How to filter search results?

27 min

Filters let you refine any dataset in GetFocus so it contains exactly the patents you need. This article covers every filter type available, when to use each one, and how to combine them effectively.

Important to know:

Filters are applied on top of your existing dataset — they do not change the underlying search query.

Filters stack: applying multiple filters narrows results further each time.

LLM-Filters

Landscaping patents is challenging. Finding the right combination of keywords to map out an entire technological domain is complex and time consuming. You might miss out on important patents because you forgot to include an important keyword, or add noise because you are adding keywords that also match on unintended domains. To help you out with this problem, we have introduced a feature called, LLM-Filters.

LLM-Filters use a large language model to read each patent and classify it as relevant or irrelevant based on your natural language instructions. They are the most powerful filter type, but also the slowest.

LLM Filter

When to use LLM-Filters

LLM-Filters are particularly useful in the following scenarios:

Traditional keyword or vector search filters aren't giving you accurate enough results

Your filtering criteria is complex and hard to express as keywords (e.g. a specific process with multiple technical conditions)

You want to filter in plain language rather than constructing Boolean expressions

Example instruction: "Find patents related to pyrolysis, a thermochemical decomposition process that operates under an inert atmosphere, typically reaching temperatures between 400°C and 650°C."

How does LLM-Filters work internally?

LLM-Filters operate by utilizing user instructions to identify which type of patents should be matched through the filter. Here's a step-by-step breakdown:

User Instruction: The process begins when you provide specific instructions detailing the kinds of patents you want to filter.

LLM Processing: The Large Language Model (LLM) reviews each patent within the dataset. The filter then scans through patents title, abstract, claims and description to classify each patent as either "relevant" or "irrelevant" based on your instructions. This step is contingent upon the size of your dataset and could take anywhere from 5 to 60 minutes.

Classification and Storage: After the LLM has finished its evaluation, the publication numbers for patents deemed "relevant" or "irrelevant" are collected and stored in our database.

Filter Application: Once the LLM-Filter process is complete, you can effortlessly apply it to the dataset. This enables quick and efficient filtering, making it easy for you to access the information that matters most.

What are the limitations of LLM-Filters?

While LLM-Filters can be incredibly useful, it's important to be aware of their limitations:

Dataset Size Restriction: Due to the cost and time associated with running LLM-Filters, only the first 10,000 results in a dataset are processed.

Processing Speed: The speed of LLM-Filters is inherently tied to the speed of the LLM itself. Although we employ parallelization techniques to boost efficiency, the average processing speed is approximately 12 patents per second.

Family Level: We process only the representative member from a patent family. This is the member that best matches your filters and is displayed by default on the family card. Consequently, other family members are not checked.

⚠️ Important: Datasets with an applied LLM-Filter become static — new patents published after the filter ran will not appear automatically. Click Re-run to process newly added patents (already-processed patents are skipped).

What are best practices when using LLM-Filtering?

To make the most of LLM-Filtering, follow these best practices:

💡 Tip: Start with fast filters (Keyword, Patent Offices, Status, Year) to reduce your dataset size before running an LLM-Filter.

💡 Tip: Be specific in your instructions. Vague instructions lead to inconsistent classification. Instead of "find patents about batteries", write "find patents describing solid-state lithium-ion batteries with ceramic electrolyte separators." Read here about prompts for LLM-Filter

Filters

Keyword Filtering

Keyword search filters your dataset to patent families that contain — or don't contain — specific words.

How to use

Open the Filters panel and expand Keyword Filtering.

Enter terms in the Include field and/or the Exclude field.

Choose which patent sections to search against (see below).

Click Filter to apply.

Keywords filter

Include vs. Exclude logic

Include uses AND by default — every keyword you add must be present in the patent family.

Example: Adding "bike" and "tire" returns only families that contain both words.

Exclude uses OR by default — any family containing an excluded keyword is removed.

Example: Excluding "saddle" and "gears" removes any family that contains either word.

The logic behind separating the default relationship between include and exclude keywords is that you typically want to zoom in on a particular concept in the dataset with include, and want to exclude many more aspects with exclude.

Which sections to search

You can match keywords against: Invention title, Abstract, Claims, and Description.

⚠️ Important: Consider unchecking Description to improve result quality. The description section often contains broad background text that isn't specific to the invention's topic, which can cause false positives.

