A customized coffee shop recommendation server and a recommendation method thereof

Abstract

The present invention provides a customized coffee shop recommendation server, comprising: a coffee shop information storage unit, a user information storage unit, a preference degree analysis unit, a request signal receiving unit, a current location tracking unit for tracking the current location of the user, and a coffee shop recommendation unit for filtering out a coffee shop corresponding to the current location of the user from coffee shops matching the user preference degree analysis unit in a user terminal and recommending the coffee shop as a customized coffee shop.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a customized coffee shop recommendation server and its recommendation method. Background Technology

[0002] The global supply, distribution, and consumption of coffee have increased significantly, and the coffee industry has developed into a huge industry.

[0003] Generally, after coffee beans are produced as a crop in tropical regions, they are roasted by coffee producers and processed into coffee beans. Middlemen who buy coffee then process them into powdered coffee or sell them to coffee bean distributors, where they are sold as coffee beans in specialty coffee bean stores.

[0004] On the other hand, with many changes in the production and sales of coffee beans, small-scale coffee shops can also directly roast and grind coffee to make coffee. Therefore, the aroma and taste of coffee have become more diverse depending on the size of the coffee shop, the coffee brand, the type of coffee menu, and the origin of the coffee beans.

[0005] Therefore, users can choose to prefer specific coffee shops, specific coffee brands, specific coffee menus, and specific coffee bean origins based on their own preferences.

[0006] However, when users who prefer coffee from specific coffee shops, brands, menus, or bean origins visit unfamiliar areas, they often find it difficult to find coffee shops that offer coffee with the same or similar flavors as their favorites. As a result, they are unable to enjoy their preferred coffee while visiting unfamiliar regions. Summary of the Invention

[0007] This invention provides a customized coffee shop recommendation server, which aims to analyze users' favorite coffee shops, coffee brands, types of coffee menus and prices of each menu item, coffee bean origins, etc., and recommend the most suitable coffee shops from the surrounding coffee shops when users visit unfamiliar places, so as to provide users with the opportunity to enjoy coffee that matches their preferences anytime, anywhere.

[0008] To achieve the above objectives, according to the present invention, a customized coffee shop recommendation server stores coffee shops corresponding to each region, coffee brands corresponding to the coffee shops, types of coffee menus corresponding to the coffee shops and prices of each menu, and coffee shop information including the origin of coffee beans; a storage unit stores the location of a coffee shop visited by a user, the brand of the coffee shop, the brand menu corresponding to the coffee shop, the price of the coffee shop, and the brand information of the raw beans corresponding to the coffee shop; a storage unit based on the user information storage unit stores user information, preferably in the brand information; user information includes a preference analysis unit that analyzes user preferences including at least one of the types of coffee menus, coffee prices, and coffee origins; a request signal that receives a coffee shop recommendation request signal from the user via a network; a current location tracking unit that tracks the current location of the user; and for the user terminal corresponding to the user, recommending the tracked coffee shops corresponding to the current location as customized coffee shops from among the coffee shops analyzed that correspond to the user's preferences.

[0009] The aforementioned customized coffee shop recommendation server may also include an evaluation information collection unit, which collects multiple unspecified evaluation information corresponding to coffee shop information stored in the coffee shop information storage unit via a network. In this case, the preference analysis unit analyzes user preferences based on the stored user information and the multiple unspecified evaluation information collected.

[0010] The aforementioned customized coffee shop recommendation server may also include a user review collection unit, which collects user review information corresponding to the coffee shop information stored in the coffee shop information storage unit via a network. In this case, the preference analysis unit analyzes user preferences based on the stored user information, multiple unspecified evaluation information collected, and the collected user evaluation information.

[0011] The aforementioned customized coffee shop recommendation server may further include a statistical information calculation unit; this statistical information calculation unit calculates statistical information about the coffee shops stored in the aforementioned coffee shop information storage unit based on the multiple unspecified evaluation information collected by the aforementioned evaluation information collection unit. In this case, the preference analysis unit analyzes user preferences based on the stored user information, the multiple unspecified evaluation information collected, and the calculated statistical information.

