# How Samsung Uses Two AI Models to Improve Sentence Generation > A method for training an AI to write better sentences by using a second model that reads or generates text in a different order to grade the first model's work. - **Patent:** US 11727263 - **Original title:** Method of updating sentence generation model and sentence generating apparatus - **Owner:** Samsung Electronics Co Ltd - **Granted:** 2023 - **Status:** Active - **Times cited:** 1 - **Field:** ai_ml, consumer_electronics, software, telecommunications ## What it does This patent describes a way to train a machine learning model to generate text more accurately. It uses two 'decoding models'—essentially the parts of an AI that pick the next word in a sentence. The first model generates a target sentence, while the second model acts as a critic by generating words in a different order (such as backward) to calculate 'reward information.' This reward information acts like a score, telling the first model how good its sentence was, which is then used to update the first model's internal weights to improve future performance. ## What it does NOT cover - Does not cover simple text generation that lacks a secondary, order-reversed validation model. - Does not cover training methods that rely solely on human-provided labels or static datasets. - Does not cover architectures that do not use neural network weight resetting as the primary training mechanism. ## The clever bit By forcing the second model to process or generate words in a different order (like backward), the system gains a structural check on the first model's output, helping to catch errors that a forward-only model might consistently repeat. ## Real-world examples 1. Samsung Bixby voice assistant responses 2. Automated machine translation software 3. Predictive text and smart reply features in mobile operating systems ## Why it matters As AI-driven text generation becomes standard in consumer electronics, improving the quality of machine-generated language without massive amounts of human-labeled data is a major technical hurdle. This approach helps Samsung refine its virtual assistants and automated translation tools by creating a self-improving loop between two different AI perspectives. ## Frequently asked questions ### What does How Samsung Uses Two AI Models to Improve Sentence Generation cover? A method for training an AI to write better sentences by using a second model that reads or generates text in a different order to grade the first model's work. ### Who owns patent US 11727263? Samsung Electronics Co Ltd owns this patent, granted in 2023. ### When does this patent expire? This patent is expected to expire on June 21, 2038, when the invention enters the public domain. ### What is patent US 11727263 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? As AI-driven text generation becomes standard in consumer electronics, improving the quality of machine-generated language without massive amounts of human-labeled data is a major technical hurdle. This approach helps Samsung refine its virtual assistants and automated translation tools by creating a self-improving loop between two different AI perspectives. ### What does this patent NOT cover? Does not cover simple text generation that lacks a secondary, order-reversed validation model. **Full plain-English explainer:** https://patentbrief.org/patent/us/11727263/method-of-updating-sentence-generation-model-and-sentence-generating-apparatus **Original patent:** https://patents.google.com/patent/US11727263 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Adapting AI Models to Fit Device Resources](https://patentbrief.org/patent/us/20220383078/data-processing-method-and-related-device) — This patent describes how a computer system can automatically shrink a large artificial intelligence model, specifically a "transformer" type, to fit the available computing power of a phone or other device. - [How to Shrink Large AI Models Using Knowledge Distillation](https://patentbrief.org/patent/us/10289962/deep-q-networks-dqn) — A method for teaching small, efficient AI models to mimic the complex decision-making patterns of much larger, more powerful neural networks. - [How AI Models Understand Language Using 'Attention'](https://patentbrief.org/patent/us/10452978/transformer-attention-mechanism) — This patent describes a neural network architecture, known as a Transformer, that uses a "self-attention" mechanism to process sequences of information, like words in a sentence, by weighing the importance of different parts of the input. - [How to Update AI on Small Devices with Slow Internet](https://patentbrief.org/patent/us/20250363357/systems-and-methods-for-deploying-and-updating-neural-networks-at-the-edge-of-a-) — This patent describes a method for efficiently updating artificial intelligence models on small, internet-connected devices, like smart cameras, by sending only the changes, or 'patches,' instead of the entire updated model, which saves bandwidth. - [Using Non-AI Systems to Improve AI Text Generation](https://patentbrief.org/patent/us/12353827/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including) — This patent describes a method where a traditional, rule-based computer system helps a Large Language Model (LLM) generate more accurate and reliable text by providing it with better context and fact-checking.