DE102019121635A1 - Method for generating a coffee bean-specific parameter data set - Google Patents

Abstract

The invention relates to a method for generating a bean-specific parameter data set for the automatic setting of brewing parameters of a coffee machine (1), comprising at least the following steps: a) Automatic acquisition of at least one piece of information about coffee beans to be used for a brewing process, Dependence of the information about the coffee beans recorded in step a) in at least one external database (7) and / or the Internet (6), c) generation of the bean-specific parameter data set depending on the information about the brewing parameters found in step b).

Description

The invention relates to a method for generating a bean-specific parameter data set for the automatic setting of brewing parameters of a coffee machine, a coffee machine for performing the method and a network with the coffee machine. The invention is particularly suitable for use in fully automatic coffee machines.

In connection with modern coffee machines, there is an effort to provide the consumer with the most satisfactory taste experience possible. For this purpose, it is desirable to be able to set the brewing parameters of the coffee machine as individually as possible to the coffee beans to be brewed. For this purpose, the brewing parameters can be set manually by a user, for example, depending on the type of coffee (s) and / or roast used. However, it is disadvantageous that each parameter must be set individually and the result must be checked after each change. This is tedious and takes a lot of time. In addition, a lot of coffee will be used until the desired setting is found. In addition, a satisfactory taste experience can often only be achieved with the specialist knowledge of a barista and therefore cannot always be achieved for the average consumer.

Proceeding from this, it is the object of the present invention to at least partially solve the problems described with reference to the prior art. In particular, a method, a coffee machine and a network are to be specified with which the most satisfactory taste experience can be provided in an automated manner, so that time-consuming manual trying out by the consumer can be dispensed with.

These objects are achieved by the features of the independent patent claims. Further advantageous refinements of the solution proposed here are specified in the dependent claims. It should be pointed out that the features listed individually in the dependent patent claims can be combined with one another in any desired, technologically sensible manner and define further embodiments of the invention. In addition, the features specified in the patent claims are specified and explained in greater detail in the description, with further preferred embodiments of the invention being presented.

1. a) Maschinelles Erfassen mindestens einer Information über für einen Brühvorgang zu verwendende Kaffeebohnen,
2. b) Maschinelles Suchen von Informationen über Brühparameter in Abhängigkeit der in Schritt a) erfassten Information über die Kaffeebohnen in mindestens einer externen Datenbank und/oder dem Internet,
3. c) Generieren des bohnenspezifischen Parameterdatensatzes in Abhängigkeit der in Schritt b) gefundenen Informationen über die Brühparameter.

What is proposed here is a method for generating a (coffee) bean-specific parameter data set for the automatic setting of brewing parameters of a coffee machine, comprising at least the following steps:

1. a) Automatic recording of at least one piece of information about coffee beans to be used for a brewing process,
2. b) Automatic search for information about brewing parameters depending on the information about the coffee beans recorded in step a) in at least one external database and / or the Internet,
3. c) Generating the bean-specific parameter data set as a function of the information about the brewing parameters found in step b).

The method advantageously allows an automated determination of the brewing parameters for coffee preparation according to the individual or the bean (s) and roast (s) used. This enables the particular advantage that the user of the coffee machine no longer has to find out these parameters experimentally through lengthy testing. In other words, the user does not have to set the brewing parameters himself. Rather, the machine can independently search for the appropriate parameters for brewing the coffee, in particular from databases on the Internet or from the Internet. This contributes to easy handling by the user. For example, he can only enter the type of coffee and / or roast and immediately receives good coffee quality from the machine, without tedious manual setting of the machine to the appropriate brewing parameters.

The bean-specific parameter data set contains in particular one or more of the following brewing parameters: coffee (flour) amount, degree of grinding, temperature, water amount, flow rate and / or compression pressure of the coffee grounds. The bean-specific parameter data set preferably contains all of these brewing parameters. Alternatively or cumulatively, the parameter data set can also include a quantity of ground coffee per water volume unit and / or a specification of the brewing process / process (change in parameters over time and / or interruption of brewing). The term coffee is understood here to mean all possible types of coffee, such as espresso.

The coffee machine usually includes a water architecture and a brewing group, which can be formed with the following components: water tank, a pump, a heater, sensors, valves, grinder, etc. The coffee machine can also be controlled via a control device, such as a circuit board and / or have a microcontroller. The control device can also, for example, via a communication interface to Example in the form of a transmitting and receiving unit in order to get access to the Internet via W-LAN, LAN etc. The communication interface can be part of the control unit or connected to it. Alternatively or cumulatively, the communication interface can comprise a Bluetooth module in order, for example, to enable communication with terminals located in the vicinity of the machine.

