Information processing system, method for processing information, control program, and recording medium

Abstract

To specify coffee beans from which a coffee beverage having a flavor desired by a subject can be extracted, on the basis of the flavor.SOLUTION: An information processing system (100) includes: an acquisition unit (61) for acquiring a target measurement signal corresponding to the odor of target coffee beans from an odor measurement device (30) for measuring the odor and outputting a measurement signal; and an estimation unit (62) for estimating target identification information corresponding to the target coffee beans by using an estimation model from the acquired target measurement signal. The estimation model is generated by machine learning using teacher data that includes, as an explanatory variable, a sample measurement signal corresponding to the odor of sample coffee beans and includes, a sample identification information corresponding to the sample coffee beans, as an object variable.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and the like. [Background technology]

[0002] In recent years, devices have been developed that evaluate specific objects based on their odors. For example, Patent Document 1 describes an odor identification device that uses coke odors and tar odors collected from potential odor sources in steel plants as reference odors and identifies the source and cause of unknown odors in steel plants and the like.

[0003] However, with conventional technology, it is difficult to identify coffee beans that match a consumer's preferences for the flavor of a coffee beverage. For this reason, in stores selling coffee beans, the coffee beans are handed directly to customers, or customers are given samples of coffee beverages actually extracted from the coffee beans, and the characteristics of the coffee beverage, such as its smell and taste, are explained to them. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2006-017467 A Summary of the Invention [Problem to be solved by the invention]

[0005] Using specialized equipment such as gas chromatography, it is possible to identify the components in coffee drinks, but the results of such analysis do not necessarily align with consumer preferences.

[0006] One aspect of the present invention is to provide an information processing system, an information processing method, and the like that can identify coffee beans that can be used to extract a coffee beverage with a desired flavor from the flavor of the coffee beverage desired by a subject. [Means for solving the problem]

[0007] An information processing system according to one aspect of the present invention comprises an acquisition unit that acquires a target measurement signal corresponding to the odor of a target coffee bean from an odor measurement device that measures odors and outputs a measurement signal, and an estimation unit that estimates target identification information corresponding to the target coffee bean from the acquired target measurement signal using an estimation model, wherein the estimation model is generated by machine learning using training data that includes a sample measurement signal corresponding to the odor of a sample coffee bean as an explanatory variable and sample identification information corresponding to the sample coffee bean as a target variable.

[0008] An information processing method according to one aspect of the present invention includes an acquisition step in which a computer acquires a target measurement signal corresponding to the odor of a target coffee bean from an odor measurement device that measures odors and outputs a measurement signal, and an estimation step in which the computer estimates target identification information corresponding to the target coffee bean from the acquired target measurement signal using an estimation model, wherein the estimation model is generated by machine learning using training data that includes a sample measurement signal corresponding to the odor of a sample coffee bean as an explanatory variable and sample identification information corresponding to the sample coffee bean as a target variable.

[0009] An information processing system according to one aspect of the present invention includes a first acquisition unit that acquires classification information classifying a plurality of coffee beans into a plurality of groups based on a plurality of measurement signals corresponding to the odors of a plurality of coffee beans, each having different identification information, output from an odor measurement device that measures odors and outputs measurement signals; a second acquisition unit that acquires subject information for each of a plurality of reference coffee beans classified into at least one of the plurality of groups, in which a subject's evaluation of the flavor of a coffee beverage extracted from the reference coffee beans is associated with the identification information corresponding to the reference coffee beans used to extract the coffee beverage; and an identification unit that analyzes the correspondence between the subject information and the classification information and identifies recommended coffee beans from the plurality of reference coffee beans that can extract a coffee beverage with the flavor desired by the subject.

[0010] An information processing method according to one aspect of the present invention includes: a first acquisition step in which a computer acquires classification information that classifies a plurality of coffee beans into a plurality of groups based on measurement signals acquired from an odor measurement device that measures the odors of the plurality of coffee beans having different identification information and outputs a measurement signal; a second acquisition step in which the computer acquires subject information that associates a subject's evaluation of the flavor of a coffee beverage extracted from each of a plurality of reference coffee beans classified into at least one of the plurality of groups with the identification information of the reference coffee beans used to extract the coffee beverage; and an identification step in which the computer analyzes the correspondence between the subject information and the classification information and identifies, from the plurality of coffee beans, coffee beans that can be used to extract a coffee beverage with the flavor desired by the subject. [Effects of the Invention]

[0011] According to one aspect of the present invention, it is possible to provide an information processing system and an information processing method that can identify coffee beans that can be used to extract a coffee drink with the flavor desired by a subject based on the flavor of the coffee drink. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a schematic diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 2] 1 is a functional block diagram showing an example of an odor measurement device according to an embodiment of the present invention. [Figure 3] FIG. 2 is a top view showing an example of the configuration of an odor sensor element. [Figure 4] FIG. 2 is a functional block diagram illustrating an example of the configuration of a specific device according to an embodiment of the present invention. [Figure 5] 10 is a flowchart showing an example of the flow of a process in which an information processing system according to an embodiment of the present invention identifies the aroma characteristics of a coffee beverage extracted from multiple coffee beans with different identification information. [Figure 6]1 is a schematic diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 7] 1 is a schematic diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 8] 1 is a functional block diagram showing an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 9] 1 is a schematic diagram illustrating an example of a configuration of an information processing system according to an embodiment of the present invention. [Figure 10] 1 is a functional block diagram showing an example of an odor measurement device according to an embodiment of the present invention. [Figure 11] 10 is a flowchart showing an example of a processing flow in which an information processing system according to an embodiment of the present invention estimates identification information of a plurality of coffee beans having mutually different identification information. DETAILED DESCRIPTION OF THE INVENTION

[0013] One embodiment of the present invention will be described below, but the present invention is not limited thereto. Furthermore, unless otherwise specified in this specification, the term "A to B" representing a numerical range means "not less than A and not more than B."

[0014] [Embodiment 1] (Information processing system 100) First, an overview of an information processing system 100 according to one embodiment of the present invention will be described using Fig. 1. Fig. 1 is a schematic diagram showing an example of the configuration of an information processing system 100 according to one embodiment of the present invention.

