system

Abstract

The system according to this embodiment aims to make it easier for single people who frequently eat out to control what they eat. [Solution] The system according to the embodiment comprises a reception unit, a calculation unit, an acquisition unit, a collection unit, and a recommendation unit. The reception unit receives user information. The calculation unit calculates the number of calories to be reduced based on the information entered by the reception unit. The acquisition unit acquires the current location information. The collection unit collects menu information from nearby restaurants based on the location information acquired by the acquisition unit. The recommendation unit recommends menus based on the menu information collected by the collection unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult for single people who eat out frequently to control what they eat, and there is a problem that dieting does not work well.

[0005] The system according to the embodiment aims to make it easier for single people who eat out frequently to control what they eat.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a calculation unit, an acquisition unit, a collection unit, and a recommendation unit. The reception unit receives user information. The calculation unit calculates the number of calories to be reduced based on the information entered by the reception unit. The acquisition unit acquires the current location information. The collection unit collects menu information from nearby restaurants based on the location information acquired by the acquisition unit. The recommendation unit recommends menus based on the menu information collected by the collection unit. [Effects of the Invention]

[0007] The system according to this embodiment makes it easier for single people who frequently eat out to control what they eat. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) SoloFit, a diet meal management agent for single individuals according to an embodiment of the present invention, is a system that calculates the number of calories to be reduced based on the user's input information such as target weight, diet period, age, height, and weight. It then acquires the user's current location information to collect menu information from nearby restaurants and recommends the optimal menu considering calorie intake and PFC balance. SoloFit calculates the number of calories to be reduced based on the user's input information such as target weight, diet period, age, height, and weight. Next, SoloFit acquires the user's current location information and collects menu information from nearby restaurants. From the collected menu information, SoloFit recommends the optimal menu to the user, considering calorie intake and PFC balance. For example, if a user is planning to eat katsu curry for lunch, SoloFit can calculate the calories of the katsu curry and recommend a sashimi set meal instead. This allows the user to easily control their calorie intake and effectively advance their diet. SoloFit can also recommend menus considering the user's budget, preferences, and current mood. This allows the user to continue dieting without difficulty and lead a healthy lifestyle. Furthermore, SoloFit also manages the user's weight. By having the user regularly input their weight, SoloFit records weight changes and manages the progress of their diet. This allows the user to check the results of their diet and maintain motivation. In this way, SoloFit supports users' diets as a diet and meal management agent for single people. Users can easily manage their meals and maintain a healthy weight. As a result, SoloFit, a diet and meal management agent for single people, can effectively support users' diets.

[0029] The diet meal management agent "SoloFit" according to this embodiment comprises a reception unit, a calculation unit, an acquisition unit, a collection unit, and a recommendation unit. The reception unit inputs user information. User information includes, but is not limited to, target weight, diet period, age, height, and weight. The reception unit, for example, stores the information entered by the user in a database. The reception unit can also provide input assistance functions when the user is entering information. For example, the reception unit suggests the next information to be entered based on the information entered by the user. The calculation unit calculates the number of calories to be reduced based on the information entered by the reception unit. For example, the calculation unit calculates the number of calories to be reduced based on the user's target weight, diet period, age, height, and weight, taking into account basal metabolic rate and activity level. For example, the calculation unit uses an algorithm that takes the user's age, gender, height, and weight as input and outputs the basal metabolic rate in order to calculate the basal metabolic rate. The acquisition unit acquires the current location information. For example, the acquisition unit acquires the current location information using GPS or Wi-Fi location information. The acquisition unit can, for example, acquire the location information of the user's smartphone. The collection unit collects menu information from nearby restaurants based on the location information acquired by the acquisition unit. The collection unit collects menu information from, for example, databases on the internet. The collection unit can acquire menu information from, for example, the official websites of restaurants or menu information provision services. The recommendation unit recommends menus based on the menu information collected by the collection unit. The recommendation unit selects the optimal menu from the collected menu information, for example, by considering calorie intake and PFC balance. The recommendation unit can recommend menus that restrict calorie intake based on, for example, the user's target weight and diet period. As a result, the diet meal management agent "SoloFit" according to this embodiment can effectively support dieting by calculating calories based on user information and recommending the optimal menu based on location information.

[0030] The reception desk inputs user information. User information includes, but is not limited to, target weight, diet period, age, height, and weight. The reception desk saves the information entered by the user to a database. Specifically, the reception desk saves the information entered by the user to the database in real time so that it can be used for subsequent processing. The information entered by the user is provided in form format, and appropriate input assistance is provided for each item. For example, when entering target weight, the difference from the current weight is automatically calculated and displayed so that the user can clearly set their goal. Also, when entering diet period, calorie reduction targets corresponding to the period are presented so that the user can set a realistic goal. Furthermore, the reception desk has an input check function to prevent user input errors. For example, range checks are performed on numerical data such as age, height, and weight, and a warning is displayed if an abnormal value is entered. In this way, the reception desk supports users in entering information accurately and efficiently.

[0031] The calculation unit calculates the number of calories to be reduced based on the information entered by the reception unit. For example, the calculation unit calculates the number of calories to be reduced based on information such as the user's target weight, diet period, age, height, and weight, taking into account basal metabolic rate and activity level. Specifically, the calculation unit uses algorithms such as the Harris-Benedict equation to calculate the user's basal metabolic rate. For example, for men, the basal metabolic rate is calculated as 66 + (13.7 × weight [kg]) + (5.0 × height [cm]) - (6.8 × age [years]), and for women, it is calculated as 655 + (9.6 × weight [kg]) + (1.8 × height [cm]) - (4.7 × age [years]). In addition to this, the calculation unit calculates the total calories burned, taking into account the user's activity level (e.g., sedentary lifestyle, light exercise, moderate exercise, vigorous exercise). Based on this information, the calculation unit calculates the amount of calories that the user needs to reduce to reach their target weight and presents a daily calorie intake target. Furthermore, the calculation unit also has a function to periodically evaluate the user's progress and adjust the calorie target as needed. This allows the calculation unit to provide the user with specific calorie targets that will help them effectively progress with their diet.