In step a), at least one piece of information about coffee beans to be used for a brewing process is automatically recorded. In other words, this means that at least one piece of information is obtained about the coffee beans that are used for a (subsequent) brewing process. The at least one piece of information about the coffee beans can be, for example, one or more of the following information: coffee type, coffee maker, roasting (roasting duration, roasting temperature), roaster, country of origin, mixing ratio for a mixture of varieties, water content, bean weight and / or bean size. Alternatively or cumulatively, the at least one piece of information about the coffee beans can include a chemical property and / or a flavor property of the coffee beans.

The machine detection can take the form of reading in manually entered information, optical reading in of an identifier and / or sensory detection via at least one sensor of the coffee machine and / or via at least one sensor of a terminal that can be connected to the coffee machine. For example, the coffee machine can include a display with touch function and / or a keyboard as the input device. Alternatively or cumulatively, an input can be made by a terminal such as a smartphone, tablet, computer or the like. In this context, information can be entered using a smartphone app or a web page, for example. It is also conceivable that information is photographed with the smartphone. A transmission can then take place via, for example, Bluetooth, W-LAN etc. to the coffee machine.

In step b), information about brewing parameters is automatically searched as a function of the information about the coffee beans recorded in step a) in at least one external database and / or the Internet. In this context, an external database is to be understood as a database that is not stored in the coffee machine itself. The automatic search can be carried out by a control device of the coffee machine and / or by a digital terminal that can be connected to the coffee machine. The external databases can be made available, for example, by coffee machine manufacturers, roasters and / or coffee manufacturers. The external databases can contain, for example, data such as parameter sets that are stored by other machines (of the same type or similar type and / or from a different manufacturer) on a (website) server, in particular, of their own and / or provided for this purpose. As an alternative or cumulative information about brewing parameters, in particular data records or brewing parameters can be searched for on websites of bloggers, hobby baristas, professional baristas, coffee machine manufacturers, roasters and / or coffee manufacturers, for example. In particular, already proposed brewing parameters and / or data or statements can be searched for as information about brewing parameters that allow conclusions to be drawn about brewing parameters.

In step c), the bean-specific parameter data set is generated as a function of the information about the brewing parameters found in step b). For example, one or more, in particular all of the above-mentioned brewing parameters are assigned a value that was found in step b) or derived from a value found or a statement found. The generation can thus include, for example, generating or determining (in particular estimating) (brewing) parameter values for the parameter data set, in particular from information, values and / or statements found. Several pieces of information, values and / or statements can also flow into the generation or determination of a (individual) parameter value. For example, in this context an already existing (initial) value of an (initial) parameter data record of the machine can be changed as a function of information found in step b). Alternatively or cumulatively, the generation of the parameter data set can also include an integration of an externally found data set into the coffee machine. The bean-specific parameter data set can then be stored in a memory (e.g. MEM) integrated in the machine. The parameters generated for subsequent production processes for the production of coffee can thus be stored in a memory of the coffee machine. In addition, it is conceivable that the parameter data set is (also) stored on an external server in order to make it available to other machines, for example. The subsequent brewing of the beans can then take place with the brewing parameters generated in this way. In other words, this means in particular that the brewing parameters of the coffee machine are set in accordance with the generated parameter data set.

According to an advantageous embodiment, it is proposed that one or more of the Steps a), b) and c) are carried out using an artificial intelligence algorithm. In particular, a machine-learning or self-learning algorithm can be used for this purpose. An artificial neural network can be used as an example of such an algorithm. Input variables of the algorithm can be the information about the coffee beans acquired in step a) and / or the information about the brewing parameters sought in step b). The stated brewing parameters can be the output variables of the algorithm. To determine the output variables, the algorithm, which is designed as an artificial neural network, for example, can include weights, nodes, neurons, transfer functions and / or threshold values. These can, for example, be adapted, added or deleted using machine learning.

The algorithm can in principle be a (pure) search algorithm. This can receive the information about the coffee beans acquired in step a) as input variables and output the information sought in step b) as output variables. The self-learning search algorithm can, for example, search for data records with the appropriate brewing parameters or information with which the appropriate brewing parameters can be generated in accordance with the type of bean on databases via the Internet.

