PROVIDING BLENDED CONSUMER GOODS

Abstract

A method for providing blended consumer goods to a user by using a blending device (BD) that is adapted to store at least two starting goods blendable into a blended consumer good comprises: recording (REC) selection data concerning selections of the blended consumer goods by the user; recording response data provided by the user concerning responses to the selected blended consumer goods; and processing (COD1-COD5, PP, DA) the data to prioritize presentation (SP, AUI) of blended consumer goods on a user interface (BD, UCD) for selection by the user. A data processing device (DPD, BD, UCD) comprises a data processing unit and a data storage (DB) for storing the recorded data and/or the processed data, wherein the data processing device is adapted to perform the method. A system comprises the data processing device and at least one blending device. The invention is particularly useful for blending fragrances, food, or beverages.

Claims

1. A method for providing blended consumer goods to a user by using a blending device (BD) that is adapted to store at least two starting goods blendable into a blended consumer good, comprising: recording (REC) selection data concerning selections of the blended consumer goods by the user; recording (REC) response data provided by the user concerning responses to the selected blended consumer goods; and processing (COD1-COD5, PP, DA) the data to prioritize presentation (SP, AUI) of blended consumer goods on a user interface (BD, UCD) for selection by the user.

2. The method according to claim 1, wherein the blended consumer goods are blended from the same types of consumer goods, including fragrances, food or beverages.

3. The method according to claim 1, wherein the selection data comprise data concerning at least three out of the group comprising: ingredients of the selected blended consumer goods; pre-mixed ingredient mixes of the selected blended consumer goods; blending data used for blending the selected blended consumer goods; personal background data attached to the selected blended consumer goods.

4. The method according to claim 3, wherein the data concerning the ingredients, the pre-mixed ingredient mixes and/or the blending data are received from at least one blending device (BD) adapted to dispense the blended consumer goods based on selections of ingredients and/or pre-mixed ingredient mixes made by the user.

5. The method according to claim 1, wherein the selection data are processed into predictor variables for statistical analysis (DA); and the response data are processed into predicted variables for (DA) statistical analysis.

6. The method according to claim 5, wherein the selection data are grouped into matrices and the response data are grouped into vectors.

7. The method according to claim 5, wherein the statistical analysis (DA) is performed as a statistical learning algorithm based on the predictor variables and/or the predicted variables; and the presentation of blended consumer goods on the user interface (BD, UCD) is changed based on an outcome of the statistical learning algorithm.

8. The method according to claim 1, wherein selection data and/or response data of other users are processed to prioritize presentation of blended consumer goods on a user interface (BD, UCD) for selection by the user.

9. A data processing device (DPD, BD, UCD) comprising a data processing unit and a data storage (DB) for storing the selection data, the response data and/or the processed data, wherein the data processing device (DPD, BD, UCD) is adapted to perform the method according to claim 1.

10. A system (DPD, BD, UCD), comprising: the data processing device (DBD, BD, UCD), comprising a data processing unit and a data storage (DB) for storing the selection data, the response data and/or the processed data, wherein the data processing device (DPD, BD, UCD) is adapted to perform the method according to claim 4, and at least one blending device (BD) adapted to dispense the blended consumer goods based on selections of ingredients and/or pre-mixed ingredient mixes made by the user, wherein the data processing device (DPD, BD, UCD) and the at least one blending device (BD) are communicatively coupled such that the data concerning the ingredients, the pre-mixed ingredient mixes and/or the blending data are receivable from the at least one blending device; the data processing device (DPD, BD, UCD) is communicatively coupled to a network to receive the response data.

11. The system (DPD, BD, UCD) according to claim 10, wherein the data processing device is adapted to transfer personalized information regarding presentation of blended consumer goods to the user interface (BD, UCD) of a respective user.

Description

[0106] FIG. 1 shows a schematic diagram depicting the method and associated devices.

[0107] The method makes use of several sources of information SOU1 to SOU4 to record REC selection data and response data associated with a blending process to produce a blend from at least two starting consumer goods. These sources SOU1 to SOU4 may include:

[0108] At least one first source of information SOU1 may provide ingredient data concerning ingredients selectable or selected to be introduced into the blend by a blending device BD. The at least one first source of information SOU1 may provide an ingredients' ID code (e.g. its CAS number), lower and/or upper known concentration limits fixed for legal or technical reasons, function(s), qualitative descriptor(s), known non-affinity with other ingredients etc. as the ingredient data or features.

[0109] At least one second source of information SOU2 may provide data concerning containers to be used together with the blending device BD. Regarding the containers, the following data/features may be recorded: batch number, serial number, qualitative or technical descriptors of pre-mixed mixes of ingredients inside the containers, other data concerning ingredient mixes etc. The second source of information SOU2 may also provide ingredient data. Thus, the first source of information SOU1 and the second source of information SOU2 may be the same source, e.g. containers being labelled accordingly.

[0110] At least one third source of information SOU3 may provide data concerning at least one blending device BD, including a blending process. In this regard, the third source of information SOU3 may provide respective blending data. In particular, the third source of information SOU3 may comprise information/data about: containers used together, amounts (e.g. in ml, grams or as a percentage) used from each container to produce a blend, log-in and log-off times and profile, an IP address of the router through which the mobile device and/or machine is connected to the system (in particular a system that comprises the co-ordination entity), features of the final blend produced, amount produced of the final blend, etc. The third source of information SOU3 may be the blending device BD.