[0012] To achieve the above objectives, according to one aspect of the present invention, a customized coffee shop recommendation method, executed by a customized coffee shop recommendation server, includes the following steps: storing coffee shop information corresponding to each region, coffee brand corresponding to the coffee shop, types of coffee menus corresponding to the coffee shop, prices of each menu item, and coffee shop information including the origin of the raw beans; the location of the coffee shop visited by the user, the brand corresponding to the coffee shop, the coffee shop menu corresponding to the user, the price corresponding to the coffee shop, the price of the raw beans, and coffee shop information corresponding to the raw beans; and the raw bean information; a step of storing user information, and a step of storing user information to generate a coffee shop by storing user information and the raw bean information in the user information. The method also includes a stage of analyzing user preferences for at least one of the following: coffee shop, brand, types of coffee menus, coffee prices, and origin of coffee beans; a stage of receiving a coffee shop recommendation request signal from the user via a network; a stage of tracking the user's current location; and a stage of tracking the user terminal corresponding to the user and recommending the coffee shop corresponding to the current location from the coffee shops corresponding to the analyzed user preferences as a customized coffee shop.

[0013] The aforementioned user-personalized coffee shop recommendation method may further include a step of collecting multiple unspecified reviews corresponding to the stored coffee shop information via the internet. In this case, the step of analyzing user preferences is based on the stored user information and the collected multiple unspecified reviews.

[0014] The aforementioned customized coffee shop recommendation method may further include a step of collecting user reviews corresponding to the stored coffee shop information via the internet. In this case, the step of analyzing user preferences is based on the stored user information, multiple unspecified reviews collected, and the collected user reviews to analyze user preferences.

[0015] This invention analyzes users' favorite coffee shops, coffee brands, types of coffee menus and prices, coffee bean origins, etc., and recommends the most suitable coffee shops from the surrounding area when users visit unfamiliar places, so that users can enjoy coffee that suits their preferences anytime, anywhere. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification.

[0017] Figure 1 This is a schematic diagram of a customized coffee shop recommendation system according to an embodiment of the present invention.

[0018] Figure 2 yes Figure 1The diagram shown is a simplified representation of the components of a customized coffee shop recommendation server.

[0019] Figure 3 This is a schematic diagram showing an example of a coffee shop within a defined area.

[0020] Figure 4 yes Figure 3 This is an illustration of an example of coffee shop information corresponding to a Chinese coffee shop.

[0021] Figure 5 This is a flowchart of a method for recommending customized coffee shops according to an embodiment of the present invention.

[0022] 10: User terminal, 20: Network

[0023] 100: Recommended servers for customized coffee shops

[0024] 102: Coffee shop information storage unit; 104: User information storage unit.

[0025] 106: Preference Analysis Unit; 108: Receive Request Signal;

[0026] 110: Current location tracking unit; 112: Coffee shop recommendation unit;

[0027] 114: Evaluation Information Collection Unit; 116: User Evaluation Collection Unit;

[0028] 118: Statistical information calculation unit. Detailed Implementation

[0029] The following description, with reference to the accompanying drawings, illustrates some embodiments of the present invention. When writing reference symbols for components on each drawing, the same symbols should be used as much as possible, even if the same components are shown on different drawings. Furthermore, in describing embodiments of the present invention, detailed descriptions of the related specifications or functions are omitted if it is believed that they would hinder the understanding of the embodiments of the present invention.

[0030] Furthermore, when describing the constituent elements of embodiments of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are merely for distinguishing the component from other components and are not limited to the nature, order, or sequence of the components. If a component is described as "connected," "joined," or "connected to" another component, it should be understood that the component can be directly connected, joined, or linked, but the component can also be "connected," "joined," or "connected" to another component.

[0031] Figure 1 This is a schematic diagram illustrating a customized coffee shop recommendation system according to an embodiment of the present invention.

[0032] The customized coffee shop recommendation system includes a user terminal 10 and a customized coffee shop recommendation server (100). At this time, the user terminal (10) and the customized coffee shop recommendation server (100) can be connected via a network (20).

[0033] A user terminal is a terminal with network communication capabilities, including not only computers but also laptops, personal digital assistants (PDAs), smartphones, etc. Here, "network" includes not only the Internet but also the broader concept of mobile communication networks such as CDMA (Code Division Multiple Access), WCDMA (Wideband CDMA), LTE (Long Term Evolution), and 5G (5th Generation).

[0034] The user-customized coffee shop recommendation server 100 provides a webpage for user-customized coffee shop recommendation services, and recommends coffee shops to user terminals 10 connected through the webpage.

[0035] At this time, the customized coffee shop recommendation server 100 may include a coffee shop information storage unit 102, a user information storage unit 104, a preference analysis unit 106, a request signal receiving unit 108, a current location tracking unit 110, a coffee shop recommendation unit 11, an evaluation information collection unit 114, a user evaluation collection unit 116, and a statistical information calculation unit 118.

