US 12236456

Summary of U.S. Patent 12,236,456

As of April 26, 2026, a search for U.S. Patent 12,236,456 in the USPTO database and CAFC 2026 dockets did not yield specific information for this patent number. Therefore, details regarding its title, assignee, inventors, filing date, issue date, abstract, and independent claims cannot be provided.

There is a high degree of uncertainty regarding the status of this patent, as it is not appearing in standard public patent information databases. This could be for a variety of reasons, including but not limited to: the patent application may be pending and not yet published, the patent number may be inaccurate, or it may be a recently issued patent for which the data has not yet been fully propagated to public databases. Without further authoritative information, a detailed analysis is not possible at this time.

Litigation History of U.S. Patent 12,236,456

Contradicting the previously generated summary, which was based on an inability to locate the patent, the full patent text has since been provided. This text indicates that the patent family, including U.S. Patent 12,236,456, has been involved in litigation.

A recent lawsuit filed on April 17, 2026, explicitly lists U.S. Patent 12,236,456 as one of the asserted patents. The assignee, VB Assets, LLC, has been active in asserting its patent portfolio, which relates to natural language processing and voice assistants, against major technology companies.

Details of the known litigation involving this specific patent are as follows:

• Case 1: VB Assets, LLC v. Google LLC et al.
  • Plaintiff: VB Assets, LLC
  • Defendants: Google LLC, XXVI Holdings Inc., Alphabet Inc., Android, Inc., and YouTube, LLC
  • Jurisdiction: U.S. District Court for the District of Delaware
  • Case Number: 1:2026cv00443
  • Filing Date: April 17, 2026
  • Status/Outcome: As of April 17, 2026, the complaint had been filed and summons were issued. The case is in its initial stages.

Additionally, the provided patent data references a case in the Texas Eastern District Court (2:25-cv-00621) linked to the patent's family. However, the specific complaint for that case would need to be reviewed to confirm if patent 12,236,456 itself was asserted. The Darts-ip database, which tracks global patent litigation, also indicates litigation history for this patent family (Family ID=39676921), though access to specific case details requires a subscription.

PTAB proceedings on file

The USPTO ODP API returns no AIA trial proceedings for this patent as of the most recent ingest.

Proceedings overview

There are no AIA trial proceedings (Inter Partes Review, Post-Grant Review, or Covered Business Method review) currently on file or recorded for U.S. Patent 12,236,456. This indicates that the patent has not been challenged through these specific administrative proceedings at the Patent Trial and Appeal Board (PTAB). For a defendant, this means the patent has not been subjected to PTAB scrutiny, and all claims remain untested by these particular challenges.

Strategic summary

As of the current date, no claims of U.S. Patent 12,236,456 have been canceled, sustained, or even tested through PTAB proceedings. All claims of the patent are therefore considered UNTESTED in the context of AIA trials.

Since no PTAB proceedings have been initiated or concluded, there is no estoppel landscape established under 35 U.S.C. § 315(e)(2). This means that a potential defendant is not barred from raising any ground of invalidity (e.g., anticipation under § 102 or obviousness under § 103) that they raised or reasonably could have raised in a hypothetical IPR. The full range of prior art and invalidity arguments remain available for any potential challenge in district court or a future PTAB filing.

The absence of PTAB activity is notable, especially given the patent's involvement in district court litigation. Well-asserted patents often become targets for IPRs or other AIA trials as defendants seek to invalidate claims more quickly and cost-effectively than in district court. This lack of PTAB challenges could suggest a strategic decision by prior defendants, a perceived difficulty in finding compelling prior art, or simply that the litigation has not yet advanced to a stage where such challenges are typically launched.

Recommended next steps

Since no PTAB activity exists for U.S. Patent 12,236,456, a defendant facing assertion of this patent should consider the following:

• Prior Art Search: Conduct a thorough prior art search to identify strong references that could form the basis of an IPR petition. The absence of previous IPRs means there's no established estoppel for prior art grounds, leaving the field open.
• Validity Analysis: Perform a detailed validity analysis of all asserted claims against any newly discovered prior art and the references already cited during prosecution (e.g., US 2002/0087326 A1, US 2005/0144068 A1, US 7,069,219 B2). This would include assessing the strength of anticipation (§ 102) and obviousness (§ 103) arguments.
• IPR Feasibility Study: If strong prior art is found, initiate an IPR feasibility study to determine the likelihood of institution and eventual invalidation of the asserted claims. The priority date of February 6, 2007, makes the patent eligible for IPR.
• Monitoring: Continuously monitor the patent for any newly filed PTAB proceedings, as well as the ongoing district court litigation (e.g., VB Assets, LLC v. Google LLC et al., 1:2026cv00443 in Delaware, and 2:25-cv-00621 in Texas Eastern District Court). Outcomes in related cases or subsequent PTAB filings could significantly impact the defensive posture.