[0015] As shown in FIG. 1, the information processing system 100 includes an odor measurement device 30, an identification device 10, and a user terminal 50. The odor measurement device 30 measures an odor and outputs a measurement signal. The identification device 10 identifies recommended coffee beans that can be used to brew the coffee beverage desired by the subject, based on classification information based on the measurement signal and subject information. The user terminal 50 transmits the measurement signal from the odor identification device 30 and receives the identification result output from the identification device 10. In the information processing system 100, as shown in FIG. 1, the user terminal 50 and the identification device 10 may be connected via a wide area communication network 40. Alternatively, the user terminal 50 and the odor measurement device 30 may be connected via a local area network connection, LTE communication, or the like, without going through a provider or the like.

[0016] The wide area communication network 40 is not particularly limited, and may be the Internet, a telephone line network, a mobile communication network, a CATV communication network, a satellite communication network, etc. The wide area communication network 40 may further be connected to a cloud server that stores the measurement signal output by the odor measuring device 30 and the identification result output by the identifying device 10. When the wide area communication network 40 is connected to the cloud server, the identifying device 10 may be realized as the cloud server.

[0017] According to the above configuration, the information processing system 100 identifies recommended coffee beans that can extract a coffee drink with the flavor desired by the subject, based on the classification information and the subject information. In other words, it can be said that the information processing system 100 identifies the coffee beans desired by the subject. Here, the classification information is information in which a plurality of coffee beans having different identification information are classified into a plurality of groups based on measurement signals. Furthermore, the subject information is information in which the subject's evaluation of the flavor of a coffee drink extracted from reference coffee beans (e.g., coffee beans with a well-known flavor) classified into one of the plurality of groups is associated with the identification information of the reference coffee beans.

[0018] In this specification, "reference coffee beans" refers to coffee beans that have been classified into at least one of the above-mentioned groups before the recommended coffee beans are identified. At least one type of coffee beverage extracted from the reference coffee beans has been evaluated in advance for flavor by a subject. The reference coffee beans may be coffee beans that are easily available, commercially available coffee beans, or coffee beans whose taste is generally well known.

[0019] In this specification, recommended coffee beans are coffee beans that can extract a coffee drink with the flavor desired by the subject, and are identified by an identification unit by analyzing the correspondence between subject information and classification information. Recommended coffee beans are identified from among reference coffee beans. That is, coffee beans that can be selected as recommended coffee beans are the same type of coffee beans as the coffee beans that can be selected as the reference coffee beans. In one embodiment, the recommended coffee beans may be selected only from reference coffee beans other than the reference coffee beans evaluated by the subject, or may be selected from all reference coffee beans.

[0020] In this specification, classification information refers to information obtained by classifying a plurality of reference coffee beans based on measurement signals measured using the odor measuring device 30. The classification information may be information output by the user terminal 50, the odor measuring device 30, or the identifying device 10, or may be information output by a device other than the identifying device 10 (for example, an information processing device used by the user of the information processing system 100) based on measurement signals output by the odor measuring device 30.

[0021] In this specification, subject information refers to information that associates a subject's evaluation of the flavor of a coffee beverage extracted from reference coffee beans with the identification information corresponding to the reference coffee beans used to extract the coffee beverage. The subject information may be information output by user terminal 50 or identification device 10, or may be information output by a device other than identification device 10 (for example, an information processing device used by a user of information processing system 100).

[0022] As a result, the information processing system 100 can identify recommended coffee beans that can extract a coffee beverage with the flavor desired by the subject, based on the measurement results of the odors derived from multiple coffee beans with different identification information. For example, the information processing system can be used as a substitute for a questionnaire to identify the subject's coffee bean preferences. The information processing system can also be used to determine the subject's criteria for purchasing coffee beans.

[0023] In this specification, the reference coffee beans may be classified into different groups. Alternatively, only some of the multiple reference coffee beans may be classified into different groups. The more types of reference coffee beans that are classified into different groups, the more accurately the coffee beans desired by the subject can be identified.

[0024] In this specification, the subject information may be information based on answers from the subject who has come into contact with the coffee beverage extracted from the reference coffee beans to predetermined questions regarding their evaluation of the flavor of the coffee beverage. In this specification, "come into contact with the coffee beverage" means smelling the coffee beverage or drinking the coffee beverage. The predetermined questions may be, for example, questions in the form of multiple-choice answers, or questions in the form of numerically evaluating each reference coffee bean. By including this information in the subject information, the recommended coffee beans desired by the subject can be more accurately identified.

[0025] In this specification, the identification information may include information indicating at least one of the origin, brand, and roasting level of each of the reference coffee beans and the recommended coffee beans. By including this information in the identification information, it is possible to identify the recommended coffee beans desired by the subject based on this information. In this specification, "roast level" refers to identification information indicating the degree to which the coffee beans have been roasted, and examples include unroasted (green beans), light roast, cinnamon roast, medium roast, high roast, city roast, full city roast, French roast, Italian roast, etc.

[0026] In this specification, the term "coffee beans" is not particularly limited as long as it refers to coffee beans, and may be in the form of either ground powder or unground beans. Furthermore, the coffee beans may be green beans or roasted coffee beans. Preferably, the coffee beans are roasted beans.

[0027] The information processing system 100 may be used by a coffee bean seller to identify consumer preferences, and may also be used by coffee bean consumers to identify their own preferences.

[0028] In the information processing system 100, the odor measurement device 30 includes multiple sensor elements. The multiple sensor elements may be capable of measuring different odor substances, or the same odor substances. The measurement signal is output from each of the multiple odor sensor elements. The sensor elements will be described below. After that, the odor measurement device 30 incorporating the odor sensor element 31 and the information processing system 100 including the odor measurement device 30 and the identification device 10 will be described in detail.

[0029] (Odor measuring device 30) The outline and effects of an odor measurement device 30 employing an odor sensor element 31 will be described below with reference to Fig. 2. Fig. 2 is a functional block diagram showing an example of the configuration of an information processing system 100 equipped with an odor measurement device 30 employing an odor sensor element 31. The odor measurement device 30 includes an odor sensor element 31 that detects odor substances, a power source 32 (power supply), a clock 33 (timer), a control unit 34, and a communication unit 35. As described above, the odor measurement device 30 may be connected to a user terminal 50.

[0030] The power supply 32 is a power source for supplying power to the odor sensor element 31. The constant voltage power supply 32 supplies a constant current (for example, a direct current of 1 mA) via lead wires to the odor sensor element 31. The voltage value supplied by the constant voltage power supply 32 is 0.5 V to 10 V, for example, 2.5 V or 5.0 V.

