阅读说明：本技术 食品处理设备及配方引导方法 (Food processing equipment and formula guiding method ) 是由 D·J·C·洛韦特 J·M·克兰斯 I·G·L·古巴·吉伦斯藤 于 2020-04-20 设计创作，主要内容包括：本发明提供一种适于标识配料和相关配方的食品处理设备。该设备包括用于接纳待处理配料的容器以及位于该容器中的混合叶片。该设备还包括用于感测在容器处接纳的配料的特性的传感器,以及,用于分析该特性的处理器和/或用于将该特性通信到远程数据处理资源以进行分析或进一步分析的通信系统,其中该分析用于标识多个添加配料的配料类型以及用于标识与添加配料类型一致的配方。该设备还包括用于引导用户遵循所标识的配方的剩余部分的输出设备。(The present invention provides a food processing apparatus adapted to identify ingredients and related recipes. The apparatus includes a container for receiving an ingredient to be treated and a mixing blade located in the container. The apparatus also includes a sensor for sensing a characteristic of an ingredient received at the receptacle, and a processor for analyzing the characteristic and/or a communication system for communicating the characteristic to a remote data processing resource for analysis or further analysis, wherein the analysis is for identifying an ingredient type of the plurality of added ingredients and for identifying a recipe consistent with the added ingredient type. The apparatus also includes an output device for guiding a user to follow the remainder of the identified recipe.)

1. A food processing apparatus (100) comprising:

a receptacle (110) for receiving an ingredient (115) to be processed;

a mixing blade (120) located in the vessel;

a sensor (130) for sensing a characteristic of the ingredient received at the container;

a processor (140) for analyzing the characteristic and/or a communication system (150) for sending the characteristic to a remote data processing resource for analysis or further analysis, wherein the analysis is for identifying an ingredient type of a plurality of added ingredients and for identifying a recipe consistent with the added ingredient type; and

an output device (160) for guiding a user to follow the identified remaining portion of the recipe,

wherein the sensor (130) comprises a weight sensor, and wherein the characteristic of the ingredient comprises a response curve generated by the weight sensor.

2. The apparatus of claim 1, wherein the sensor (130) comprises a camera (300), and wherein the characteristic of the ingredient comprises a visual ingredient indicator generated by the camera.

3. The device according to claim 2, wherein the device further comprises a base unit adapted to be detachably coupled to the container and the camera (300) is located within the base unit, and wherein the device comprises a light guide (310) adapted to guide light from an upper portion of the container to the camera.

4. The device of claim 3, wherein the receptacle (110) comprises a handle, and wherein at least a portion of the light guide (310) is located within the handle.

5. The apparatus of any of claims 1 to 4, wherein the sensor (130) comprises one or more of:

a microphone, wherein the characteristic of the ingredient comprises an audio signal of the ingredient received in the receptacle;

a torque sensor coupled to the mixing blade (120), wherein the characteristic of the ingredient comprises a mixing resistance;

an electrical measurement sensor, wherein the characteristic of the furnish comprises the furnish impedance;

a temperature sensor, wherein the characteristic of the ingredient comprises a temperature of the ingredient; and

a gas sensor, wherein the characteristic of the ingredient comprises a gas composition of air within the container.

6. The device according to any of claims 1 to 5, wherein the communication system (150) is adapted to communicate with a smart device of a user.

7. The apparatus according to any one of claims 1 to 6, wherein the processor (140) is adapted to apply a machine learning algorithm to the characteristic of the ingredient for analyzing the characteristic of the ingredient.

8. A system (400) for guiding a user according to a recipe identified based on an identified ingredient, the system comprising:

one or more food processing devices (100) according to claim 7;

a remote data processing resource (410) coupled to the food processing device through a communication system, the remote data processing resource comprising a collaborative machine learning algorithm, and wherein the system is adapted to:

updating the machine learning algorithm of the one or more food processing devices based on the collaborative machine learning algorithm; and

uploading the analyzed characteristics of the ingredients from the one or more food processing devices to the remote data processing resource for use in the collaborative machine learning algorithm.