[0032] The acquisition unit acquires the current location information. The acquisition unit acquires the current location information using, for example, GPS or Wi-Fi location information. Specifically, the acquisition unit uses the location information service of the user's smartphone to acquire the current location information in real time. By using GPS, highly accurate location information can be acquired outdoors, and by using Wi-Fi location information, relatively highly accurate location information can be acquired indoors. The acquisition unit periodically updates this location information and maintains the latest location information according to the user's movement. In addition, the acquisition unit has a function to adjust the frequency of location information acquisition, so that location information can be acquired with the necessary accuracy while suppressing battery consumption. For example, by increasing the frequency of location information acquisition when the user is moving and decreasing the frequency when the user is stationary, location information can be managed efficiently. As a result, the acquisition unit can accurately grasp the user's current location and use it for subsequent processing.

[0033] The collection unit collects menu information from nearby restaurants based on location information obtained by the acquisition unit. The collection unit collects menu information from, for example, databases on the internet. Specifically, the collection unit can obtain menu information from the official websites of restaurants or from menu information provision services. Based on location information, the collection unit identifies restaurants within a certain range from the user's current location and collects menu information from those restaurants. For example, the collection unit obtains the latest menu information by accessing the official websites of restaurants and scraping the menu information. It can also obtain menu information from multiple restaurants at once by using a menu information provision service. The collection unit stores the acquired menu information in a database so that it can be used by the subsequent recommendation unit. Furthermore, the collection unit manages the update frequency of the menu information and can always provide the latest menu information by regularly obtaining the latest information. As a result, the collection unit can efficiently collect menu information from nearby restaurants based on the user's current location and provide information to support dieting.

[0034] The recommendation unit recommends menus based on menu information collected by the data collection unit. For example, the recommendation unit selects the optimal menu from the collected menu information, taking into account calorie intake and PFC balance. Specifically, the recommendation unit can recommend menus that restrict calorie intake based on the user's target weight and diet period. The recommendation unit uses AI to analyze menu information and proposes personalized menus that take into account the user's preferences and allergy information. For example, if a user desires a low-calorie, high-protein meal, the recommendation unit will prioritize suggesting menus that meet those conditions. The recommendation unit can also learn the user's past selection history and suggest menus that match the user's preferences. Furthermore, the recommendation unit analyzes the nutritional information of menus in detail and proposes healthy meals by optimizing the PFC balance (balance of protein, fat, and carbohydrates). In this way, the recommendation unit can provide the optimal menu tailored to the user's diet goals and support effective dieting.

[0035] The recommendation system can recommend menu items considering the user's budget, preferences, and current mood. For example, it can recommend low-priced menu items based on the user's budget. It can also recommend menu items that do not contain ingredients the user is allergic to, considering their preferences. It can also recommend light meals or desserts, considering the user's current mood. By considering the user's budget, preferences, and current mood, the system can recommend more appropriate menu items.

[0036] The recommendation function can recommend menus considering calorie intake and PFC balance. For example, it can recommend low-calorie menus to limit calorie intake. It can also recommend menus with a good balance of protein, fat, and carbohydrates, considering PFC balance. It can also recommend menus that limit calorie intake based on the user's target weight and diet period. In this way, by considering calorie intake and PFC balance, it can support healthy eating.

[0037] The calculation unit can calculate the number of calories to be reduced based on information such as the user's target weight, diet period, age, height, and weight. For example, the calculation unit can calculate the daily calorie intake based on the user's target weight. The calculation unit can also set a weekly weight loss target, taking into account the diet period. For example, the calculation unit can calculate the basal metabolic rate based on the user's age, height, and weight. This allows for more effective dieting support by performing calorie calculations based on the user's individual information.

[0038] The reception desk can accept regular entries of the user's weight. For example, the reception desk may encourage the user to enter their weight daily. For example, the reception desk may recommend that the user enter their weight once a week. For example, the reception desk may accept that the user enters their weight once a month. This allows the user to track their weight progress by regularly entering their weight.

[0039] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. For example, the reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user.

[0040] The input unit can provide real-time input assistance based on the user's input. For example, when the user starts typing, the input unit can provide predictive text functionality to assist with input. The input unit can also display relevant suggestions in real time and provide options while the user is typing. The input unit can also prompt the user to confirm their input before they complete the input, preventing errors. By providing real-time input assistance, the efficiency of input can be improved.

[0041] The reception desk can provide information relevant to the user's input, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can automatically display information relevant to that region. For example, if the user is on the move, the reception desk can also provide information based on the user's current location in real time. For example, if the user is in a specific location, the reception desk can prioritize displaying information relevant to that location. This allows for the provision of more relevant information by considering geographical location.

[0042] The reception desk can analyze a user's social media activity and suggest relevant input content. For example, the reception desk can suggest relevant input content based on information the user has shared on social media. The reception desk can also analyze a user's interests and preferences from their social media activity and customize the input content. For example, the reception desk can suggest relevant input content based on information about accounts the user follows on social media. In this way, by analyzing social media activity, it is possible to suggest input content relevant to the user.

[0043] The calculation unit can analyze the user's past dieting history and propose the optimal calorie calculation method. For example, the calculation unit can propose the optimal calorie calculation method based on the user's past successful dieting methods. The calculation unit can also select an effective calorie calculation method from the user's past dieting history. For example, the calculation unit can analyze the user's past dieting history and propose the most effective calorie calculation method. In this way, by analyzing past dieting history, the optimal calorie calculation method can be proposed.

[0044] The calculation unit can adjust the frequency of calorie calculations according to the user's lifestyle. For example, if the user is busy, the calculation unit can reduce the frequency of calorie calculations to alleviate the burden. For example, if the user has more time, the calculation unit can increase the frequency of calorie calculations to obtain more accurate results. For example, the calculation unit can also suggest the optimal frequency of calorie calculations according to the user's lifestyle. In this way, the burden can be reduced by adjusting the frequency of calorie calculations according to the lifestyle.

[0045] The calculation unit can provide information related to calorie calculation, taking into account the user's geographical location. For example, if the user is in a specific region, the calculation unit can perform calorie calculations based on the food information of that region. For example, if the user is on the move, the calculation unit can also provide real-time calorie calculations based on the user's current location. For example, if the user is in a specific location, the calculation unit can also perform calorie calculations based on the food information of that location. This allows for more relevant calorie calculations by taking geographical location into consideration.