The algorithm can be trained, for example, by what is known as monitored learning. The algorithm can be trained, for example, with specially generated training data and a target / actual comparison. Alternatively or cumulatively, feedback from the user can be requested for training. For example, it can be provided that the consumer of the coffee brewed with the machine-generated parameters can enter whether he is satisfied with the taste. In this context, the user can, for example, specify which taste property should change and how (e.g. less bitter and / or stronger).

To execute and / or train the algorithm, for example, a control unit of the coffee machine and / or a terminal device that can be connected to the coffee machine can have a processor with sufficient performance (for high data throughputs) to use a self-learning (search) algorithm (artificial Intelligence) to retrieve and analyze data from the Internet. After step c) the assemblies of the machine can be controlled via the control unit of the machine in accordance with the parameter data records that were determined from the Internet using the self-learning algorithm.

According to a further advantageous embodiment, it is proposed that one or more of steps a), b) and c) be carried out by the coffee machine itself. At least step c) or steps b) and c) are preferably carried out by the coffee machine itself. Steps a) and b) and c) of the coffee machine itself are particularly preferably carried out.

According to a further advantageous embodiment, it is proposed that one or more of steps a), b) and c) be carried out by a digital terminal that can be connected to the coffee machine. The terminal can be, for example, an (external) computer, a smartphone and / or a tablet. At least step a) or steps a) and b) are preferably carried out by the terminal.

According to a further advantageous embodiment, it is proposed that at least one piece of information about the coffee beans is obtained in step a) by reading in an identifier. The identifier can be, for example, a product name (bean brand), an optically readable code such as a barcode or a QR code and / or an RFID code. The reading-in takes place in particular by machine. A corresponding scanner (optical, RFID, etc.) can, for example, be part of the coffee machine or be formed with a terminal (e.g. in the form of a camera of a terminal). In principle, an identifier entered manually, e.g. via a keyboard, can also be read in.

In order to read in the information about the coffee bean, an image of the packaging of the coffee beans can particularly preferably be recorded, in particular of a label attached to the packaging. This can be done, for example, by means of a camera in the coffee machine or in the terminal. An image evaluation can then be carried out using a program on the coffee machine or the end device (e.g. using a corresponding app), in particular with the aim of recognizing or identifying the bean manufacturer, the type of bean and / or the roast.

According to a further advantageous embodiment, it is proposed that in step a) at least one piece of information about the coffee beans is automatically obtained during the production of coffee with the coffee machine. Thus, for example, a particularly initial or first brewing process can be used to obtain, in particular to measure, the information about the coffee beans. For example, an in particular chemical, taste and / or physical property can be used as at least one piece of information about the coffee beans in this context the coffee beans and / or a powder formed therefrom can be obtained.

In this context it is particularly preferred if a property of the coffee beans used is sensed (measured) as information about the coffee beans. This can be done with a corresponding, for example chemical, sensor of the coffee machine.

Obtaining information and / or parameters during the brewing process can also contribute to a continuous improvement of the bean-specific parameters by self-learning of the coffee machine during coffee production for subsequent coffee beverages that are produced in particular with this machine (or other machines). An exchange of information obtained between the coffee machines via the Internet is also conceivable in this context.

According to a further advantageous embodiment, it is proposed that Internet forums be searched in step b). For example, predefined forums can be searched. This specification can be updated in particular at regular intervals. In the case of an adaptive algorithm, it can also be provided that it searches for and selects suitable forums independently. In this context, the algorithm can, for example, use one or more of the following criteria to select forums: content density, topicality, professionalism of the authors.

According to a further advantageous embodiment, it is proposed that in step c), in particular, machine-learned user preferences are also taken into account. The user can enter his particular taste preferences (bitterness, strength, temperature), for example by way of feedback and / or an initial specification, directly to the machine or indirectly via a terminal. Furthermore, the machine and / or the algorithm can learn from the user preferences and / or take them into account when searching for and / or selecting the information and parameters.

According to a further advantageous embodiment, it is proposed that the generated bean-specific parameter data set be made available to further users and / or coffee machines via at least one external database and / or the Internet. Thus, an advantageous networking of machines can be achieved. This networking can include, for example, an exchange or provision of the brewing parameter data from, in particular, an integrated memory of the machine via the Internet.

According to a further aspect, a coffee machine is also proposed, comprising a brewing system, a control device and a communication interface, the control device being set up to carry out a method proposed here.

The coffee machine can furthermore comprise an input device via which at least one piece of information about the coffee beans can be provided to the control device. The input device can be, for example, a touch display and / or a keyboard. Alternatively or cumulatively, the input device can comprise an (optical) scanner.

