Prior art — US Patent 12236456

Prior Art Analysis for U.S. Patent 12,236,456

This analysis reviews prior art references that are relevant to the claims of U.S. Patent 12,236,456. The analysis focuses on potential anticipation under 35 U.S.C. § 102, which requires that a single prior art reference disclose each and every element of a claimed invention. The key inventive concept of patent 12,236,456, as detailed in independent claims 1 and 11, appears to be the specific feedback loop where a user's interaction with a presented purchase opportunity (an advertisement) is used to build a user profile, and that updated profile is then used to interpret a subsequent natural language utterance from the user.

The following references, which were cited during the prosecution of the patent family, are analyzed for their potential to anticipate the claims.

1. U.S. Patent Application Publication No. 2002/0087326 A1 (Lee et al.)

Full Citation: US 2002/0087326 A1, "Method and apparatus for providing advertisement in a voice-based information search system"
Publication Date: July 4, 2002
Filing Date: December 28, 2000
Brief Description: Lee discloses a system for delivering voice advertisements in response to a user's spoken query to an information system. The system identifies keywords from the user's utterance to retrieve both the requested information and a relevant advertisement. Ad selection can be based on the query's keywords and pre-existing user profile information. The system also describes tracking user responses to advertisements to measure their effectiveness.
Anticipation Analysis (§ 102): This reference does not anticipate independent claims 1 or 11. While Lee teaches receiving a voice utterance, determining context (keywords), selecting and delivering a voice advertisement based on that context, and tracking user interaction, it fails to teach the complete claimed feedback loop. Specifically, Lee does not disclose using the tracked interaction with an advertisement to build or update a user-specific profile that is then used to interpret a subsequent natural language utterance. Lee's tracking is described in the context of measuring aggregate ad effectiveness rather than personalizing the natural language interpretation for a specific user based on their ad interactions.

2. U.S. Patent Application Publication No. 2005/0144068 A1 (Kopra et al.)

Full Citation: US 2005/0144068 A1, "Advertising system for a voice-based services platform"
Publication Date: June 30, 2005
Filing Date: December 23, 2003
Brief Description: Kopra describes a system for presenting targeted advertisements within a voice-services platform, such as directory assistance. The system selects ads based on the user's spoken request, location, and demographic data. Kopra explicitly discloses tracking user interactions with the presented ads (e.g., requests for more information or to be connected to the advertiser) and using this data to "compile a history of the user's preferences" to select more relevant ads in the future.
Anticipation Analysis (§ 102): This reference is highly relevant but does not appear to anticipate claims 1 or 11. Kopra teaches nearly all elements of the claim: receiving an utterance, selecting an ad based on context, delivering the ad, tracking interaction, and updating a user history (profile) based on that interaction. It also teaches using this history to select future ads. However, there is a subtle but critical distinction from the claim language. Claim 1 requires using the updated user profile to "interpreting... a subsequent second natural language utterance." This implies the profile informs the core natural language understanding (NLU) process to determine the meaning of the user's words. Kopra, in contrast, appears to use the user history to select a better ad after the subsequent utterance has already been interpreted. Because Kopra does not explicitly teach using the ad-interaction history to modify the NLU process itself, it fails to anticipate this specific limitation.

3. U.S. Patent No. 7,069,219 B2 (Drucker et al.)

Full Citation: US 7,069,219 B2, "System and method for dynamically generating a statistical model for use in a natural language processing system"
Issue Date: June 27, 2006
Filing Date: May 29, 2001
Brief Description: Drucker discloses a natural language processing system that learns from user interactions to improve its performance. The system updates its underlying statistical models based on user feedback. For example, when an utterance is ambiguous, the system can provide clarification options; the user's choice is then used to retrain the model, making future interpretations more accurate. This describes a feedback loop for improving the core interpretation of language.
Anticipation Analysis (§ 102): This reference does not anticipate claims 1 or 11. While Drucker provides a strong teaching for the concept of tracking user interaction to update a model (profile) and using that updated model to interpret subsequent utterances, it is missing the entire advertising context. Drucker does not disclose selecting, delivering, or tracking interactions with "purchase opportunities." The feedback mechanism it describes is based on a user clarifying the meaning of their own utterance, not on their interaction with a commercial advertisement. Therefore, it fails to teach key limitations of the claims.