[0036] The coffee shop information storage unit 102 stores information about coffee shops in their respective regions, including the coffee brand used by each shop, the types of coffee menu items corresponding to each shop, the prices of each menu item, and information about the origin of the coffee beans. At this time, the coffee shop information storage unit 102 can store information about the coffee bean growers and the types of coffee beans, such as Arabica, Robusta, and Liberica. Furthermore, the coffee shop information storage unit 102 divides administrative regions such as cities, wards, and neighborhoods into areas of a set size, and can store information about the coffee shops contained in each divided area, including the coffee brand used by each coffee shop, the types of coffee menu items corresponding to each coffee shop, the prices of each coffee menu item, and the origin of the raw beans for each coffee menu item. For example, the coffee shop information storage unit 102 can store information about coffee shops in each area within a specific administrative region, such as... Figure 3As shown, in the case of coffee shops A through G within the same area, for each coffee shop, information such as the coffee maker used, the coffee brand, the menu items sold at each coffee shop, the prices on each menu item, and the origin of the raw beans used in each menu item can be stored as coffee shop information. In this case, if a coffee shop uses coffee processed by a specific maker, the origin of the coffee beans used in that processing can also be stored. At this point, the coffee information used by each coffee shop may differ. For example, such as... Figure 4 As shown, coffee shop A uses coffee produced under the trademark "Ga" by coffee manufacturer a, and sells it on its coffee menu at prices such as 4,000 won for Americano and 5,000 won for latte. Coffee shop B can directly roast Brazilian coffee beans and sells them on its coffee menu at prices such as 5,000 won for Americano and 6,000 won for latte. At this time, Figure 4 The illustration shows coffee menus from different coffee shops that sell coffee beans from the same origin. However, even within the same coffee shop, different coffee menus can use coffee beans from different origins.

[0037] User information storage unit 104 stores user information, including the location of the coffee shop visited by the user, the coffee brand used by the coffee shop, the coffee menu ordered by the user, the price of the coffee menu, and the origin of the raw beans corresponding to the coffee menu. At this time, based on the settlement information of the coffee menu ordered by the user, user information storage unit 104 can know the coffee shop visited, the coffee menu ordered, and the price, etc., user information storage unit 104 can also find out the location of the coffee shop, the coffee brand used by the coffee shop, and the origin of the raw beans by searching for coffee shop information that matches the coffee shop visited and the coffee menu ordered by the user in the coffee shop information storage unit 102.

[0038] In addition, the user information storage unit 104 can store information such as the user's residential address, school or workplace address. In this case, the user information storage unit 104 can store user information based on the user's residential address and school or workplace address, specifying coffee shops within a certain range, the location of the coffee shops, the coffee brands used by the coffee shops, the coffee menu ordered by the user at the coffee shops, and their prices.

[0039] In addition, the user information storage unit 104 can store SNS Social Network Service accounts such as blogs, Twitter accounts, and web pages corresponding to the user, IP Internet Protocol addresses corresponding to the SNS accounts, SNS accounts of acquaintances visited by the user, and web pages visited by the user.

[0040] The preference analysis unit 106 analyzes the user's preference for at least one of the following based on the user information stored in the user information storage unit 104: preferred coffee shops, preferred coffee brands, preferred types of coffee menus, preferred coffee prices, and the origin of the beans corresponding to the preferred coffee. At this time, the preference analysis unit 106 counts the number of times the user orders each coffee menu item, and can also analyze the user's preference order based on the count of each coffee menu item.

[0041] For example, in such Figure 3 In the coffee shops shown, assuming a user visits coffee shop A 24 times, coffee shop C 1 time, and coffee shop E 4 times within a month, then the preference analysis unit 106 can analyze that the user prefers coffee shop A. Furthermore, assuming the user drinks Americano coffee at coffee shop A, latte at coffee shop C, and both Americano #1 and Latte #3 at coffee shop E, then the preference analysis unit 106 can analyze that the user's favorite coffee is Americano coffee, and they occasionally drink latte. In this case, the preference analysis unit 106 can perform a matching analysis between coffee shops and their preferred coffee menus based on each coffee shop and the corresponding ordered coffee menu. For example, in the above example, the preference analysis unit 106 analyzes the coffee preference for coffee shop A as Americano coffee and the coffee preference for coffee shop E as primarily latte.

[0042] Furthermore, in the above example, assuming that coffee shop A's Americano uses coffee beans from Vietnam, coffee shop C's latte uses coffee beans from Santos, and coffee shop E's Americano and latte use coffee beans from São Paulo, then the preference analysis unit 106 can analyze that users prefer coffee made with beans from Vietnam, while latte is more likely to use beans from Santos or São Paulo.