Furthermore, the coffee machine can include at least one sensor for detecting at least one piece of information about the coffee beans. It is preferably a sensor for detecting an in particular chemical, taste and / or physical property of the coffee beans or a powder formed therefrom. The at least one sensor can be, for example, one or more of the following sensors: Pressure sensor for detecting the flow resistance for water through ground coffee beans, in particular in a portafilter of the coffee machine, optical sensors for detecting a color and / or a shade of ground coffee beans in particular in a portafilter of the coffee machine, optical sensor to detect a color and / or a shade of unmilled coffee beans, especially on a bean holder of the coffee machine, artificial nose (chip to detect odors based on chemical compounds), force sensor to determine the compressibility (displacement force Characteristic) of a stamp on the portafilter to compress the coffee powder and / or chemical sensor, such as sensor for (chemical) detection of the salt content, sensor for (chemical) detection of bitter substances, sensor for (chemical) detection of oils, sensor for (chemical) ) Capture acid and / or sensor for (chemical) detection of caffeine.

According to a further aspect, a network is proposed comprising at least one coffee machine proposed here and at least one digital terminal and / or at least one server that can be connected to the coffee machine via the communication interface. The at least one server can provide information and parameters from at least one coffee machine manufacturer, roaster, coffee maker and / or service provider, for exchanging data between coffee machines and / or for outsourcing computing operations and / or data from the coffee machine and / or serve the end device.

For example, a first server can provide a web service from a coffee machine manufacturer, roaster, coffee manufacturer and / or service provider for recognizing types of coffee and a second server can be a publicly available server (for example Internet sources). To outsource computing operations and / or data from the coffee machine and / or the terminal device, one of the servers or a third server can be set up for (main component) analysis of, for example, odor components, taste components and / or chemical components. For this purpose, it can include, for example, an artificial neural network for analysis and / or even for determining certain types of coffee from the components. The server can use data from the at least one sensor of the coffee machine as an input variable. As soon as the type of coffee has been determined by the server, associated brewing parameters can be searched for from one of the servers or at least one other server.

The details, features and advantageous configurations discussed in connection with the method can correspondingly also occur in the case of the coffee machine and / or the network presented here, and vice versa. In this respect, reference is made in full to the statements made there for a more detailed characterization of the features.

• 1: ein hier vorgeschlagenes Netzwerk mit einer hier vorgeschlagenen Kaffeemaschine,
• 2: ein hier vorgeschlagenes Netzwerk mit einer weiteren hier vorgeschlagenen Kaffeemaschine, und
• 3: ein Ablaufdiagramm eines hier vorgeschlagenen Verfahrens.

The solution presented here and its technical environment are explained in more detail below with reference to the figures. It should be pointed out that the invention is not intended to be restricted by the exemplary embodiments shown. In particular, unless explicitly stated otherwise, it is also possible to extract partial aspects of the facts explained in the figures and to combine them with other components and / or findings from other figures and / or the present description. They show schematically:

• 1 : a network proposed here with a coffee machine proposed here,
• 2 : a network proposed here with another coffee machine proposed here, and
• 3 : a flow diagram of a method proposed here.

1 shows schematically a network proposed here with a coffee machine proposed here 1 . The network also includes at least one server. The coffee machine 1 includes a brewing system 8th , a control unit 3 and a communication interface 4th . The control unit 3 is required to carry out a procedure described here (cf. 3 ) furnished. Also includes the coffee maker 1 according to 1 for example an input device 2 via which the control unit 3 at least one piece of information about the coffee beans can be provided.

The input device is for this purpose 2 exemplarily formed in the manner of a scanning unit. Thus shows 1 also an example that at least one piece of information about the coffee beans can be obtained by reading in an identifier such as a barcode, QR code and / or RFID code.

The communication interface 4th can for example be formed in the manner of a transmission and reception module which can work with W-LAN, Bluetooth, RFID and / or LAN technology. Via the communication interface 4th can the coffee machine 1 for example with a home network and / or a modem 5 and about it with the internet 6th be connected and access websites available through it. This means that Internet forums can also be searched.

You can also use the Internet 6th also databases 7th that are stored on one or more of the servers. In addition, a generated bean-specific parameter data record can be used via at least one external database 7th and / or the internet 6th be made available to other users and / or coffee machines.

The input device 2 is here for the interaction between user and coffee machine 1 sufficient so that in 1 communication with a terminal 9 not happened. Thus, the variant simulates 1 also represents an example that several (all) of steps a), b) and c) of the coffee machine 1 can be done by yourself.