Inventors

• Tom Freeman: Employer not explicitly stated at the time of the original priority application filing (February 6, 2007). He was an assignor to Voicebox Technologies, Inc. on October 13, 2021.
• Mike Kennewick: Employer not explicitly stated at the time of the original priority application filing (February 6, 2007). He was an assignor to Voicebox Technologies, Inc. on October 13, 2021.

Both inventors assigned their interest to Voicebox Technologies, Inc., suggesting they were likely associated with Voicebox Technologies, Inc. at the time of the invention or filing of the earliest application in the chain (U.S. Pat. No. 7,818,176, which lists Voicebox Technologies, Inc. as the assignee).

Original assignee

The original operating company that developed the underlying technology and to which the inventors initially assigned their rights appears to be Voicebox Technologies, Inc. (later Voicebox Technologies Corporation). Voicebox Technologies, Inc. was known for developing natural language understanding and voice recognition software, a core component of the patent's claims.

Voicebox Technologies, Inc. was subsequently involved in a merger and then transferred rights to VB Assets LLC. VB Assets LLC, the entity that filed the application leading to US12236456, is identified as the "Original Assignee" on Google Patents for this specific patent. VB Assets LLC's primary line of business appears to be patent assertion, and they are currently active, as evidenced by ongoing litigation. It is not clear that VB Assets LLC shipped a product embodying the claims.

Assignment timeline

The USPTO Assignment Center (https://assignmentcenter.uspto.gov/) was searched for patent number 12236456. The search results corroborate the legal events listed on Google Patents.

• 2021-10-13 (executed) / recorded 2021-10-13 — Reel 059952/0466

  • Conveyance: Assignment of Assignors Interest
  • Assignor: FREEMAN, TOM; KENNEWICK, MIKE
  • Assignee: VOICEBOX TECHNOLOGIES, INC.
  • Correspondent: LINDEMAN, TERRY D. (TERRY D. LINDEMAN, PS, P.O. BOX 10606, BAINBRIDGE IS, WA 98110-0606). This correspondent will recur in this chain.
  • Context: Transfer of inventor rights to the original operating company.

• 2021-10-13 (executed) / recorded 2021-10-13 — Reel 059952/0469

  • Conveyance: Merger
  • Assignor: VOICEBOX TECHNOLOGIES, INC.
  • Assignee: VOICEBOX TECHNOLOGIES CORPORATION
  • Correspondent: LINDEMAN, TERRY D. (TERRY D. LINDEMAN, PS, P.O. BOX 10606, BAINBRIDGE IS, WA 98110-0606). This correspondent will recur in this chain.
  • Context: Internal corporate reorganization/name change of the operating company.

• 2021-10-13 (executed) / recorded 2021-10-13 — Reel 059952/0472

  • Conveyance: Nunc Pro Tunc Assignment
  • Assignor: VOICEBOX TECHNOLOGIES CORPORATION
  • Assignee: VB ASSETS, LLC
  • Correspondent: LINDEMAN, TERRY D. (TERRY D. LINDEMAN, PS, P.O. BOX 10606, BAINBRIDGE IS, WA 98110-0606). This correspondent will recur in this chain.
  • Context: Transfer of patent rights from the operating company to a new entity, VB Assets, LLC.

• 2025-04-08 (executed) / recorded 2025-04-09 — Reel 065751/0867

  • Conveyance: Security Interest
  • Assignor: VB ASSETS, LLC
  • Assignee: CONTINGENCY CAPITAL FUND A LP
  • Correspondent: HAHN, ERIC (Ropes & Gray LLP, PRUDENTIAL TOWER, 800 BOYLSTON STREET, BOSTON, MA 02199-3600).
  • Context: VB Assets, LLC granted a security interest in the patent to Contingency Capital Fund A LP, likely for financing or litigation funding.

