How to filter search results?
Just as you can use vector search to create initial search results, you can also use it to filter them. The Vector Search filter allows you to filter search results by using phrases, sentences, or even entire paragraphs. Unlike Keyword search, it does not search for exact words or combinations thereof, but rather filters based on meaning. For example, if your initial search query was "car seat", you might enter "artificial leather, also known as synthetic leather or faux leather, is a material designed to etc." into the vector search filter to filter for patent families that describe artificial leather car seats without the normal limitations associated with keyword search. The vector search filter always works better with concepts rather than keywords, so consider entering sentences or paragraphs over keywords to make the most use of this filter. 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.
Use the positive field to indicate what you do want in your search results. When using Positive search, multiple entries will be interpreted as having an AND relationship. In other words, multiple entries into the Positive search field will include patents that are similar to Input A AND Input B
Use the negative field to indicate what you don't want in your search results. A Negative search will interpret multiple entries as having an OR relationship. In other words, multiple entries into the Negative search field will exclude patents that are similar to Input A OR Input B.
Keyword search allows you to filter your dataset for patents that contain certain keywords. There is an include and an exclude option in the keyword filtering section.
Every keyword you include MUST be present in the patent family to still show up in your search results. If you add multiple keywords in include, the default relationship between the keywords is AND. In other words, if you add "bike" and "tire" as keywords, only patent families that contain both words will be displayed in your results.
In exclude, the default relationship between keywords is OR. Any patent family that contains an excluded keyword will be ignored. In other words, if you exclude "saddle" and "gears" all patent families that contain either word will be excluded. 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.
Below include/exclude you can select which segments of the patent families to match your keyword filters with. Keyword search is completed in the patent sections such as Invention title, Abstract, Claims, and Description. Typically, patent subjects will be mentioned in the Title, Abstract, or Claims. 💡To improve search results, consider unchecking "Description" as this section often contains many words that are not directly associated with the topic of the invention itself.
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.
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.
Wildcards: * and ?
- Asterisk (*)
- Replaces zero or more characters.
- Example: combus* finds documents mentioning “combus,” “combust,” “combustion,” etc.
- Question Mark (?)
- Replaces a single character.
- Example: te?t can match “test,” “tent,” “text,” etc.
Warning: Using too many wildcards (e.g., a* b* c*) can overload the system, as it has to search for every possible combination of letters matching those patterns.
3. Grouping Keywords with Parentheses: ()
- Parentheses are useful to ensure that multiple words are included exactly in sequence.
- Example: (bike tire) returns items mentioning “bike tire” as a phrase.
- In other words, “tire” must directly follow “bike.”
- ❗When applying the bool modifier the sequence requirement will be ignored.
- Example: bool: (bike tire) returns items mentioning both (“bike” AND “tire”) without enforcing that “tire” follows “bike.”
4. Exact Phrase Search: ""
- Surrounding your search terms with quotation marks looks for the exact phrase in the same order.
- Example: "high catalytic activity of carbonic anhydrase"
- This finds documents where all these words appear in exactly that order.
5. Proximity Searches with Tilde: ~
- Proximity queries let the words appear within a certain distance of each other, and in any order.
- Example: "machine learning"~5
- This matches documents where “machine” and “learning” occur within five words of each other (in any order).
- 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.
The Publication Numbers filter helps to find specific patents in the search results using their publication number. The standard patent publication format in Odin is: "2 digit country code-patent number-kind code". For example: "US-20180226168-A1".
To filter the search results by organizations, use the Organization filter. There are two ways to use this filter: by selecting/deselecting an organization from the list, or by entering a specific name in the organization search field and then selecting or deselecting that organization. Searching for an organization's name will allow you to select that particular organization's portfolio within your search set. Simply tick/untick which organizations you want to in or exclude and hit 'Filter' to update your dataset.
With the Ultimate Owner filter, organizations are grouped by their ultimate owners. Use this filter option to make sure you find all patent families owned by a particular organization, even if specific patent families are assigned to a daughter organization.
There are two ways to use this filter: by selecting/deselecting an ultimate owner from the list, or by entering a specific organization name in the ultimate owner search field and then selecting or deselecting that ultimate owner. Searching for an ultimate owner name will allow you to select that particular organization's portfolio within your search set. Simply tick/untick which organizations you want to in or exclude and hit 'Filter' to update your dataset.
If you are only interested in patent families with members in specific patent offices, use the patent office filter. Similar to organizations, there are two ways to use it: selecting/deselecting a patent office from the list, or searching for a specific patent office and then selecting/deselecting it. Hit 'Filter' when you have applied your selection to update your dataset.
If you only want to see patent families that have members with a certain status, you can filter by status. Open the status filter and tick/untick which statuses you want to include in your dataset. 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.
Hit 'Filter' to update your dataset when you have applied your selection.
With the publication year filter, you can select a pre-determined or custom publication year date range for your dataset. Standard filters are: Last year, last 3 years, last 5 years, last 10 years, and last 20 years. You can also add a custom date range by entering the year range (e.g. 2010-2017) you want to apply to your data set. Hit 'Filter' to update your dataset when you have entered your selection.
With the expiration year filter, you can select a pre-determined or custom expiration year date range for your dataset. Standard filters are: Next year, next 3 years, next 5 years, next 10 years, and next 20 years. You can also add a custom date range by entering the year range (e.g. 2025-2030) you want to apply to your dataset. Hit 'Filter' to update your dataset when you have entered your selection.
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. The height of the bars on the chart indicates the number of patents at each similarity level.
For example, a similarity threshold of 83,7 will only include patent families in your search results if they are at least 83,7% similar to your query. 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. To determine the appropriate similarity threshold for your search results, you might consider scrolling through your list of results to identify a similarity score at which your results start getting irrelevant. If you identify such a threshold, this would be a good number to place your similarity threshold.
