Digital processing systems and methods for implementing and managing artificial intelligence functionalities in applications

Nadav Grinberg (Haifa) , Etay Liberman (Tel Aviv) , Vlad Mystetskyi (Petah Tikva)

Authors on Pith no claims yet

Pith reviewed 2026-05-06 03:50 UTC · model claude-opus-4-7

classification patents

keywords AI agent routingcontext-aware model selectionmulti-model orchestrationquery classificationresponse post-processingfeedback-driven routingapplication-embedded AI

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The pith

A query-to-agent router picks one AI model from a pool based on the query's inferred context, then post-processes the answer before returning it.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The disclosure describes an application that does not bind itself to a single AI model. Instead, when a user submits a query, the system first classifies the query to extract a context, then consults a repository that pairs contexts with specific AI agents, and routes the query to whichever agent matches. The chosen agent's response can be transformed before it reaches the user, and feedback signals can shape later routing decisions. Configurable interface elements within the application each retain the ability to reach multiple agents. The contribution being claimed is the orchestration pattern itself — context inference plus a context-to-agent lookup plus response shaping plus feedback — embedded as the answering mechanism of an end-user application.

Core claim

The patent claims a system that, inside an application offering AI features, takes a user's query, analyzes it to infer a context, looks that context up in a stored map from contexts to AI agents, picks one agent from a pool of several, sends the query there, optionally modifies the returned response, and delivers it back to the user. The same application may host multiple configurable interface elements, each able to talk to multiple agents. The asserted point of novelty is the context-driven routing layer combined with response post-processing and feedback-informed future routing.

What carries the argument

A context-to-agent repository: a stored association between a plurality of contexts and differing AI agents, queried at runtime by a context inferred from the user's input. This lookup table is what turns a single user query into a choice among a pool of models, and it is the structural object on which the independent claims rest.

If this is right

Applications adopting this pattern would treat AI agents as interchangeable backends behind a routing layer rather than as fixed providers.
Per-context routing tables become a maintainable artifact: adding a new agent or retiring one is a repository edit
not an application rewrite.
Response modification as a first-class step normalizes outputs across heterogeneous agents
which matters when downstream UI elements expect a consistent shape.
Feedback-driven re-routing turns model selection into an online learning problem at the application layer
not the model layer.
Interface elements that can each reach multiple agents allow a single screen to mix answers from different models without the user noticing.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same architecture maps cleanly onto cost-and-quality cascades: cheap model first
escalate by context
which the claim language appears broad enough to read on.
Whether the claim survives prosecution likely depends on whether examiners treat "analyzing the query for determining a context" as distinct from generic intent classification already common in dialog systems.
Response modification before delivery is the limitation most likely to do real distinguishing work
because pure routers without post-processing are the most heavily pre-dated prior art.
Feedback-score-based future selection is effectively a contextual bandit over agents
framing it that way in litigation would invite a large body of pre-2023 prior art on bandit-based model selection.

Load-bearing premise

That choosing among several AI models based on what the user is asking — and tweaking the answer before showing it — was a new enough idea on the filing date to deserve a patent, given that model routers, expert gating, and cascaded model selection were already widely described and shipped in public software.

What would settle it

Locate, dated before August 14, 2023, a publicly available system or publication describing an application that (i) infers a context from a user query, (ii) selects one AI agent from a pool by consulting a context-to-agent mapping, (iii) modifies the agent's response prior to delivery, and (iv) updates future selection based on feedback scores. A single such reference reading on all four limitations would collapse the novelty premise of the independent claim.

Figures

Figures reproduced from USPTO: patent/us-12619833 by Etay Liberman (Tel Aviv), Nadav Grinberg (Haifa), Vlad Mystetskyi (Petah Tikva).

**Sheet 1.** Drawing sheet 1 from US 12619833. view at source ↗

**Sheet 2.** Drawing sheet 2 from US 12619833. view at source ↗

**Sheet 3.** Drawing sheet 3 from US 12619833. view at source ↗

**Sheet 4.** Drawing sheet 4 from US 12619833. view at source ↗

read the original abstract

Systems and methods are disclosed for selection operations for improving quality of Artificial Intelligence responses. The operations include accessing an application that employs AI functionality, receiving from a user, via the application, a query for which a response is sought from an AI agent, analyzing the query for determining a context, based on the context, selecting a particular AI agent from a pool of a plurality of AI agents, to which the query should be sent for response, and directing the query to the selected AI agent.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 1 invented entities

As a patent, the document does not rest on axioms or invented physical entities. The 'ledger' reframed for this genre is: (a) implicit assumption that a context classifier exists and is reliable; (b) implicit assumption that a context→agent table is the right abstraction; (c) no fitted parameters in the scientific sense — the 'score' of dependent claim 3 is left unspecified and is operator-tunable. No new entities are postulated beyond software components that already exist in the LLM-orchestration ecosystem.

free parameters (2)

feedback-derived score (claim 3)
The score function used to rank agents from user feedback is left entirely unspecified. Designer-chosen, not derived.
context→agent repository contents
The mapping from contexts to AI agents is configured by an operator; it is the central tunable of the system and is not derived from any principle.

axioms (2)

domain assumption A query's 'context' can be reliably extracted such that routing on it improves response quality.
Asserted in the preamble ('improving quality of AI responses') but not demonstrated; standard premise of router-based LLM systems.
domain assumption Modifying the AI agent's response before delivery preserves correctness while improving user-facing quality.
Implicit in claim 1's 'modifying, by the application, the received response' step.

invented entities (1)

'platform element' configurable to communicate with multiple AI agents independent evidence
purpose: Provides the in-app surface through which the routing system is invoked; the abstraction that ties the patent to a project-management-style UI.
Corresponds to existing UI widgets in monday.com-style products; not a postulated novel object, but a labeled abstraction over commodity UI components.

pith-pipeline@v0.9.0 · 16554 in / 4932 out tokens · 157553 ms · 2026-05-06T03:50:12.959640+00:00 · methodology