Timeline diagram

timeline
    title Ownership of US 12236456
    2007 : Original application filed
    2021 : Inventors assign to Voicebox Inc
         : Voicebox Inc merges to Voicebox Corp
         : Voicebox Corp assigns to VB Assets LLC
    2025 : VB Assets grants security interest
    2026 : Litigation filed

NPE / troll-pattern signals

1. Shell-entity transfer — present. The transfer from Voicebox Technologies Corporation to VB Assets, LLC on 2021-10-13 (Reel 059952/0472) is a strong signal. Voicebox Technologies was an operating company. VB Assets, LLC, as identified in the litigation summary, is actively asserting patents and does not appear to ship products embodying the claims, fitting the profile of a licensing-only entity. The subsequent security interest granted to Contingency Capital Fund A LP (Reel 065751/0867) further supports this as a financing mechanism often used by NPEs.
2. Known asserter in the chain — present. VB Assets, LLC is the current assignee and is identified in the "Litigation History" section as having filed suit against major technology companies (e.g., VB Assets, LLC v. Google LLC et al., Case Number: 1:2026cv00443). This aligns with the profile of a high-frequency plaintiff.
3. Repeat correspondent across the chain — present. Terry D. Lindeman, PS (P.O. BOX 10606, BAINBRIDGE IS, WA 98110-0606) is listed as the correspondent for three consecutive assignments on 2021-10-13 (Reel 059952/0466, Reel 059952/0469, Reel 059952/0472). This consistent use of the same legal counsel across multiple transfers within a short period is a classic NPE pattern.
4. Cascading transfers — present. There were three consecutive assignments on the same day, October 13, 2021 (Reel 059952/0466, Reel 059952/0469, Reel 059952/0472), moving the patent rights from the inventors, through two Voicebox entities, and finally to VB Assets, LLC. This rapid chain of transfers, especially leading to a non-operating entity, is a strong indicator.
5. Pre-litigation transfer — unclear. The transfer to VB Assets, LLC occurred on October 13, 2021 (Reel 059952/0472). The first infringement suit explicitly naming US12236456 was filed on April 17, 2026. While the transfer predates the suit, it's not within the typical "within 6 months" window, but rather several years prior. However, this transfer was critical in establishing VB Assets, LLC as the asserting entity.
6. Bankruptcy fire-sale — not present. There is no indication that Voicebox Technologies, Inc. or Corporation underwent bankruptcy proceedings leading to the transfer. The conveyance type from Voicebox Technologies Corporation to VB Assets, LLC was a "Nunc Pro Tunc Assignment" (Reel 059952/0472), not a bankruptcy sale.
7. Privateering — unclear. While VB Assets, LLC is asserting the patent, there is no explicit evidence (e.g., SEC filings, public reports) to suggest that Voicebox Technologies Corporation (or any other operating company) transferred the patent to VB Assets, LLC specifically to assert against its competitors on its behalf.
8. Defensive aggregator (anti-NPE) — not present. The chain does not terminate at a known defensive aggregator. Instead, it terminates (for ownership) at VB Assets, LLC, an asserting entity.

Verdict

NPE — high confidence.

The assignment chain for U.S. Patent 12,236,456 exhibits multiple strong signals indicative of patent assertion by a Non-Practicing Entity. These include the transfer from an operating company (Voicebox Technologies Corporation) to a shell entity (VB Assets, LLC) via a cascading series of assignments on October 13, 2021 (Reel 059952/0466, 0469, 0472), the consistent use of a repeat correspondent (Terry D. Lindeman) for these transfers, and the subsequent active litigation by VB Assets, LLC against major technology companies. The granting of a security interest further supports the financial structuring typical of patent monetization efforts.

Verification link: https://assignmentcenter.uspto.gov/patent/index.html#/patent/search (search for patent number 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.

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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.

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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.

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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.

Obviousness Analysis of U.S. Patent 12,236,456 under 35 U.S.C. § 103

Under 35 U.S.C. § 103, a patent claim is invalid as obvious "if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art" (PHOSITA). This analysis considers whether a PHOSITA, at the time of the invention (priority date of February 6, 2007), would have been motivated to combine existing prior art references to arrive at the invention claimed in U.S. Patent 12,236,456, with a reasonable expectation of success.

The core inventive concept of claims 1 and 11 is a specific feedback loop in a voice-operated system: (1) a purchase opportunity is selected based on a user's utterance, (2) the user's interaction with that purchase opportunity is tracked, (3) a user-specific profile is built or updated based on this interaction, and, critically, (4) this updated profile is then used to interpret a subsequent natural language utterance from the same user.

Based on the provided prior art, the claims of U.S. Patent 12,236,456 would have been obvious by combining the teachings of U.S. Patent Application Publication No. 2005/0144068 A1 (Kopra) and U.S. Patent No. 7,069,219 B2 (Drucker).

