US 11080758

Here is a concise summary of US patent 11080758:

US Patent 11080758 Summary

• Title: System and method for delivering targeted advertisements and/or providing natural language processing based on advertisements
• Assignee: VB Assets LLC
• Inventors: Tom Freeman, Mike Kennewick
• Filing Date: November 19, 2018
• Issue Date: August 3, 2021
• Abstract: The patent describes a system and method utilizing natural language models for delivering targeted advertisements and/or natural language processing based on advertisements. It involves providing an advertisement for a product or service, receiving a user's natural language utterance, interpreting that utterance based on the advertisement, and determining if any pronouns in the utterance refer to the product, service, or its provider.

Plain-Language Overview of Independent Claims:

• Independent Claim 1 (Method): This claim describes a computer-implemented method involving several steps:

  1. A natural language voice input (utterance) is fed into a speech recognition engine.
  2. Words and phrases are recognized from this voice input by the engine.
  3. A context for the voice input is determined based on these recognized words and phrases.
  4. A "purchase opportunity" (an advertisement or offering to buy) is selected based on this determined context.
  5. This selected purchase opportunity is delivered to a user's electronic device.
  6. The system tracks how the user interacts with this delivered purchase opportunity, specifically noting if the device completes a transaction related to it.
  7. A personalized profile for the user is created or updated based on this tracked interaction.
  8. Finally, this updated user profile is used to interpret any future natural language voice inputs from the user, which then helps in selecting subsequent purchase opportunities.

• Independent Claim 23 (System): This claim describes a system that includes one or more physical processors. These processors are specifically programmed with computer instructions to perform all the steps outlined in Independent Claim 1. Essentially, it covers the hardware and software configuration designed to execute the method described in Claim 1.

Litigation Status (as of April 26, 2026):
While the specific patent number US11080758 did not appear in the CAFC 2026 dockets searched, the Google Patents record for US11080758 indicates that the patent family is involved in litigation. This includes:

• A PTAB case, IPR2025-01240, which was filed but "Not Instituted - Procedural".
• A US case filed in the Delaware District Court (Case number 1:24-cv-00839).
• The first worldwide family litigation has been filed.

Known litigation involving US patent 11080758:

1. District Court Case:

   • Plaintiff(s): VB Assets, LLC
   • Defendant(s): Amazon.com Services, LLC
   • Jurisdiction: Delaware District Court
   • Case Number: 1:24-cv-00839
   • Filing Date: July 18, 2024
   • Outcome or Current Status: Complaint for Patent Infringement (Active/Pending).

2. PTAB Case:

   • Case Number: IPR2025-01240
   • Outcome or Current Status: Not Instituted - Procedural. The specific petitioner, patent owner, and filing date for this IPR were not explicitly found in the provided search results.

Proceedings overview

US Patent 11080758 has been the subject of one AIA trial proceeding, IPR2025-01240, which resulted in a discretionary denial of institution. This means the patent claims were not substantively reviewed, and all claims of the patent remain untested and are therefore sustained. This outcome generally strengthens the patent owner's position as it indicates the PTAB declined to proceed with the challenge.

IPR2025-01240 — Amazon.com Services LLC v. VB Assets, LLC

• Type: Inter Partes Review
• Filed: 2025-07-01
• Status: Discretionary Denial. The petition was denied institution by the Director of the USPTO.
• Judge panel: John A. Squires, Under Secretary of Commerce for Intellectual Property and Director of the United States Patent and Trademark Office. During this period (October-November 2025), Director Squires assumed personal control over all IPR institution decisions and frequently issued summary denials.
• Petition grounds: Details regarding the specific claims challenged, prior art references, and statutory bases (§ 102 / § 103 / § 112) for the petition were not provided in the summary notice of denial.
• Institution decision: Denied on 2025-11-20. Director Squires' policy involved issuing summary notices for institution decisions, often without detailed reasoning for denials, especially for routine cases. The denial was procedural, indicating it was based on discretionary factors rather than a full review of the merits.
• Final Written Decision: Not applicable, as institution was denied.
• Settlement / termination: Not applicable.
• Appeal: Institution decisions are generally final and non-appealable.
• Defensive value: The discretionary denial means that the claims of US11080758 were not invalidated by this IPR. For a defendant facing assertion of this patent, it signals that an IPR challenge by Amazon.com Services LLC (or its privies) on the same grounds may be difficult due to potential estoppel or the continued application of the Director's discretionary denial policies.

Strategic summary

All claims of US11080758 remain UNTESTED by the PTAB. The single IPR filed against this patent, IPR2025-01240, was denied institution on discretionary grounds by the USPTO Director, meaning no substantive review of the claims occurred. Therefore, the patent has not been narrowed through IPR, and all claims as granted are currently intact.

Regarding the estoppel landscape, as institution was denied, the petitioner (Amazon.com Services LLC) and its privies would likely be barred under 35 U.S.C. § 315(e)(2) from raising any ground that was raised or reasonably could have been raised in IPR2025-01240 in future district court or ITC proceedings. However, because the denial was discretionary and likely a summary notice without detailed reasoning, the exact scope of grounds that "could have been raised" and are therefore estopped might be less clear compared to a merits-based denial. Other prior-art grounds not presented or not reasonably presentable in the denied petition might still be available to other potential defendants or even to Amazon.com Services LLC if it can demonstrate distinct grounds or circumstances.

A pattern signal observed is that this IPR was denied institution during a period (late 2025) where USPTO Director John Squires had centralized control over institution decisions and was frequently issuing summary denials without detailed explanations, often resulting in a significantly lower institution rate. The petitioner, Amazon.com Services LLC, has been involved in numerous PTAB cases, both as petitioner and patent owner, with multiple petitions against VB Assets LLC patents, many of which also resulted in procedural denials around the same time period as IPR2025-01240.

Recommended next steps

The discretionary denial of IPR2025-01240 means all claims of US11080758 are still presumed valid and have not been challenged on the merits at the PTAB.

For a defendant facing assertion of this patent:

• Given the discretionary denial, a direct re-filing of an IPR by Amazon.com Services LLC (or its privies) on the same or substantially similar grounds as IPR2025-01240 would face significant hurdles due to the non-appealable nature of institution decisions and potential estoppel.
• Investigate the specific arguments and prior art presented in IPR2025-01240, if publicly accessible, to understand the scope of what was "raised or reasonably could have been raised" and thus potentially estopped. While the denial was summary, the petition itself would contain these details.
• Consider new prior art or different invalidity theories not covered by the denied petition, which could form the basis of a new IPR challenge by a different petitioner, or potentially even by Amazon if the grounds are sufficiently distinct from those deemed "could have been raised."
• Understand the context of the Director's policy on discretionary denials around late 2025. This policy shift made institution more difficult, and a new petition would need to consider how to overcome such discretionary factors.

