Drinks Machine Backend Voice Recognition Segmentation
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Solution Overview
Problem
Current voice recognition systems for drinks preparation machines, such as Alexa, struggle with accurately recognizing individualized recipe names due to variations in pronunciation and lack the ability to store unique recipe names for third-party providers, leading to unreliable voice command recognition and limited customization options.
Innovation Solution
A method that uses a machine backend to manage and store individualized preparation prescripts with unique identifiers, allowing voice recognition systems to process a limited set of common identifiers, increasing recognition reliability by differentiating between distinct voice commands and enabling users to access personalized recipes across multiple machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If voice recognition systems process a large number of individualized recipe names, then customization options increase, but recognition reliability decreases due to pronunciation variations and command differentiation difficulties
Solution Approach 1:
The patent segments the recipe identification process into two levels: generic identifiers (processed by voice recognition) and individualized identifiers (stored locally). This segmentation allows the system to maintain high recognition reliability at the generic level while enabling customization through local storage, resolving the contradiction between versatility and reliability.
Solution Approach 2:
The patent introduces an intermediary layer (local storage device) between the voice recognition system and the recipe database. This intermediary stores individualized recipe names and maps them to generic identifiers, allowing the voice system to work with a limited set of reliable commands while still providing extensive customization through the intermediary layer.
2Adaptability or versatility
If voice recognition systems store unique recipe names for third-party providers, then individualized recipe management improves, but system complexity increases beyond current infrastructure capabilities
Solution Approach 1:
The patent divides the recipe management system into centralized (generic identifiers) and decentralized (individualized identifiers) components. This segmentation allows third-party providers to implement individualized recipe management using simple local storage devices without requiring complex changes to the central voice recognition infrastructure, thus increasing versatility while controlling system complexity.
Solution Approach 2:
The patent enables individualized recipe management through self-service local storage devices that automatically map generic identifiers to user-specific recipe names. This self-service approach allows users to manage their own customized recipes without requiring complex centralized management infrastructure, resolving the contradiction between versatility and complexity.
3Reliability
If a limited set of common identifiers is used for voice commands, then recognition reliability increases, but the ability to access personalized recipes decreases
Solution Approach 1:
The patent segments identifier functionality into generic identifiers for voice recognition (ensuring reliability) and individualized identifiers stored locally (enabling personalization). The local storage device acts as a translation layer that maps reliable generic commands to personalized recipe names, allowing the system to simultaneously achieve high recognition reliability and extensive personalized access.
Solution Approach 2:
The patent uses copying by storing multiple representations of recipe identifiers: generic identifiers in the voice recognition system and individualized copies in local storage. This copying approach allows the limited set of generic identifiers to reliably trigger voice recognition while the copied individualized identifiers provide personalized recipe access, resolving the contradiction between reliability and versatility.
Data Source
AI summary
A method for producing a product by way of a drinks preparation machine, including the steps of, by way of a machine backend for a drinks preparation machine, receiving a backend order. The backend order includes an identifier for the identification of a preparation prescript, as well as a configuration identifier. A preparation prescript is determined by way of determining, in the case that the identifier is the same as an individual identifier, a preparation prescript that in one of several user profiles is assigned to the individual identifier. The user profile is determined in accordance with the configuration identifier. By way of the machine backend, a machine order is generated to the drinks preparation machine, wherein the machine order specifies the preparation prescript. By way of the drinks preparation machine, the product is produced in accordance with the preparation prescript.