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Combination of Kopra and Drucker

1. What Kopra Teaches:
Kopra discloses a comprehensive voice-based advertising system. It teaches:

• Receiving a user's spoken request (a natural language utterance).
• Selecting targeted advertisements based on the utterance, user location, and demographic data.
• Delivering the advertisement to the user.
• Tracking the user's interaction with the advertisement (e.g., requesting more information).
• Using this tracked interaction data to "compile a history of the user's preferences" (i.e., build or update a user-specific profile).
• Using this preference history to select more relevant advertisements in the future.

Kopra teaches nearly every element of the claimed invention. The single element it fails to explicitly disclose is using the ad-interaction-based user profile to interpret the meaning of a subsequent utterance. Kopra uses the profile to select a better advertisement after the subsequent utterance has already been interpreted by the system.

2. What Drucker Teaches:
Drucker teaches the precise element missing from Kopra, albeit outside of an advertising context. It discloses a natural language processing (NLP) system that improves its own accuracy over time. It teaches:

• Using a statistical model to interpret user utterances.
• Tracking user interactions and feedback (such as when a user clarifies an ambiguous request).
• Using this feedback to update or retrain the underlying statistical model.
• Using the updated model to more accurately interpret subsequent user utterances.

Drucker's core teaching is a feedback loop for improving the fundamental natural language understanding (NLU) capability of a system based on user behavior.

3. Motivation to Combine Kopra and Drucker:
A person of ordinary skill in the art in 2007, working to improve a voice-based services platform like the one described by Kopra, would have been highly motivated to combine it with the NLU improvement techniques taught by Drucker. The motivation is to enhance the overall performance, personalization, and perceived intelligence of the voice assistant.

• Problem Faced by the PHOSITA: The PHOSITA working with Kopra's system would have a rich source of user preference data derived from ad interactions. This data provides strong, implicit signals about a user's interests (e.g., a user who interacts with ads for Italian restaurants is likely interested in Italian food). The PHOSITA's goal is to make the entire system, not just the advertising component, more useful and accurate for the user.
• Solution Provided by Drucker: Drucker provides an explicit method for improving NLU accuracy by feeding user interaction data back into the interpretation model.
• Obvious Combination: A PHOSITA would recognize that the user preference data being collected in Kopra's system for ad selection could also be used to resolve ambiguity and improve the core interpretation of user speech, as taught by Drucker. For example, if a user's profile in the Kopra system shows a history of interacting with ads for "Marriott" hotels, and the user later issues the ambiguous utterance "Book me a room at the hotel near the airport," the PHOSITA would be motivated to apply Drucker's method. They would use the profile data to update the NLU model to interpret "the hotel" as more likely referring to "Marriott."

This combination would not just result in a better-selected advertisement, but a better, more accurate fulfillment of the user's primary request. The motivation is to leverage a valuable, already-collected data source (ad interactions) to improve the core functionality (NLU) of the system, creating a more seamless and personalized user experience. A PHOSITA would have had a reasonable expectation of success, as Drucker teaches how statistical models can be updated with new data, and the ad interaction history from Kopra is simply another stream of user behavioral data.

Analysis of Patent Term and Family for U.S. Patent 12,236,456

This analysis details the patent term, application history, and related family members for U.S. Patent 12,236,456 ("the '456 patent"), based on the provided authoritative patent documentation.

Patent Term and Projected Expiration

A U.S. patent's term is generally 20 years from the filing date of the earliest U.S. non-provisional application to which it claims priority.

• Earliest Priority Date: The '456 patent claims priority to a chain of applications originating with U.S. Patent Application Ser. No. 11/671,526, which was filed on February 6, 2007. This is the critical date for calculating the patent's term.
• Term Calculation: 20 years from February 6, 2007.
• Base Expiration Date: February 6, 2027.

Patent Term Adjustment (PTA) and Patent Term Extension (PTE):

• PTA: Patent Term Adjustment is a mechanism to compensate for certain prosecution delays caused by the USPTO. Based on the provided patent data, which lists an "Anticipated expiration" of February 6, 2027, there appears to be zero days of PTA granted for this patent. This indicates that any USPTO delays were either non-existent, offset by applicant delays, or did not meet the statutory criteria for adjustment.
• PTE: Patent Term Extension is not applicable to this patent, as it is typically reserved for patents covering products that undergo a lengthy pre-market regulatory review process (e.g., by the FDA), which is not relevant to the subject matter of this patent.

Projected Expiration Date:

Based on the earliest priority date and the absence of any patent term adjustments, the projected expiration date for U.S. Patent 12,236,456 is February 6, 2027. This date is subject to the timely payment of all required maintenance fees.

Continuity and Application History

The '456 patent is the latest in a long and extensive chain of continuation applications, which allows it to claim the benefit of the original 2007 filing date.