Inventors

• Tom Freeman (Voicebox Technologies, Inc.)
• Mike Kennewick (Voicebox Technologies, Inc.)

No unusual patterns observed regarding inventor departure.

Original assignee

The original assignee, as per the filing of US11080758, is VB Assets LLC. However, the priority date traces back to an application filed by Voicebox Technologies, Inc. (U.S. Pat. No. 7,818,176, filed Feb. 6, 2007). Voicebox Technologies Corporation was known for its natural language understanding and speech recognition software, particularly for embedded devices and automotive applications. It is unclear if VB Assets LLC directly shipped a product embodying the claims of this specific patent. Voicebox Technologies Corporation was acquired by Nuance Communications in 2018. VB Assets LLC's current operating status is "Active".

Assignment timeline

• 2018-11-19 (executed) / recorded 2018-11-19 — Reel 004873/0080

  • Conveyance: ASSIGNMENT OF ASSIGNORS INTEREST
  • Assignor: FREEMAN, TOM; KENNEWICK, MIKE
  • Assignee: VOICEBOX TECHNOLOGIES, INC.
  • Correspondent: BRENT E. RRICE, STOEL RIVES LLP, 600 ATLANTIC AVENUE SUITE 2100 BOSTON MA 02210
  • Context: Transfer from inventors to Voicebox Technologies, Inc.

• 2018-11-19 (executed) / recorded 2018-11-19 — Reel 004873/0080

  • Conveyance: MERGER
  • Assignor: VOICEBOX TECHNOLOGIES, INC.
  • Assignee: VOICEBOX TECHNOLOGIES CORPORATION
  • Correspondent: BRENT E. RICE, STOEL RIVES LLP, 600 ATLANTIC AVENUE SUITE 2100 BOSTON MA 02210. This correspondent recurs in this chain.
  • Context: Internal corporate reorganization/merger.

• 2018-11-19 (executed) / recorded 2018-11-19 — Reel 004873/0080

  • Conveyance: NUNC PRO TUNC ASSIGNMENT
  • Assignor: VOICEBOX TECHNOLOGIES CORPORATION
  • Assignee: VB ASSETS, LLC
  • Correspondent: BRENT E. RICE, STOEL RIVES LLP, 600 ATLANTIC AVENUE SUITE 2100 BOSTON MA 02210. This correspondent recurs in this chain.
  • Context: Transfer of patent assets to VB Assets, LLC.

• 2019-04-12 (executed) / recorded 2019-04-12 — Reel 004958/0468

  • Conveyance: SECURITY INTEREST
  • Assignor: VB ASSETS, LLC
  • Assignee: DELPHI ASSET MANAGEMENT CORPORATION
  • Correspondent: JENNIFER L. HUEY, MCDERMOTT WILL & EMERY LLP, 227 WEST MONROE STREET, SUITE 4400, CHICAGO, ILLINOIS 60606-5096
  • Context: Securitization of patent assets.

• 2019-06-13 (executed) / recorded 2019-06-13 — Reel 004992/0594

  • Conveyance: RELEASE BY SECURED PARTY
  • Assignor: DELPHI ASSET MANAGEMENT CORPORATION
  • Assignee: VB ASSETS, LLC
  • Correspondent: Jennifer L Huey, McDermott Will & Emery LLP, 444 W Lake St., Suite 4400, Chicago, IL 60606
  • Context: Release of security interest.

• 2025-04-08 (executed) / recorded 2025-04-08 — Reel 006121/0446

  • Conveyance: SECURITY INTEREST
  • Assignor: VB ASSETS, LLC
  • Assignee: CONTINGENCY CAPITAL FUND A LP
  • Correspondent: John D. Level, Cooley LLP, 1299 Pennsylvania Ave. NW, Suite 700, Washington, DC 20004-2975
  • Context: Securitization of patent assets.

Timeline diagram

timeline
    title Ownership of US 11080758
    2007 : Priority date from Voicebox Tech
    2018 : Inventors assign to Voicebox
         : Voicebox Inc merges into Voicebox Corp
         : Voicebox Corp assigns to VB Assets LLC
    2019 : VB Assets grants security interest
         : Security interest released
    2021 : Patent issued to VB Assets LLC
    2025 : VB Assets grants new security interest

NPE / troll-pattern signals

1. Shell-entity transfer — present. The assignment from Voicebox Technologies Corporation to VB ASSETS, LLC on 2018-11-19 (Reel 004873/0080) appears to be a transfer to a licensing-only entity. Voicebox Technologies Corporation was acquired by Nuance, and the creation of "VB ASSETS, LLC" to hold the patent assets after this event is a strong indicator of a shell entity for monetization. VB Assets LLC does not appear to ship products.
2. Known asserter in the chain — unclear. While VB Assets, LLC holds the patent, it is not explicitly listed on public NPE lists like RPX or Unified Patents directories in a readily identifiable manner. The recent litigation filings (PTAB case IPR2025-01240 and US case in Delaware District Court) suggest an assertion strategy.
3. Repeat correspondent across the chain — present. Brent E. Rice of Stoel Rives LLP is listed as the correspondent for all three assignments on 2018-11-19 (Reel 004873/0080). This recurrence is a signal, especially as these assignments occurred in quick succession and involved the transfer to VB Assets, LLC.
4. Cascading transfers — present. There are three consecutive assignments recorded on the same day, 2018-11-19 (Reel 004873/0080), culminating in the transfer to VB Assets, LLC. This rapid series of transfers, particularly with the same correspondent, is a strong indicator.
5. Pre-litigation transfer — unclear. The patent issued on 2021-08-03. While litigation has recently been filed (IPR2025-01240 filed 2025-01-14, and a Delaware District Court case filed 2024-03-01 per Unified Patents), the most recent assignment of ownership (to VB Assets, LLC) occurred in 2018, which is more than 6 months prior to these filings. The security interest granted in 2025 is not an ownership transfer.
6. Bankruptcy fire-sale — not present. There is no indication that Voicebox Technologies Corporation or VB Assets LLC filed for bankruptcy.
7. Privateering — unclear. While Voicebox Technologies Corporation was acquired by Nuance Communications, the transfer of this patent to VB Assets, LLC could be a form of privateering where Nuance is using VB Assets, LLC to assert patents, but explicit evidence of this relationship for assertion purposes is not present in the public record of assignments.
8. Defensive aggregator (anti-NPE) — not present. The chain does not terminate with any known defensive aggregators.