9. A recipe guidance method (500), comprising:

sensing (510) a characteristic of an ingredient as the ingredient is received in a container, the characteristic of the ingredient comprising a response curve generated by a weight sensor;

analyzing (520) and/or communicating the characteristic of the ingredient to a remote data processing unit for analysis or further analysis of the characteristic of the ingredient; and

identifying (530) an ingredient type of the ingredient received at the container based on the analysis of the characteristic of the ingredient;

identifying (540) a recipe based on the identified ingredient type; and

guiding (550) a user to follow the identified remaining portion of the recipe.

10. The method of claim 9, wherein identifying the ingredient type comprises: applying a machine learning algorithm to the ingredient characteristics.

11. The method of claim 10, wherein the method further comprises:

acquiring a collaborative machine learning algorithm from a remote data processing resource; and

updating the machine learning algorithm based on the collaborative machine learning algorithm.

12. The method of claim 11, wherein the method further comprises:

obtaining user input indicating whether the identified recipe has been followed; and

updating the collaborative machine learning algorithm based on the analyzed characteristic of the ingredient if the user input indicates that the identified recipe has been followed.

13. The method of any of claims 9-12, wherein the method further comprises suggesting additional ingredients based on the identified formula.

14. A computer program comprising computer program code means adapted to perform the method of any of claims 9 to 13 when the computer program is run on a computer.

Technical Field

The invention relates to the field of food processing equipment, in particular to the field of intelligent food processing equipment.

Background

Food processing equipment, such as mixers (blenders), is common in most homes. In order to use the apparatus to prepare food in a desired and consistent manner, the user typically follows a recipe.

Traditionally, the recipes are organized into a recipe book; however, it is now common to access a given recipe over the internet when needed. When access to a recipe is required, a disadvantage of accessing the recipe is that it may be difficult to reposition the recipe for subsequent use. This may result in a partially memorized recipe being followed by the user, which may result in sub-optimal and disappointing food preparation.

Further, some recipes may require certain steps to be followed in a particular order to achieve a desired result, which may not be known or understood by inexperienced users.

Therefore, there is a need for a more user friendly food processing apparatus.

Disclosure of Invention

The invention is defined by the claims.

According to an example according to an aspect of the present invention, there is provided a food processing apparatus comprising:

a container for receiving an ingredient to be treated;

a mixing blade located in the vessel;

a sensor for sensing a characteristic of an ingredient received at the container;

a processor for analyzing the characteristic and/or a communication system for transmitting the characteristic to a remote data processing resource for analysis or further analysis, wherein the analysis is for identifying ingredient types of the plurality of added ingredients and for identifying recipes consistent with the added ingredient types; and

an output device for guiding a user to follow a remaining portion of the identified recipe.

The apparatus provides automatic identification of ingredients added to a container of a food processing apparatus.

The automatic identification of the most recently added ingredient may then be used to guide the user to follow a given recipe.

In one embodiment, the sensor comprises a weight sensor, and wherein the characteristic of the ingredient comprises a response curve generated by the weight sensor.

When an ingredient is added to the container (e.g., when the ingredient impacts the base of the container), the resulting response curve may provide information about the type of ingredient added to the container.

In one embodiment, the sensor comprises a camera, and wherein the characteristic of the ingredient comprises a visual ingredient indicator produced by the camera.

The camera may be used to help identify the ingredients, for example by image identification capabilities included in the analysis, which are executed by the processor.

In another embodiment, the device further comprises a base unit adapted to be detachably connected to the container, and the camera is located within the base unit, wherein the device comprises a light guide adapted to guide light from an upper portion of the container to the camera.

In this way, the recently added ingredients are visible to the camera. For example, the light guide may be built into the handle of the container.

In another embodiment, the container comprises a handle, wherein at least a portion of the light guide is located within the handle.

In this way, the light guide may be at least partially accommodated within a structure having an additional function.