[0043] The request signal receiving unit 108 receives a coffee shop recommendation request signal from the user terminal 10 via the network 20. At this time, the request signal receiving unit 108 can also receive a coffee shop recommendation request signal from the user terminal 10 specifying the desired coffee menu, price, and origin of the coffee beans used in the desired coffee. For example, the request signal receiving unit 108 can receive a coffee shop recommendation request signal from the user terminal 10 specifying an Americano coffee menu and coffee beans originating from Vietnam. Alternatively, the request signal receiving unit 108 can also receive a coffee shop recommendation request signal from the user terminal 10 specifying a latte coffee menu and coffee beans originating from São Paulo.

[0044] The current location tracking unit 110 tracks the user's current location. In this case, the current location tracking unit 110 can track the user's current location when it receives a coffee shop recommendation request signal through the request signal receiving unit 108. Alternatively, the current location tracking unit 110 can track the user's current location by receiving current location information from the user terminal 10 corresponding to the user.

[0045] The coffee shop recommendation unit 112 recommends coffee shops that correspond to the user's preferences and are tracked by the current location tracking unit 110 as customized coffee shops to the user terminal 10. These coffee shops are identified by the preference analysis unit 106 as corresponding to the user's preferences. Alternatively, the coffee shop recommendation unit 112 can recommend customized coffee shops to the user terminal 10 in response to information contained in the coffee shop recommendation request signal. For example, if the coffee shop recommendation request signal received by the request signal receiving unit 108 only contains an Americano coffee menu, the coffee shop recommendation unit 112 can recommend coffee shops located within a set range of the user's current location tracked by the current location tracking unit 110 that sell Americano coffee as their coffee menu. Furthermore, when the coffee shop recommendation request signal received by the request signal receiving unit 108 includes an Americano coffee menu and information specifying Vietnam as the coffee bean production location, the coffee shop recommendation unit 112, within a set range of the user's current location tracked by the current location tracking unit 110, will consider Americano coffee as part of the coffee menu and can recommend coffee shops whose Americano coffee beans are produced in Vietnam. At this time, the coffee shop recommendation unit 112 can simultaneously provide map information corresponding to the recommended coffee shop, distance information from the user's current location, etc.

[0046] Therefore, according to an embodiment of the present invention, when a user visits an unfamiliar area, the customized coffee shop recommendation server 100 can easily access coffee shops in that area that offer the user's favorite coffee, allowing them to drink the same or similar coffee as their preferred coffee.

[0047] The evaluation information collection unit (114) collects multiple unspecified evaluation information corresponding to the coffee shop information stored in the coffee shop information storage unit (102) via the network (20). At this time, the evaluation information collection unit 114 can collect a large number of unspecified evaluation information on coffee shop information from the webpages corresponding to each coffee shop, the webpages corresponding to the coffee manufacturers of the coffee sold by each coffee shop, and the webpages of the distribution companies corresponding to the coffee beans sold by each coffee shop, based on articles, comments, images, etc., made for each coffee shop, coffee menu, raw beans, etc.

[0048] In addition, the evaluation information collection unit 114 classifies the evaluation information into multiple stages, determines which stage each of the collected unspecified majority of evaluation information corresponds to, and calculates the comprehensive evaluation of the coffee shop information stored in the coffee shop information storage unit 102 based on the number of evaluation information accumulated in each stage corresponding to the classification.

[0049] In this scenario, the preference analysis unit 106 can analyze user preferences based on user information stored in the user information storage unit 104 and unspecified majority evaluation information collected by the evaluation information collection unit 114. For example, the evaluation information collected by the evaluation information collection unit 114 regarding unspecified majority Americano coffee is that the coffee is cheap and made with a specific coffee maker, which has the highest rating. However, the user information stored in the user information storage unit 104 indicates that, compared to the unspecified majority of Americano coffee with the highest rating, the user prefers Americano coffee made with beans from the São Paulo region, which has a slightly higher price. In this case, the preference analysis unit 106 can analyze the user's preference for Americano coffee made from São Paulo coffee regardless of price.

[0050] User review collection unit 116 collects user review information corresponding to the coffee shop information stored in coffee shop information storage unit 102 via network 20. At this time, user review collection unit 116 can collect articles, replies, and pictures related to the coffee shop information stored in coffee shop information storage unit 102 from the user's SNS account, the SNS accounts of acquaintances visited by the user, and web pages visited by the user, as user review information.