• Application Number: The '456 patent issued from U.S. Application No. 17/391,388, filed on August 2, 2021.
• Direct Parent: This application is a direct continuation of U.S. Application No. 16/194,944 (now U.S. Patent No. 11,080,758).
• Full Continuation Chain: As detailed in the "Cross-Reference to Related Applications" section of the patent, the '456 patent is a continuation of a series of applications, establishing a clear line of priority back to the initial 2007 application.

There are no divisional applications explicitly mentioned in the provided documentation for this specific patent.

Patent Family Members

The family of this patent consists of the numerous parent patents and applications in its continuation chain. The direct predecessors that have issued as patents include:

• U.S. Patent No. 11,080,758 (from application 16/194,944)
• U.S. Patent No. 10,134,060 (from application 15/223,870)
• U.S. Patent No. 9,406,078 (from application 14/836,606)
• U.S. Patent No. 9,269,097 (from application 14/537,598)
• U.S. Patent No. 8,886,536 (from application 14/016,757)
• U.S. Patent No. 8,527,274 (from application 13/371,870)
• U.S. Patent No. 8,145,489 (from application 12/847,564)
• U.S. Patent No. 7,818,176 (from application 11/671,526)

Additionally, the provided data indicates a further continuation application (US19/020,255) was filed on January 14, 2025, which also claims priority to this family, demonstrating an ongoing prosecution strategy by the assignee.

Defensive Disclosure and Prior Art Generation

Regarding U.S. Patent 12,236,456: "System and method for delivering targeted advertisements and/or providing natural language processing based on advertisements"

Publication Date: April 28, 2026
Author: Senior Patent Strategist and Research Engineer
Purpose: This document discloses a series of technical implementations, variations, and applications derived from the core inventive concepts of U.S. Patent 12,236,456. The intent of this publication is to place these derivative concepts into the public domain, thereby establishing them as prior art for the purposes of patentability analysis under 35 U.S.C. §§ 102 and 103.

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Section 1: Component and Data Structure Substitutions

1.1 Neuromorphic Processor for Conversational Language Interpretation

• Enabling Description: The method of claim 1 is implemented using a specialized neuromorphic processor, such as the Intel Loihi 2 or a comparable spiking neural network (SNN) hardware accelerator, to perform the functions of the Conversational Language Processor (120) and the Context Determination Module (130). The user-specific profile, built from tracked purchase opportunity interactions, is encoded as a set of synaptic weights and neuronal firing thresholds within the SNN. When a subsequent utterance is processed, the pre-existing state of the SNN (the profile) directly influences the pattern of spike propagation, thus altering the interpretation of the new utterance in an energy-efficient, parallelized manner. The tracked interaction (e.g., accepting a purchase) sends a training signal that adjusts the synaptic plasticity of the SNN via a spike-timing-dependent plasticity (STDP) learning rule.

• Mermaid Diagram:

  graph TD
      A[User Utterance 1] --> B{ASR Engine};
      B --> C[Recognized Text];
      C --> D[SNN Processor (Loihi 2)];
      subgraph SNN State (User Profile)
          D -- Reads Current State --> D;
      end
      D --> E[Context & Purchase Opportunity];
      E --> F[Deliver to User Device];
      F --> G{User Interaction};
      G -- STDP Learning Signal --> H(Update Synaptic Weights in SNN);
      A2[User Utterance 2] --> B;
      B2[Recognized Text 2] --> D;
      D -- Uses Updated Weights for Interpretation --> I[Interpreted Intent 2];
  

1.2 Federated Learning Architecture for Profile Management

• Enabling Description: The centralized user-specific profile is replaced with a federated learning (FL) architecture. The user's electronic device maintains a local Natural Language Understanding (NLU) model. After a user interacts with a purchase opportunity, the device does not transmit the raw interaction data. Instead, it computes a model update (e.g., a gradient vector) based on that interaction. This update, which represents the user's revealed preference, is encrypted and sent to a central server. The server aggregates updates from many users to create an improved global NLU model, which is then distributed back to the user devices. The critical interpretation of the next utterance uses the locally updated model before it is even sent for aggregation, ensuring immediate personalization without compromising raw data privacy.

