables with more than the recommended score may not be displayed at all, or may be displayed with low saturation, achromatic or dull colors.

When the user selects a specific recommended cafe, detailed information about a table in the cafe may be provided as shown in FIG. 4 . 4 is a diagram illustrating a UI for guiding table information according to an embodiment of the present invention.

As shown, a table mini layout (V) is displayed, and additionally, interior photos, operating hours, menu boards, and the like may be provided. In addition, detailed information about the table, for example, a distance from another table, a table shape, a height, a height difference between a table and a chair, and information about a noise level may be displayed.

In the mini-arrangement diagram (V), tables can be displayed in colors with different brightness and saturation according to the recommended score, and through this, the user can easily distinguish the degree of matching with the taste of the table.

In addition, whether the taste set by the user for each taste item matches the table information may be provided as a bar-type UI. The higher the degree of matching, the higher the brightness or saturation of the table may be displayed. According to another example, not only the color but also the size of the table may be differently adjusted based on the recommendation score.

Alternatively, according to an example, a UI for viewing the cafe taste information set by another user based on the cafe recommendation unit 40 recommendation score may be provided to the user.

The table reservation unit 50 may reserve a table and a food and beverage menu according to a user's selection.

A reservation item is displayed at the bottom of FIG. 4 , and a user can reserve a table and pay for a desired beverage using a subsequent UI.

According to an example, a table may be reserved by selecting a table on the interior layout diagram of a cafe, and when a large number of people want to reserve a table, it may be executed at once through a function of adding the number of people.

Alternatively, according to an example, it may be set to additionally pay a certain amount when reserving a table.

In addition, the user may be provided with an order status UI that allows the user to check whether the ordered food or drink is ready at any time. Algorithms can be run to cancel.

Whether the table is used can be checked by scanning the barcode (QR code) attached to the reserved table.

5 is a diagram illustrating a UI for utilizing a user community according to an embodiment of the present invention.

A user may be provided with a review writing UI that can write a review after using the cafe, and if he or she writes a review, he or she can acquire coffee mileage “coffee beans” and the like.

In addition, as an example, the cafe table recommendation device may provide a UI such as 'My Coffee Story' where users can read user information and articles written by other users, and use keyword and filter search functions to find content that the user wants to search. It is also possible to provide a UI such as a coffee bulletin board where users can search or individually leave reviews of cafes they have used.

In addition, the user may update his/her coffee taste information by modifying the cafe taste information stored through other users.

According to the cafe table recommendation device as described above, by providing the real-time usage status of the registered cafes, the inefficiency of moving from one cafe to another because there are no empty seats in the cafe can be eliminated, and the overall information of the cafe space and more detailed table-by-table information can be eliminated. By providing the ability to view information and even a reservation function, the convenience of users can be greatly increased.

In addition, by recommending small cafes that you would not have recognized if you had not been recommended through the recommendation method according to your taste, you can expect to expand your personal spatial experience and revitalize the alley commercial area at the same time. In addition, it is possible to recommend a space in a table unit rather than a cafe unit, so it is expected that cafe operators will be able to improve the business feasibility of their stores through table unit marketing.

6 is a control flowchart of a method for recommending a cafe table according to an embodiment of the present invention.

Referring to FIG. 6 , the cafe table recommendation method is summarized as follows.

A user UI is provided for the user to set cafe taste information for the table (S610).

The cafe taste information may include essential item information and optional item information.

Required item information may include preconditions for table search such as at least one of the presence or absence of an outlet, whether a toilet is provided, whether or not a smoking room exists, and an interior concept. It may include at least one of time, congestion level, table shape, and operating hours.

In addition, according to this embodiment, in order to actively reflect the user's taste in the table selection

The user UI may include a weight item that can give weight to the selection item information. The weight set by the user may be used to derive a recommendation score.

Thereafter, the table recommendation apparatus may compare the cafe taste information with the previously stored table information on the cafe to derive a recommendation score based on the degree of matching between the cafe taste information and the table information ( S620 ).

Table information may be collected in real time based on indoor positioning technology and sensors attached to the table, and such table information and user preference information may be updated in real time.

The cafe analysis unit 30 may derive the sum of values obtained by multiplying a weight value by a value obtained by quantifying the degree of matching for each item included in the selection item information as a recommendation score.

The user may set a desired recommendation score when searching for a table, and the cafe recommendation unit 40 may provide the user with cafe information including a table corresponding to the cafe taste information based on the recommendation score (S630).

In order to provide detailed information about the table, tables having different colors or sizes according to the recommendation score may be displayed, and a UI for determining the degree of matching may also be provided.

The user may view cafe taste information set by other users based on the recommendation score, and may also receive information on a recommendation table provided to other users.

When a user selection for the recommended cafe occurs, a UI for reserving a table and food and beverage menu according to the user's selection may be provided to the user (S640).

Additionally, the user may use the community accompanying the use of the cafe, obtain a predetermined mileage when writing a review, etc., and may be provided with a UI to view all the history related to the use of the cafe at any time.