[0046] The calculation unit can analyze a user's social media activity and suggest a relevant calorie calculation method. For example, the calculation unit can calculate calories based on meal information shared by the user on social media. The calculation unit can also analyze the user's interests and preferences from their social media activity and customize the calorie calculation method. For example, the calculation unit can suggest a calorie calculation method based on information about accounts the user follows on social media. In this way, by analyzing social media activity, it can suggest a calorie calculation method relevant to the user.

[0047] The acquisition unit can analyze the user's past location history and select the optimal acquisition method. For example, the acquisition unit can propose the optimal location acquisition method based on places the user has visited in the past. For example, the acquisition unit can select an effective acquisition method from the user's past location history. For example, the acquisition unit can analyze the user's past location history and propose the most efficient acquisition method. In this way, by analyzing past location history, it is possible to propose the optimal location acquisition method.

[0048] The acquisition unit can filter location information based on the user's current activity status when acquiring it. For example, if the user is on the move, the acquisition unit can filter the location information based on the user's current activity status. For example, if the user is in a specific location, the acquisition unit can also filter the location information based on the activity status of that location. For example, the acquisition unit can analyze the user's current activity status and provide optimal location information. By filtering location information based on the user's current activity status, more accurate information can be provided.

[0049] The acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring location information. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring location information related to that region. For example, if the user is on the move, the acquisition unit can also acquire location information based on the user's current location in real time. For example, if the user is in a specific location, the acquisition unit can also prioritize acquiring location information related to that location. This allows for the provision of more relevant information by considering geographical location information.

[0050] The acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring location information. For example, the acquisition unit can obtain relevant information based on location information shared by the user on social media. The acquisition unit can also analyze the user's interests and preferences from their social media activity and customize the location information. For example, the acquisition unit can obtain relevant location information based on information about accounts the user follows on social media. This allows the system to provide location information relevant to the user by analyzing their social media activity.

[0051] The data collection unit can analyze a user's past dining history and propose the optimal method for collecting menu information. For example, the unit can propose the optimal collection method based on menu information from restaurants the user has previously visited. The unit can also select an effective menu information collection method from a user's past dining history. For example, the unit can analyze a user's past dining history and propose the most efficient method for collecting menu information. This allows the unit to propose the optimal menu information collection method by analyzing past dining history.

[0052] The data collection unit can filter menu information based on the user's current eating habits. For example, if a user has a specific eating habit, the data collection unit can filter menu information based on that habit. For example, if a user has a specific dietary restriction, the data collection unit can also filter menu information based on that restriction. For example, the data collection unit can analyze the user's current eating habits and provide optimal menu information. By filtering menu information based on current eating habits, more appropriate information can be provided.

[0053] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting menu information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of menu information related to that region. For example, if the user is on the move, the data collection unit can also collect menu information based on the user's current location in real time. For example, if the user is in a specific location, the data collection unit can prioritize the collection of menu information related to that location. This allows for the provision of more relevant information by considering geographical location.

[0054] The data collection unit can analyze users' social media activity and collect relevant information when gathering menu information. For example, the data collection unit can collect relevant menu information based on dining information shared by users on social media. The data collection unit can also analyze users' interests and preferences from their social media activity and customize menu information accordingly. For example, the data collection unit can collect relevant menu information based on information about accounts that users follow on social media. This allows the system to provide users with relevant menu information by analyzing their social media activity.

[0055] The recommendation system can analyze a user's past dining history and recommend the most suitable menu items. For example, the recommendation system can recommend the most suitable menu items based on the menus of restaurants the user has visited in the past. The recommendation system can also select effective menu items based on the user's past dining history. For example, the recommendation system can analyze the user's past dining history and recommend the most efficient menu items. In this way, by analyzing past dining history, the system can recommend the most suitable menu items.

[0056] The recommendation system can filter menu recommendations based on the user's current eating habits. For example, if a user has a specific eating habit, the recommendation system can filter menus based on that habit. It can also filter menus based on specific dietary restrictions if a user has them. Furthermore, the recommendation system can analyze the user's current eating habits and provide the most suitable menu. By filtering menus based on current eating habits, it can provide more relevant information.

[0057] The recommendation system can prioritize recommending menu items that are highly relevant to the user, taking into account their geographical location. For example, if a user is in a specific region, the recommendation system will prioritize recommending menu items related to that region. The recommendation system can also recommend menu items in real time based on the user's current location, for example, if the user is on the move. Furthermore, if a user is in a specific location, the recommendation system can prioritize recommending menu items related to that location. This allows the system to provide more relevant menu items by considering geographical location information.

[0058] The recommendation system can analyze a user's social media activity when recommending menu items and recommend relevant items. For example, the recommendation system can recommend relevant menu items based on food and drink information shared by the user on social media. The recommendation system can also customize menus by analyzing the user's interests and preferences from their social media activity. For example, the recommendation system can recommend relevant menu items based on information about accounts the user follows on social media. In this way, by analyzing social media activity, the system can provide menu items that are relevant to the user.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] SoloFit can collect information about the taste and texture of food to improve user satisfaction with meals and incorporate it into recommendations. For example, if a user prefers the taste or texture of a particular ingredient, the system can recommend a menu based on that information. It can also analyze the characteristics of menus that users have previously given high ratings to and recommend similar menus. Furthermore, the accuracy of recommendations can be improved by users providing feedback after meals. As a result, users can enjoy more satisfying meals and it becomes easier to stick to their diet.

[0061] SoloFit can recommend menus that take into account the user's meal timing. For example, it can suggest appropriate menus depending on when the user eats breakfast, lunch, or dinner. If the user has a habit of snacking, it can also recommend low-calorie snacks suitable for that time of day. Furthermore, if it is difficult for the user to eat meals at certain times, it can suggest menus that are easy to carry. This allows users to choose appropriate menus that match their meal timing, maximizing the effectiveness of their diet.

[0062] SoloFit can recommend menus that take into account the user's exercise habits. For example, it can suggest high-protein meals for the user to consume after exercise. It can also recommend meals high in carbohydrates for the user to replenish energy before exercise. Furthermore, it can suggest low-calorie meals for days when the user does not exercise. This allows users to eat appropriately according to their exercise habits, thereby enhancing the effectiveness of their diet.