In one embodiment, the sensor comprises one or more of:

a microphone, wherein the characteristic of the ingredient comprises an audio signal of the ingredient received in the receptacle;

a torque sensor coupled to the mixing blade, wherein the characteristic of the ingredient comprises a mixing resistance;

an electrical measurement sensor, wherein the characteristic of the furnish comprises furnish impedance;

a temperature sensor, wherein the characteristic of the ingredient comprises a temperature of the ingredient; and

a gas sensor, wherein the characteristic of the ingredient comprises a gas composition of air within the container.

In this way, additional ingredient characteristics may be considered in identifying the ingredient.

In one embodiment, the communication system is adapted to communicate with a smart device of a user.

In this way, a user can interact with the food processing device in a convenient manner without the need to build additional user interaction capabilities in the food processing device itself.

In one embodiment, the processor is adapted to apply a machine learning algorithm to the characteristics of the ingredient to analyze the characteristics of the ingredient.

In this way, the ability of the food processing device to identify ingredients and related recipes can be developed and improved with use.

According to an example in accordance with an aspect of the present invention, there is provided a system for guiding a user according to a recipe identified based on an identified ingredient, the system comprising

One or more food processing apparatus as described above;

a remote data processing resource coupled to the food processing device through the communication system, the remote data processing resource comprising a collaborative machine learning algorithm, and wherein the system is adapted to:

updating a machine learning algorithm of one or more food processing devices based on a collaborative machine learning algorithm; and

uploading the analyzed characteristics of the ingredients from the one or more food processing devices to a remote data processing resource for use in a collaborative machine learning algorithm.

In this way, a central server (with which any number of devices may communicate) may be used to train and update the machine learning algorithm, thereby increasing the data set used to train the machine learning algorithm.

According to an example according to an aspect of the present invention, there is provided a recipe guidance method including:

sensing a characteristic of the ingredient while the ingredient is contained in the container;

analyzing the characteristic of the ingredient and/or communicating the characteristic of the ingredient to a remote data processing unit for analysis or further analysis of the characteristic of the ingredient; and

identifying an ingredient type of an ingredient received at a container based on an analysis of a characteristic of the ingredient;

identifying a recipe based on the identified ingredient type; and

the user is guided to follow the remainder of the identified recipe.

In one embodiment, identifying the ingredient type includes applying a machine learning algorithm to the ingredient characteristic.

In another embodiment, the method further comprises:

obtaining a collaborative machine learning algorithm from a remote data processing resource;

the machine learning algorithm is updated based on the collaborative machine learning algorithm.

In another embodiment, the method further comprises:

obtaining user input indicating whether the identified recipe has been followed; and

if the user input indicates that the identified recipe has been followed, the collaborative machine learning algorithm is updated based on the analyzed ingredient characteristics.

In this way, only data corresponding to a correctly followed recipe is used to form part of the collaborative machine learning algorithm, thereby increasing the accuracy of the algorithm. For example, a user may follow a certain recipe and, after each step, may press a "next" button on a user interface of the food processing device. By pressing this "next" button, the user input may implicitly confirm that the ingredient of the previous recipe step has been added, which may then be used to annotate the sensor data.

In one embodiment, the method further comprises suggesting additional ingredients based on the identified recipe.

According to an example according to an aspect of the present invention, there is provided a computer program comprising computer program code means adapted to perform the above-mentioned method when the computer program is run on a computer.

These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

Drawings

For a better understanding of the present invention, and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

FIG. 1 shows a schematic view of a food processing apparatus;

FIG. 2 shows an example graph of a response curve generated by a weight sensor when an ingredient is received in a container;

figures 3a and 3b show schematic views of an embodiment of the food processing apparatus of figure 1;

FIG. 4 shows a schematic representation of a system for guiding a user according to a recipe identified based on an identified ingredient; and

fig. 5 illustrates a recipe guidance method.

Detailed Description

The present invention will be described with reference to the accompanying drawings.