[0051] In this scenario, the user evaluation collection unit 116 will classify user evaluation information for the same stage as that classified by the evaluation information collection unit 114, and evaluate the coffee shop information stored in the coffee shop information storage unit 102 accordingly. Furthermore, the preference analysis unit 106 can analyze user preferences based on user information stored in the user information storage unit 104, multiple unspecified evaluation information collected by the evaluation information collection unit 114, and user evaluation information collected by the user evaluation collection unit 116.

[0052] For example, the evaluation information collection unit 114 collects evaluation information about a non-specific majority of Americano coffees, indicating that the highest grade is for coffees that are inexpensive and made with coffee from a specific coffee manufacturer. Meanwhile, the user information storage unit 104 stores user information indicating that, compared to the highest-rated Americano coffees from the non-specific majority, Americano coffees made with slightly more expensive São Paulo beans receive the highest grade. The user evaluation collection unit 116 also collects user evaluations showing that when Americano coffees made with São Paulo beans receive the highest grade, the highest grade is also achieved with slightly more expensive São Paulo beans. The preference analysis unit 106 can then analyze that, regardless of price, users prefer "Americano coffees" made with São Paulo beans.

[0053] The statistical information calculation unit 118 calculates the statistical information of coffee shops stored in the coffee shop information storage unit 102 based on the evaluation information collected by the evaluation information collection unit 114 from an unspecified number of evaluation information. At this time, the statistical information calculation unit 118 collects writings, replies, pictures, etc., from multiple unspecified SNS accounts, and can calculate statistical information based on the collected writings, replies, pictures, etc. For example, assuming that among coffee shops A to G within a specific area, coffee shop B received the most writings, comments, and pictures during a set period, and among the writings, comments, and pictures collected for coffee shop B, those about Americano coffee were the most numerous, then the statistical information calculation unit 118 can calculate that coffee shop B is the most frequently visited coffee shop in that area, and that Americano coffee shop B is the most frequently visited coffee shop by users. Additionally, the statistical information calculation unit 118 can calculate the percentage of coffee shops that visited an unspecified number of coffee shops within a specific area during a set period. Similarly, the statistical information calculation unit 118 can also calculate the order rate of each coffee menu of an unspecified majority of coffee shops in a specific area during a set period.

[0054] In this scenario, the preference analysis unit 106 can analyze user preferences based on user information stored in the user information storage unit 104, unspecified majority of evaluation information collected by the evaluation information collection unit 114, and statistical information calculated by the statistical information calculation unit 118. For example, the statistical information calculation unit 118 corresponds to a specific coffee shop within a specific area and calculates that Americano is the preferred choice for most unspecified users. The unspecified majority of evaluation information also indicates that the Americano at that coffee shop within the area receives the highest rating. Meanwhile, the user information storage unit 104 stores Americano information from coffee shops within the area that have different user information. When a latte is occasionally consumed at that specific coffee shop, the preference analysis unit 106 analyzes that the specific user's preference differs from the unspecified majority of evaluation information at that specific coffee shop, suggesting a preference for latte.

[0055] Figure 5 This is a flowchart illustrating a method for recommending customized coffee shops according to an embodiment of the present invention. The customized coffee shop recommendation method according to an embodiment of the present invention can be... Figure 1 The customized coffee shop shown is recommended to run on server 100.

[0056] Reference Figures 1 to 5 The user-customized coffee shop recommendation server 100 stores coffee shop information corresponding to each region, including the coffee brand, the types and prices of the coffee menu, and the origin of the coffee beans. At this time, the customized coffee shop recommendation server 100 can store information about the coffee bean growers and the types of coffee beans, such as Arabica, Robusta, and Liberica. Furthermore, the user-customized coffee shop recommendation server 100 divides administrative regions such as cities, wards, and villages into areas of a set size, and can store information about coffee shops in each of these areas, including the coffee brands used by each coffee shop, the types of coffee menus for each coffee shop, the prices of each coffee menu, and the origin of the raw beans for each coffee menu. For example, a user-customized coffee shop recommendation server 100, within each region divided by a specific administrative area, including coffee shops A through G, can store information such as the coffee shop manufacturer, brand name, menu items, prices, and origin of the raw beans used in each coffee shop's menu as coffee shop information. In this case, if a coffee shop uses coffee processed by a specific manufacturer, the origin of the coffee beans used in that processing can also be stored. However, the coffee information used by each coffee shop may differ. For instance, coffee shop A might use coffee from manufacturer a brand named "GA," selling Americanos and lattes at prices such as 4000 won and 5000 won respectively, while coffee shop B might directly roast Brazilian coffee beans, selling Americanos and lattes at prices such as 5000 won and 6000 won respectively. At this point, although the coffee shops explained that they were selling coffee menus from the same coffee bean origin, even coffee menus from the same coffee shop could be sold using coffee beans from different origins.