[0031] The clock 33 measures the time. The clock 33 transmits the measured time to the control unit 34. The clock 33 may be a clock whose time is set by the user, or may be a radio-controlled clock.

[0032] The control unit 34 controls all the components of the odor measuring device. The control unit 34 also outputs the odor detected by the odor sensor element 31 as a measurement signal. The control unit 34 may output the odor measurement signal according to the time measured by the clock 33.

[0033] The communication unit 35 transmits the measurement signal output by the control unit 34. The communication unit 35 transmits the measurement signal to the wide area communication network 40, and the transmitted measurement signal is acquired by the identifying device 10.

[0034] The odor measuring device 30 may further include a housing, although this is not an essential component. The housing is a container capable of containing air containing odor substances. When the odor measuring device 30 includes a housing, the odor sensor element 31 is installed inside the housing.

[0035] The odor measuring device 30 outputs a measurement signal that indicates the change over time in the electrical conductivity of the odor sensor element 31 before and after an odor substance is adsorbed to the odor sensor element 31. This makes it possible to detect and distinguish various odor substances.

[0036] <Odor sensor element 31> 3 is a top view showing an example of the configuration of an odor sensor element 31. The odor sensor element 31 includes an odorant receiving layer 315 containing the above-described resin composition, a first metal wiring 313A, and a second metal wiring 313B. Note that, hereinafter, when there is no need to distinguish between the first metal wiring 313A and the second metal wiring 313B, they may be referred to as metal wiring 313.

[0037] The first metal wiring 313A and the second metal wiring 313B are each metal wirings that function as electrodes for measuring changes in the electrical conductivity of the odorant receiving layer 315 (i.e., the resin composition). That is, the first metal wiring 313A and the second metal wiring 313B are spaced apart from each other, and the odorant receiving layer 315 is in contact with at least a portion of the first metal wiring and at least a portion of the second metal wiring. In one example, the first metal wiring 313A and the second metal wiring 313B are metal wirings that are not in direct contact with each other, and may be metal wirings that are approximately parallel to each other, as shown in FIG. 2.

[0038] 3, metal wiring 313 including first metal wiring 313A and second metal wiring 313B may be disposed on substrate 311. Substrate 311 may be a substrate such as glass epoxy commonly used in electronic circuits. Metal wiring 313 may be metal wiring such as copper or gold. The thickness of each of first metal wiring 313A and second metal wiring 313B as viewed in a direction perpendicular to the surface of the substrate may be, for example, 10 μm to 2 mm.

[0039] The odorant receiving layer 315 may be in contact with at least a portion of the first metal wiring 313A and at least a portion of the second metal wiring 313B. The odorant receiving layer 315 may be arranged to fill the area between the first metal wiring 313A and the second metal wiring 313B, as shown in FIG.

[0040] When the electrical conductivity of the odorant receiving layer 315 (i.e., the electrical conductivity of the odor sensor element 31) is low, it is desirable that the distance between the first metal wiring 313A and the second metal wiring 313B be a predetermined distance (for example, 500 μm) or less.

[0041] The odorant receiving layer may contain a resin composition. The resin composition includes a resin and may further contain one or more types selected from a surfactant and a filler (e.g., a conductive carbon material). In this specification, "odorant receiving layer" refers to a layer that adsorbs the odorant to be identified. The odorant receiving layer 315 is formed from the above-mentioned resin composition. The odorant receiving layer 315 may be provided as part of the odor sensor element 31. The electrical resistance of this odorant receiving layer 315 changes in response to the adsorption of odorants, etc. In other words, the odor sensor element 31 is an odor detection device equipped with such an odorant receiving layer 315, and the odor measurement method of the odor sensor element 31 may be a chemiresistor type. Furthermore, the odor sensor element 31 is not limited to the above-mentioned chemiresistor type odor sensor element, and may include one or more types of odor sensor elements used in known odor sensors, etc.

[0042] When the odor sensor element 31 is a chemiresistor type containing a resin composition, the change in electrical conductivity over time differs between when odorant A is adsorbed and when odorant B, which is different from odorant A, is adsorbed, making it possible to detect and distinguish various odorants. In the odor measurement device 30 described below, multiple odor sensor elements 31 are arranged, each including a substrate 311 on which a structure for detecting odorants (metal wiring 313 and an odorant receiving layer 315) is provided. Each substrate 311 is provided with multiple sets including multiple odorant receiving layers 315. Each of the multiple odor sensor elements 31 may be equipped with a constant-voltage power supply and a voltmeter. In the odor measurement device 30, each substrate 311 may be provided with one structure for detecting odorants (metal wiring 313 and an odorant receiving layer 315). Alternatively, in the odor measurement device 30, multiple sets of structures for detecting odorants (metal wiring 313 and an odorant receiving layer 315) may be arranged on a single substrate 311. In the latter case, a constant voltage power supply and a voltmeter are connected to each of the sets provided on the substrate 311.

[0043] The resin compositions contained in the odorant receiving layers 315 of the multiple odor sensor elements 31 included in the odor measurement device 30 may be the same or different. If the odorant receiving layers 315 included in the multiple odor sensor elements 31 have the same composition, each of the multiple odorant receiving layers 315 can detect the same odorant. Furthermore, if the multiple odor sensor elements 31 each include odorant receiving layers 315 with different compositions, each of the multiple odorant receiving layers 315 will respond differently to the odorant. In this way, by providing multiple sets of components for detecting odorants, the accuracy of odorant identification in the odor measurement device 30 can be improved.

[0044] The above-described odor measuring device 30 can output the change over time in the electrical conductivity of the odor sensor element 31 for each odor substance when various odor substances are adsorbed onto the odor sensor element 31. By applying this odor measuring device 30, it is possible to compare the change over time in the electrical conductivity of the odor sensor element 31 when, for example, an odor substance contained in the odor of coffee bean A is adsorbed onto the odor sensor element 31 with the change over time in the electrical conductivity of the odor sensor element 31 when an odor substance contained in the odor of coffee bean B is adsorbed onto the odor sensor element 31. Based on the results of such comparison, it is possible to realize an identification device 10, described below, that can identify the coffee beans desired by a subject from the odor substances adsorbed onto the odor sensor element 31.