It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the devices, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems, and methods of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings. It should be understood that the figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the figures to indicate the same or similar parts.

The present invention provides a food processing apparatus adapted to identify ingredients and related recipes. The apparatus includes a container for receiving an ingredient to be treated and a mixing blade located in the container. The apparatus further comprises a sensor for sensing a characteristic of the ingredient received at the receptacle; and a processor for analyzing the characteristic and/or a communication system for communicating the characteristic to a remote data processing resource for analysis or further analysis, wherein the analysis is for identifying ingredient types of the plurality of added ingredients and for identifying recipes consistent with the added ingredient types. The apparatus also includes an output device for guiding a user to follow the remainder of the identified recipe.

FIG. 1 shows a schematic view of a food processing apparatus 100 according to one aspect of the present invention.

The food processing apparatus 100 includes a container 110 for receiving an ingredient 115 to be processed and a mixing blade 120 located within the container.

The food processing apparatus 100 further comprises a sensor 130 in communication with the container 110. The sensor is adapted to sense a characteristic of the ingredient 115 received at the container.

For example, the sensor may comprise a weight sensor, and the characteristic of the ingredient may comprise a response curve generated by the weight sensor. The weight sensor or scale may be considered a spring, and the compression or expansion of the spring corresponds to the load placed on the scale. Each time an ingredient is thrown or poured into the receptacle 110 of the food processing apparatus, the scale will not immediately settle to the exact weight of the added ingredient, but will follow a short up and down response curve until equilibrium is reached. The weight sensor may also be adapted to measure the pressure and/or pressure variations of the ingredient located in the container. This is described below with reference to fig. 2.

Each type of ingredient (e.g., fluid, fruit, ice, etc.) affects the scale of the food processing equipment differently and, therefore, produces a different response curve. Thus, the form of the response curve may be used to derive information about the type of ingredient that has been received in the container, which information may be used to identify the type of ingredient.

Furthermore, different types of ingredients will have different response curves, even if they have the same weight. For example, the shape of the response curve for a particular ingredient may depend on: the weight of the ingredients; the elasticity of the ingredients (e.g., nuts can bounce in air and fall off again before standing); the shape of the ingredients; the fluidity/viscosity of the ingredient (e.g., a liquid ingredient may be detected from a particular response curve, however liquids with highly different viscosity levels will also provide a different response, e.g., water versus oil versus a creamy substance (such as yogurt or cream)); throw into or pour into; and ingredients already present in the mixer.

The response curve for the same ingredient will vary depending on the ingredient that was previously added. For example, assuming that there is already 200ml of milk/water in the food processing apparatus, the addition of fruit or nuts will produce a completely different response curve than if no liquid had been previously added to the apparatus.

As an example, if the user follows an unknown recipe, the output for the user may take the form:

197g of liquid

123g of hard fruit

166g of soft fruit

56g of ice cubes.

However, if the recipe is recognized by the device, known to the user or manually identified by the user, the above output may be refined as follows:

197g of milk

123g of hazelnut

86g of bananas

80g of apple

56g of ice cubes.

The food processing apparatus 100 further comprises a processor 140 for analyzing the characteristic and/or a communication system 150 for communicating the characteristic to a remote data processing resource for analysis or further analysis of the characteristic of the ingredient.

The analysis identifies a plurality of ingredient types of the added ingredients based on the characteristics of the ingredients measured by the sensors 130. In addition, the processor or remote processing resource identifies a recipe consistent with the type of ingredient added. The processor may be adapted to identify the ingredient and the associated recipe by a machine learning algorithm trained to identify the ingredient based on the sensed ingredient characteristic. An embodiment of such an algorithm is described below with reference to fig. 4.

The food processing device 100 further comprises an output device 160 for guiding the user to follow the rest of the identified recipe. The output device may include, for example, a display and/or a speaker for communicating with a user.