[0063] SoloFit can recommend cuisines from different countries and regions to increase the variety of meals a user can eat. For example, if a user is tired of Japanese food, it can suggest different cuisines such as Italian or Mexican. It can also recommend dishes using specific ingredients if the user prefers them. Furthermore, if a user wants to try new dishes, it can provide easy-to-make recipes. This allows users to enjoy variety in their diet while staying on track with their weight loss efforts.

[0064] SoloFit can recommend menus that take into account the nutritional balance of the user's diet. For example, if a user is deficient in vitamins or minerals, it can suggest menus that are rich in those nutrients. It can also recommend menus that are lower in certain nutrients if the user is consuming them in excess. Furthermore, if a user has a specific health condition, it can suggest menus with a nutritional balance appropriate for that condition. This allows users to diet while maintaining a healthy nutritional balance.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk enters the user's information. This information includes target weight, diet period, age, height, and weight. The reception desk can also save the information entered by the user in a database and provide input assistance functions. Step 2: The calculation unit calculates the number of calories to be reduced based on the information entered by the reception unit. The calculation unit calculates the number of calories to be reduced based on the user's target weight, diet period, age, height, weight, etc., taking into account basal metabolic rate and activity level. Step 3: The acquisition unit acquires the current location information. The acquisition unit can acquire the current location information using GPS or Wi-Fi location information and obtain the location information of the user's smartphone. Step 4: The collection unit collects menu information from nearby restaurants based on the location information acquired by the acquisition unit. The collection unit can collect menu information from databases on the internet, or obtain menu information from the official websites of restaurants or from menu information provision services. Step 5: The recommendation unit recommends menus based on the menu information collected by the collection unit. The recommendation unit selects the optimal menu from the collected menu information, taking into account calorie intake and PFC balance, and recommends menus that restrict calorie intake based on the user's target weight and diet period.

[0067] (Example of form 2) SoloFit, a diet meal management agent for single individuals according to an embodiment of the present invention, is a system that calculates the number of calories to be reduced based on the user's input information such as target weight, diet period, age, height, and weight. It then acquires the user's current location information to collect menu information from nearby restaurants and recommends the optimal menu considering calorie intake and PFC balance. SoloFit calculates the number of calories to be reduced based on the user's input information such as target weight, diet period, age, height, and weight. Next, SoloFit acquires the user's current location information and collects menu information from nearby restaurants. From the collected menu information, SoloFit recommends the optimal menu to the user, considering calorie intake and PFC balance. For example, if a user is planning to eat katsu curry for lunch, SoloFit can calculate the calories of the katsu curry and recommend a sashimi set meal instead. This allows the user to easily control their calorie intake and effectively advance their diet. SoloFit can also recommend menus considering the user's budget, preferences, and current mood. This allows the user to continue dieting without difficulty and lead a healthy lifestyle. Furthermore, SoloFit also manages the user's weight. By having the user regularly input their weight, SoloFit records weight changes and manages the progress of their diet. This allows the user to check the results of their diet and maintain motivation. In this way, SoloFit supports users' diets as a diet and meal management agent for single people. Users can easily manage their meals and maintain a healthy weight. As a result, SoloFit, a diet and meal management agent for single people, can effectively support users' diets.

[0068] The diet meal management agent "SoloFit" according to this embodiment comprises a reception unit, a calculation unit, an acquisition unit, a collection unit, and a recommendation unit. The reception unit inputs user information. User information includes, but is not limited to, target weight, diet period, age, height, and weight. The reception unit, for example, stores the information entered by the user in a database. The reception unit can also provide input assistance functions when the user is entering information. For example, the reception unit suggests the next information to be entered based on the information entered by the user. The calculation unit calculates the number of calories to be reduced based on the information entered by the reception unit. For example, the calculation unit calculates the number of calories to be reduced based on the user's target weight, diet period, age, height, and weight, taking into account basal metabolic rate and activity level. For example, the calculation unit uses an algorithm that takes the user's age, gender, height, and weight as input and outputs the basal metabolic rate in order to calculate the basal metabolic rate. The acquisition unit acquires the current location information. For example, the acquisition unit acquires the current location information using GPS or Wi-Fi location information. The acquisition unit can, for example, acquire the location information of the user's smartphone. The collection unit collects menu information from nearby restaurants based on the location information acquired by the acquisition unit. The collection unit collects menu information from, for example, databases on the internet. The collection unit can acquire menu information from, for example, the official websites of restaurants or menu information provision services. The recommendation unit recommends menus based on the menu information collected by the collection unit. The recommendation unit selects the optimal menu from the collected menu information, for example, by considering calorie intake and PFC balance. The recommendation unit can recommend menus that restrict calorie intake based on, for example, the user's target weight and diet period. As a result, the diet meal management agent "SoloFit" according to this embodiment can effectively support dieting by calculating calories based on user information and recommending the optimal menu based on location information.

[0069] The reception desk inputs user information. User information includes, but is not limited to, target weight, diet period, age, height, and weight. The reception desk saves the information entered by the user to a database. Specifically, the reception desk saves the information entered by the user to the database in real time so that it can be used for subsequent processing. The information entered by the user is provided in form format, and appropriate input assistance is provided for each item. For example, when entering target weight, the difference from the current weight is automatically calculated and displayed so that the user can clearly set their goal. Also, when entering diet period, calorie reduction targets corresponding to the period are presented so that the user can set a realistic goal. Furthermore, the reception desk has an input check function to prevent user input errors. For example, range checks are performed on numerical data such as age, height, and weight, and a warning is displayed if an abnormal value is entered. In this way, the reception desk supports users in entering information accurately and efficiently.

[0070] The calculation unit calculates the number of calories to be reduced based on the information entered by the reception unit. For example, the calculation unit calculates the number of calories to be reduced based on information such as the user's target weight, diet period, age, height, and weight, taking into account basal metabolic rate and activity level. Specifically, the calculation unit uses algorithms such as the Harris-Benedict equation to calculate the user's basal metabolic rate. For example, for men, the basal metabolic rate is calculated as 66 + (13.7 × weight [kg]) + (5.0 × height [cm]) - (6.8 × age [years]), and for women, it is calculated as 655 + (9.6 × weight [kg]) + (1.8 × height [cm]) - (4.7 × age [years]). In addition to this, the calculation unit calculates the total calories burned, taking into account the user's activity level (e.g., sedentary lifestyle, light exercise, moderate exercise, vigorous exercise). Based on this information, the calculation unit calculates the amount of calories that the user needs to reduce to reach their target weight and presents a daily calorie intake target. Furthermore, the calculation unit also has a function to periodically evaluate the user's progress and adjust the calorie target as needed. This allows the calculation unit to provide the user with specific calorie targets that will help them effectively progress with their diet.

