Obviousness

A person having ordinary skill in the art (POSA) in the field of conversational speech systems, as of the August 10, 2005 priority date of US8620659, would have been familiar with fundamental technologies and concepts relating to user interaction, natural language processing, speech recognition, and context management. The "Prior art keywords" provided in the patent (user, natural language, request, context, speech) represent these generally known concepts in the field. The patent itself acknowledges the state of the art, stating that "Speech recognition has steadily improved in accuracy and today is successfully used in a wide range of applications. Natural language processing has been applied to the parsing of speech queries." However, it notes that "current systems do not reliably provide a complete environment for users to submit speech and/or non-speech communications through natural language queries that are processed to provide natural responses." This background clearly establishes the problem of unreliability and the motivation to improve naturalness in conversational interfaces.

A POSA, motivated to overcome these acknowledged "significant barriers" and enhance the reliability and naturalness of human-machine conversational interfaces, would have been incentivized to combine and adapt these known concepts to create systems capable of learning from errors.

Combinations of Prior Art Concepts and Motivation for Obviousness:

1. Combination of "Speech," "Natural Language," and "User" for Request Processing with Error Detection:

   • Prior Art Concepts: A POSA would begin with systems that receive a "user's" "request" through "speech" input, and employ "natural language" processing to interpret these requests.
   • Motivation: Given the inherent inaccuracies of speech recognition and natural language processing, particularly in conversational settings, a POSA would be motivated to detect when the system makes an error to prevent user frustration and improve overall performance. The patent highlights the problem that "verbal communications and machine processing of requests... may be fundamentally incompatible."
   • Obvious Step: It would be obvious for a POSA to identify system "misrecognitions" by observing immediate "user" feedback, such as a user overriding a command, repeating the original request, or issuing a stop command. These are natural human reactions to system errors and provide direct signals for error detection.

2. Combination of "Context," "User," and Misrecognition Analysis for Personalized Adaptation:

   • Prior Art Concepts: The importance of "context" and "user" profiles (or "personalized cognitive models" as an advanced form thereof) for tailoring interactions and disambiguation would be known to a POSA. "Cognitive research on human interaction shows that verbal communication... typically relies heavily on context and domain knowledge of the target person."
   • Motivation: To create a more reliable and "natural environment" by adapting the system to individual user patterns and improving interpretation based on past interactions.
   • Obvious Step: Upon detecting "misrecognitions" from a "user," a POSA would be motivated to analyze these events statistically (e.g., determining the "frequency occurrence of misrecognitions for particular commands") and use this analysis to "update the corresponding personalized cognitive model" for that specific "user." This personalized learning would leverage user-specific "context" to refine future interpretations.

3. Combination of "Speech" and Misrecognition Analysis for System Tuning:

   • Prior Art Concepts: The underlying "speech" recognition engine is a critical component prone to errors.
   • Motivation: To directly address the root causes of misrecognition within the core speech processing components to achieve overall system improvement.
   • Obvious Step: A POSA would find it obvious to use the collected and analyzed "misrecognition" data to "determine personalized tuning parameters for the speech recognition components of the system." For example, if the system consistently misrecognizes a specific utterance from a user, the speech recognition engine could be adaptively tuned (e.g., by adjusting acoustic models or grammar weights for that user) to improve future recognition accuracy for that particular user's speech.

4. Combination of "Natural Language," "Context," and Personalized Models for Proactive Disambiguation:

   • Prior Art Concepts: Systems for understanding "natural language" "requests" use "context" to resolve ambiguities.
   • Motivation: To make the conversational interface more intelligent and efficient by reducing the need for explicit clarification from the user when inputs are ambiguous or uncertain.
   • Obvious Step: With an updated "personalized cognitive model" reflecting learned patterns of user interaction and misrecognition, a POSA would logically enable the conversational speech analyzer to "proactively select a next best (or nth best) match" for a received "natural language" input. This uses the enhanced "context" and user-specific knowledge to make more informed decisions, thereby improving the overall robustness and naturalness of the system.

In conclusion, the adaptive misrecognition analysis engine and its associated functionalities, as broadly described in US8620659, would have been an obvious combination of known concepts (user, natural language, request, context, speech) for a person having ordinary skill in the art at the time of the invention. The motivation would stem from the recognized need to improve the reliability and naturalness of conversational human-machine interfaces by learning from system errors and adapting to individual user behaviors, a common problem-solving approach in the field of intelligent systems and human-computer interaction.