For example, the food processing device may detect that the user has added 100g of strawberries and 100g of bananas to the container. This may be identified by the processor as the initial ingredient of the smoothie. Thus, the output device may indicate to the user that the recipe has been identified as smoothie. At this point, the user may provide feedback to the food processing device: the identified recipe is indeed correct. The output device may then generate instructions how the user should handle in order to complete the recipe, for example by generating visual or audible instructions to instruct the user to add for example 200ml of milk to the container.

The food processing apparatus may be any apparatus adapted to receive one or more ingredients to be processed. For example, the food processing apparatus may be a mixer as shown in fig. 1. In another example, the food processing apparatus may be a soup maker, in which case the apparatus shown in fig. 1 may further comprise a heating element for heating the ingredients after they have been processed.

It should be noted that the scales described above may be adapted to only distinguish the type of ingredient received in the receptacle, and not the exact ingredient itself. The exact ingredients can then be determined based on the type of ingredient and the context of the recipe.

The type of ingredient may be determined as one of: preparing a water sample; oil sample batching; a creamy ingredient; eggs; hardening the fruits; softening the fruits; vegetables; ice cubes; pulsing; a nut; meat; and powders. It should be noted that this list is not exhaustive and that the food processing device may classify ingredients with any other ingredient type.

By adding a plurality of weight sensors, for example one weight sensor at the centre of the base of the container and four weight sensors at the sides, the weight sensors can be made more accurate and robust, which means that the device can take into account four additional response curves when identifying the ingredients.

Alternatively or additionally, the sensor may comprise a camera and the characteristic of the ingredient may comprise a visual ingredient indicator produced by the camera.

To identify the ingredient received at the receptacle 110, the camera may be placed such that it can see what is introduced at the top of the receptacle and have a view from the top down such that the most recent addition of the receptacle is in the view of the camera. In this way, the camera can view each ingredient added to the mixer. Various embodiments of a camera within a food processing unit are discussed below with reference to fig. 3.

The camera sensor and the weight sensor may be used individually or in combination to improve the accuracy of ingredient identification. For example, the camera sensor may better detect which type of ingredient is received in the sensor; however, weight sensor response curve based identification may be more appropriate for estimating the total weight and identifying the amount of both the weight of an individual block (grams per individual block, block size, and number of blocks).

The chunk size information may be used to give feedback to the user (e.g., "next time please cut the banana into smaller chunks") or to automatically adjust the motor settings for the mixing blade or select the appropriate mixing tool (e.g., knife, fork, or spoon).

Furthermore, the combination of the weight sensor and the camera sensor may overcome situations where the ingredients look very similar visually (e.g. milk and cream) but would result in different response curves for the weight sensor.

In addition to the weight sensor and the camera sensor, the sensor 130 may include additional sensors that may be used in conjunction with the weight sensor and/or the camera sensor.

For example, the sensor 130 may also comprise a microphone, in which case the characteristic of the ingredient also comprises an audio signal of the ingredient received in the container.

In this case, the sound of the ingredient entering the container may be used as part of the ingredient identification process. For example, ice cubes will produce different audio signals for soft fruits and soft fruits will produce different audio signals for liquids. Thus, the audio signal may be used to help distinguish the ingredients from each other.

The same microphone may be used to detect the mixed sound to automatically adapt to the food processor settings. The microphone may be embedded in the power base of the food processor, such as the base unit. Alternatively, the microphone is not comprised in the food processor, but is located in a smart speaker present in the same room (e.g. kitchen) and communicatively linked to the food processor by a communication system within the food processing device.

Further, the sensor 130 may comprise a torque sensor coupled to the mixing blade 120 of the food processing apparatus, in which case the characteristic of the ingredient comprises a mixing resistance.

In other words, additional data points relating to the ingredient characteristics can be extracted from the resistance curve of the motor driving the mixing blade 120 of the food processing apparatus. Different ingredients and different combinations of ingredients provide different resistances to the mixing blade and can therefore be used to differentiate the ingredients.

Further, sensor 130 may comprise an electrical measurement sensor, in which case the characteristic of the furnish comprises furnish impedance.