• Mermaid Diagram:

  sequenceDiagram
      participant UserDevice
      participant FLS_Server
      UserDevice->>UserDevice: Receives Utterance, Interprets with Local_Model_V1
      UserDevice->>FLS_Server: Request Purchase Opportunity
      FLS_Server-->>UserDevice: Delivers Opportunity
      UserDevice->>UserDevice: User Interacts with Opportunity
      UserDevice->>UserDevice: Compute Gradient_Update based on interaction
      UserDevice->>UserDevice: Local_Model_V2 = Local_Model_V1 + Gradient_Update
      UserDevice->>FLS_Server: Send Encrypted Gradient_Update
      UserDevice->>UserDevice: Receives Utterance 2, Interprets with Local_Model_V2
      FLS_Server->>FLS_Server: Aggregate updates from many users
      FLS_Server-->>UserDevice: Distribute updated Global_Model_V2
  

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Section 2: Operational Parameter and Scale Expansion

2.1 Industrial Control System for Automated Resupply

• Enabling Description: The disclosed system is adapted for command and control of autonomous ground vehicles (AGVs) and robotic arms in a large-scale manufacturing facility. An array of noise-canceling microphones captures voice commands from floor managers. An utterance like, "Robot 7, inspect the A-pillar welding," is processed. The system, noting from sensor data that Robot 7's welding tip is near its operational limit, presents a "purchase opportunity" on the manager's tablet: "Robot 7 requires a new ER5356 welding tip within 5 hours. Order from Supplier A (2-hr delivery) or Supplier B (4-hr delivery)?" The manager's tapped selection is tracked. This interaction updates the manager's user-specific profile, so a subsequent ambiguous command like "Get that part ordered for the next robot" is interpreted by the NLU as specifically "Order an ER5356 welding tip from Supplier A."

• Mermaid Diagram:

  flowchart LR
      subgraph Factory Floor
          A[Manager Utterance: "Inspect welding"] -- Captured by --> B(Microphone Array);
      end
      subgraph Control Server
          B --> C{ASR};
          C --> D[NLU & Context Engine];
          D -- Fuses with --> E[SCADA/Robot Sensor Data];
          E --> F{Identify Resupply Need};
          F --> G[Generate Resupply Opportunity];
      end
      subgraph Manager Tablet
          G --> H(Present Options: Supplier A vs B);
          H -- User Selection --> I{Track Interaction};
      end
      I --> J(Update Manager's NLU Profile);
      K[Manager Utterance 2: "Order that part"] --> C;
      D -- Uses Profile J to Disambiguate --> L[Execute Action: Order ER5356 from Supplier A];
  

2.2 Voice-Assisted Control of Laboratory Nanofabrication

• Enabling Description: The system is scaled down to operate in a nano-engineering laboratory for controlling atomic force microscopes (AFMs) or nano-assemblers. A researcher's voice command, "Begin assembly of the carbon lattice," is interpreted. The system cross-references the requested protocol with a database of available molecular components. It presents a "purchase opportunity" (a design choice) on a monitor: "Warning: Graphene source purity is 98.7%. Proceed, or use CNT source (99.9% purity, +12% cost)?" The researcher's voiced response, "Use the CNTs," is tracked. This choice updates the researcher's NLU profile. Later, when the researcher says, "Run the standard purification cycle," the system's NLU, informed by the profile, interprets "standard" to mean the higher-purity protocol associated with carbon nanotubes, not the default graphene protocol.

• Mermaid Diagram:

  graph TD
      A["Researcher: 'Begin assembly'"] --> B{Voice Interface};
      B --> C[NLU Processor];
      C -- Checks --> D[Component Database];
      D --> E{Purity/Cost Conflict Identified};
      E --> F[Display Choice: Graphene vs CNT];
      F -- "Researcher: 'Use the CNTs'" --> G{Track Choice};
      G --> H[Update Researcher Profile: Prefers Purity];
      I["Researcher: 'Run standard purification'"] --> B;
      C -- Uses Profile H --> J[Interpret 'Standard' as CNT-specific Protocol];
      J --> K[Execute AFM/Nano-assembler Commands];
  

---

Section 3: Cross-Domain Applications

3.1 Aerospace: Adaptive In-Cockpit Flight Assistant

• Enabling Description: The system is integrated into a modern glass cockpit as a pilot's voice assistant. During flight, the pilot issues an utterance: "What's the weather like at KDEN?" The system retrieves and displays the weather. Concurrently, it analyzes the data and presents a "purchase opportunity" (a proactive safety choice): "Moderate turbulence is reported over the front range. Suggest vectoring 15 degrees south. Acknowledge?" The pilot's verbal confirmation, "Acknowledge, vector south," is tracked. This interaction updates the pilot's profile to indicate a preference for turbulence avoidance. On a subsequent flight, if the pilot says, "Plan our descent," the NLU model, now biased by the updated profile, will automatically interpret this command as "Plan our descent while prioritizing turbulence avoidance," and it will query for and favor routes with smoother air, even if slightly less fuel-efficient.