[0111] At least one fourth source of information SOU4 may provide data associated with at least one user, e.g. personal background data lake a user's social profile and/or consumer profile or response data like likes or don't likes of resulting blends produced or to be produced, ratings of resulting blends produced or to be produced etc. The at least one fourth source of information SOU4 may comprise the user interface and/or social networks. Thus, the fourth source of information SOU4 may provide background data associated with a certain user and/or response data associated with at least this user.

[0112] The blends produced by this method may in particular be fragrances, food, or beverages.

[0113] The recorded data may be stored in a database DB. The database DB may be part of a data processing device DPD like the blending device BD, a user's computing device UCD, a remote server, and/or the cloud etc.

[0114] In one variant, the first to third sources of information SOU1, SOU2, SOU3 are the part of at least one blending device BD, e.g. the blending device BD and its containers. To this effect, the blending device BD may read the data associated with the first to third sources of information SOU1, SOU2, SOU3 and transfer these data to the database DB. For example, the blending device BD may transfer relevant data from the first to third sources of information SOU1, SOU2, SOU3 to the database DB after having finished a blending process. Alternatively, the data processing device DPD may poll or prompt the blending device BD to transfer the data, e.g. after expiry of a certain time interval.

[0115] The fourth source of information SOU4 may comprise the user interface of the bending device BD (i.e., the blending device BD itself), the user's computing device UCDin particular if also providing the user interfaceand/or social networks, in particular independent of dedicated computing devices. Thus, in a variant, the blending device BD may also transfer data of the fourth source of information SOU4 to the database DB after having finished a blending process, e.g. transfer a user's background data like a user's ID. In particular, the blending device BD is also the fourth source of information SOU4.

[0116] The data stored in the database DB may be coded according to pre-defined coding schemes. In particular, the method may perform or execute coding schemes or algorithms COD1 to COD5 according to the type or group of data stored in the database DB. For example, ingredient data may be coded (converted) into numerical values by a coding algorithm COD1. Data describing containers and mix of ingredients in common containers, respectively, may be coded into numerical values by a coding algorithm COD2. Blending data may be coded into numerical values by a coding algorithm COD3. Personal background data may be coded into numerical values by a coding algorithm COD4. Response data may be coded into numerical values by a coding algorithm COD5. The coding may be performed by the data processing device DPD.

[0117] The coded data may be grouped into predictor variables and predicted variables. In particular, the selection data (ingredient data, data describing containers and pre-mixed ingredient mixes in common containers, respectively, blending data and personal background data) are processed into predictor variables while the response data are processed into predicted variables. The selection data may be arranged in one or more matrices while the response data may be arranged in one or more vectors. The arranging may be performed by the data processing device DPD.

[0118] The coded data may be pre-processed in a pre-processing step PP performing, e.g. cleansing, integrating, and/or aggregating etc. the data. The pre-processing may be performed by the data processing device DPD.

[0119] Then, a statistical data analysis DA may be performed. The statistical data analysis DA may include processing the pre-processed data to perform a statistical learning algorithm based on the predictor variables and the predicted variables in order to improve/prioritize the presentation of blended consumer goods on the user interface based on an outcome of the statistical learning algorithm. The statistical data analysis DA may be performed by the data processing device DPD.

[0120] The statistical data analysis DA may comprise statistical supervised and unsupervised learning to make predictions, e.g., on users' responses (corresponding to predicted variables), inference on patterns and relationships between predictor variables) or combinations of both to provide results, e.g. in the following three areas:

[0121] Business intelligence, BI: [0122] Inference of new combinations of ingredients with a high probability to be liked by users; [0123] Dynamic clustering of users by social features and preferences; and/or [0124] Sales, ratings and usage of combination of ingredients, recipes and resulting blends.

[0125] Smart proposals, SP: [0126] Recipes and resulting blends having a high probability to be liked by the user may be presented to the user via the user interface; [0127] Guidance or tips to combine certain ingredients, resulting in final blends with a high probability to be liked by the user.

[0128] Adaptive user interface, AUI: [0129] The user interface may learn from user feedback and may adopt descriptors to be used on the user interface for describing blends of ingredients and resulting blends most useful or meaningful for the user.

[0130] All three areas, but in particular the smart proposals and the adaptive user interface areas, can be used to improve/prioritize the presentation of blended consumer goods on the user interface.

[0131] Of course, the invention is not restricted to the described embodiments.

LIST OF REFERENCE SIGNS

[0132] AUI Adaptive user interface [0133] BI Business intelligence [0134] BD Blending device [0135] COD1 Coding algorithm [0136] COD2 Coding algorithm [0137] COD3 Coding algorithm [0138] COD4 Coding algorithm [0139] COD5 Coding algorithm [0140] DA Statistical data analysis [0141] DB Database [0142] DPD Data processing device [0143] PP Pre-processing step [0144] REC Recording step [0145] SOU1 First source of information [0146] SOU2 Second source of information [0147] SOU3 Third source of information [0148] SOU4 Fourth source of information [0149] SP Smart proposals [0150] UCD User's computing device