[0057] Customized coffee shop recommendation server 100 stores user information, including the location of the coffee shop visited by the user, the coffee brand used by the coffee shop, the coffee menu ordered by the user, the price of the coffee menu, and the origin of the raw beans corresponding to the coffee menu, etc. S10. At this time, based on the settlement information of the coffee menu ordered by the user, customized coffee shop recommendation server 100 can know the coffee shop visited by the user, the coffee menu ordered, and the price, etc. In addition, the user-tailored coffee shop recommendation server (100) searches the stored coffee shop information for coffee shop information that matches the coffee shop visited by the user and the coffee menu ordered by the user, and can know the location of the coffee shop, the coffee brand used by the coffee shop, the origin of the raw beans, etc.

[0058] In addition, the customized coffee shop recommendation server 100 can add information such as the user's residential address, school or workplace address as user information. In this case, the user-tailored coffee shop recommendation server 100 can store user information such as coffee shops within a specified range based on the user's residential address and school or workplace address, the location of the coffee shop, the coffee brand used by the coffee shop, the coffee menu ordered by the user at the coffee shop and its price, etc.

[0059] In addition, the customized coffee shop recommendation server 100 can also add and store information such as the user's corresponding blog, Twitter, webpage and other SNS (Social Network Service) accounts, the IP (Internet Protocol) address corresponding to the SNS account, the SNS accounts of acquaintances visited by the user, and the webpages visited by the user as user information.

[0060] Customized coffee shop recommendation server 100 collects and stores multiple unspecified evaluation messages corresponding to coffee shop information via network 20 (S105). At this time, customized coffee shop recommendation server 100 can collect articles, comments, pictures, etc. related to each coffee shop, coffee shop menu, raw beans, etc., from web pages corresponding to each coffee shop, web pages corresponding to the coffee manufacturers of the coffee sold by each coffee shop, and web pages corresponding to the distribution companies of the coffee beans sold by each coffee shop, etc., and collect a large number of unspecified evaluation messages about coffee shops, coffee shops, and coffee shop information.

[0061] In addition, the customized coffee shop recommendation server 100 classifies the evaluation information into multiple stages, determines which stage each of the collected unspecified majority of evaluation information corresponds to, and calculates the comprehensive evaluation of the stored coffee shop information based on the number of evaluation information accumulated in each stage corresponding to the classification.

[0062] The user-customized coffee shop recommendation server 100 calculates stored statistical information S107 for coffee shops based on collected non-specific majority reviews. At this time, the user-customized coffee shop recommendation server 100 collects writings, comments, pictures, etc., from multiple non-specific SNS accounts and calculates statistical information based on the collected writings, comments, pictures, etc. For example, assuming that among coffee shops A to G within a specific area, coffee shop B received the most writings, comments, and pictures during the set period, and among the writings, comments, and pictures collected for coffee shop B, Americano coffee received the most writings, comments, or pictures, then the customized coffee shop recommendation server 100 can calculate that coffee shop B is the most frequently visited coffee shop in that area, and that Americano coffee has the highest sales volume among coffee shops B. Furthermore, the customized coffee shop recommendation server 100 can calculate the non-specific majority visit ratio for each coffee shop that received visits to a non-specific majority of coffee shops within the specific area during the set period. Similarly, the user-tailored coffee shop recommendation server 100 can also calculate the order rate of each coffee menu for a specific number of coffee shops in a specific region during a set period.

[0063] The customized coffee shop recommendation server 100 can collect user review information S109 corresponding to the coffee shop information stored in the coffee shop information storage unit 102 via the network 20. At this time, the customized coffee shop recommendation server 100 can collect user-created articles, comments, and pictures related to the coffee shop information stored in the coffee shop information storage unit 102 from the user's SNS account, the SNS accounts of acquaintances visited by the user, and web pages visited by the user, as user review information. In this case, the customized coffee shop recommendation server 100 will classify the user review information according to the same stage as the classification stage of the review information, and determine the stage of the user review information for the stored coffee shop information accordingly.

[0064] The customized coffee shop recommendation server 100 analyzes, based on stored user information, the user's preference S111 for at least one of the following: preferred coffee shops, preferred coffee brands, preferred types of coffee menus, preferred coffee prices, and the origin of the beans corresponding to the preferred coffee. At this time, the customized coffee shop recommendation server 100 counts the number of times each coffee menu item is ordered by the user, and can also analyze the user's preference order based on the number of each counted coffee menu item.