Verdict

NPE — high confidence

The strong indicators include the transfer to a likely shell entity (VB Assets, LLC) on 2018-11-19 (Reel 004873/0080) following the acquisition of Voicebox Technologies, the cascading transfers on the same day involving the same correspondent, and the involvement of this patent in recent litigation (as indicated by the PTAB IPR2025-01240 and Delaware District Court case, mentioned in the Google Patents legal status section, though not directly in the assignment records). These factors collectively point to an NPE assertion strategy.

Verification: https://assignmentcenter.uspto.gov/ (search for patent number 11080758).

To identify the most relevant prior art for US patent 11080758, I will examine the patent's cited references. The USPTO's Patent Public Search tool is an appropriate resource for this task. Prior art refers to any evidence that an invention is already known, which can include previously patented inventions, descriptions in printed publications, or public use/sale of the invention before the effective filing date of the claimed invention.

Based on the full patent text of US11080758, the following prior art documents are referenced:

Cited U.S. Patent Documents:

• U.S. Pat. No. 7,398,209

  • Full Citation: U.S. Pat. No. 7,398,209, entitled "Systems and Methods for Responding to Natural Language Speech Utterance," issued July 8, 2008.
  • Filing Date: June 3, 2003 (as U.S. patent application Ser. No. 10/452,147).
  • Brief Description: This patent describes a system that interprets natural language voice-based inputs and generates responses. It includes a speech recognition engine that recognizes words and phrases in an utterance using dictionary and phrase tables, with fuzzy set possibilities or prior probabilities dynamically updated. This system is foundational for natural language processing in voice user interfaces.
  • Potential Anticipation (35 U.S.C. § 102): This patent could potentially anticipate elements of claims related to the core functionality of natural language utterance processing, speech recognition, and context determination (e.g., portions of claims 1 and 23 concerning "providing a natural language utterance as an input to a speech recognition engine," "receiving... words or phrases, recognized from the natural language utterance," and "determining... a context for the natural language utterance based on the recognized words or phrases"). The dynamic updating of probabilities for recognition could also anticipate aspects of how the speech recognition engine functions.

• U.S. Pat. No. 7,693,720

  • Full Citation: U.S. Pat. No. 7,693,720, entitled "Mobile Systems and Methods for Responding to Natural Language Speech Utterance," issued April 6, 2010.
  • Filing Date: June 15, 2003 (as U.S. patent application Ser. No. 10/618,633).
  • Brief Description: This patent describes mobile systems and methods for responding to natural language speech utterances, building upon similar principles as U.S. Pat. No. 7,398,209 but specifically adapted for mobile environments.
  • Potential Anticipation (35 U.S.C. § 102): Similar to U.S. Pat. No. 7,398,209, this patent could potentially anticipate elements of claims related to natural language utterance processing and speech recognition, particularly in a mobile device context (e.g., portions of claims 1 and 23 involving the initial stages of processing a natural language utterance on an electronic device).

• U.S. Pat. No. 7,634,409

  • Full Citation: U.S. Pat. No. 7,634,409, entitled "Dynamic Speech Sharpening," issued December 15, 2009.
  • Filing Date: August 31, 2006 (as U.S. patent application Ser. No. 11/513,269).
  • Brief Description: This patent focuses on techniques for enhancing the interpretation of a user utterance, specifically "dynamic speech sharpening." This suggests methods for improving the accuracy and understanding of spoken input.
  • Potential Anticipation (35 U.S.C. § 102): This patent could potentially anticipate aspects of claims 1 and 23 related to the refinement or enhancement of the speech recognition engine's preliminary interpretations, as well as the overall accuracy of interpreting the natural language utterance.

• U.S. Pat. No. 7,640,160

  • Full Citation: U.S. Pat. No. 7,640,160, entitled "Systems and Methods for Responding to Natural Language Speech Utterance," issued December 29, 2009.
  • Filing Date: August 5, 2005 (as U.S. patent application Ser. No. 11/197,504).
  • Brief Description: This patent describes methods for generating context-based interpretations and responses to natural language voice-based inputs, specifically utilizing domain agents to competitively generate interpretations.
  • Potential Anticipation (35 U.S.C. § 102): This patent is highly relevant to claims 1 and 23, particularly concerning the determination of context for the natural language utterance and the use of conversational language processors and domain agents for interpretation (e.g., claims 5, 6, 7 in the method and system claims).

• U.S. Pat. No. 7,949,529

  • Full Citation: U.S. Pat. No. 7,949,529, entitled "Mobile Systems and Methods of Supporting Natural Language Human-Machine Interactions," issued May 24, 2011.
  • Filing Date: August 29, 2005 (as U.S. patent application Ser. No. 11/212,693).
  • Brief Description: This patent details mobile systems and methods for supporting natural language human-machine interactions, focusing on conversational interaction and context-based interpretations.
  • Potential Anticipation (35 U.S.C. § 102): Similar to U.S. Pat. No. 7,640,160, this patent could potentially anticipate aspects of claims 1 and 23 related to context determination and conversational interaction, especially in a mobile setting.

• U.S. Pat. No. 7,620,549

  • Full Citation: U.S. Pat. No. 7,620,549, entitled "System and Method of Supporting Adaptive Misrecognition in Conversational Speech," issued November 17, 2009.
  • Filing Date: August 10, 2005 (as U.S. patent application Ser. No. 11/200,164).
  • Brief Description: This patent describes a system and method for adaptive misrecognition in conversational speech, allowing for reinterpretation of current and previous utterances based on new information. It also mentions accessing an environmental model to determine user location, activity, and actions.
  • Potential Anticipation (35 U.S.C. § 102): This patent could potentially anticipate aspects of claims 1 and 23 related to the interpretation of natural language utterances, particularly where "adaptive misrecognition" could influence how the system understands and processes speech. Furthermore, the use of an "environmental model" to determine context (user location, activity, action) directly relates to elements in claims 9 and 10.

• U.S. Pat. No. 8,073,681

  • Full Citation: U.S. Pat. No. 8,073,681, entitled "System and Method for a Cooperative Conversational Voice User Interface," issued December 6, 2011.
  • Filing Date: October 16, 2006 (as U.S. patent application Ser. No. 11/580,926).
  • Brief Description: This patent describes a system and method for a cooperative conversational voice user interface, aiming to enhance responses and predict user behavior based on cognitive models.
  • Potential Anticipation (35 U.S.C. § 102): This patent could potentially anticipate aspects of claims 1 and 23 related to the conversational nature of the interaction and the system's ability to enhance responses and predict user behavior.

General Application of 35 U.S.C. § 102 (Novelty):

Under 35 U.S.C. § 102, an invention is considered "novel" if no single piece of prior art discloses every element of the claimed invention before its effective filing date. The purpose of prior art analysis is to determine if an invention was already known or obvious at the time of its creation. For a prior art reference to anticipate a claim under § 102, it must disclose, either expressly or inherently, all the limitations of the claim. If a single prior art reference (e.g., one of the patents listed above) contains every element of an independent claim in US11080758, then that claim would not be novel. This analysis focuses on the prior art cited by US11080758 itself, as these are the references the examiner considered most relevant during prosecution.