As described above, the present invention can match each space with an individual through a customized recommendation of a cafe and further individual tables in consideration of the preferences and tastes of cafe users, and provides real-time table status, resulting in time inefficiency in wandering around several cafes. Provided are a cafe table recommendation device and a cafe table recommendation method that can eliminate

In addition, through the service of the present invention, it is possible to help cafe operators to reflect the table shape desired by users in the cafe, and to increase the convenience of users by tying the coupons and mileage concepts that existed individually in various cafes into one. can Furthermore, if the service to which the present invention is applied is activated so that information and evaluations of many cafes can be easily accessed, the overall service quality of the corresponding industry can be improved.

In addition, it can be used in various fields such as public city planning, traffic planning, and pedestrian planning by acquiring integrated data on users' preferences, table usage space status, and frequency, and various decision making or research in the public and private fields. It can also be used in other processes.

In addition, if the data obtained through the service to which the present invention is applied is sufficiently accumulated in the form of big data

It is expected that it will be possible to commercialize the data in a form related to the collection, processing, and processing of the data.

7 is a diagram illustrating a computing device according to an embodiment of the present invention. The computing device TN100 of FIG. 7 may be a device described herein (eg, a cafe table recommendation device, etc.).

In the embodiment of FIG. 7 , the computing device TN100 may include at least one processor TN110 , a transceiver device TN120 , and a memory TN130 . In addition, the computing device TN100 may further include a storage device TN140 , an input interface device TN150 , an output interface device TN160 , and the like. Components included in the computing device TN100 may be connected by a bus TN170 to communicate with each other.

The processor TN110 may execute a program command stored in at least one of the memory TN130 and the storage device TN140. The processor TN110 may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to an embodiment of the present invention are performed. The processor TN110 may be configured to implement procedures, functions, and methods described in connection with an embodiment of the present invention. The processor TN110 may control each component of the computing device TN100.

Each of the memory TN130 and the storage device TN140 may store various information related to the operation of the processor TN110. Each of the memory TN130 and the storage device TN140 may be configured as at least one of a volatile storage medium and a nonvolatile storage medium. For example, the memory TN130 may include at least one of a read only memory (ROM) and a random access memory (RAM).

The transceiver TN120 may transmit or receive a wired signal or a wireless signal. The transceiver TN120 may be connected to a network to perform communication.

On the other hand, the embodiment of the present invention is not implemented only through the apparatus and/or method described so far, and a program for realizing a function corresponding to the configuration of the embodiment of the present invention or a recording medium in which the program is recorded may be implemented. And, such an implementation can be easily implemented by those skilled in the art from the description of the above-described embodiments.

Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention as defined in the following claims are also presented. It belongs to the scope of the invention.

Claims (8)

In the cafe table recommendation device,
a user UI unit providing a user UI for a user to set cafe taste information for a table;
a cafe analysis unit that compares the cafe taste information with pre-stored table information on cafes and derives a recommendation score based on a degree of matching between the cafe taste information and the table information;
a cafe recommendation unit that provides cafe information including a table matching the cafe taste information to a user based on the recommendation score;
A cafe table recommendation device comprising a table reservation unit for reserving a table and a food and beverage menu according to a user's selection.
The method of claim 1,
The cafe taste information includes essential item information and optional item information,
The essential item information includes at least one of whether there is an outlet, whether a toilet is provided, whether or not there is a smoking room, and an interior concept,
The selection item information includes at least one of illuminance, noise, operating time, congestion, table shape, and operating time.
3. The method of claim 2,
The user UI is a cafe table recommendation apparatus, characterized in that it includes a weight item for assigning a weight to the selected item information.
4. The method of claim 3,
The cafe table recommendation apparatus, characterized in that the cafe analyzer derives a sum of values obtained by multiplying a value obtained by multiplying a value obtained by quantifying the degree of matching for each item included in the selection item information by the weight as the recommendation score.
5. The method of claim 4,
The cafe recommender unit recommends a cafe table, characterized in that it provides the cafe information having a different color or size to the user based on the recommendation score.
5. The method of claim 4,
The user UI unit, based on the recommendation score, cafe table recommendation apparatus, characterized in that the user can browse the cafe taste information set by another user to provide a UI.
The method of claim 1,
The table information is a cafe table recommendation device, characterized in that it is collected in real time based on indoor positioning technology and a sensor attached to the table.
In the cafe table recommendation method,
A user UI providing step of providing a user UI for the user to set cafe taste information for the table;
a cafe analysis step of comparing the cafe taste information with pre-stored table information on cafes and deriving a recommendation score based on a degree of matching between the cafe taste information and the table information;
a cafe recommendation step of providing cafe information including a table corresponding to the cafe taste information to a user based on the recommendation score;
A method for recommending a cafe table, characterized in that it includes a table reservation step of reserving a table and a food and beverage menu according to a user's selection.

Images