Alternatively or cumulatively to the input device 2 can the coffee machine 1 also contain at least one sensor (not shown here) for detecting at least one piece of information about the coffee beans. As a result, at least one piece of information about the coffee beans can be automatically obtained during the production of coffee with the coffee machine. In this context, a property of the coffee beans used can be sensed as information about the coffee beans.

2 shows schematically a network proposed here with a further coffee machine proposed here 1 . In 2 is the coffee maker 1 via the communication interface 4th with at least one digital device 9 , such as a computer and / or a smartphone, so that, for example, an input can be made via a website or an app that is sent via the terminal 9 can be called. The end device 9 is here for the interaction between the user and the coffee machine 1 sufficient so that the input device 2 in 2 is not provided as an example.

Thus shows 2 also an example that one or more of steps a), b) and c) of one with the coffee machine 1 connectable, digital device 9 can be carried out. A combination of the interaction options via the input device is of course also conceivable 2 and at least one terminal 9 .

3 schematically shows a flowchart of a method proposed here. The method is used to generate a bean-specific parameter data set for the automatic setting of brewing parameters of a coffee machine 1 . The sequence of steps a), b) and c) shown with blocks 110, 120 and 130 is only exemplary and can be run through at least once in the sequence shown in a regular operating sequence.

In block 110, according to step a), at least one piece of information about coffee beans to be used for a brewing process is automatically recorded. In block 120, according to step b), information about brewing parameters is automatically searched as a function of the information about the coffee beans recorded in step a) in at least one external database 7th and / or the internet 6th . In block 130, according to step c), the bean-specific parameter data set is generated depending on the information about the brewing parameters found in step b).

One or more of steps a), b) and c) can be carried out using an artificial intelligence algorithm. In step c), in particular, machine-learned user preferences can also be taken into account.

A method, a coffee machine and a network are thus specified which at least partially solve the problems described with reference to the prior art. In particular, a method, a coffee machine and a network are specified with which the most satisfactory taste experience can be provided in an automated manner, so that time-consuming manual trying out by the consumer can be dispensed with.

List of reference symbols

1

coffee machine

2

Input device

3

Control unit

4th

Communication interface

5

Communication system

6

Internet

7th

Database

8th

Brewing system

9

End device

Claims (14)

Method for generating a bean-specific parameter data set for automatically setting brewing parameters of a coffee machine (1), comprising at least the following steps: a) Automatic recording of at least one piece of information about coffee beans to be used for a brewing process, b) Automatic search for information about brewing parameters as a function of the information about the coffee beans recorded in step a) in at least one external database (7) and / or the Internet (6), c) Generating the bean-specific parameter data set as a function of the information about the brewing parameters found in step b). Procedure according to Claim 1 , wherein one or more of steps a), b) and c) are carried out using an artificial intelligence algorithm. Procedure according to Claim 1 or 2 , one or more of steps a), b) and c) being carried out by the coffee machine (1) itself. Method according to one of the preceding claims, wherein one or more of steps a), b) and c) are carried out by a digital terminal (9) which can be connected to the coffee machine (1). Method according to one of the preceding claims, wherein in step a) at least one piece of information about the coffee beans is obtained by reading in an identifier. Method according to one of the preceding claims, wherein in step a) at least one piece of information about the coffee beans is automatically obtained during the production of coffee with the coffee machine. Procedure according to Claim 6 , wherein a property of the coffee beans used is sensed as information about the coffee beans. Method according to one of the preceding claims, wherein Internet forums are searched in step b). Method according to one of the preceding claims, wherein in step c) in particular machine-learned user preferences are also taken into account. Method according to one of the preceding claims, wherein the generated bean-specific parameter data set is made available to further users and / or coffee machines via at least one external database (7) and / or the Internet (6). Coffee machine (1), comprising a brewing system (8), a control device (3) and a communication interface (4), the control device (3) for performing a method according to one of the Claims 1 to 10 is set up. Coffee machine (1) Claim 11 , further comprising an input device (2) via which at least one piece of information about the coffee beans can be provided to the control device (3). Coffee machine (1) Claim 11 or 12 , having at least one sensor for detecting at least one piece of information about the coffee beans. Network comprising at least one coffee machine (1) according to one of the Claims 11 to 13 and at least one digital terminal (9) and / or at least one server that can be connected to the coffee machine (1) via the communication interface (4).

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