SaaS Semantic Analysis Platform Secure Data Pipelines

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Solution Overview

Problem

Large organizations face challenges in efficiently processing and analyzing vast amounts of communications data due to the tedious and inefficient manual approach, which does not scale well. Additionally, privacy concerns in traditional systems make it difficult to ensure confidentiality and privacy of data, preventing many organizations from utilizing semantic analysis systems.

Innovation Solution

A software-as-a-service (SaaS) platform is implemented with separate processing pipelines for each entity, ensuring that data remains private and secure. Unique identification codes are assigned to data associated with each entity, and a semantic analysis mechanism performs analysis by accessing semantic databases to vectorize the data and direct it to appropriate storage mechanisms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual approach is used to process communications, then data privacy is maintained, but processing efficiency is low and does not scale

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidtime consumption

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processing of communications with automated semantic analysis systems that use natural language processing, machine learning, and AI algorithms to automatically categorize, analyze, and route communications, dramatically improving processing efficiency while maintaining data privacy through secure architecture

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces secure intermediaries including encryption layers, secure data pipelines, and privacy-preserving computation mechanisms that enable automated processing while acting as mediators to protect data confidentiality between the processing system and external entities

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If traditional semantic analysis systems are used, then data processing capability is provided, but data privacy and confidentiality cannot be ensured

Engineering Contradiction:

Improvesemantic analysis capabilityVSAvoiddata privacy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent segments the semantic analysis system into isolated processing environments for different entities, with dedicated data pipelines and storage mechanisms that prevent cross-contamination of data, ensuring that each entity's data remains confidential while still providing full semantic analysis capabilities

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates secure, isolated processing environments analogous to inert atmospheres, where data is processed in encrypted containers with restricted access, preventing unauthorized interaction between different entities' data while maintaining full analytical functionality within each secure boundary

Inventive Principle:

Principle #39Inert atmosphere (Inert environment)

3Reliability

If separate processing pipelines are implemented for each entity, then data privacy is maintained, but system complexity increases

Engineering Contradiction:

Improvedata confidentialityVSAvoidsystem architecture complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements a universal secure pipeline architecture where a single multi-functional processing framework serves multiple entities simultaneously, with dynamic configuration that adapts to different entity requirements, reducing overall system complexity compared to completely separate dedicated pipelines for each entity

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If automated semantic analysis is implemented, then processing efficiency improves, but data security concerns arise

Engineering Contradiction:

Improvecommunication processing efficiencyVSAvoiddata security risks

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The patent implements beforehand cushioning through proactive security measures including pre-encryption of data at ingestion, predetermined access control policies, and pre-configured security protocols that cushion against potential security breaches before they can occur, enabling automated processing while mitigating data security risks

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

Data Source

PatentUS12288033B2Method and system for securely storing private data in a semantic analysis system

Publication Date: 2025.04.29 ORACLE INT CORP

AI summary

Disclosed is an approach for allowing an entity to perform semantic analysis in a SaaS semantic analysis platform upon private data possessed by one or more entities. In one or more embodiments, separate processing pipelines may be provided to the plurality of entities thereby keeping private data secure within the semantic analysis platform. In one or more embodiments, a common processing pipeline is provide with data associated a first entity being assigned a first identification code, and data associated with a second entity being assigned a second identification code.