Keywords filter

Creating Custom Boolean Keyword Filters

Normally, when you enter multiple keywords (e.g., bike tyre) in an “include” field, they’re combined with an AND operator by default—meaning the system looks for patents or documents containing both words. To override that default, you can type bool: before your keywords and manually include other Boolean operators.

💡 Important: To override the default logic, prefix your keywords with bool: and write your own Boolean expression.

Example

bool: bike OR bicycle (in the “include” field)

This searches for items that contain either “bike” or “bicycle” (or both). This function can be useful to cover various ways of referring to the same concept.

It bypasses the default requirement that both words must be present.

Keywords filter

Important Boolean Operators for Custom Boolean Filters

Operator	What it does	Example
bool: (prefix)	Enables custom Boolean mode	bool: bike OR bicycle
*	Matches zero or more characters	combus* → combustion, combust…
?	Matches exactly one character	te?t → test, text, tent…
( )	Groups words that must appear together as a phrase	(bike tire) → "bike tire" in sequence
" "	Exact phrase match in fixed order	"high catalytic activity"
~	Proximity search - words within N words of each other	"machine learning"~5

⚠️ Important: When using bool: mode, parentheses ( ) no longer enforce word sequence — they group terms with AND logic instead. For strict phrase matching in Boolean mode, use " " quotes.

Custom Boolean Filtering Summary

Use bool: to override default AND and manually apply other operators like OR.

Use * and ? to allow flexible character matching, but be cautious with wildcards to avoid performance issues.

Use () to group words that must appear right next to each other.

Use "" for exact phrases in a fixed order.

Use ~ to allow two words to appear near each other, with a specified distance.

By combining these tools, you gain fine-grained control over your searches, enabling you to include or exclude results more precisely.

⚠️ Important: Avoid overusing wildcards (e.g. a* b* c*). The system has to evaluate every possible character combination, which can slow down or overload the search.

Publication Numbers

The Publication Numbers filter under Advanced Filters, helps to find specific patents in the search results using their publication number.

The standard patent publication format: [2-letter country code]-[patent number]-[kind code] Example: US-20180226168-A1

To filter for multiple patents at once, separate publication numbers with a new line, semicolon, pipe (|), or tab.

Publication Numbers filter

Organization and Ultimate Owner filters

Use these filters to scope your dataset by who owns the patents.

Organization — filters by the direct assignee listed on the patent.

Ultimate Owner — groups patents by the parent company at the top of the corporate hierarchy. Use this to capture patents held by subsidiaries under a single umbrella.

How to use (both filters)

Open the filter panel and expand Organization or Ultimate Owner.

Either scroll the list and tick/untick entries, or type a name in the search field to find a specific company.

Click Filter to apply.

💡 Tip: Use Ultimate Owner when researching a large corporation to ensure you capture patents filed by its subsidiaries and acquired companies.

Ultimate Owner filter

Patent Offices

Limits results to patent families that have at least one member filed at the selected office(s).

How to use

Expand Patent Offices in the Filters panel.

Select or search for the offices you want.

Click Filter.

Patent Office filter

Status

If you only want to see patent families that have members with a certain status, you can filter by status. There are 5 statuses: 'Active', 'In-Force', 'Pending', 'Inactive', and 'Unknown'.

In Force families have at least 1 member that is currently granted and still in force.

Pending families are those where all patent family members are currently still pending.

Active families are those which are either in force or pending.

Inactive families are those where all patent family members have expired for any reason.

Unknown families are those where we do not have good information from the patent office about the current state of the patent family members, and thus the family as a whole.

How to use

Expand Patent Offices in the Filters panel.

Select or search for the offices you want.

Click Filter.

Publication year, Priority year, Application year, Expiration year filters

Both filters work the same way — you select a date range, and only patent families within that range are shown.

Publication year

Preset options: Last year, last 3, 5, 10, or 20 years. Custom: enter a range like 2010–2017.

Expiration year

Preset options: Next year, next 3, 5, 10, or 20 years. Custom: enter a range like 2025–2030.

Click Filter to apply after entering your selection.

Publication Year filter

In the Advanced Filters section you can find filters for Priority Year and Application Year. Both filters work the same way — you select a date range, and only patent families within that range are shown.

💡 All date filters can be combined, giving you greater flexibility and precision when exploring your dataset.

Click Filter to apply after entering your selection.