[0045] (Specific device 10) The following describes the overview and effects of the identification device 10. The identification device 10 is a device that identifies the recommended coffee beans desired by the subject from the measurement signal output from the above-mentioned odor measurement device 30. The identification device 10 performs identification based on two types of information, namely classification information and subject information, and is therefore able to identify the recommended coffee beans desired by the subject from among reference coffee beans.

[0046] 4 is a functional block diagram showing an example of the configuration of the identifying device 10. The identifying device 10 includes a control unit 1 that controls each unit of the identifying device 10, and a storage unit 2 that stores various data used by the identifying device 10, but is not limited to this configuration. For example, the storage unit 2 may be an external device attached to the identifying device 10. Furthermore, the identifying device 10 may be connected to the wide area communication network 40 as described above.

[0047] <Control Unit 1> First, we will explain the control unit 1. The control unit 1 includes a first acquisition unit 11, a second acquisition unit 12, and an identification unit 13. Furthermore, some of the blocks included in the control unit 1 may be omitted from the control unit 1 by assigning their functions to another device that can communicate with the identification device 10.

[0048] The first acquisition unit 11 acquires classification information based on the measurement signal output from the odor measurement device 30. Specifically, the first acquisition unit 11 acquires classification information that classifies the coffee beans to be measured into a plurality of groups based on the measurement signal from each of the plurality of odor sensor elements 31. The first acquisition unit 11 may acquire the classification information in real time, or may acquire the classification information at predetermined time intervals (for example, 0.1 second intervals). The first acquisition unit 11 preferably acquires the classification information every first hour. The first acquisition unit 11 may store the acquired classification information in the storage unit 2.

[0049] The classification information may be information output by any information processing device (e.g., a computer used by a user of the information processing system 100) that has the function of analyzing the measurement signal acquired from the odor measuring device 30. The classification information may be transmitted from the any information processing device to the specific device 10 via the wide area communication network 40. In one embodiment, the classification information may be stored in any storage medium (e.g., an SD card, a USB memory, etc.).

[0050] The first time period may be, for example, but is not particularly limited to, 1 minute, 5 minutes, 10 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 6 hours, 8 hours, 10 hours, or more.

[0051] The second acquisition unit 12 acquires subject information in which a subject's evaluation of the flavor of a coffee beverage extracted from each of a plurality of reference coffee beans classified into at least one of the plurality of groups is associated with identification information of the reference coffee beans used to extract the coffee beverage. The second acquisition unit 12 may acquire subject information 22 from the storage unit 2, or may acquire the subject's evaluation and the identification information stored in the storage unit 2 and then associate these pieces of information to acquire the subject information.

[0052] The subject information may be information output by any information processing device (e.g., a computer used by a user of the information processing system 100) that has the function of associating the subject's evaluation with the identification information of the reference coffee beans. The subject information may be transmitted from the any information processing device to the identification device 10 via the wide area communication network 40. The information processing device that outputs the subject information may be the same as or different from the information processing device that outputs the classification information. In one embodiment, the subject information may be stored in any storage medium (e.g., an SD card, a USB memory, etc.).

[0053] The identification unit 13 identifies the recommended coffee beans desired by the subject based on the classification information and the subject information. The identification unit 13 may perform the identification by, for example, class classification. The identification unit 13 may also perform the identification by regression analysis. The identification unit 13 may further identify multiple types of recommended coffee beans desired by the subject and rank them. The identification unit 13 may store identification result data in the storage unit 2.

[0054] The identification unit 13 transmits the identification result to the user terminal 50 via the wide area communication network 40 based on the user terminal data 23. The identification unit 13 may transmit the identification result directly to the user terminal 50, or may transmit the identification result to a web page or the like on the wide area communication network 40 that is accessible by the user terminal 50.

[0055] The identifying device 10 may be equipped with another device that has a function of acquiring the identification result of the identifying device 10. In this case, the identifying device 10 may transmit the identification result identified by the identifying unit 13 through the other device.

[0056] <Storage section 2> Next, a description will be given of the storage unit 2. The storage unit 2 may store classification information 21, subject information 22, and user terminal data 23.

[0057] Classification information 21 is information obtained by measuring the aroma of a plurality of coffee beans having different identification information and classifying the plurality of coffee beans into a plurality of groups based on the obtained measurement signals. In one embodiment, classification information 21 may further include identification information of the plurality of coffee beans.

[0058] The subject information 22 is information in which the subject's evaluation of the flavor of a coffee beverage extracted from each of a plurality of reference coffee beans classified into at least one of the plurality of groups is associated with the identification information of the reference coffee beans used to extract the coffee beverage. In one embodiment, the subject information 22 may be information in which the subject's evaluation is associated with the identification information in advance, or the association may be established when the information is acquired by the second acquisition unit 12.

[0059] In one embodiment, the subject information 22 may be input by an input unit included in the control device 10. The subject information 22 may be partially or entirely input by the input unit. The subject information 22 may also be updated by information input from the input unit.

[0060] The target person may be a coffee bean consumer, a coffee bean purchaser, a coffee bean seller, a coffee bean producer, etc. Furthermore, the user of the information processing system and the target person may be the same or different.

[0061] The user terminal data 23 is data relating to a terminal owned by a user of the information processing system 100. The user terminal data 23 is data for linking the user terminal 50 with a measurement signal transmitted from the user terminal 50. Examples of users of the information processing system 100 include coffee bean sellers and coffee bean consumers.

[0062] The user terminal 50 is not particularly limited, but examples thereof include a communication terminal (such as a mobile phone or smartphone) owned by the subject, a computer owned by the subject, and the like.

[0063] (Processing performed by information processing system 100) An outline of an information processing method according to one embodiment of the present invention will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an outline of an information processing method by the information processing system 100.

[0064] In step S1, the odor measuring device 30 measures the odors of multiple coffee beans with different identification information and outputs the results as a measurement signal. At this time, the odor measuring device 30 may output the odor measurement results to the identifying device 10 in real time, or may output the results every first hour as described above.

[0065] In step S2, the first acquisition unit 11 acquires classification information that classifies the plurality of coffee beans into a plurality of groups based on the measurement signal output from the odor measurement device 30 (first acquisition step). The first acquisition unit 11 may acquire the classification information in real time, or may acquire it every time a predetermined time (for example, one second) has elapsed. The first acquisition unit 11 may store the acquired classification information in the storage unit 2. The first acquisition unit 11 may acquire the classification information via the wide area communication network 40, or may acquire classification information stored in another storage medium.

