Web FAQ Data Transformation for Speech Interface Playback
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Solution Overview
Problem
Current approaches fail to effectively transform graphical and textual web content into a suitable format for real-time playback in speech applications, overloading users' short-term memory with complex instructions and failing to account for the differences between web and telephone interfaces.
Innovation Solution
A method involving an automatic transformation module that classifies FAQs into complexity levels (simple, medium, complex, complex-complex) by parsing web data into speech acts, renumbering steps, and preserving causality, allowing for streamlined playback suitable for human short-term memory and telephone interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If web FAQs are directly fetched and played back in speech applications, then the content is available in real-time, but the user interface compatibility deteriorates due to fundamental differences between web and telephone interfaces
Solution Approach 1:
The patent introduces a transformation module as an intermediary between web data and speech application playback. This module converts web-oriented FAQ content into speech-appropriate formats by parsing HTML/XML structures, extracting relevant text, and reformulating instructions for auditory consumption, thereby resolving the interface incompatibility while maintaining real-time playback capability
Solution Approach 2:
The transformation module changes the parameters of FAQ content by converting visual/web-oriented formatting into speech-appropriate structures. This includes transforming graphical content references into text descriptions, breaking down complex multi-action instructions into sequential speech commands, and adjusting the granularity of information presentation to suit auditory processing
2Adaptability or versatility
If graphical content in FAQs is translated into text for speech playback, then the content becomes accessible over telephone, but the translation accuracy deteriorates due to loss of visual context
Solution Approach 1:
The transformation module creates a textual copy of graphical content by extracting and describing visual elements from web pages. It parses HTML structures to identify graphical content and generates corresponding text descriptions that preserve the essential information, enabling telephone accessibility while maintaining content fidelity through structured extraction rather than simple conversion
3Ease of manufacture
If multiple actions or conjunctions in FAQ instructions are kept as single steps, then the original web format is preserved, but the user's short term memory is overloaded during speech playback
Solution Approach 1:
The transformation module segments complex FAQ instructions into smaller, manageable speech steps. It parses multi-action instructions like 'hold down the action key and then page down' and separates them into distinct sequential commands, reducing cognitive load on users' short-term memory while maintaining the original instructional intent and format structure
4Loss of information
If lengthy lists of instructions are played back in speech applications, then complete information is provided, but the user's short term memory is excessively tasked making problem resolution difficult
Solution Approach 1:
The transformation module divides lengthy instruction lists into segmented, modular speech segments that can be processed incrementally. It structures long FAQs as sequences of manageable steps with clear transitions, allowing users to retain information in short-term memory longer and follow complex procedures without being overwhelmed by the full list presented at once
Solution Approach 2:
The module implements periodic pauses and repetitions in the speech playback structure, breaking continuous lengthy instructions into periodic segments. This allows users to process and act on information in intervals rather than attempting to hold entire lengthy lists in memory simultaneously, facilitating better problem resolution while maintaining information completeness
Data Source
AI summary
In one example, this invention presents a method of providing the same self-service content that is available on the web interface to users contacting by telephone, knowing that the web and telephone are fundamentally different user interfaces. In one embodiment, this patent seeks to protect the general idea of how to playback web data in real-time to the user over the speech interface. For this purpose, a method is presented comprising of the general steps through which the web data is initially sent to an automatic transformation module. Then, that transformation module refines or re-structures the web data to make it suitable for the speech interface. The algorithm in the module is predicated on the user interface principles of cognitive complexity and limitations on short term memory based on which FAQ types are classified into one of the following four classes: simple, medium, complex, and complex-complex.