Google Patent Uses Context Beyond Queries

Google has recently filed a patent describing how an AI assistant could use at least five real-world contextual signals to improve its responses and create more natural conversations. This system goes beyond simply analysing the keywords in a user’s query, aiming instead to engage users with contextually relevant questions and dialogue.

The patent explains that the assistant would consider factors such as the environment, user data, conversation history, and the intent behind the dialogue. These elements help the AI generate answers that feel more human-like and tailored to the user’s situation, marking a shift from traditional keyword-based search systems.

It is important to note that filing a patent is primarily about securing legal protection and does not necessarily mean that Google is actively using this technology yet. The patent includes examples based on spoken dialogue but clearly states the system is not limited to audio inputs alone.

Users could interact with the assistant through various input methods, including speaking, typing, or touch. The patent is titled “Using Large Language Model(s) In Generating Automated Assistant response(s)” and covers AI assistants that receive input from these different sources.

There are five key factors that influence how the AI assistant modifies its responses: time, location, and environmental context; user-specific data; dialogue intent and previous interactions; input method (text, touch, or speech); and system or device context. While the first four guide the response itself, the fifth determines whether the AI should use the advanced language model or revert to a more basic reply.

Time, Location, And Environmental

The patent highlights three key contextual factors—time, location, and environmental conditions—that provide information beyond what keywords alone can offer. These factors influence how the AI assistant tailors its responses, showing how AI interactions are evolving beyond simple keyword matching. Although these contextual elements are not directly related to AI Overviews or AI Mode, they demonstrate how AI-assisted conversations with data can become more dynamic and relevant.

For example, the patent describes a scenario where a user tells their assistant they are going surfing. A typical AI might respond with a generic remark like “Have fun” or “Enjoy your day.” However, the AI system covered by the patent would use location and time data to create a more specific and useful reply, such as warning about the possibility of rain. These tailored replies are known as modified assistant outputs.

The patent explains that these modified outputs aim to deepen engagement by asking contextually appropriate questions, for instance, “How long have you been surfing?” It may also provide helpful information relevant to the user’s location, such as, “If you’re heading to Example Beach again, be prepared for some light showers.” In this way, the assistant’s responses become more personal and connected to the ongoing conversation.

User-Specific Context

The patent outlines several user-specific contexts that the large language model (LLM) can draw on to create more personalised and relevant responses. These include user profile information, such as preferences for food or activities, which help tailor the assistant’s replies to individual tastes.

It also considers data from software applications, like apps that are currently or recently in use, to provide context-aware assistance. Additionally, the assistant takes into account the history of the ongoing or previous conversations, allowing it to maintain continuity and relevance during interactions.

The patent highlights these user-related contextual signals with an example: “Moreover, the context of the dialog session can be determined based on one or more contextual signals that include, for example, ambient noise detected in an environment of the client device, user profile data, software application data, … dialog history of the dialog session between the user and the automated assistant, and/or other contextual signals.” This shows how a wide range of factors can influence the assistant’s responses.

Related Intents

The patent highlights an interesting feature where a user’s food preferences help shape the assistant’s understanding of a query. For example, when a user says they are hungry, the system can identify related intents, such as the type of cuisine the user likes or the intention to eat at a restaurant.

It explains that the large language model (LLM) can generate follow-up questions based on these related intents. For instance, it might ask about the user’s preferred cuisine or check which nearby restaurants are open.

The patent describes this process as: “In this example, the additional assistant query can correspond to, for example, ‘what types of cuisine has the user indicated he/she prefers?’ (e.g., reflecting a related cuisine type intent associated with the intent of the user indicating he/she would like to eat), ‘what restaurants nearby are open?’ (e.g., reflecting a related restaurant lookup intent associated with the intent of the user indicating he/she would like to eat) … In these implementations, additional assistant output can be determined based on processing the additional assistant query.” This approach allows the assistant to provide more relevant and personalised responses.

Takeaways

Google’s patent explains how AI assistants can use real-world context to deliver more relevant and natural responses in conversations. It highlights several factors that shape these replies, including time, location, environmental conditions, user-specific information, conversation history, system settings, and input types such as text, speech, or touch.

By using large language models (LLMs), the assistant can offer personalised answers or ask follow-up questions that connect with the user’s situation. For example, it might mention local weather when someone plans an outdoor activity or suggest food options based on a person’s tastes.

This patent is significant because AI assistants are becoming a part of everyday life for millions, affecting industries like publishing, ecommerce, local businesses, and SEO. It shows how Google’s AI systems move beyond simple keyword matching to provide context-aware, tailored interactions—such as recommending nearby restaurants or offering relevant weather updates before events.