Unified AI Data Formatting for Interface-Specific Responses

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Solution Overview

Problem

Existing knowledge systems struggle to process and scale with disparate data types from various sources, leading to inefficiencies such as incorrect data retrieval, slow query execution, and resource strain due to the need for separate workflows for each data type.

Innovation Solution

An artificial-intelligence-based system translates disparate data types into a unified data type that can be automatically converted into interface-specific representations, using machine-learning models to preprocess, classify, and augment data for efficient communication across different interfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If separate workflows are used for each data type, then data processing accuracy is maintained, but system complexity and resource usage increase

Engineering Contradiction:

Improvedata processing accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements a universal data processing system that can handle multiple data types (structured, semi-structured, unstructured) through a single unified workflow. The system uses a general-purpose machine learning model that automatically adapts to different data formats, eliminating the need for separate specialized workflows for each data type while maintaining processing accuracy.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces a data translation layer that acts as an intermediary between disparate data sources and the processing system. This layer automatically translates various data formats into a unified internal representation, allowing the system to maintain simple processing workflows while handling diverse data types accurately.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If separate workflows are used for each data type, then data processing accuracy is maintained, but processing time increases

Engineering Contradiction:

Improvedata processing accuracyVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent merges multiple separate data processing workflows into a single unified process. By combining the handling of structured, semi-structured, and unstructured data into one workflow, the system eliminates redundant processing steps and reduces overall processing time while maintaining accuracy through the universal machine learning model.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent performs preliminary data translation and normalization automatically, so that when data needs to be processed, it is already in the optimal format. This pre-processing step eliminates the need for time-consuming format conversion during the main processing workflow, significantly reducing processing time.

Inventive Principle:

Principle #10Preliminary action

3Quantity of substance

If data is scraped from additional sources, then data quantity increases, but ability to process disparate data types decreases

Engineering Contradiction:

Improvedata quantityVSAvoidability to process disparate data types

Core Design Contradiction:

Quantity of substanceVSAdaptability or versatility

Solution Approach 1:

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent dynamically adjusts processing parameters based on the detected data type and structure. The machine learning model automatically modifies processing parameters such as tokenization methods, feature extraction techniques, and normalization approaches to match the specific characteristics of the input data, enabling effective processing of diverse data types from additional sources.

Inventive Principle:

Principle #35Parameter changes

4Quantity of substance

If poorly structured data is processed, then data quantity increases, but query execution time increases

Engineering Contradiction:

Improvedata quantityVSAvoidquery execution time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual or rule-based data structuring mechanisms with an automated machine learning-based system. The ML model automatically infers data structure and relationships from unstructured data, eliminating the need for complex manual processing steps and reducing query execution time significantly.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12386836B2Methods and systems for implementing a unified data format for artificial intelligence systems

Publication Date: 2025.08.12 LIVEPERSON INC

AI summary

Systems and method are provided for artificial-intelligence-based formatting of data into interface-specific representations. A computing device may receive datasets including information structured for presentation through various interfaces. The computing device may train a machine-learning model using feature vectors defined from the datasets. The machine-learning model may be trained to generate interface-specific representations of data. The computing device may then receive a query through a first type of interface and execute the trained machine-learning model using the query and an identification of the first type of interface. The machine-learning model may generate a response to the query that includes a structure tailored for interfaces that correspond to the first type of interface. The computing device may then facilitate a transmission of the response to the query through an interface that corresponds to the first type of interface.