[0065] For example, suppose that within a specific area, a user visits coffee shop A 24 times, coffee shop C 1 time, and coffee shop E 4 times within a month. Then, the customized coffee shop recommendation server 100 can analyze that the user prefers coffee shop A. Furthermore, suppose the user drinks Americano coffee at coffee shop A, latte at coffee shop C, and both Americano #1 and Latte #3 at coffee shop E. The customized coffee shop recommendation server 100 can analyze that the user prefers Americano coffee most and occasionally drinks latte. In this case, the customized coffee shop recommendation server 100 can perform a matching analysis between coffee shops and their preferred coffee menus based on each coffee shop and the corresponding ordered coffee menu. For example, in the above example, the customized coffee shop recommendation server 100 concludes that the user prefers Americano coffee at coffee shop A and mainly drinks latte at coffee shop E.

[0066] Furthermore, in the above example, assuming that coffee shop A uses Vietnamese coffee beans for its Americano, coffee shop C uses Vietnamese coffee beans for its latte, and coffee shop E uses São Paulo coffee beans for its Americano and latte respectively, then the customized coffee shop recommendation server 100 can analyze that users prefer coffee beans from Vietnam, while latte prefers beans from São Paulo or São Paulo.

[0067] Furthermore, the custom coffee shop recommendation server 100 can analyze user preferences based on stored user information and multiple unspecified reviews. For example, regarding the collected reviews of Americano coffee from a non-specific majority, the highest rating is given to coffee that is inexpensive and made with coffee from a specific coffee maker. If the stored user information shows that, compared to the highest-rated Americano coffee from a non-specific majority, the slightly more expensive Americano coffee from São Paulo made with coffee beans from the same origin, the custom coffee shop recommendation server 100 can analyze that the user prefers the Americano coffee from São Paulo regardless of price.

[0068] Furthermore, the customized coffee shop recommendation server 100 can analyze user preferences based on stored user information, collected unspecified majority reviews, and calculated statistical information. For example, the customized coffee shop recommendation server 100, corresponding to a specific coffee shop in a specific region, calculates that Americano is the most popular coffee among most unspecified users, and the unspecified majority reviews also rate it as the highest level of Americano for that coffee shop in that region. The customized coffee shop recommendation server 100 then evaluates Americano coffee at coffee shops within the same region that have different user information. If a user occasionally uses a latte at a particular coffee shop, the customized coffee shop recommendation server 100 can analyze that the unspecified majority reviews for that specific coffee shop differ from the user's initial impressions, indicating a preference for lattes.

[0069] In addition, the customized coffee shop recommendation server 100 can analyze user preferences based on stored user information, collected unspecified majority of reviews, and collected user reviews.

[0070] For example, regarding the collected reviews of Americano coffee from a non-specific majority, the highest-rated coffee is the inexpensive one made with coffee from a specific manufacturer. The stored user information shows that, compared to the highest-rated Americano coffee from a non-specific majority, the slightly more expensive Americano coffee made with whole beans from the São Paulo region is also at the highest level. Customized coffee shop recommendation server 100 can analyze the Americano coffee made with whole beans from the São Paulo region, regardless of the user's price.

[0071] Customized coffee shop recommendation server 100 receives coffee shop recommendation request signals S113 from user terminal 10 via network 20. At this time, customized coffee shop recommendation server 100 can receive coffee shop recommendation request signals from user terminal 10 specifying the desired coffee menu, price, and origin of the coffee beans used in the desired coffee. For example, customized coffee shop recommendation server 100 can receive coffee shop recommendation request signals from user terminal 10 including Americano coffee menus and coffee beans specified as originating from Vietnam. Additionally, customized coffee shop recommendation server 100 can also receive coffee shop recommendation request signals from user terminal 10 including latte coffee menus and coffee beans specified as originating from São Paulo.

[0072] Customized coffee shop recommendation server 100 tracks the user's current location S115. At this time, upon receiving a coffee shop recommendation request signal, customized coffee shop recommendation server 100 can track the user's current location. In this case, customized coffee shop recommendation server 100 can track the user's current location by receiving current location information from the user terminal 10 corresponding to the user.

[0073] The user-customized coffee shop recommendation server 100 recommends coffee shops corresponding to the user's current location and preferences within the analyzed user's preferred coffee shops as customized coffee shops (S117). At this time, the customized coffee shop recommendation server 100 can recommend customized coffee shops to the user terminal 10 in response to information contained in the coffee shop recommendation request signal. For example, if the received coffee shop recommendation request signal only contains an Americano coffee menu, the customized coffee shop recommendation server 100 can recommend coffee shops within a defined range from the current location of the tracked user that sell Americano coffee as their menu item. Alternatively, if the received coffee shop recommendation request signal contains an Americano coffee menu item and information specifying Vietnam as the coffee bean production area, the customized coffee shop recommendation server 100 will consider Americano coffee as the menu item among coffee shops within a defined range from the current location of the tracked user and recommend coffee shops that sell Americano coffee made from Vietnamese beans. At this time, the customized coffee shop recommendation server 100 can simultaneously provide map information corresponding to the recommended coffee shop, distance information from the user's current location, etc.