[0066] In step S3, the second acquisition unit 12 acquires subject information in which the subject's evaluation of the flavor of a coffee drink extracted from reference coffee beans is associated with identification information of the reference coffee beans (second acquisition step). The second acquisition unit 12 may acquire the subject information from the storage unit 2.

[0067] In step S4, the identification unit 13 analyzes the correspondence between the subject information and the classification information, and outputs the result of identifying recommended coffee beans that can extract a coffee drink with the flavor desired by the subject (identification step). The identification unit 13 may store the output identification result in the storage unit 2.

[0068] In step S5, the identification result output by the identification unit 13 is transmitted (output) by the identification unit 13 to the user terminal 50 via the wide area communication network 40 based on the user terminal data 23.

[0069] Because the aroma of coffee beans and the coffee drink extracted from those coffee beans does not necessarily match the analysis results of odorous substances contained in the smell derived from the coffee beans, it has been difficult to identify the recommended coffee beans desired by the subject and to classify the coffee beans.By using information processing system 100, identification unit 13 performs identification using the subject's evaluation of the flavor of the coffee drink extracted from each reference coffee bean and identification information of the reference coffee beans used to extract the coffee drink, making it possible to identify the coffee beans desired by the subject.

[0070] [Embodiment 2] An overview of an information processing system 100a according to another embodiment of the present invention will be described below with reference to Fig. 6. Fig. 6 is a schematic diagram showing an example of the configuration of an information processing system 100a different from those of embodiments 1 and 2. Note that descriptions of matters that have already been described will be omitted.

[0071] 6, in the information processing system 100a, the measurement signal output by the odor measuring device 30 is transmitted to the identifying device 10 via the wide area communication network 40. In addition, the user terminal 50 receives the identification result output from the identifying device 10 based on the measurement signal via the wide area communication network 40.

[0072] The information processing system 100a includes an identifying device 10, an odor measuring device 30, and a user terminal 50. The information processing system 100a may include a wide area communication network 40 as needed. As described above, in the information processing system 100a, the odor measuring device 30, the identifying device 10, and the user terminal 50 may be connected via the wide area communication network 40.

[0073] In the information processing system 100a, the user terminal data 23 may further include data linking the device number of the odor measuring device 30 with the terminal number of the user terminal 50 in addition to the data described above.

[0074] With the above configuration, it is possible to transmit the identification results to a user in an area distant from the odor measuring device 30, for example, a person who plans to purchase coffee beans using the Internet, etc. Furthermore, if the coffee beans desired by the subject are identified at multiple locations and the information processing system 100a is equipped with multiple odor measuring devices 30 installed at each of the multiple locations, it is possible to transmit the identification results based on the measurement signals measured at the multiple locations. Furthermore, if the information processing system 100a is used by users at multiple locations, the information processing system 100a is equipped with multiple user terminals 50, and the identification results based on the measurement signals can be transmitted to each of the user terminals 50 at the multiple locations.

[0075] [Embodiment 3] An overview of an information processing system 100b according to another embodiment of the present invention will be described below with reference to Fig. 7. Fig. 7 is a schematic diagram showing an example of the configuration of the information processing system 100b. Note that descriptions of matters that have already been described will be omitted.

[0076] 7, in the information processing system 100b, the measurement signal output by the odor measuring device is transmitted directly to the identifying device 10a. Based on the acquired measurement signal, the identifying device 10a outputs the results of an evaluation of the odor characteristics derived from multiple coffee beans with different identification information. In other words, in the information processing system 100b, the identifying device 10a can be said to be integrated with the user terminal 50. Furthermore, in the information processing system 100b, the identifying device 10a and the odor measuring device 30 may be connected via a local area network connection, LTE communication, or the like, without going through a provider or the like.

[0077] (Configuration of information processing system 100b) 8 is a functional block diagram showing an example of the configuration of an information processing system 100b different from those in embodiments 1 and 2. The information processing system 100b includes an identifying device 10a and an odor measuring device 30. The information processing system 100b may also include a wide area communication network 40 as necessary.

[0078] The identifying device 10a includes a control unit 1a that controls all the components of the identifying device 10a, a storage unit 2a that stores various data used by the identifying device 10a, and an output unit 15 that outputs the identification results.

[0079] <Control unit 1a> The control unit 1a includes a first acquisition unit 11, a second acquisition unit 12, an identification unit 13, and an output control unit .

[0080] The output control unit 14 causes the output unit 15 to output the identification result output by the identification unit 13. The output mode of the output unit 15 is not particularly limited, and may be, for example, a display output, a print output, or an audio output.

[0081] With the above configuration, since identifying device 10a is integrated with user terminal 50, the time lag until an identifying result is obtained is reduced.

[0082] [Embodiment 4] An overview of an information processing system 100c according to another embodiment of the present invention will be described below. Information processing system 100c includes an acquisition unit that acquires a target measurement signal corresponding to the odor of a target coffee bean from an odor measurement device that measures odors and outputs a measurement signal, and an estimation unit that estimates target identification information corresponding to the target coffee beans from the acquired target measurement signal using an estimation model, the estimation model being generated by machine learning using training data that includes a sample measurement signal corresponding to the odor of a sample coffee bean as an explanatory variable and sample identification information corresponding to the sample coffee bean as a target variable.

[0083] In this specification, the term "sample coffee beans" refers to coffee beans used to create training data used in machine learning of an estimation model. The sample coffee beans may be the same coffee beans as those that may be selected as reference coffee beans.

[0084] In this specification, the target coffee beans are not particularly limited as long as they are coffee beans whose odors are measured by the odor measuring device 30. The target coffee beans may have the same identification information as the sample coffee beans, or they may have different identification information.

[0085] Fig. 9 is a schematic diagram showing an example of the configuration of an information processing system 100c. Note that a description of matters already explained in [Embodiment 1] to [Embodiment 3] will be omitted. As shown in Fig. 9, the information processing system 100c includes an odor measurement device 30 that measures the odor of target coffee beans and outputs a target measurement signal, an estimation device 60 that uses an estimation model to estimate target identifying information corresponding to the target coffee beans from the target measurement signal, and a user terminal 50 that transmits the measurement signal to the estimation device 60 and receives the estimation result output from the estimation device 60.