Similarity

The Similarity (%) filter allows you to set a similarity threshold to your dataset. E.g. the minimum similarity score to still be included in your search results. It can be adjusted by setting a specific number in the input field or by simply dragging the bar.

Similarity thresholds are an important part of vector search. Since vector search works through grouping and including similar documents to your query, at some point you will start getting less desirable results. For example, if you search for 'dog food', your top results are likely to all contain dog food patent family members. However, at some point further down the similarity list, there may be 'cat food' patent families, as cat food is at least somewhat related to dog food. The height of the bars on the chart indicates the number of patents at each similarity level.

How to use

Expand Similarity in the Filters panel.

Drag the slider or type a value (e.g. 83.7).

The bar chart shows how many patents fall at each similarity level — use it to identify where results start becoming less relevant.

Click Filter to apply.

💡 Tip: Scroll through your results before setting a threshold. Find the point where results start feeling off-topic, note the similarity score at that point, and use it as your cutoff.

Advanced Filter

Vector search in Filters

Just as you can use vector search to create initial search results, you can also use it to filter them. The Vector Search filter under Advanced Filters allows you to filter search results by using phrases, sentences, or even entire paragraphs. Unlike keyword search, the vector search filter works on meaning rather than exact words. Use it when the concept you're looking for can be expressed in many different ways.

How to use

Open the Filters panel and expand Advanced Filters → Vector Search.

Enter a phrase, sentence, or paragraph in the Positive field (what you want) and/or the Negative field (what you don't want).

Click Filter to apply.

‼️ Please note that the maximum input for the vector search filter is 384 words, any input after 384 words will not be vectorized and will thus not affect the filtering outcome.

Vector Search filter

Positive vs. Negative fields

Positive uses AND logic — multiple entries must each be semantically present.

Negative uses OR logic — patent families similar to any negative entry are excluded.

💡 Tip: Write sentences and paragraphs, not keywords. The more context you give, the more accurate the semantic match.

Example: instead of "synthetic leather", write "artificial leather, also known as synthetic leather or faux leather, is a material designed to simulate the look and feel of genuine leather."

Country of Origin

These filters scope your dataset by the country where the patent-holding organisation is headquartered — regardless of where the patents are filed.

Country of Origin (Ultimate Owner) — uses the HQ country of the top-level parent company.

Country of Origin (Organization) — uses the HQ country of the direct assignee.

How to use (both filters)

Expand the relevant Country of Origin filter.

Select/deselect countries from the list, or type a country name to search.

Click Filter to apply.

Country of Origin filters

How to filter search results?

﻿﻿LLM-Filters﻿﻿

﻿﻿When to use LLM-Filters﻿﻿

﻿﻿How does LLM-Filters work internally?﻿﻿

﻿﻿What are the limitations of LLM-Filters?﻿﻿

﻿﻿What are best practices when using LLM-Filtering?﻿﻿

﻿Filter﻿s﻿

﻿﻿Keyword Filtering ﻿﻿

﻿Include vs. Exclude logic﻿

﻿Which sections to search﻿

﻿﻿Creating Custom Boolean Keyword Filters﻿﻿

﻿﻿Important Boolean Operators ﻿for ﻿Custom Boolean Filters﻿﻿

﻿﻿Custom Boolean Filtering Summary﻿﻿

﻿﻿Publication Numbers﻿﻿

﻿Organization ﻿and ﻿Ultimate ﻿Owner﻿ filters﻿

﻿﻿Patent Offices﻿﻿

﻿Status﻿

﻿Publication year, Priority year, Application year, Expiration year filters﻿

﻿Similarity ﻿

﻿﻿Advanced Filter﻿﻿

﻿﻿Vector search in Filters﻿﻿

﻿﻿﻿Country of Origin﻿﻿

LLM-Filters

When to use LLM-Filters

How does LLM-Filters work internally?

What are the limitations of LLM-Filters?

What are best practices when using LLM-Filtering?

Filters

Keyword Filtering

Include vs. Exclude logic

Which sections to search

Creating Custom Boolean Keyword Filters

Important Boolean Operators for Custom Boolean Filters

Custom Boolean Filtering Summary

Publication Numbers

Organization and Ultimate Owner filters

Patent Offices

Status

Publication year, Priority year, Application year, Expiration year filters

Similarity

Advanced Filter

Vector search in Filters

Country of Origin