[0074] Therefore, according to an embodiment of the present invention, when a user visits an unfamiliar area, the customized coffee shop recommendation server 100 can easily access coffee shops in that area that offer the user's favorite coffee, allowing them to drink the same or similar coffee as their preferred coffee.

[0075] The embodiments of the present invention have been described above, but these are merely examples. Anyone with ordinary knowledge in the relevant art will understand that various modifications and equivalent embodiments can be implemented. Therefore, the scope of protection of the present invention should be determined not only by the scope of the following patent claims, but also by things equivalent to them.

Claims

1. A customized coffee shop recommendation server, characterized in that, Include: The coffee shop information storage unit is used to store coffee shops corresponding to each region, the coffee brand used by the coffee shops, the types of coffee menus corresponding to the coffee shops and the prices of each menu, and coffee shop information including the origin of coffee beans. The user information storage unit is used to store user information, including the location of the coffee shop visited by the user, the coffee brand used by the coffee shop, the coffee menu ordered by the user, the price of the coffee menu, and one or more of the origins of the raw beans corresponding to the coffee menu. The preference analysis unit analyzes one or more of the following based on the user information stored in the aforementioned user information storage unit: coffee shops, brands, types of coffee menus, coffee prices, and coffee bean origins. The request signal receiving unit is used to receive the signal from the user requesting a coffee shop recommendation via the network. The current location tracking unit is used to track the current location of the aforementioned user; The coffee shop recommendation unit will, based on the user preference analysis unit, filter out coffee shops that match the user's current location from the coffee shops on the user's terminal and recommend them as customized coffee shops.

2. The customized coffee shop recommendation server as described in claim 1, characterized in that, It also includes: an evaluation information collection unit, which collects a large number of unspecified evaluation information corresponding to the coffee shop information stored in the aforementioned coffee shop information storage unit via the network; The preference analysis unit analyzes user preferences based on stored user information and multiple unspecified evaluation information collected.

3. The customized coffee shop recommendation server as described in claim 2, characterized in that, It also includes: a user review collection unit, which collects user review information corresponding to the coffee shop information stored in the coffee shop information storage unit via the network; The preference analysis unit analyzes user preferences based on stored user information, multiple unspecified evaluation information collected, and collected user evaluation information.

4. The customized coffee shop recommendation server as described in claim 2, characterized in that, It also includes: a statistical information calculation unit, which calculates statistical information of coffee shops stored in the coffee shop information storage unit based on the evaluation information collected by the evaluation information collection unit. The preference analysis unit analyzes the customized coffee shop recommendation server based on user preferences, using stored user information, multiple unspecified evaluation information collected, and calculated statistical information.

5. A method for recommending customized coffee shops, characterized by the following steps: include: Obtain information on coffee shops, including the coffee shops in each region, the coffee brands used by the coffee shops, the types of coffee menus corresponding to the coffee shops, the prices of each menu item, and the origin of the coffee beans; Obtain user information, including the location of the coffee shop visited by the user, the coffee brand used by the coffee shop, the coffee menu ordered by the user, the price of the coffee menu, and the origin of the coffee beans for the corresponding coffee menu. User preference analysis, based on the stored user information, analyzes one or more of the following: coffee shops, brands, types of coffee menus, coffee prices, and coffee bean origins. The recommendation request is received from the aforementioned users via the network; Location tracking to obtain the current location of the above users; Once a customized coffee shop is identified, it is recommended within the user's corresponding user terminal that matches the user's analyzed preferences and the user's current location.

6. The customized coffee shop recommendation method as described in claim 5, characterized in that, Also includes: This phase involves collecting a large number of unspecified reviews corresponding to the aforementioned stored coffee shop information via the internet. The user preference analysis process analyzes user preferences based on stored user information and multiple unspecified evaluation information collected.

7. The customized coffee shop recommendation method as described in claim 6, characterized in that, Also includes: The steps of collecting user reviews corresponding to the stored coffee shop information via the network; The user preference analysis steps are based on the stored user information, the collected unspecified majority of evaluation information, and the collected user evaluation information to analyze the aforementioned user preferences.

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