[0071] The acquisition unit acquires the current location information. The acquisition unit acquires the current location information using, for example, GPS or Wi-Fi location information. Specifically, the acquisition unit uses the location information service of the user's smartphone to acquire the current location information in real time. By using GPS, highly accurate location information can be acquired outdoors, and by using Wi-Fi location information, relatively highly accurate location information can be acquired indoors. The acquisition unit periodically updates this location information and maintains the latest location information according to the user's movement. In addition, the acquisition unit has a function to adjust the frequency of location information acquisition, so that location information can be acquired with the necessary accuracy while suppressing battery consumption. For example, by increasing the frequency of location information acquisition when the user is moving and decreasing the frequency when the user is stationary, location information can be managed efficiently. As a result, the acquisition unit can accurately grasp the user's current location and use it for subsequent processing.

[0072] The collection unit collects menu information from nearby restaurants based on location information obtained by the acquisition unit. The collection unit collects menu information from, for example, databases on the internet. Specifically, the collection unit can obtain menu information from the official websites of restaurants or from menu information provision services. Based on location information, the collection unit identifies restaurants within a certain range from the user's current location and collects menu information from those restaurants. For example, the collection unit obtains the latest menu information by accessing the official websites of restaurants and scraping the menu information. It can also obtain menu information from multiple restaurants at once by using a menu information provision service. The collection unit stores the acquired menu information in a database so that it can be used by the subsequent recommendation unit. Furthermore, the collection unit manages the update frequency of the menu information and can always provide the latest menu information by regularly obtaining the latest information. As a result, the collection unit can efficiently collect menu information from nearby restaurants based on the user's current location and provide information to support dieting.

[0073] The recommendation unit recommends menus based on menu information collected by the data collection unit. For example, the recommendation unit selects the optimal menu from the collected menu information, taking into account calorie intake and PFC balance. Specifically, the recommendation unit can recommend menus that restrict calorie intake based on the user's target weight and diet period. The recommendation unit uses AI to analyze menu information and proposes personalized menus that take into account the user's preferences and allergy information. For example, if a user desires a low-calorie, high-protein meal, the recommendation unit will prioritize suggesting menus that meet those conditions. The recommendation unit can also learn the user's past selection history and suggest menus that match the user's preferences. Furthermore, the recommendation unit analyzes the nutritional information of menus in detail and proposes healthy meals by optimizing the PFC balance (balance of protein, fat, and carbohydrates). In this way, the recommendation unit can provide the optimal menu tailored to the user's diet goals and support effective dieting.

[0074] The recommendation system can recommend menu items considering the user's budget, preferences, and current mood. For example, it can recommend low-priced menu items based on the user's budget. It can also recommend menu items that do not contain ingredients the user is allergic to, considering their preferences. It can also recommend light meals or desserts, considering the user's current mood. By considering the user's budget, preferences, and current mood, the system can recommend more appropriate menu items.

[0075] The recommendation function can recommend menus considering calorie intake and PFC balance. For example, it can recommend low-calorie menus to limit calorie intake. It can also recommend menus with a good balance of protein, fat, and carbohydrates, considering PFC balance. It can also recommend menus that limit calorie intake based on the user's target weight and diet period. In this way, by considering calorie intake and PFC balance, it can support healthy eating.

[0076] The calculation unit can calculate the number of calories to be reduced based on information such as the user's target weight, diet period, age, height, and weight. For example, the calculation unit can calculate the daily calorie intake based on the user's target weight. The calculation unit can also set a weekly weight loss target, taking into account the diet period. For example, the calculation unit can calculate the basal metabolic rate based on the user's age, height, and weight. This allows for more effective dieting support by performing calorie calculations based on the user's individual information.

[0077] The reception desk can accept regular entries of the user's weight. For example, the reception desk may encourage the user to enter their weight daily. For example, the reception desk may recommend that the user enter their weight once a week. For example, the reception desk may accept that the user enters their weight once a month. This allows the user to track their weight progress by regularly entering their weight.

[0078] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk can provide detailed input options and suggest customizable input methods. If the user is in a hurry, for example, the reception desk can prioritize voice input to allow for quick information entry. This allows for a more comfortable input experience by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. For example, the reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user.

[0080] The input unit can provide real-time input assistance based on the user's input. For example, when the user starts typing, the input unit can provide predictive text functionality to assist with input. The input unit can also display relevant suggestions in real time and provide options while the user is typing. The input unit can also prompt the user to confirm their input before they complete the input, preventing errors. By providing real-time input assistance, the efficiency of input can be improved.

[0081] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, the reception desk will prioritize inputting important information and postpone other information. If the user is relaxed, the reception desk can encourage the input of detailed information and facilitate the overall input process. If the user is in a hurry, the reception desk can prioritize inputting the most important information and complete the process quickly. This allows for more appropriate input by prioritizing input according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The reception desk can provide information relevant to the user's input, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can automatically display information relevant to that region. For example, if the user is on the move, the reception desk can also provide information based on the user's current location in real time. For example, if the user is in a specific location, the reception desk can prioritize displaying information relevant to that location. This allows for the provision of more relevant information by considering geographical location.

[0083] The reception desk can analyze a user's social media activity and suggest relevant input content. For example, the reception desk can suggest relevant input content based on information the user has shared on social media. The reception desk can also analyze a user's interests and preferences from their social media activity and customize the input content. For example, the reception desk can suggest relevant input content based on information about accounts the user follows on social media. In this way, by analyzing social media activity, it is possible to suggest input content relevant to the user.