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.

Patent Term Adjustments (PTA)

Patent Term Adjustment (PTA) is granted to compensate patent owners for certain delays caused by the USPTO during the prosecution of a utility or plant patent application. It adds additional time to the standard 20-year patent term.

To determine the specific PTA for US11080758, access to the patent's prosecution history in USPTO's Patent Center or Open Data Portal is generally required. Without direct access to this specific data for US11080758, a precise PTA cannot be stated. However, the Google Patents record for US11080758 does not indicate any PTA granted in the "Legal status" section.

Patent Term Extensions (PTE)

Patent Term Extension (PTE) is available for patents on certain human drugs, food or color additives, medical devices, animal drugs, and veterinary biological products to restore time lost during the pre-market government approval process from a regulatory agency like the FDA.

Based on the subject matter of US11080758, which relates to delivering targeted advertisements and natural language processing, it is highly unlikely to be eligible for Patent Term Extension under 35 U.S.C. § 156, as it does not fall within the categories of products requiring pre-market regulatory approval.

Continuation Applications

US11080758 is itself a continuation patent. Its "CROSS-REFERENCE TO RELATED APPLICATIONS" section states that it is a continuation of U.S. patent application Ser. No. 15/223,870 (which issued as U.S. Pat. No. 10,134,060).

The patent family information indicates a direct chain of continuation applications leading up to US11080758:

• US11080758B2 is a continuation of US15/223,870 (US10134060B2).
• US15/223,870 is a continuation of US14/836,606 (US9406078B2).
• US14/836,606 is a continuation of US14/537,598 (US9269097B2).
• US14/537,598 is a continuation of US14/016,757 (US8886536B2).
• US14/016,757 is a continuation of US13/371,870 (US8527274B2).
• US13/371,870 is a continuation of US12/847,564 (US8145489B2).
• US12/847,564 is a continuation of US11/671,526 (US7818176B2).

Divisional Applications

The provided patent text and Google Patents information do not explicitly mention any divisional applications directly stemming from US11080758.

Related Family Members

The patent family associated with US11080758 includes several applications claiming priority back to the original filing date of February 6, 2007. These include:

• Priority Applications:
  • US16/194,944 (US11080758B2) - This patent itself.
  • US17/391,388 (US12236456B2).
  • US19/020,255 (US20250384463A1).
• Applications Claiming Priority (which represent the continuation chain):
  • US11/671,526 (US7818176B2).
  • US12/847,564 (US8145489B2).
  • US13/371,870 (US8527274B2).
  • US14/016,757 (US8886536B2).
  • US14/537,598 (US9269097B2).
  • US14/836,606 (US9406078B2).
  • US15/223,870 (US10134060B2).
  • US16/194,944 (US11080758B2).

Projected Expiration Date

For US utility patents filed on or after June 8, 1995, the patent term generally expires 20 years from the earliest filing date of the patent application, plus any Patent Term Adjustment (PTA) or Patent Term Extension (PTE), and considering any terminal disclaimers.

US11080758 claims priority back to February 6, 2007. Therefore, the base 20-year term would typically end on February 6, 2027.

As of the current date (May 18, 2026), the Google Patents record for US11080758 indicates an "Anticipated expiration" date of 2027-02-06. This date aligns with the 20-year term from the earliest priority date, suggesting that no significant PTA was granted, or if it was, it was offset by applicant delay, or no PTE was applied.

Defensive Disclosure: Enhancements and Alternative Implementations of US Patent 11080758

This document serves as a defensive disclosure for potential future advancements related to the technology described in US Patent 11080758. The aim is to articulate obvious variations and integrations that a person having ordinary skill in the art (PHOSITA) would readily conceive, thereby establishing prior art to limit the patentability of incremental improvements by third parties.

Derivative Variations for Core Claims (Method Claim 1 and System Claim 23)

The following derivatives explore alternative materials, expanded operational parameters, cross-domain applications, integration with emerging technologies, and inverse/failure modes for the core claims of US11080758, particularly focusing on the processes of natural language utterance input, recognition, context determination, purchase opportunity selection and delivery, interaction tracking, and user profile management.

---

1. Material & Component Substitution

Derivative 1.1: Piezoelectric MEMS Microphone Array with Low-Power DSP

Enabling Description:
Instead of a conventional electret condenser microphone or standard MEMS microphone (Input device 105), this derivative utilizes a multi-element piezoelectric Micro-Electro-Mechanical System (MEMS) microphone array for enhanced directionality and noise cancellation, particularly in high-noise environments. The analog audio signals from the array are fed into a dedicated ultra-low-power Digital Signal Processor (DSP) chip, optimized for edge computing. This DSP performs preliminary acoustic model inference, including noise reduction using a Wiener filter algorithm and beamforming via generalized sidelobe canceller (GSC) techniques to isolate the user's speech. The processed, denoised, and beamformed audio stream, rather than raw audio, is then digitized by a low-power Analog-to-Digital Converter (ADC) and transmitted to the Automatic Speech Recognizer (ASR) 110. This reduces the computational load and power consumption at the main processor, extending battery life for the electronic device. The DSP firmware can be updated over-the-air (OTA) to adapt to new acoustic environments or implement improved noise suppression algorithms.

graph TD
    A[User Utterance] --> B(Piezoelectric MEMS Mic Array);
    B --> C{Ultra-Low-Power DSP};
    C -- Wiener Filter & GSC --> D[Processed Audio Stream];
    D --> E(Low-Power ADC);
    E --> F[Digitized Audio Data];
    F --> G[ASR Engine (110)];
    G --> H[Words/Phrases];

Derivative 1.2: Liquid Crystal Display (LCD) with Haptic Feedback for Purchase Opportunity Delivery

Enabling Description:
For delivering the selected purchase opportunity (Operation 325) on an electronic device (210), this derivative replaces traditional LED or OLED displays with an advanced transflective Liquid Crystal Display (LCD) panel that offers excellent readability in direct sunlight and reduced power consumption for static content. Complementing the visual display, the system integrates a haptic feedback actuator array, such as a Linear Resonant Actuator (LRA) or Eccentric Rotating Mass (ERM) motor, beneath the display surface. When a purchase opportunity is presented, the system triggers specific haptic patterns to draw the user's attention to interactive elements (e.g., a "Buy Now" button vibrates upon display, or a "Scroll for More" prompt generates a directional haptic pulse). This multimodal output (visual and tactile) enhances user engagement and interaction (monitored in Operation 330), especially in contexts where visual attention is limited, or an audible response is impractical. The haptic feedback parameters (intensity, frequency, duration) are dynamically tuned by the advertising application (160) based on user profile (240) preferences or global interaction patterns (345) to optimize click-through rates.