More specifically, electrical measurement sensors may be added to the metal blades of the hybrid blade 120. The bioimpedance spectra of the ingredients in contact with the mixing blade may be used to determine the salt content and lipid content of the ingredients, which may be used to refine the ingredients (e.g., for a camera, cream and milk both look similar but have very different lipid contents).

Further, the sensors 130 may also include temperature sensors adapted to determine the temperature of the ingredients, and/or gas sensors adapted to analyze the air within the container to identify the ingredients.

FIG. 2 illustrates an example plot of a response curve 200 generated by a weight sensor when receiving a batch material in a container, where the x-axis represents time and the y-axis represents a detected weight.

As can be seen from the response curve, the weight begins to increase dramatically as the ingredient is received at the container. The weight measurement is then oscillated until the reading settles between error bars 210, at which point typically a digital scale will give a reading. However, the processor 140 or remote data processing resource may analyze the entire response curve to identify the ingredient received.

Fig. 3a and 3b show a schematic representation of an embodiment of a food processing device 100' comprising a camera sensor 300 and a light guide 310.

The simplest implementation of a camera capable of capturing such a view is to house the camera in the lid of the container so that the camera has a top view of the ingredients within the container.

In a mixer with a large form factor, it is relatively simple to add a camera placed on top of the cover to the power supply and the computing processing unit. When the top cover is opened, a camera placed on the top cover can visually capture what is added to the container. When the lid is closed, the camera may be protected by the lid mating with the top of the container.

However, most mixers designed for home use will have a different, more particularly smaller, form factor. In mixers with such smaller form factors (although not impossible), it is not possible to position the camera directly on the lid of the container, since the camera needs to be connected to a power supply and processing unit. Placing these components in a container or lid has the added complexity that these components need to be dishwasher proof and therefore resistant to high temperature and humid environments.

This problem can be solved by positioning the camera in a base unit that is detachably connected to the container, which means that the container can be removed for cleaning without the camera.

However, simply positioning the camera in the base unit below the container will result in the camera only being able to capture ingredients that were first added to the container, after which its field of view will be blocked.

Fig. 3a shows a solution to this problem, wherein a camera 300 is in communication with a light guide 310 accommodated within the container wall.

The light guide system or periscope system can transmit light from the top of the vessel to the bottom of the vessel to enter the camera. In this way, the camera can still record which ingredients are added to the container without being blocked.

Fig. 3b shows an example where the light guide is located within the handle 315 of the receptacle. The handle may be made opaque so that external light cannot enter the camera.

It should be noted that any of the sensors described above may be located in the base unit of the food processing apparatus and connected to the container by any suitable means. For example, a weight sensor may be located in the base sensor and adapted to measure the weight of the container and the ingredient, from which the weight of the container may be subtracted. In another example, an electrical measurement sensor may be located in the base unit and electrically coupled to the mixing blade via an electrically conductive mechanical coupling unit in the base unit.

Fig. 4 shows a schematic representation of a system 400 for guiding a user according to a recipe identified based on an identified ingredient.

The system includes one or more food processing appliances 100 as described above and a remote data processing resource 410 connected to the food processing appliances via a communication system 150.

The remote data processing resource includes a collaborative machine learning algorithm. The collaborative machine learning algorithm is a machine learning algorithm that combines training data of different mixers, each of which may have an on-board machine learning algorithm, and combines them into one training algorithm.

In this way, the system may be adapted to update the machine learning algorithm of the one or more food processing devices based on the collaborative machine learning algorithm and upload the analyzed characteristics of the ingredients from the one or more food processing devices to the remote data processing resource for training the collaborative machine learning algorithm.

One or more food processing appliances 100 may communicate with the remote data processing resource 410 through a user intelligent device connected to each food processing appliance's communication system 150.