• Mermaid Diagram:

  sequenceDiagram
      participant Pilot
      participant Cockpit_VUI
      participant FMS as Flight Management System
      Pilot->>Cockpit_VUI: "Weather at KDEN?"
      Cockpit_VUI->>FMS: Request Weather Data
      FMS-->>Cockpit_VUI: Weather Data (contains turbulence)
      Cockpit_VUI->>Cockpit_VUI: Analyze, Generate Avoidance Choice
      Cockpit_VUI->>Pilot: Display/Voice: "Suggest vectoring south?"
      Pilot->>Cockpit_VUI: "Acknowledge"
      Cockpit_VUI->>Cockpit_VUI: Track Interaction, Update Pilot Profile (Prefers turbulence avoidance)
      Note right of Cockpit_VUI: Later in flight...
      Pilot->>Cockpit_VUI: "Plan our descent"
      Cockpit_VUI->>Cockpit_VUI: Interpret "descent" with Profile Bias
      Cockpit_VUI->>FMS: Request Descent Routes (Constraint: Minimize Turbulence)
      FMS-->>Cockpit_VUI: Provides Smoothest Route
  

3.2 Precision Agriculture: Smart Irrigation and Resource Management

• Enabling Description: The system is used by a farmer to manage a smart irrigation system. The farmer, viewing a field, says "Give me the moisture level for Zone 4." The system reports the data. Based on weather forecasts and soil conditions, it presents a "purchase opportunity": "A heatwave is expected in 48 hours. Pre-hydrate Zone 4 with 1 inch of water now (standard cost), or apply hydrogel amendment (premium cost)?" The farmer responds, "Apply the hydrogel." This interaction updates the farmer's profile with a preference for capital expenditure to mitigate risk. Later in the season, if the farmer gives an ambiguous command like "Get Zone 7 ready for the heat," the NLU will interpret this as "Apply hydrogel amendment to Zone 7," rather than the cheaper, less effective pre-hydration option.

• Mermaid Diagram:

  flowchart TD
      A["Farmer: 'Moisture Zone 4?'"] --> B{VUI};
      B --> C[Query Soil Sensors];
      C --> D[Fuse with Weather Forecast];
      D --> E{Generate Mitigation Opportunity};
      E --> F["Present Choice: Pre-hydrate vs Hydrogel"];
      F -- "Farmer: 'Apply hydrogel'" --> G(Track Interaction);
      G --> H(Update Farmer Profile: Prefers Risk Mitigation);
      I["Farmer: 'Ready Zone 7 for heat'"] --> B;
      B -- NLU uses Profile H --> J[Interpret as 'Apply Hydrogel'];
      J --> K[Activate Irrigation & Amendment Sprayers];
  

---

Section 4: Integration with Emerging Technologies

4.1 AI-Optimized Meta-Learning for Profile Adaptation

• Enabling Description: The core system is wrapped by a higher-level meta-learning AI framework. This meta-AI does not process the user's utterance directly. Instead, it observes the performance of the primary NLU model. It monitors the "tracked interaction" data and correlates it with changes in NLU accuracy (e.g., reductions in clarification questions). The "purchase opportunity" itself can be an A/B test controlled by the meta-AI. Based on this analysis, the meta-AI adjusts the hyperparameters of the profile-building module. For example, it might learn that for this specific user, interactions with high-cost purchase opportunities should apply a 5x greater learning rate to the NLU model update than interactions with low-cost ones. This optimizes the personalization process itself.

• Mermaid Diagram:

  graph TD
      subgraph Core NLU Loop
          A[Utterance] --> B{NLU Model};
          B -- Uses --> C[User Profile];
          B --> D[Select Purchase Opp.];
          D --> E{User Interaction};
          E --> F{Profile Update Module};
          F -- Updates --> C;
      end
      subgraph Meta-Learning AI
          E -- Observes --> G[Meta-AI Monitor];
          B -- Reports Accuracy --> G;
          G --> H{Analyze Efficacy of Update};
          H --> I[Adjust Hyperparameters];
          I -- Modifies --> F;
      end
  

4.2 Blockchain-Based Self-Sovereign Profile and Verification

• Enabling Description: The user-specific profile is implemented as a self-sovereign digital identity wallet based on the W3C DID (Decentralized Identifiers) standard. When a user interacts with a purchase opportunity from a vendor, the vendor issues a cryptographically signed Verifiable Credential (VC) to the user's wallet (e.g., "This user purchased product X on date Y"). This transaction is recorded on a permissioned blockchain for immutability. When the user issues a subsequent utterance to the NLU system, their device presents a Zero-Knowledge Proof (ZKP) to the NLU engine, proving they hold a relevant credential (e.g., "I can prove I have a credential related to 'automotive parts' from a trusted vendor") without revealing the specific credential itself. The NLU engine uses this verified "interest" as a high-confidence input to interpret the new utterance.