graph TD
    A[Selected Purchase Opportunity (320)] --> B{Advertising App (160)};
    B --> C[Transflective LCD Display];
    B --> D[Haptic Feedback Actuator Array];
    C --> E[Visual Presentation];
    D --> F[Tactile Alert];
    E & F --> G[User Interaction (330)];
    G --> H[Interaction Tracking (345)];

Derivative 1.3: Ferroelectric RAM (FeRAM) for User Profile Storage

Enabling Description:
To enhance the robustness and speed of user profile storage (Operation 345, user-specific profile, global profiles), this derivative employs Ferroelectric RAM (FeRAM) in place of conventional NAND flash or DRAM for storing frequently accessed portions of the user-specific profile (Claim 1, step 7) and temporary contextual data. FeRAM offers non-volatility, extremely fast write speeds (comparable to DRAM), and high endurance, making it ideal for frequent, granular updates to user preferences, immediate context, and short-term interaction history without incurring the write-wear limitations of flash memory. The user-specific profile module (240) directly interfaces with the FeRAM module, minimizing latency for profile updates and retrieval, which is critical for real-time interpretation of subsequent natural language utterances (Claim 1, step 8) and rapid advertisement selection (Claim 1, step 4). A larger, slower mass storage (e.g., SSD) would still be used for archival or less volatile profile data.

graph TD
    A[Tracked Interaction Pattern (345)] --> B{User Profile Module (240)};
    B --> C[FeRAM Module];
    C -- Fast R/W & Non-Volatile --> D[User-Specific Profile];
    B --> E[Subsequent Natural Language Utterance];
    E --> F[ASR Engine (110)];
    F --> G{Conversational Language Processor (120)};
    G -- Accesses D --> H[Interpreted Utterance];
    H --> I[Subsequent Purchase Opportunity Selection];

---

2. Operational Parameter Expansion

Derivative 2.1: Ultra-Low Latency, High-Frequency Trading Advertisement System

Enabling Description:
This derivative implements the system for selecting and presenting purchase opportunities (Claim 1, steps 4 & 5) within a high-frequency trading (HFT) environment, where user utterances are interpreted as commands for financial transactions and purchase opportunities are real-time, rapidly changing investment opportunities or financial products. The system operates with sub-millisecond latency. Natural language utterances (e.g., "Buy 10,000 shares of AAPL at market") are captured via ultra-low-latency microphones connected directly to FPGA-accelerated ASR and NLP modules. The "context" (Claim 1, step 3) is determined by market conditions, news sentiment, and the user's portfolio in real-time. "Purchase opportunities" are dynamically generated algorithmic trading signals or direct stock purchase options. These are delivered (Claim 1, step 5) via specialized low-latency optical fiber networks to secure trading terminals, with interaction tracking (Claim 1, step 6) involving immediate order execution and confirmation messages, and user-specific profiles (Claim 1, step 7) continuously updated with trading history, risk tolerance, and profit/loss metrics.

graph TD
    A[Voice Input (HFT Trader)] -- Ultra-Low Latency Mic --> B(FPGA-Accelerated ASR);
    B -- Sub-ms --> C(FPGA-Accelerated NLP);
    C -- Market Data & Portfolio --> D{Real-time Context Determination};
    D --> E[Dynamic Purchase Opportunity Selection];
    E -- Optical Fiber Network --> F[Secure Trading Terminal];
    F -- Order Execution --> G[Interaction Tracking (Order Book)];
    G --> H[User-Specific Trading Profile Update];
    H --> C;

Derivative 2.2: Deep-Sea Autonomous Underwater Vehicle (AUV) Command & Control with Limited-Bandwidth Voice Prompts

Enabling Description:
The core method (Claim 1) is adapted for operating an Autonomous Underwater Vehicle (AUV) at extreme pressures (up to 600 bar) and low-bandwidth acoustic communication channels. The "electronic device" is a ruggedized AUV control system. A human operator on a surface vessel provides natural language utterances (e.g., "Deploy sonar array to 500 meters," "Scan seabed sector Gamma-7") through a specialized, highly compressed voice communication protocol (e.g., using linear predictive coding (LPC) or vector quantization). The ASR (110) on the AUV is a highly robust, acoustic-model-optimized engine that works with severely degraded audio quality. "Context" (Claim 1, step 3) includes current depth, mission parameters, battery life, and environmental sensor data (e.g., water temperature, currents). "Purchase opportunities" (Claim 1, step 4) are not commercial ads, but rather options for AUV resource allocation, task prioritization, or suggested actions (e.g., "Warning: low battery, return to surface?"). These are delivered (Claim 1, step 5) as synthesized, concise voice prompts or critical data displays over the acoustic modem, acknowledging bandwidth limitations. User profiles (Claim 1, step 7) track operator preferences for mission parameters, safety protocols, and response formats.

sequenceDiagram
    participant O as Operator (Surface)
    participant S as Surface Control System
    participant A as AUV Control System
    participant SR as AUV Speech Recognition (110)
    participant NL as AUV Natural Language Processor (120)
    participant C as AUV Context Module (130)
    participant PO as AUV "Purchase Opportunity" Selector
    participant U as AUV User Profile Module (240)

    O->>S: "Deploy sonar array to 500 meters." (Natural Language Utterance)
    S->>A: Compressed Voice Data (Low-Bandwidth Acoustic Link)
    A->>SR: Process Compressed Voice
    SR->>NL: Words/Phrases
    NL->>C: Determine Context (Depth, Mission, Battery)
    C->>PO: Request AUV Action/Suggestion
    PO->>A: Selected Action/Suggestion (e.g., "Confirm deployment?")
    A->>S: Synthesized Voice Prompt (Low-Bandwidth Acoustic Link)
    S->>O: "Confirm deployment?"
    O->>S: "Confirm."
    S->>A: Compressed Voice Data
    A->>SR: Process "Confirm"
    SR->>NL: Update Context
    NL->>U: Track Interaction (Profile Update)
    A->>A: Execute Deployment

---

3. Cross-Domain Application

Derivative 3.1: Surgical Assistant Voice Control and Supply Recommendation System (Medical)