[0086] According to the above configuration, the information processing system 100c estimates target identification information corresponding to the target coffee beans based on the target measurement signal. Here, the target identification information includes information indicating at least one of the origin, brand, and roast level of the target coffee beans.

[0087] 1, the information processing system 100 may have a user terminal 50 and an estimation device 60 connected via a wide area communication network 40. The user terminal 50 and the odor measuring device 30 may also be connected via a local area network connection, LTE communication, or the like, without going through a provider or the like.

[0088] (Estimation device 60) Below, we will explain the overview and effects of estimation device 60. Estimation device 60 is a device that estimates target identification information of target coffee beans using an estimation model from the measurement signals output by the above-mentioned odor measurement device 30. Estimation device 60 performs estimation using an estimation model 71 that has been generated by machine learning using training data that includes sample measurement signals corresponding to the odor of the sample coffee beans as explanatory variables and includes sample identification information corresponding to the sample coffee beans as a target variable.

[0089] 10 is a functional block diagram showing an example of the configuration of the estimation device 60. The estimation device 60 includes a control unit 6 that controls each unit of the estimation device 60, and a storage unit 7 that stores various data used by the estimation device 60, but is not limited to this configuration. For example, the storage unit 7 may be a device external to the estimation device 60. Furthermore, the estimation device 60 may be connected to the wide area communication network 40 as described above.

[0090] <Control unit 6> The control unit 6 includes an acquisition unit 61, an estimation unit 62, and an output control unit 63. In addition, some of the blocks included in the control unit 6 may be omitted from the control unit 6 by assigning their functions to another device that can communicate with the estimation device 60.

[0091] The acquisition unit 61 acquires the target measurement signal 72 output from the odor measurement device 30. The acquisition unit 61 may acquire the target measurement signal every hour, every 30 minutes, every 10 minutes, every 5 minutes, every minute, every 30 seconds, or every second, for example. The acquisition unit 61 may store the acquired target measurement signal 72 in the storage unit 7. The acquisition unit 61 may acquire the target measurement signal 72 from the odor measurement device 30, or may acquire the target measurement signal 72 stored in a storage medium or the like.

[0092] If necessary, the acquiring unit 61 may acquire information other than the target measurement signal 72. Examples of information other than the target measurement signal 72 include information based on measurement results measured by a sensor other than the odor measuring device 30 (e.g., a humidity sensor, a temperature sensor, an air pressure sensor, etc.).

[0093] The estimation unit 62 estimates object identifying information 73 corresponding to the object coffee beans from the object measurement signal 72 acquired by the acquisition unit 61, using the estimation model 71. The estimation unit 62 may store the estimated object identifying information 73 in the storage unit 7.

[0094] The output control unit 63 outputs the target identification information 73 output by the estimation unit 62 to the user terminal 50 via the wide area communication network 40. The output control unit 63 may transmit the target identification information directly to the user terminal 50, or may transmit the target identification information to a web page or web service on the wide area communication network 40 that is accessible by the user terminal 50.

[0095] The output control unit 63 may be another device that has an output control function and is capable of acquiring the target identification information from the estimation device 60. In this case, the estimation device 60 may transmit the target identification information estimated by the estimation unit 62 via the other device. In one embodiment, the output control unit 63 may further have a function as a communication unit.

[0096] <Storage section 7> The storage unit 7 stores an estimation model 71, a target measurement signal 72, and target identification information 73.

[0097] The estimation model 71 is generated by machine learning using training data that includes sample measurement signals corresponding to the aroma of sample coffee beans as explanatory variables and sample identification information corresponding to the sample coffee beans as a response variable. In this specification, "sample coffee beans" and "target coffee beans" are not particularly limited as long as they are coffee beans that can be selected as the above-mentioned "coffee beans." Furthermore, in this specification, "sample identification information" and "target identification information" are not particularly limited as long as they are information that can be included in the above-mentioned "identification information."

[0098] Data preprocessing and feature extraction may be further performed in the machine learning of the estimation model 71. Furthermore, a machine learning algorithm may be used in the machine learning for generating the estimation model 71.

[0099] Data preprocessing and feature extraction may be further performed in the machine learning of the estimation model 71. Furthermore, a machine learning algorithm may be used in the machine learning for generating the estimation model 71.

[0100] (Feature extraction) The learning data for generating the estimation model 71 by machine learning may be the measured values ​​themselves or may be feature quantities extracted from the measured values. The feature quantities may be, for example, statistics, differential and integral values, peak detection values, or autocorrelation values. Examples of statistical quantities include the mean value, variance, maximum value, minimum value, the difference between the maximum and minimum values, and standard deviation. Examples of differential and integral values ​​include the differential value (the slope of a graph showing the change in measured values ​​over time) and the integral value (the area of ​​the region defined by the curve showing the change in measured values ​​and the horizontal axis (e.g., the time axis) in a graph showing the change in measured values ​​over time). Examples of peak detection values ​​include the number and height of peaks in the change in measured values ​​(e.g., the change over time). Examples of autocorrelation values ​​include the difference in the change in measured values ​​(e.g., the change over time). These feature quantities can be extracted from measured values ​​based on known methods.

[0101] (Pretreatment method) The training data for generating the estimation model 71 by machine learning may be used for machine learning without preprocessing, or may be used after predetermined preprocessing as necessary. Furthermore, if preprocessing is performed, it may be performed before, after, or both before and after the feature extraction. Preprocessing may be performed by known methods. Known methods include correction, noise removal, standardization, data transformation, smoothing, and data expansion. Examples of correction include integration, addition, subtraction, and division using output ratios, independent component analysis (ICA), or statistics based on the measurement results of a standard gas using multiple sensor elements or commercially available sensors (e.g., temperature sensors or humidity sensors). Examples of noise removal include removal of outliers, white noise, and other noise. Examples of standardization include normalization and regularization of features. Examples of data transformation include removal of data trends, frequency transformation, and logarithmic transformation. Examples of smoothing include taking a moving average and difference of data. Examples of data expansion include adding the same sample data (for example, adding data assuming a normal distribution), adding new sample data (for example, adding data related to the mixture ratio of vectors), and the like.

