Obviousness

Obviousness Analysis under 35 U.S.C. § 103 for US11080758

This analysis examines whether the claims of US Patent 11080758, which has a priority date of February 6, 2007, would have been obvious to a person having ordinary skill in the art (POSITA) at that time, based on the prior art references explicitly cited within the patent itself.

The Person Having Ordinary Skill in the Art (POSITA)

A POSITA in the field of voice user interfaces, natural language processing (NLP), and advertising around February 2007 would possess knowledge of:

• Speech recognition technologies, including phonetic dictation and dictionary/phrase tables.
• Natural language understanding, context determination, and dialogue management in conversational systems.
• The use of user profiles and environmental models to enhance system interpretation and response generation.
• Online advertising models, including targeted advertisement selection based on keywords, demographics, and user behavior.
• E-commerce functionalities, such as facilitating online transactions and purchase opportunities.
• Techniques for tracking user interactions with digital content, particularly advertisements (e.g., click-through rates, conversions), and using this data to refine targeting and user profiles.

Identified Prior Art References

The US11080758 patent itself incorporates by reference and describes the use of several earlier-filed U.S. patents and patent applications, which serve as foundational prior art for the claimed invention. These include:

Core NLP, Speech Recognition, and Conversational Interface Systems:

• U.S. Pat. No. 7,398,209 (originally U.S. patent application Ser. No. 10/452,147, filed Jun. 3, 2003) entitled “Systems and Methods for Responding to Natural Language Speech Utterance.” This patent teaches a system (System 100) including a speech recognition engine (Automatic Speech Recognizer 110) that recognizes words and phrases, dynamically updates probabilities, and provides interpretations to a conversational language processor (120). The conversational language processor includes a voice search engine (125), context determination module (130), and agents (135) for cooperative, conversational interaction. It also covers the use of user profiles and environmental models.
• U.S. Pat. No. 7,693,720 (originally U.S. patent application Ser. No. 10/618,633, filed Jun. 15, 2003) entitled “Mobile Systems and Methods for Responding to Natural Language Speech Utterance.” This patent likely extends the concepts of 7,398,209 to mobile contexts.
• U.S. Pat. No. 7,640,160 (originally U.S. patent application Ser. No. 11/197,504, filed Aug. 5, 2005) and U.S. Pat. No. 7,949,529 (originally U.S. patent application Ser. No. 11/212,693, filed Aug. 29, 2005), both teaching context-based interpretations and responses to natural language speech.
• U.S. Pat. No. 7,620,549 (originally U.S. patent application Ser. No. 11/200,164, filed Aug. 10, 2005) for adaptive misrecognition.
• U.S. Pat. No. 8,073,681 (originally U.S. patent application Ser. No. 11/580,926, filed Oct. 16, 2006) for a cooperative conversational voice user interface.

These references collectively establish that the fundamental components of receiving natural language voice input, performing speech recognition, interpreting utterances, determining context, maintaining user profiles, and engaging in conversational interaction were known in the art prior to the '758 patent's priority date.

Obviousness Rationale and Combination of Prior Art

The independent claims (e.g., Claim 1 and Claim 23) of US11080758 describe a method and system that essentially integrates these advanced natural language processing and voice interaction capabilities with the selection, delivery, and tracking of "purchase opportunities" (a form of targeted advertisement), and uses user interaction data to refine future selections.

A combination of the NLP/voice UI systems taught by U.S. Pat. No. 7,398,209 (or its broader family of related patents) with the general knowledge and existing practices in targeted advertising and e-commerce would have rendered the claims of US11080758 obvious to a POSITA.

Motivation to Combine:

The '758 patent's own "Background of the Invention" section explicitly articulates the problems that a POSITA would be motivated to solve:

1. Complexity of human-to-machine interfaces: The background highlights that increased functionality in devices makes them difficult to use, leading to users abandoning simple tasks like purchasing a ringtone due to complex menu navigation. This presents a clear motivation to streamline interactions, particularly for commercial activities, using intuitive voice commands.
2. Limitations of existing voice user interfaces: The patent states that many existing voice UIs require users to memorize specific syntaxes or keywords and fail to engage users in productive, cooperative dialogue. This points to a need for more natural and flexible voice interactions, especially when guiding users toward transactions.
3. Missed marketing opportunities: The background explicitly identifies a "lack of adequate voice user interfaces" leading to "missed opportunities for providing valuable and relevant information to users" and that "providers of goods and services may lose out on potential business." It further states that "existing techniques for marketing, advertising... fail to effectively utilize voice-based information." These statements directly provide a compelling commercial and technical motivation for combining sophisticated voice user interfaces with advertising and e-commerce functionalities.

How a POSITA Would Combine the Elements:

Given the identified problems and motivations, a POSITA would find it obvious to:

1. Integrate "purchase opportunity" selection into the NLP system: U.S. Pat. No. 7,398,209 provides the framework for receiving a natural language utterance, recognizing words/phrases, and determining context. A POSITA, aware of the desire to monetize these interactions (as highlighted in the '758 patent's background), would naturally integrate a module (e.g., "advertising application 160" or "electronic commerce application 170" as mentioned in '758, or a similar known e-commerce module) to select a "purchase opportunity" based on the context determined by the NLP system. If a user asks about a product, offering a way to buy it would be a logical extension.
2. Leverage existing user profiles for targeted delivery: U.S. Pat. No. 7,398,209 already teaches the use of "user profiles and preferences" for dynamically updating speech recognition. In the context of advertising, it was well-known in 2007 to use user demographic data and preferences for targeting advertisements. Combining these two known uses of user profiles for more precise advertisement selection would be a predictable application of known techniques.
3. Implement interaction tracking: Tracking user engagement (e.g., click-throughs, conversions, purchases) with advertisements was a standard practice in online advertising long before 2007. Extending this tracking to interactions with voice-presented or displayed "purchase opportunities" (e.g., "tracking module 255" in '758) would be a straightforward implementation for a POSITA seeking to measure and optimize the effectiveness of these opportunities.
4. Use tracked interactions to update user profiles and refine future selections: The concept of a feedback loop, where user behavior (like interacting with an ad or completing a transaction) informs and refines future personalized recommendations or ad selections, was also a well-established practice in information retrieval and advertising systems by 2007. The '758 patent explicitly states that "tracking user interaction with advertisements may be used to build user-specific and/or global statistical profiles... Accordingly, the tracking information may be used... to improve subsequent performance and relevance of advertisements selected." This is a predictable outcome of combining known tracking and profiling techniques.
5. Utilize updated profiles for subsequent utterance interpretation: The continuous refinement of user profiles based on ad interactions, and then using these enhanced profiles to better interpret subsequent natural language utterances and select even more relevant purchase opportunities, represents a logical and obvious feedback mechanism to improve the overall user experience and monetization potential.

Conclusion

Therefore, a POSITA, motivated by the stated problems of inadequate voice user interfaces and missed advertising opportunities, would have found it obvious to combine the natural language processing, speech recognition, and conversational interaction capabilities taught by prior art such as U.S. Pat. No. 7,398,209, with the well-known principles and techniques of targeted advertising, e-commerce, and user behavior tracking. The result of such a combination, encompassing the elements of selecting and delivering purchase opportunities, tracking user interactions, building user profiles based on these interactions, and using these profiles to interpret subsequent utterances for further ad selection, would have been a predictable improvement to existing systems for addressing the identified deficiencies.