Enabling Description:
In a surgical theater, the "electronic device" (210) is an integrated surgical console. A surgeon issues natural language utterances (e.g., "Scalpel, size 10," "Increase suction," "Check patient vitals") to control instruments and access patient data. The ASR (110) is trained on medical terminology and low-whisper speech patterns. The "context" (Claim 1, step 3) is derived from the current surgical phase (e.g., incision, dissection, hemostasis, closure), real-time patient physiological data (from IoT sensors), and the active surgical protocol. "Purchase opportunities" (Claim 1, step 4) are presented not as traditional advertisements, but as recommendations for surgical supplies, instruments, or medications based on the determined context (e.g., "Recommend larger sutures?", "Consider hemostatic agent X for current bleeding?"). These recommendations are delivered (Claim 1, step 5) audibly via an integrated speaker system and visually on a sterile display within the surgeon's line of sight. Interaction tracking (Claim 1, step 6) monitors whether the surgeon accepts the recommendation (e.g., "Yes, provide agent X") or rejects it, and if a supply is actually used. This data builds a user-specific profile (Claim 1, step 7) for the surgeon, detailing preferred instruments, response to recommendations, and efficiency metrics, enabling more targeted and personalized suggestions in future surgeries.

stateDiagram-v2
    state "Surgical Phase: Incision" as Incision
    state "Surgical Phase: Dissection" as Dissection
    state "Surgical Phase: Hemostasis" as Hemostasis
    state "Surgical Phase: Closure" as Closure

    [*] --> Incision : Start Surgery
    Incision --> Dissection : Incision Complete
    Dissection --> Hemostasis : Tissue Separated
    Hemostasis --> Closure : Bleeding Controlled
    Closure --> [*] : Surgery Complete

    state "Voice Command Received" as Command
    state "ASR & NLP Processing" as Process
    state "Context Determination (Surgical Phase, Patient Data)" as Context
    state "Supply Recommendation Engine" as Recommend
    state "Present Recommendation (Audio/Visual)" as Present
    state "Surgeon Interaction (Accept/Reject)" as Interact
    state "Profile Update & Tracking" as Profile

    Incision --> Command
    Dissection --> Command
    Hemostasis --> Command
    Closure --> Command

    Command --> Process : Utterance
    Process --> Context : Words/Phrases
    Context --> Recommend : Current Context
    Recommend --> Present : Supply Recommendation
    Present --> Interact : Feedback
    Interact --> Profile : Interaction Data
    Profile --> Recommend : Updated Profile (for next recommendation)
    Interact --> Command : Next Command/Implicit Request

Derivative 3.2: Aerospace Maintenance Guidance & Part Ordering System (Aerospace)

Enabling Description:
For aerospace maintenance technicians, the "electronic device" (210) is a head-mounted augmented reality (AR) display with integrated microphone. A technician performing repairs on an aircraft engine issues natural language utterances (e.g., "What is the torque specification for this bolt?", "Show me the hydraulic line diagram," "Order new fuel filter"). The ASR (110) processes these commands, and the NLP engine (120) identifies requests for information or actions. The "context" (Claim 1, step 3) is determined by the specific aircraft tail number, the component being serviced (identified via visual recognition from the AR camera), and the current step in the maintenance checklist (accessed from a digital logbook). "Purchase opportunities" (Claim 1, step 4) are dynamically presented within the AR display as options to order replacement parts, specialized tools, or schedule follow-up inspections from approved suppliers. These are delivered (Claim 1, step 5) as overlay graphics in the AR view and audible confirmations. Interaction tracking (Claim 1, step 6) records part orders, confirmation of part receipt, and technician efficiency. The user-specific profile (Claim 1, step 7) for each technician tracks frequently ordered parts, preferred suppliers, and completion rates, informing subsequent part recommendations and tool suggestions.

graph LR
    A[Technician Utterance] --> B(AR Headset Mic);
    B --> C[ASR & NLP Module];
    C --> D{Context Determination};
    D -- Aircraft ID, Component, Maint Step --> E[Maintenance Database];
    E --> D;
    D --> F[Part & Tool Recommendation Engine];
    F --> G[Augmented Reality Display];
    G --> H[Visual & Audio Presentation];
    H --> I[Technician Interaction (Order/Accept)];
    I --> J[Interaction Tracking (Order Log)];
    J --> K[Technician Profile Update];
    K --> F;

Derivative 3.3: Livestock Management and Feed Procurement System (AgriTech)

Enabling Description:
In a modern agricultural setting, the "electronic device" (210) is a ruggedized tablet or handheld device used by a farm manager, integrated with IoT sensors in barns and fields. The manager issues natural language utterances (e.g., "Check health status of pen 3," "Order 500 pounds of cattle feed," "Locate cow ID 123"). The ASR (110) converts these to text. "Context" (Claim 1, step 3) is determined by GPS location on the farm, identified livestock pens (via RFID readers or computer vision), current weather conditions, and real-time animal health metrics (from IoT biosensors). "Purchase opportunities" (Claim 1, step 4) include recommendations for feed supplements, veterinary supplies, or equipment upgrades based on animal health trends, feed consumption rates, or upcoming weather events. These are delivered (Claim 1, step 5) as visual prompts on the tablet display and audible alerts. Interaction tracking (Claim 1, step 6) records accepted orders, feed dispenses, and observed animal health improvements. The user-specific profile (Claim 1, step 7) for the farm manager and for specific livestock groups tracks feed preferences, supplier reliability, and optimal health intervention strategies, enabling more precise, data-driven procurement and management suggestions.

flowchart TD
    A[Farm Manager Utterance] --> B(Ruggedized Tablet Mic);
    B --> C(ASR & NLP);
    C --> D{Context Determination};
    D -- GPS, RFID, IoT Biosensors, Weather --> E[Farm Data Platform];
    E --> D;
    D --> F[Feed & Supply Recommendation Engine];
    F --> G[Tablet Display & Audio Alert];
    G --> H[Manager Interaction (Order/Accept)];
    H --> I[Interaction Tracking (Supply Chain/Usage)];
    I --> J[Manager/Livestock Profile Update];
    J --> F;

---

4. Integration with Emerging Tech

Derivative 4.1: AI-Driven Multi-Objective Optimization for Advertisement Selection

Enabling Description:
The advertisement selection module (250, Claim 1, step 4) is augmented with an Artificial Intelligence (AI) driven multi-objective optimization engine, specifically using a deep reinforcement learning (DRL) agent. This DRL agent observes the "state" of the system, which includes the determined context (235, Claim 1, step 3), the current user-specific profile (240, Claim 1, step 7), available advertisement inventory (260), and real-time advertiser bidding data. The "actions" available to the DRL agent are to select a specific set of purchase opportunities or advertisements. The "reward function" for the DRL agent is a composite score considering multiple objectives: maximizing click-through rate (CTR), maximizing conversion rate (transaction completion), minimizing ad fatigue (by diversifying ad types/sources), and adhering to advertiser budget constraints (from tracking module 255). The DRL agent continuously learns and refines its ad selection policy based on tracked interaction patterns (255, Claim 1, step 6) and transaction completions. This moves beyond simple scoring to a dynamic, adaptive optimization of ad delivery for both user satisfaction and advertiser ROI.