[0102] (machine learning algorithms) Machine learning algorithms that can be used to create the estimation model 71 include regression analysis, classification, trees, time series analysis, neural networks, and clustering. Regression analysis includes, for example, logistic regression, Lasso regression, elastic net regression, support vector regression (SVR), linear regression, ridge regression, and ensemble regression. Classification includes, for example, k-nearest neighbor method, support vector classification (SVC), Naive Bayes classifier, stochastic gradient descent (SGD), and kernel approximation. Trees include, for example, decision trees, regression trees, random forests, boosting (lightGBM, XGboost), and stacking. Time series include, for example, AR, MA, ARIMA, and state space. Examples of neural networks include multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), residual neural networks (ResNets), transformers, and graph neural networks (GNNs). Examples of clustering methods include Gaussian mixture models (GMMs), k-means, mini k-means, variational Gaussian mixture models (VBGMMs), and kernel approximation.

[0103] The target measurement signal 72 includes a measurement signal output from the odor measuring device 30. In one embodiment, the target measurement signal 72 may include information based on measurement results measured by a sensor other than the odor measuring device 30 (e.g., a humidity sensor, a temperature sensor, an air pressure sensor, etc.).

[0104] The target identifying information 73 is information corresponding to the target coffee bean estimated by the estimation unit 62. The target identifying information 73 may include only one type of information, or may include multiple types of information.

[0105] (Processing performed by information processing system 100c) An outline of an information processing method according to one embodiment of the present invention will be described with reference to Fig. 11. Fig. 11 is a flowchart showing an outline of an information processing method by the information processing system 100c.

[0106] In step S11, the acquisition unit 61 acquires a target measurement signal corresponding to the odor of the target coffee beans output by the odor measuring device 30 (acquisition step). The acquisition unit 61 may acquire the target measurement signal 72 from the odor measuring device 30, or may acquire it from a storage medium or the like in which the target measurement signal 72 is saved.

[0107] In step S12, the estimation unit 62 outputs the result of estimating the object identification information 73 based on the object measurement signal 72 (estimation step). The estimation unit 62 may store the output object identification information 73 in the storage unit 2.

[0108] In step S13, the output control unit 63 transmits (outputs) the object identification information 73 to the user terminal 50 via the wide area communication network 40.

[0109] Because the aroma of coffee beans does not necessarily match the analysis results of odorous substances contained in the smell derived from the coffee beans, it has been difficult to estimate the identification information of coffee beans whose brand, etc. is unknown. By using information processing system 100c, estimation unit 72 performs estimation using a sample measurement signal corresponding to the odor of the sample coffee beans and sample identification information corresponding to the sample coffee beans, making it possible to identify information related to the target coffee beans.

[0110] [Software implementation example] In the information processing systems 100, 100a, 100b, and 100c, the control blocks (particularly the control units 1 and 1a) may be realized by logic circuits (hardware) formed on an integrated circuit (IC chip) or the like, or may be realized by software.

[0111] In the latter case, the information processing systems 100, 100a, and 100b each include a computer that executes instructions from a program, which is software that realizes each function. The computer includes, for example, one or more processors and a computer-readable recording medium storing the program. The object of the present invention is achieved when the processor in the computer reads and executes the program from the recording medium. The processor may be, for example, a CPU (Central Processing Unit). The recording medium may be a "non-transitory tangible medium," such as a ROM (Read Only Memory), a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The system may also include a RAM (Random Access Memory) for loading the program. The program may be supplied to the computer via any transmission medium capable of transmitting the program (such as a communication network or broadcast waves). Note that one aspect of the present invention may also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

[0112] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

[0113] 〔summary〕 An information processing system according to a first aspect of the present invention comprises: an acquisition unit that acquires a target measurement signal corresponding to the odor of a target coffee bean from an odor measurement device that measures an odor and outputs a measurement signal; and an estimation unit that estimates target identification information corresponding to the target coffee bean from the acquired target measurement signal using an estimation model, wherein the estimation model is generated by machine learning using training data that includes a sample measurement signal corresponding to the odor of a sample coffee bean as an explanatory variable and sample identification information corresponding to the sample coffee bean as a target variable.

[0114] In the information processing system of aspect 2 of the present invention, in aspect 1, the target identification information and the sample identification information each include information indicating at least one of the origin, brand, and roast level of the target coffee beans and the sample coffee beans.

[0115] In the information processing system according to aspect 3 of the present invention, in the above-mentioned aspect 1 or 2, the target coffee beans and the sample coffee beans are in the form of powdered beans or unground beans.

[0116] In the information processing system according to a fourth aspect of the present invention, in any one of the first to third aspects, the target coffee beans and the sample coffee beans are roasted coffee beans.

[0117] An information processing system according to a fifth aspect of the present invention comprises: a first acquisition unit that acquires classification information classifying a plurality of coffee beans into a plurality of groups based on a plurality of measurement signals corresponding to the odors of a plurality of coffee beans, each having different identification information, output from an odor measuring device that measures odors and outputs measurement signals; a second acquisition unit that acquires subject information that associates a subject's evaluation of the flavor of a coffee beverage extracted from each of a plurality of reference coffee beans classified into at least one of the plurality of groups with the identification information of the reference coffee beans used to extract the coffee beverage; and an identification unit that analyzes the correspondence between the subject information and the classification information and identifies coffee beans from the plurality of coffee beans that can be used to extract a coffee beverage with the flavor desired by the subject.

[0118] An information processing system according to a sixth aspect of the present invention may be the information processing system of the fifth aspect, wherein the plurality of reference coffee beans are coffee beans classified into different groups.

[0119] An information processing system according to aspect 7 of the present invention is the information processing system according to aspect 5 or 6, wherein the subject information may be information based on responses from the subject who has come into contact with the coffee beverage extracted from the reference coffee beans to predetermined questions regarding their evaluation of the flavor of the coffee beverage.

[0120] An information processing system according to aspect 8 of the present invention is the information processing system of any one of aspects 1 to 3, wherein the identification information includes information indicating at least one of the origin, brand, and roast level of each of the reference coffee bean and the plurality of coffee beans.

[0121] An information processing system according to a ninth aspect of the present invention is the information processing system of any one of the first to fourth aspects, wherein the reference coffee beans and the plurality of coffee beans may be in either powdered or unground bean form.

[0122] An information processing system according to a tenth aspect of the present invention is the information processing system of any one of the first to fifth aspects, wherein the reference coffee bean and the plurality of coffee beans are roasted coffee beans.