A plurality of labeled training data points may be provided to the collaborative machine learning algorithm each time the user follows the recipe. For example, if the recipe indicates that the user must add 50 grams of strawberry, the subsequent response curve of the scale provides a marked data point. These marked data points can be measured and uploaded to a remote data processing resource. All of the labeled data points from one or more food processing devices may be combined to provide a large labeled data set that may be used to train a collaborative machine learning algorithm that may detect an ingredient from a response curve and any other sensor signals associated with a characteristic of the ingredient.

To train the algorithm to learn the response curves of different ingredients, the following method may be followed.

A set of labeled response curves (one for each individual ingredient added) is generated each time the user follows the recipe. This phase may also include any other sensor signals associated with the characteristics of the ingredient. The tagged list of response curves is sent to a back-end server or remote data processing resource. In this way, a large amount of label training data is generated. On the back-end server, a training collaborative machine learning algorithm is trained to identify the type of ingredient from the labeled response curve. Neural networks may be used for this purpose. The trained algorithm is then reassigned to the food processing device.

Collaborative machine learning algorithms rely on the order in which users follow a recipe to obtain an accurate data set. Before starting the mixing process, the user may be prompted to confirm whether they have followed the recipe sequence. If they do not confirm that the recipe order has been followed, data from the session may be excluded from the collaborative learning process.

It should be noted that in the case of sensors comprising cameras, the training of the collaborative machine learning algorithm described above can process strongly distorted images as long as the distortion loss is not significant in the different mixers and mixer models and as long as the distortions are the same or similar, so that the distortions can be taken into account in the training of the collaborative algorithm. In some cases, only the target color information is sufficient to distinguish between the various ingredients used in the formulation.

Fig. 5 illustrates a recipe guidance method 500.

The method begins at step 510 with sensing a characteristic of an ingredient as the ingredient is received in a container. Any suitable sensing device, such as those described above, may be used to sense the characteristics of the ingredient.

In step 520, the characteristics of the ingredients are analyzed and/or communicated to a remote data processing unit for analysis or further analysis.

In step 530, an ingredient type of an ingredient received at a container is identified based on the analysis of the ingredient characteristic.

The steps of analyzing and identifying the ingredient type may be performed using the algorithms described above.

In step 540, a recipe is identified based on the identified ingredient type. The recipe may be identified in a similar manner as the identification of the ingredient type. In other words, the recipe may be identified by applying a machine learning algorithm that may be cooperatively trained to the identified ingredients.

The recipe can be automatically identified, or a suggested recipe can be provided to the user based on the identified ingredients, which the user can then confirm is correct. Further, the user may be presented with a list of suggested recipes, from which the user may then select a desired recipe.

The system may know multiple recipes; however, the user may add a new recipe to the known recipe.

In step 550, the user is guided to follow the remainder of the identified recipe. The guidance can be adjusted based on the ingredient already present in the container.

Further, the recipe guidance may include suggesting additional ingredients based on the identified recipe. For example, a given recipe may include many known variations, some of which include alternative ingredients. In this way, the user's taste can be evaluated based on commonly used recipes and used to select variations that are consistent with the user's taste. This can also be used for ingredients that avoid allergy to the user. For example, a user may select a preference for nut-free in a recipe guidance setting, which may then be used to provide a recipe change that does not contain the indicated ingredient (e.g., nut).

In summary, the present invention provides an apparatus, system and method for identifying ingredients received at a receptacle of a food processing apparatus and providing recipe guidance to a user based on the identified ingredients.

It should be noted that the user may not always follow the recipe step by step according to the guidance provided. By automatically detecting the type of ingredient when received at the container, it is possible to handle different recipe sequences followed by the user. In addition, users may have a set of favorite recipes they regularly make. Automatic detection of a formula can be used to help track a user's nutritional intake and provide suggestions for new formulas or suggestions for changing their conventional formula to improve their nutritional value.

Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. If a computer program is discussed above, it may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems. If the term "adapted" is used in the claims or the description, it is to be noted that the term "adapted" is intended to be equivalent to the term "configured to". Any reference signs in the claims shall not be construed as limiting the scope.