sequenceDiagram
    participant U as User Utterance
    participant ASR as Speech Recognition (110)
    participant NLP as Conversational Language Processor (120)
    participant CD as Context Determination (130)
    participant UPM as User Profile Module (240)
    participant ADINV as Ad Inventory (260)
    participant DRL as AI-Driven DRL Agent (250)
    participant TM as Tracking Module (255)
    participant ED as Electronic Device (210)

    U->>ASR: Voice Input
    ASR->>NLP: Words/Phrases
    NLP->>CD: Determine Context (State Variable 1)
    NLP->>UPM: Retrieve User Profile (State Variable 2)
    UPM->>DRL: Current User Profile
    CD->>DRL: Determined Context
    ADINV->>DRL: Available Advertisements & Bids
    DRL-->>DRL: Evaluate Actions (Ad Selection) based on Multi-Objective Reward
    DRL->>ED: Deliver Selected Purchase Opportunity
    ED->>TM: User Interaction (Click/Transaction)
    TM->>DRL: Reward Feedback (CTR, Conversion, Fatigue, Budget)
    TM->>UPM: Update User Profile (Learning/Adaptation)
    DRL-->>DRL: Policy Update (Reinforcement Learning)

Derivative 4.2: IoT Sensor-Driven Context Enrichment and Proactive Purchase Opportunities

Enabling Description:
The "context for the natural language utterance" (Claim 1, step 3) is significantly enriched by real-time data from a network of interconnected Internet of Things (IoT) sensors. These sensors include:

• Environmental Sensors: Temperature, humidity, ambient noise level, light levels around the user's electronic device (210).
• Physiological Sensors: Wearable sensors monitoring user heart rate, galvanic skin response, or posture (inferring stress, activity level).
• Proximity Sensors: Bluetooth Low Energy (BLE) beacons or Ultra-Wideband (UWB) sensors indicating proximity to specific retail locations, product displays, or other IoT-enabled objects.
  The sensor data is continuously streamed to the context determination module (130) and user profile module (240), influencing both the interpretation of ambiguous utterances and the selection of purchase opportunities (Claim 1, step 4). For example, a user's utterance "I'm hungry" combined with physiological data indicating low blood sugar and proximity to a particular restaurant type could trigger a proactive delivery of a tailored food purchase opportunity. Interaction tracking (Claim 1, step 6) includes the specific sensor states that correlated with positive ad engagement, further refining the user-specific profile (Claim 1, step 7) for IoT-driven contextual advertising.

graph TD
    A[User Utterance] --> B(ASR & NLP);
    B --> C{Context Determination (130)};
    C -- Real-time Data --> D[IoT Sensor Network];
    D --> E[Environmental Sensors];
    D --> F[Physiological Sensors];
    D --> G[Proximity Sensors];
    E & F & G --> C;
    C --> H[User Profile Module (240)];
    H --> I[Advertisement Selection Module (250)];
    I --> J[Deliver Purchase Opportunity (210)];
    J --> K[User Interaction Tracking (255)];
    K --> H;

Derivative 4.3: Blockchain-Verified Purchase Opportunities and Supply Chain Transparency

Enabling Description:
The delivery of purchase opportunities (Claim 1, step 5) is enhanced by integrating blockchain technology for enhanced transparency and trust. Each "purchase opportunity" (Claim 1, step 4) is linked to a unique token or smart contract on a distributed ledger (e.g., Ethereum or Hyperledger Fabric). This blockchain record immutably stores verified attributes of the product or service, such as its origin, manufacturing process, ethical sourcing certifications, and authenticity data. When a purchase opportunity is delivered to the electronic device (210), the system concurrently fetches relevant, verifiable blockchain data. This information is presented to the user alongside the advertisement, allowing them to verify claims about quality, sustainability, or ethical practices. The "completion of a transaction" (Claim 1, step 6) is recorded as an update to the smart contract on the blockchain, providing an auditable and tamper-proof record of the purchase. The user-specific profile (Claim 1, step 7) stores preferences for blockchain-verified products and tracks engagement with transparent supply chain information, influencing the selection of subsequent, verifiable purchase opportunities.

flowchart TD
    A[Natural Language Utterance] --> B(ASR & NLP);
    B --> C(Context Determination);
    C --> D[Select Purchase Opportunity];
    D -- Product/Service ID --> E[Blockchain Oracle];
    E -- Query Smart Contract --> F[Distributed Ledger (Blockchain)];
    F --> G[Verifiable Product Data];
    D & G --> H[Deliver Purchase Opportunity (210)];
    H --> I[User Interaction & Transaction (330)];
    I -- Transaction Details --> F;
    F --> J[Immutable Transaction Record];
    J --> K[User Profile Update (345)];
    K --> C;

---

5. The "Inverse" or Failure Mode

Derivative 5.1: Critical Safety Override with Limited Functionality (Industrial Control)

Enabling Description:
This derivative applies the system to critical industrial control environments where safe failure is paramount. The "electronic device" (210) is a supervisory control interface for heavy machinery or a chemical process plant. A natural language utterance (Claim 1, step 1) might be a command (e.g., "Shut down reactor B," "Initiate emergency purge cycle"). The ASR (110) and NLP (120) process these. The "context" (Claim 1, step 3) includes real-time sensor data from the plant, fault alerts, and safety integrity levels. In the event of a system failure (e.g., loss of primary power, network instability affecting the advertising server, ASR malfunction), the system immediately transitions to a "limited functionality" mode focused exclusively on safety. All "purchase opportunities" (Claim 1, step 4) are suppressed. Instead, the system proactively delivers "safe failure instructions" or "emergency procedure prompts" (effectively "inverse purchase opportunities" aimed at avoiding undesired outcomes) via a redundant, hard-wired audible output and simplified graphical interface. Interaction tracking (Claim 1, step 6) records all operator confirmations of safety prompts and system responses during the failure event. The user-specific profile (Claim 1, step 7) stores operator-specific emergency response protocols and observed performance under duress, enabling more tailored critical alerts in future emergencies.

stateDiagram-v2
    state "Normal Operation" as Normal
    state "Limited Functionality (Safety Mode)" as SafetyMode

    Normal --> SafetyMode : System Failure Detected (Power Loss, ASR Malf.)

    state "Voice Command" as VoiceCommand
    state "ASR/NLP" as ASRNLP
    state "Context/Safety Logic" as ContextSafety
    state "Ad Selection (Suppressed)" as AdSelect
    state "Deliver PO" as DeliverPO
    state "Track Interaction" as Track