[0123] An information processing method according to an eleventh aspect of the present invention includes: an acquisition step in which a computer acquires a target measurement signal corresponding to the odor of a target coffee bean, the target measurement signal being output from an odor measurement device; and an estimation step in which the computer estimates target identification information corresponding to the target coffee bean from the acquired target measurement signal using an estimation model, wherein the estimation model is generated by machine learning using training data that includes, as an explanatory variable, a sample measurement signal corresponding to the odor of a sample coffee bean and, as a target variable, sample identification information corresponding to the sample coffee bean. An information processing method according to aspect 12 of the present invention includes: a first acquisition step of acquiring classification information classifying a plurality of coffee beans into a plurality of groups based on a plurality of measurement signals output from an odor measuring device that measures odors and outputs measurement signals, the measurement signals corresponding to the odors of a plurality of coffee beans having different identification information from each other; a second acquisition step of a computer acquiring subject information for each of a plurality of reference coffee beans classified into at least one of the plurality of groups, in which a subject's evaluation of the flavor of a coffee beverage extracted from the reference coffee beans is associated with the identification information corresponding to the reference coffee beans used to extract the coffee beverage; and an identification step of a computer analyzing the correspondence between the subject information and the classification information and identifying coffee beans from the plurality of reference coffee beans that can be used to extract a coffee beverage with the flavor desired by the subject.

[0124] A control program according to aspect 13 of the present invention is a control program for causing a computer to function as the information processing system described in aspects 1 to 4, and is a control program for causing a computer to function as the acquisition unit and the estimation unit.

[0125] The control program according to aspect 14 of the present invention is a control program for causing a computer to function as the information processing system of any one of aspects 5 to 10, and is a control program for causing a computer to function as the first acquisition unit, the second acquisition unit, and the identification unit.

[0126] A recording medium according to a fifteenth aspect of the present invention is a computer-readable recording medium on which the control program according to the thirteenth or fourteenth aspect is recorded. [Explanation of symbols]

[0127] 10, 10a specific equipment 30 Odor measuring device 31 Odor sensor element 40 Wide Area Communication Network 50 User terminal 100, 100a, 100b Information Processing Systems 311 Substrate 313A 1st metal wiring 313B 2nd metal wiring 315 Odorant receptor layer W lead wire S2 First acquisition step S3 Second acquisition step S4 Specific step S11 Acquisition step S12 Estimation step

Claims

1. an acquisition unit that acquires a target measurement signal corresponding to the odor of the target coffee beans from an odor measurement device that measures the odor and outputs a measurement signal; an estimation unit that estimates object identification information corresponding to the object coffee beans from the acquired object measurement signal using an estimation model, the estimation model is generated by machine learning using training data that includes, as an explanatory variable, a sample measurement signal corresponding to the aroma of a sample coffee bean, and includes, as a target variable, sample identification information corresponding to the sample coffee bean; Information processing system.

2. the target identification information and the sample identification information each include information indicating at least one of the origin, brand, and roast level of the target coffee beans and the sample coffee beans, The information processing system according to claim 1 .

3. The target coffee beans and the sample coffee beans are in the form of powdered beans or unground beans. The information processing system according to claim 1 .

4. The information processing system according to claim 1 , wherein the target coffee beans and the sample coffee beans are roasted coffee beans.

5. a first acquisition unit that acquires classification information that classifies the plurality of coffee beans into a plurality of groups based on a plurality of measurement signals that correspond to the odors of a plurality of coffee beans having different identification information, the measurement signals being output from an odor measurement device that measures the odor and outputs measurement signals; a second acquisition unit that acquires, for each of a plurality of reference coffee beans classified into at least one of the plurality of groups, subject information in which a subject's evaluation of the flavor of a coffee beverage extracted from the reference coffee beans is associated with the identification information corresponding to the reference coffee beans used to extract the coffee beverage; an identification unit that analyzes the correspondence between the subject information and the classification information and identifies, from the plurality of reference coffee beans, recommended coffee beans that can extract a coffee beverage with a flavor desired by the subject; Information processing system.

6. Each of the plurality of reference coffee beans is coffee beans classified into a different group. The information processing system according to claim 5 .

7. The subject information includes: the information being based on answers to predetermined questions from the subject who has come into contact with the coffee beverage extracted from the reference coffee beans regarding an evaluation of the flavor of the coffee beverage; The information processing system according to claim 5 .

8. the identification information includes information indicating at least one of the origin, brand, and roast level of each of the reference coffee bean and the plurality of coffee beans; The information processing system according to claim 5 .

9. the reference coffee bean and the plurality of coffee beans are in the form of powdered beans or unground beans; The information processing system according to claim 5 .

10. The information processing system according to claim 5 , wherein the reference coffee bean and the plurality of coffee beans are roasted coffee beans.

11. an acquisition step in which a computer acquires a target measurement signal corresponding to the odor of the target coffee beans output from the odor measuring device; an estimation step in which a computer estimates object identification information corresponding to the object coffee bean from the acquired object measurement signal using an estimation model; the estimation model is generated by machine learning using training data that includes, as an explanatory variable, a sample measurement signal corresponding to the aroma of a sample coffee bean, and includes, as a target variable, sample identification information corresponding to the sample coffee bean; Information processing methods.

12. a first acquisition step in which a computer acquires classification information that classifies the plurality of coffee beans into a plurality of groups based on a plurality of measurement signals output from an odor measurement device that measures odors and outputs measurement signals, the measurement signals corresponding to the odors of a plurality of coffee beans having different identification information from each other; a second acquisition step in which the computer acquires subject information, which associates, for each of a plurality of reference coffee beans classified into at least one of the plurality of groups, the subject's evaluation of the flavor of a coffee beverage extracted from the reference coffee beans with the identification information corresponding to the reference coffee beans used to extract the coffee beverage; an identifying step in which a computer analyzes the correspondence between the subject information and the classification information and identifies, from the plurality of reference coffee beans, coffee beans that can be used to extract a coffee beverage with a flavor desired by the subject; An information processing method, including:

13. A control program for causing a computer to function as the information processing system according to claim 1 , the control program causing the computer to function as the acquisition unit and the estimation unit.

14. 6. A control program for causing a computer to function as the information processing system according to claim 5, the control program causing a computer to function as the first acquisition unit, the second acquisition unit, and the identification unit.

15. A computer-readable recording medium on which the control program according to claim 13 or 14 is recorded.

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