[0084] The calculation unit can estimate the user's emotions and adjust the calorie calculation method based on the estimated emotions. For example, if the user is stressed, the calculation unit can provide a simple calorie calculation method to reduce the burden. For example, if the user is relaxed, the calculation unit can also provide a detailed calorie calculation method to obtain accurate results. For example, if the user is in a hurry, the calculation unit can perform a quick calorie calculation and provide the results. This allows for more appropriate calorie calculation by adjusting the calorie calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The calculation unit can analyze the user's past dieting history and propose the optimal calorie calculation method. For example, the calculation unit can propose the optimal calorie calculation method based on the user's past successful dieting methods. The calculation unit can also select an effective calorie calculation method from the user's past dieting history. For example, the calculation unit can analyze the user's past dieting history and propose the most effective calorie calculation method. In this way, by analyzing past dieting history, the optimal calorie calculation method can be proposed.

[0086] The calculation unit can adjust the frequency of calorie calculations according to the user's lifestyle. For example, if the user is busy, the calculation unit can reduce the frequency of calorie calculations to alleviate the burden. For example, if the user has more time, the calculation unit can increase the frequency of calorie calculations to obtain more accurate results. For example, the calculation unit can also suggest the optimal frequency of calorie calculations according to the user's lifestyle. In this way, the burden can be reduced by adjusting the frequency of calorie calculations according to the lifestyle.

[0087] The calculation unit can estimate the user's emotions and determine the priority of calorie calculations based on the estimated emotions. For example, if the user is stressed, the calculation unit will prioritize important calorie calculations and postpone other calculations. For example, if the user is relaxed, the calculation unit can prioritize detailed calorie calculations to obtain accurate results. For example, if the user is in a hurry, the calculation unit can perform calorie calculations quickly and provide results. This allows for more appropriate calorie calculations by determining the priority of calorie calculations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The calculation unit can provide information related to calorie calculation, taking into account the user's geographical location. For example, if the user is in a specific region, the calculation unit can perform calorie calculations based on the food information of that region. For example, if the user is on the move, the calculation unit can also provide real-time calorie calculations based on the user's current location. For example, if the user is in a specific location, the calculation unit can also perform calorie calculations based on the food information of that location. This allows for more relevant calorie calculations by taking geographical location into consideration.

[0089] The calculation unit can analyze a user's social media activity and suggest a relevant calorie calculation method. For example, the calculation unit can calculate calories based on meal information shared by the user on social media. The calculation unit can also analyze the user's interests and preferences from their social media activity and customize the calorie calculation method. For example, the calculation unit can suggest a calorie calculation method based on information about accounts the user follows on social media. In this way, by analyzing social media activity, it can suggest a calorie calculation method relevant to the user.

[0090] The acquisition unit can estimate the user's emotions and adjust the timing of location information acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can reduce the frequency of location information acquisition to alleviate the burden. For example, if the user is relaxed, the acquisition unit can increase the frequency of location information acquisition to obtain more accurate information. For example, if the user is in a hurry, the acquisition unit can quickly acquire location information and provide it in real time. This makes it possible to acquire more appropriate location information by adjusting the timing of location information acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The acquisition unit can analyze the user's past location history and select the optimal acquisition method. For example, the acquisition unit can propose the optimal location acquisition method based on places the user has visited in the past. For example, the acquisition unit can select an effective acquisition method from the user's past location history. For example, the acquisition unit can analyze the user's past location history and propose the most efficient acquisition method. In this way, by analyzing past location history, it is possible to propose the optimal location acquisition method.

[0092] The acquisition unit can filter location information based on the user's current activity status when acquiring it. For example, if the user is on the move, the acquisition unit can filter the location information based on the user's current activity status. For example, if the user is in a specific location, the acquisition unit can also filter the location information based on the activity status of that location. For example, the acquisition unit can analyze the user's current activity status and provide optimal location information. By filtering location information based on the user's current activity status, more accurate information can be provided.

[0093] The acquisition unit can estimate the user's emotions and determine the priority of location information to acquire based on the estimated emotions. For example, if the user is stressed, the acquisition unit will prioritize acquiring important location information and postpone other information. For example, if the user is relaxed, the acquisition unit can also prioritize acquiring detailed location information to obtain accurate information. For example, if the user is in a hurry, the acquisition unit can quickly acquire important location information and provide it in real time. This allows for the provision of more appropriate information by prioritizing location information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring location information. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring location information related to that region. For example, if the user is on the move, the acquisition unit can also acquire location information based on the user's current location in real time. For example, if the user is in a specific location, the acquisition unit can also prioritize acquiring location information related to that location. This allows for the provision of more relevant information by considering geographical location information.

[0095] The acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring location information. For example, the acquisition unit can obtain relevant information based on location information shared by the user on social media. The acquisition unit can also analyze the user's interests and preferences from their social media activity and customize the location information. For example, the acquisition unit can obtain relevant location information based on information about accounts the user follows on social media. This allows the system to provide location information relevant to the user by analyzing their social media activity.

[0096] The data collection unit can estimate the user's emotions and adjust the method of collecting menu information based on the estimated emotions. For example, if the user is stressed, the data collection unit can provide a simple method of collecting menu information to reduce the burden. For example, if the user is relaxed, the data collection unit can also provide a detailed method of collecting menu information to obtain accurate information. For example, if the user is in a hurry, the data collection unit can quickly collect menu information and provide the results. In this way, by adjusting the method of collecting menu information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The data collection unit can analyze a user's past dining history and propose the optimal method for collecting menu information. For example, the unit can propose the optimal collection method based on menu information from restaurants the user has previously visited. The unit can also select an effective menu information collection method from a user's past dining history. For example, the unit can analyze a user's past dining history and propose the most efficient method for collecting menu information. This allows the unit to propose the optimal menu information collection method by analyzing past dining history.

[0098] The data collection unit can filter menu information based on the user's current eating habits. For example, if a user has a specific eating habit, the data collection unit can filter menu information based on that habit. For example, if a user has a specific dietary restriction, the data collection unit can also filter menu information based on that restriction. For example, the data collection unit can analyze the user's current eating habits and provide optimal menu information. By filtering menu information based on current eating habits, more appropriate information can be provided.

[0099] The data collection unit can estimate the user's emotions and determine the priority of menu information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important menu information and postpone other information. For example, if the user is relaxed, the data collection unit can also prioritize collecting detailed menu information to obtain accurate information. For example, if the user is in a hurry, the data collection unit can quickly collect important menu information and provide the results. This allows for the provision of more appropriate information by prioritizing menu information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting menu information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of menu information related to that region. For example, if the user is on the move, the data collection unit can also collect menu information based on the user's current location in real time. For example, if the user is in a specific location, the data collection unit can prioritize the collection of menu information related to that location. This allows for the provision of more relevant information by considering geographical location.