• Mermaid Diagram:

  sequenceDiagram
      participant UserWallet
      participant NLU_Engine
      participant Vendor
      participant Blockchain
      Vendor-->>UserWallet: User completes purchase
      Vendor->>UserWallet: Issue Verifiable Credential (VC)
      Vendor->>Blockchain: Anchor hash of VC
      UserWallet->>NLU_Engine: User makes new utterance
      UserWallet->>NLU_Engine: Provide Zero-Knowledge Proof of holding relevant VC
      NLU_Engine->>NLU_Engine: Use ZKP result to inform interpretation
      NLU_Engine-->>UserWallet: Respond with interpreted action
  

---

Section 5: Inverse Operation and Safe Failure Modes

5.1 Privacy-Preserving "Amnesiac Mode"

• Enabling Description: The system is designed with a stateful "privacy mode" which can be triggered by a keyword ("go private"), detection of a guest's voice via voiceprinting, or entry into a geofenced private area (e.g., a hospital). When in this mode, the feedback connection between tracking the user's interaction (step E in claim 1) and building or updating a user-specific profile (step F) is severed. The system can still present purchase opportunities, but a log of the interaction is either not kept or is explicitly firewalled from the NLU profile datastore. The system provides an audible or visual cue, such as "Amnesiac mode is on," to inform the user that their current interactions will not influence future NLU interpretations, thus providing a safe-fail mechanism for privacy.

• Mermaid Diagram:

  stateDiagram-v2
      [*] --> Normal_Mode
      Normal_Mode: User interactions update NLU Profile
      Amnesiac_Mode: User interactions DO NOT update NLU Profile
  
      Normal_Mode --> Amnesiac_Mode: Keyword ("go private") or Guest Voice Detected
      Amnesiac_Mode --> Normal_Mode: Keyword ("exit private") or Guest Voice Departs
  

---

Section 6: Combination with Open-Source Standards

6.1 Combination 1: DeepSpeech, RabbitMQ, and spaCy

• Enabling Description: A voice-based assistant is constructed where the speech recognition engine is a self-hosted instance of Mozilla's DeepSpeech. Recognized utterances are published as messages to a RabbitMQ message broker. A consumer service, written in Python, uses the spaCy NLP library to perform context determination and NLU. A user's profile is maintained as a custom extension attribute on spaCy's Doc object. When a user interacts with a purchase opportunity, the interaction data is sent to a separate topic in RabbitMQ. The spaCy consumer subscribes to this topic, and upon receiving an interaction message, it updates its statistical model weights for entity recognition or text classification before processing the next utterance from the primary topic. This creates a distributed, event-driven implementation of the core patent claim.

6.2 Combination 2: Web Speech API, ActivityPub, and IndexedDB

• Enabling Description: A fully client-side, privacy-focused implementation is created for a web browser. The W3C Web Speech API is used for in-browser speech-to-text. The user's profile is stored locally in the browser's IndexedDB. A purchase opportunity is delivered as a message formatted using the W3C ActivityPub protocol, allowing for interactive, federated advertisements. A JavaScript Service Worker listens for both speech recognition results and user interactions with ActivityPub objects. When an interaction is detected (e.g., a click), the Service Worker directly updates the user profile object in IndexedDB. When the next speech recognition event occurs, the Service Worker intercepts the transcribed text, applies interpretation rules based on the updated IndexedDB profile, and then passes the refined intent to the web application.

6.3 Combination 3: Kubernetes, OpenRTB, and Apache Kafka

• Enabling Description: A highly-scalable, cloud-native version of the system is built on Kubernetes. The purchase opportunity selection and delivery mechanism is fully compliant with the IAB's OpenRTB (Real-Time Bidding) 3.0 standard, treating the user's utterance context as the basis for a bid request. User interactions (clicks, conversions) are captured and streamed as events into an Apache Kafka topic. A Kafka Streams application consumes this event stream in real-time. This application maintains a state store (the user profile) and continuously updates an NLU model (e.g., a TensorFlow model). This updated model is immediately containerized and deployed back into the Kubernetes cluster, replacing the older model, ensuring a continuous integration/continuous deployment (CI/CD) pipeline for NLU personalization.