Chat with Set

chat with set chat with set lets you analyze an entire patent dataset using natural language instead of reading patents one by one, you ask a question and the ai reads your dataset and answers for you in this section, we explain to you how this feature works and give you some best practices when using "chat with set" chat with set allows you to ask questions about a set of patents and get insights across the entire dataset it helps you quickly understand trends, identify relevant inventions, and explore your results without reviewing each patent individually chat with set includes two modes chat with set – explore chat with set – deep dive these modes are designed for different levels of analysis, from quick exploration to structured, in depth workflows how it works how it works chat with set works on top of your current result set it does not change the dataset itself, but helps you analyze it through natural language queries depending on the mode you choose, the system either generates a single aggregated answer ( explore explore ), or builds and maintains a structured analysis layer across all patents ( deep dive deep dive ) the workflow is always the same you ask a question the ai determines whether any filters need to be applied it retrieves the relevant patent data it answers your question using the patent data chat with set – explore chat with set – explore chat with set – explore chat with set – explore is designed for fast, high level understanding of your result set what you can do what you can do ask broad questions about your patent set get summarized insights across multiple patents quickly explore themes, trends, and relationships how explore works how explore works when you ask a question, the system analyzes the patents in your current set and generates a single, aggregated answer this mode is optimized for speed, simplicity, and high level exploration when to use explore when to use explore use explore when you want a quick overview of a dataset are exploring a new topic need directional insights before deeper analysis limitations of explore limitations of explore answers are aggregated and not structured per patent limited visibility into which patents support a conclusion may miss details that exist only in patent descriptions for each response, the ai draws on the title, abstract, claims, organization, and patent office fields of the patents in your dataset at the moment, llms have a limited context window (think of this as its memory) this means that the llm cannot query your entire dataset at once if it is too large as a rule of thumb, the llm will do the following fewer than 1,000 patent families the ai will analyze all families in the dataset more than 1,000 patent families the ai will sample and analyze up to 1,000 families the above mentioned number of families is a rule of thumb the exact number of families analyzed depends on text length domains with especially long abstracts or claims will result in fewer families being processed, and vice versa chat with set – deep dive chat with set – deep dive chat with set – deep dive chat with set – deep dive is designed for detailed, structured analysis across a set of patents your question is applied to each patent in your dataset (up to the first 200) instead of one combined answer, you get an individual answer for every patent what deep dive does what deep dive does deep dive analyzes multiple patents at the same time and builds a resource table in the background — think of it as a spreadsheet that grows as you ask questions 💡 as you work, deep dive builds a table in the background — resource table resource table — each row is a patent, each column is a question you've asked this table stays available throughout your chat session and can be exported (e g to xls) you can keep refining your analysis as you go add new questions (up to 50 columns) update or rephrase existing ones remove columns you no longer need all changes are applied consistently across the dataset this makes it easy to compare patents side by side and build up a structured view of your dataset 💡 the resource table stays available throughout your conversation and evolves as you keep working how deep dive works how deep dive works select deep dive mode ask a question deep dive checks whether it can answer right away or needs to add structure to the resource table if a table update is needed, it proposes a plan — you can approve or adjust it before anything runs the analysis is applied across all patents you get both a summary answer and the detailed per patent results in the resource table 👉 before deep dive makes any changes to the resource table (e g adding or updating a column), it first shows you a plan — what it will do, what prompt it will apply, and what to expect you can approve, adjust, or revise the plan before it runs when to use deep dive when to use deep dive use deep dive when you need to go beyond a high level overview detailed, patent level insights side by side comparisons across patents structured outputs like matrices or feature tables higher accuracy grounded in patent descriptions 💡 typical use cases include competitive benchmarking, freedom to operate (fto) analysis, feature extraction (materials, processes, etc ), and art matrix creation understanding which patents were considered understanding which patents were considered after every response, you can see exactly which patents the ai used to answer your question 👉 click "click here to see the patents that were considered" to expand the list this helps you verify the answer is grounded in the right data especially useful when working with large or filtered datasets hyperlinks in answers hyperlinks in answers whenever the ai mentions a specific patent in its response, it will hyperlink it directly click the link to open and explore that patent further using filters with chat with set using filters with chat with set filtering manually before you start you can apply any filters from the right hand panel before using chat with set the ai will only analyze the patents currently visible in your filtered dataset example filter by us patents + a specific organization first → chat with set will only answer based on those patents letting chat with set apply filters automatically chat with set can also apply certain filters on its own, based on how you phrase your question here's what it can control filter example prompt publication year "what are the main innovation trends in the past 3 years?" patent office "identify the main topics of invention in chinese patents in this dataset " organization "analyze all inventions by samsung and explain their innovation efforts " publication number "compare patents us 10462849 b1 and us 10383371 b2 and explain the main differences " status "analyze all pending patents and tell me about their main topics " for other filters such as similarity or keyword filters you still need to apply them manually before using chat with set each question you ask is a fresh interaction the ai re queries the dataset for every question, including follow ups this means the same question asked twice may return slightly different answers if it can be interpreted in more than one way follow up questions do not carry context forward automatically phrase each question with full context if needed saving chat with set saving chat with set you can save any chat with set conversation for future reference to save a chat click the save button in the right upper corner of the chat panel click the pen icon to rename the chat before saving a success message will confirm it's been saved click "open in folder" to jump straight to it to find saved chats click "show insights" in your workspace all saved chats, comparisons, and insights are stored here linked to the datasets each item is labeled by type so you can tell them apart at a glance you can view your saved chats by clicking on "show insights " here, you'll find all your saved chats, comparisons, and insights, conveniently stored in one place saved insights are easily distinguishable by the labels displayed beneath each item 💡 you can save multiple chats for the same dataset they'll all be accessible from that dataset's insights panel suggested use cases suggested use cases not sure where to start? here are some prompts to inspire you trend analysis trend analysis you could ask what are the latest trends in this domain? what are the main trends in this domain over the past 5 years? what are the topics being discussed in this dataset and how many inventions belong to each topic? what are the patent numbers that relate to \[your topic]? list the publication numbers in a comma separated list organization and portfolio analysis organization and portfolio analysis what are the top 3 startups in this domain? provide a summary of their most important inventions compare the portfolio's of organization x and organization y and explain the main differences to me comparing inventions and tracking development comparing inventions and tracking development what are the differences between patent x and patent y? how has this technological domain developed? compare 5 years ago with today for prompt examples, and prompting tricks, read prompting like a pro docid\ ednbozjs3cjme3ag 9xp2