    Normal --> VoiceCommand : Normal Utterance
    VoiceCommand --> ASRNLP
    ASRNLP --> ContextSafety
    ContextSafety --> AdSelect : Normal Flow
    AdSelect --> DeliverPO : Purchase Opportunity
    DeliverPO --> Track : User Interaction
    Track --> Normal : Profile Update

    SafetyMode --> VoiceCommand : Emergency Utterance (e.g., "Emergency Stop")
    ASRNLP --> ContextSafety : Prioritize Safety
    ContextSafety --> DeliverSafetyInstructions : Suppress Ads, Deliver Critical Prompts
    DeliverSafetyInstructions --> Track : Operator Confirmation
    Track --> SafetyMode : Log Event, Continue Monitoring

Derivative 5.2: Privacy-Centric "Ghost Mode" with Anonymized Profile Updates (Consumer Electronics)

Enabling Description:
For consumer electronic devices (210), this derivative introduces a "Ghost Mode" that prioritizes user privacy by operating in a low-power, limited-functionality state with anonymized profile updates. When activated by a natural language utterance (e.g., "Activate Ghost Mode") or a pre-set schedule, the system substantially reduces the detail and frequency of data collection. The "speech recognition engine" (110) operates locally and uses a smaller, less detailed acoustic model, providing basic command recognition but avoiding detailed phonetic transcription that could be used for speaker identification. "Context determination" (130) is restricted to on-device sensors (e.g., time of day, general activity based on accelerometer) without sending geolocation or detailed application usage to a remote server. All "purchase opportunity" selection (Claim 1, step 4) is disabled or replaced with generic, non-targeted public service announcements. Any "interaction pattern" tracking (Claim 1, step 6) is heavily aggregated and anonymized at the device level (e.g., "User interacted with voice assistant 5 times today, no ads shown") before being sent for "user-specific profile" updates (Claim 1, step 7). The remote profile is updated with an anonymized, generalized profile ("User prefers privacy mode at night"), rather than granular data, ensuring the "subsequent natural language utterance" interpretation still benefits from general preferences while protecting specific user habits.

graph TD
    A[User Utterance] --> B(ASR & NLP - Local/Limited);
    B -- "Activate Ghost Mode" --> C{Privacy Monitor};
    C -- High Confidence --> D[Ghost Mode Active];
    D --> E[Local Context (Limited Sensors)];
    D --> F[No Remote Ad Selection];
    D --> G[Anonymized Interaction Tracking];
    G --> H[Aggregated Profile Update (Remote)];
    H --> I[Generalized User Profile];
    D --> J[Subsequent Utterance];
    J --> B;
    B --> E;
    E --> I;

---

Combination Prior Art Scenarios with Open-Source Standards

These scenarios demonstrate how the techniques of US11080758 could be combined with widely available open-source standards, potentially rendering further incremental improvements obvious.

1. Integration with W3C Web Speech API and OpenStreetMap Data

Scenario Description:
An implementation of US11080758 leverages the W3C Web Speech API (specifically its Speech Recognition interface) as the front-end for the "speech recognition engine" (110) in a web-based or progressive web application (PWA) context. The natural language utterance is captured by a standard browser microphone input, processed by the browser's native or a cloud-based speech recognition service exposed via the Web Speech API. The "words or phrases" (Claim 1, step 2) are then fed to a backend "conversational language processor" (120). For "context determination" (Claim 1, step 3) related to location-based requests (e.g., "Find me a coffee shop"), the system integrates with OpenStreetMap (OSM) data. The recognized location entities (e.g., "Main Street," "Eiffel Tower") are used to query a local or remote OSM database via its Overpass API, enriching the geographical context. "Purchase opportunities" (Claim 1, step 4) for businesses are then selected based on this OSM-derived context, cross-referenced with local business listings (also potentially crowd-sourced via OSM or similar). Interaction tracking (Claim 1, step 6) logs user clicks on these business listings. The "user-specific profile" (Claim 1, step 7) could include preferences for types of establishments or travel methods, enhancing subsequent suggestions based on the rich, open-source geographic data.

2. Utilization of Kaldi ASR Toolkit and Common Voice Dataset for Domain-Specific Context

Scenario Description:
The "speech recognition engine" (110) described in US11080758 is implemented using the Kaldi open-source speech recognition toolkit. For a specific domain, such as automotive infotainment, the acoustic models within Kaldi are fine-tuned and augmented using domain-specific speech data from the Mozilla Common Voice dataset (or a similar open-source voice corpus for a particular language/domain). When a user provides a "natural language utterance" (Claim 1, step 1) related to, for example, vehicle climate control (e.g., "Set temperature to 72 degrees"), Kaldi's ASR processes it. The resulting "words or phrases" (Claim 1, step 2) are then passed to a natural language understanding (NLU) module built on an open-source framework like SpaCy or NLTK. "Context for the natural language utterance" (Claim 1, step 3) is derived from vehicle sensor data (temperature, fan speed) and the user's previously configured comfort settings (part of the "user-specific profile" (Claim 1, step 7)). "Purchase opportunities" (Claim 1, step 4) in this context might be for vehicle maintenance services (e.g., "Your AC filter needs replacement, would you like to schedule service?"), offered by an open-source telematics platform. Interaction tracking (Claim 1, step 6) monitors user acceptance of these proactive service suggestions, further refining the profile and the relevance of subsequent maintenance-related purchase opportunities.

3. Integration with Apache Solr/Lucene for Advertisement Indexing and GDPR-Compliant User Profiles

Scenario Description:
For storing and retrieving the vast inventory of "advertisements" and their associated parameters (e.g., contexts, semantic indicators, target demographics, marketing criteria, 260 in FIG. 2), the system described in US11080758 utilizes an Apache Solr (or Lucene) open-source search platform. Advertisers upload "advertisements" (Claim 1, step 4) to this Solr index, which allows for highly efficient, faceted searching based on keywords from the "determined context" (Claim 1, step 3) and attributes from "user profiles" (Claim 1, step 7). Critically, for managing "user-specific profiles" (Claim 1, step 7) and "interaction patterns" (Claim 1, step 6), the system adheres to General Data Protection Regulation (GDPR) principles, a widely recognized standard for data privacy. User data in the profile is pseudonymized or anonymized by default, and explicit consent mechanisms (implemented using open-source consent management platforms) are required for any personalized data processing. The user profile module (240) stores consent records and ensures that "subsequent purchase opportunities" (Claim 1, step 8) are selected only based on data for which the user has granted consent, or on anonymized aggregates. Interaction tracking respects user privacy settings by filtering or anonymizing sensitive data points before updating the Solr-indexed profiles, ensuring legal compliance alongside personalized ad delivery.