[0101] The data collection unit can analyze users' social media activity and collect relevant information when gathering menu information. For example, the data collection unit can collect relevant menu information based on dining information shared by users on social media. The data collection unit can also analyze users' interests and preferences from their social media activity and customize menu information accordingly. For example, the data collection unit can collect relevant menu information based on information about accounts that users follow on social media. This allows the system to provide users with relevant menu information by analyzing their social media activity.

[0102] The recommendation system can estimate the user's emotions and adjust its menu recommendations based on those emotions. For example, if the user is stressed, the recommendation system can provide simple menu recommendations to reduce their burden. If the user is relaxed, the recommendation system can also provide detailed menu recommendations to provide more accurate information. If the user is in a hurry, the recommendation system can quickly recommend menus and provide results. By adjusting the menu recommendations according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The recommendation system can analyze a user's past dining history and recommend the most suitable menu items. For example, the recommendation system can recommend the most suitable menu items based on the menus of restaurants the user has visited in the past. The recommendation system can also select effective menu items based on the user's past dining history. For example, the recommendation system can analyze the user's past dining history and recommend the most efficient menu items. In this way, by analyzing past dining history, the system can recommend the most suitable menu items.

[0104] The recommendation system can filter menu recommendations based on the user's current eating habits. For example, if a user has a specific eating habit, the recommendation system can filter menus based on that habit. It can also filter menus based on specific dietary restrictions if a user has them. Furthermore, the recommendation system can analyze the user's current eating habits and provide the most suitable menu. By filtering menus based on current eating habits, it can provide more relevant information.

[0105] The recommendation system can estimate the user's emotions and prioritize the menu recommendations based on those emotions. For example, if the user is stressed, the recommendation system will prioritize important menu recommendations and postpone other information. If the user is relaxed, the recommendation system can also prioritize detailed menu recommendations to obtain accurate information. If the user is in a hurry, the recommendation system can quickly recommend important menu items and provide results. This allows for the provision of more appropriate information by prioritizing menus according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The recommendation system can prioritize recommending menu items that are highly relevant to the user, taking into account their geographical location. For example, if a user is in a specific region, the recommendation system will prioritize recommending menu items related to that region. The recommendation system can also recommend menu items in real time based on the user's current location, for example, if the user is on the move. Furthermore, if a user is in a specific location, the recommendation system can prioritize recommending menu items related to that location. This allows the system to provide more relevant menu items by considering geographical location information.

[0107] The recommendation system can analyze a user's social media activity when recommending menu items and recommend relevant items. For example, the recommendation system can recommend relevant menu items based on food and drink information shared by the user on social media. The recommendation system can also customize menus by analyzing the user's interests and preferences from their social media activity. For example, the recommendation system can recommend relevant menu items based on information about accounts the user follows on social media. In this way, by analyzing social media activity, the system can provide menu items that are relevant to the user.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] SoloFit can collect information about the taste and texture of food to improve user satisfaction with meals and incorporate it into recommendations. For example, if a user prefers the taste or texture of a particular ingredient, the system can recommend a menu based on that information. It can also analyze the characteristics of menus that users have previously given high ratings to and recommend similar menus. Furthermore, the accuracy of recommendations can be improved by users providing feedback after meals. As a result, users can enjoy more satisfying meals and it becomes easier to stick to their diet.

[0110] SoloFit can recommend menus that take into account the user's meal timing. For example, it can suggest appropriate menus depending on when the user eats breakfast, lunch, or dinner. If the user has a habit of snacking, it can also recommend low-calorie snacks suitable for that time of day. Furthermore, if it is difficult for the user to eat meals at certain times, it can suggest menus that are easy to carry. This allows users to choose appropriate menus that match their meal timing, maximizing the effectiveness of their diet.

[0111] SoloFit can recommend menus that take into account the user's exercise habits. For example, it can suggest high-protein meals for the user to consume after exercise. It can also recommend meals high in carbohydrates for the user to replenish energy before exercise. Furthermore, it can suggest low-calorie meals for days when the user does not exercise. This allows users to eat appropriately according to their exercise habits, thereby enhancing the effectiveness of their diet.

[0112] SoloFit can recommend cuisines from different countries and regions to increase the variety of meals a user can eat. For example, if a user is tired of Japanese food, it can suggest different cuisines such as Italian or Mexican. It can also recommend dishes using specific ingredients if the user prefers them. Furthermore, if a user wants to try new dishes, it can provide easy-to-make recipes. This allows users to enjoy variety in their diet while staying on track with their weight loss efforts.

[0113] SoloFit can recommend menus that take into account the nutritional balance of the user's diet. For example, if a user is deficient in vitamins or minerals, it can suggest menus that are rich in those nutrients. It can also recommend menus that are lower in certain nutrients if the user is consuming them in excess. Furthermore, if a user has a specific health condition, it can suggest menus with a nutritional balance appropriate for that condition. This allows users to diet while maintaining a healthy nutritional balance.

[0114] SoloFit can estimate a user's emotions and recommend menus based on those emotions. For example, if a user is feeling stressed, it can suggest a menu using ingredients that have a relaxing effect. If a user is tired, it can recommend a high-calorie menu to replenish energy. Furthermore, if a user is feeling happy, it can suggest a dessert to maintain that mood. This allows users to eat appropriate meals according to their emotions and maintain their motivation for dieting.

[0115] SoloFit can estimate a user's emotions and adjust meal timing based on those emotions. For example, if a user is feeling stressed, it can delay meal times to allow time to relax. If a user is in a hurry, it can suggest quick-to-eat meals. Furthermore, if a user is relaxed, it can recommend meals that can be enjoyed at a leisurely pace. This allows users to choose appropriate meal timings based on their emotions, potentially enhancing the effectiveness of their diet.

[0116] SoloFit can estimate a user's emotions and adjust the amount of food they eat based on those emotions. For example, if a user is stressed, it can reduce the amount of food to avoid putting a strain on their digestion. If a user is relaxed, it can suggest a normal amount of food. Furthermore, if a user is feeling happy, it can add a small dessert to maintain that feeling. This allows users to eat the appropriate amount of food according to their emotions, maximizing the effectiveness of their diet.

[0117] SoloFit can estimate a user's emotions and adjust the type of meal based on those emotions. For example, if a user is feeling stressed, it can suggest relaxing herbal teas or soups. If a user is tired, it can recommend high-protein meals to replenish energy. Furthermore, if a user is feeling happy, it can suggest fruits or desserts to maintain that mood. This allows users to eat appropriate meals according to their emotions and maintain their motivation for dieting.

[0118] SoloFit can estimate a user's emotions and adjust the dining environment based on those emotions. For example, if a user is feeling stressed, it can suggest eating in a quiet environment. If a user is relaxed, it can suggest enjoying their meal while listening to music. Furthermore, if a user is in a hurry, it can suggest easily portable meals. This allows users to choose an appropriate dining environment according to their emotions, potentially enhancing the effectiveness of their diet.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk enters the user's information. This information includes target weight, diet period, age, height, and weight. The reception desk can also save the information entered by the user in a database and provide input assistance functions. Step 2: The calculation unit calculates the number of calories to be reduced based on the information entered by the reception unit. The calculation unit calculates the number of calories to be reduced based on the user's target weight, diet period, age, height, weight, etc., taking into account basal metabolic rate and activity level. Step 3: The acquisition unit acquires the current location information. The acquisition unit can acquire the current location information using GPS or Wi-Fi location information and obtain the location information of the user's smartphone. Step 4: The collection unit collects menu information from nearby restaurants based on the location information acquired by the acquisition unit. The collection unit can collect menu information from databases on the internet, or obtain menu information from the official websites of restaurants or from menu information provision services. Step 5: The recommendation unit recommends menus based on the menu information collected by the collection unit. The recommendation unit selects the optimal menu from the collected menu information, taking into account calorie intake and PFC balance, and recommends menus that restrict calorie intake based on the user's target weight and diet period.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the reception unit, calculation unit, acquisition unit, collection unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs user information. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the number of calories to be reduced. The acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires the current location information. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects menu information from nearby restaurants. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal menu. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, calculation unit, acquisition unit, collection unit, and recommendation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and inputs user information. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and calculates the number of calories to be reduced. The acquisition unit is implemented, for example, by the control unit 46A of the smart glasses 214 and acquires the current location information. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects menu information from nearby restaurants. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and recommends the optimal menu. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, calculation unit, acquisition unit, collection unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs user information. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates the number of calories to be reduced. The acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires the current location information. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects menu information from nearby restaurants. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal menu. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, calculation unit, acquisition unit, collection unit, and recommendation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs user information. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates the number of calories to be reduced. The acquisition unit is implemented by, for example, the control unit 46A of the robot 414 and acquires current location information. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects menu information from nearby restaurants. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends the optimal menu. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A reception area where user information is entered, A calculation unit that calculates the number of calories to be reduced based on the information entered by the reception unit, A unit that acquires the current location information, A collection unit collects menu information of nearby restaurants based on location information acquired by the acquisition unit, The system includes a recommendation unit that recommends menus based on menu information collected by the collection unit. A system characterized by the following features. (Note 2) The recommendation unit is, The menu is recommended based on the user's budget, preferences, and current mood. The system described in Appendix 1, characterized by the features described herein. (Note 3) The recommendation unit is, We recommend menus that take into account calorie intake and macronutrient balance (PFC balance). The system described in Appendix 1, characterized by the features described herein. (Note 4) The calculation unit, The system calculates the number of calories to reduce based on the user's target weight, diet duration, age, height, and weight. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Accepts periodic input of the user's weight. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Provides real-time input assistance based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Provide information relevant to the input content, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 12) The calculation unit, The system estimates the user's emotions and adjusts the calorie calculation method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The calculation unit, It analyzes the user's past dieting history and suggests the optimal calorie calculation method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The calculation unit, Adjust the frequency of calorie calculations according to the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 15) The calculation unit, It estimates the user's emotions and determines the priority of calorie calculations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit, The system provides information related to calorie calculation, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit, We analyze users' social media activity and suggest relevant calorie calculation methods. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of location data acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The acquisition unit is, Analyze the user's past location history and select the optimal method for acquiring it. The system described in Appendix 1, characterized by the features described herein. (Note 20) The acquisition unit is, When acquiring location information, filtering is performed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The acquisition unit is, The system estimates the user's emotions and determines the priority of location information to acquire based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The acquisition unit is, When acquiring location information, the system prioritizes acquiring highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The acquisition unit is, When acquiring location information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is The system estimates the user's emotions and adjusts how menu information is collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is We analyze users' past dining history and propose the optimal method for collecting menu information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned collection unit is When collecting menu information, filtering is performed based on the user's current eating habits. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned collection unit is It estimates the user's emotions and determines the priority of menu information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned collection unit is When collecting menu information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned collection unit is When collecting menu information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The recommendation unit is, It estimates the user's emotions and adjusts the menu recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recommendation unit is, It analyzes the user's past dining history and recommends the most suitable menu items. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recommendation unit is, When recommending menu items, filter them based on the user's current eating habits. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recommendation unit is, It estimates the user's emotions and determines the priority of recommended menu items based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The recommendation unit is, When recommending menu items, the system prioritizes recommending highly relevant menu items by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The recommendation unit is, When recommending menu items, the system analyzes the user's social media activity and recommends relevant menu items. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where user information is entered, A calculation unit that calculates the number of calories to be reduced based on the information entered by the reception unit, A unit that acquires the current location information, A collection unit collects menu information of nearby restaurants based on location information acquired by the acquisition unit, The system includes a recommendation unit that recommends menus based on menu information collected by the collection unit. A system characterized by the following features.

2. The recommendation unit is, The menu is recommended based on the user's budget, preferences, and current mood. The system according to feature 1.

3. The recommendation unit is, We recommend menus that take into account calorie intake and macronutrient balance (PFC balance). The system according to feature 1.

4. The calculation unit, The system calculates the number of calories to reduce based on the user's target weight, diet duration, age, height, and weight. The system according to feature 1.

5. The aforementioned reception unit is Accepts periodic input of the user's weight. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is Provides real-time input assistance based on user input. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is Provide information relevant to the input content, taking into account the user's geographical location. The system according to feature 1.

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