Find decisions by describing the situation you are researching

With the Docket Navigator MCP, you can research decisions by describing the factual and legal pattern you are looking for in ordinary language.

You do not need to know which Docket Navigator filters to use or how Docket Navigator categorizes the issue. Tell your AI assistant what happened, what legal issue matters, what type of decision you need, or what outcome you are interested in. The assistant can use Docket Navigator’s structured litigation data to identify the relevant concepts and find matching decisions.

This workflow is useful when your research question sounds more like:

Find decisions where courts excluded a damages expert because the expert valued the whole accused product without separating out the value of the patented feature.

than:

Search for Document Type X + Legal IssueThe procedural and substantive legal concepts considered in the court or agency’s decision (ex. Contributory Infringemen... More Y + Court Z.

The goal is to let you describe the research problem the way you would describe it to another lawyer.

When to use fact-pattern decision research

Use this workflow when you want to find decisions based on one or more of the following:

• A factual pattern
• A legal or procedural issue
• A type of motion
• A particular outcome
• A court or judge
• A combination of these factors

You can be specific without knowing Docket Navigator terminology.

For example:

Find cases where a court rejected a damages methodology because the expert did not separate the value of the patented feature from the rest of the product.

Find decisions denying a preliminary injunction because the plaintiff could not establish irreparable harm.

Find decisions addressing whether reliance on an opinion of counsel defeated a willfulness argument.

The more useful factual context you provide, the more precisely your AI assistant can identify the decisions you are trying to find.

How it works

Your AI assistant interprets your request and maps the concepts in your description to Docket Navigator’s structured litigation data. That can include identifying the relevant type of motion, legal issue, outcome, court, judge, date range, or other Docket Navigator criteria even when you did not use those terms yourself.

The resulting decisions can include Docket Navigator’s editorial annotations describing the court’s reasoning, along with links back to the underlying Docket Navigator documents. This allows the assistant to explain why a decision matches your request, rather than simply returning cases that contain similar words.

You can then continue the research conversationally by narrowing, broadening, or asking questions about the results.

When you have a useful result set, you can also ask your assistant to save the research to a Docket Navigator binder so that it becomes a persistent research artifact.

Example: Challenging a damages expert’s failure to separate the value of a patented feature

Assume you are a senior associate defending a patent case involving a large industrial machine. The asserted patent covers one subsystem within the machine. The plaintiff’s damages expert calculates a reasonable royalty using revenue from sales of the complete machine but does not separately value what the patented subsystem contributes.

You are considering a motion to exclude or limit the expert.

Instead of trying to determine which Docket Navigator filters correspond to that situation, you could ask:

I’m defending a patent case over a large multi-component industrial machine. The plaintiff’s damages expert ran a reasonable royalty off revenue from the entire machine and never separated out what the one patented subsystem actually contributes. I want to move to exclude him. Find me the district court decisions where courts have thrown out or cut back a damages expert for exactly that failure, and show me how the courts described what was wrong with the expert’s method.

What Docket Navigator can identify from that request

The request never uses the terms Daubert or apportionment. Those are nevertheless important concepts behind the research question. Using the Docket Navigator MCP, your AI assistant can recognize that:

• “Move to exclude him” and “thrown out or cut back a damages expert” describe a challenge to expert testimony.
• “Never separated out what the patented subsystem contributes” describes a damages apportionment issue.
• “Revenue from the entire machine” may raise the Entire Market Value Rule as a related issue.
• A reference to a particular court, judge, or time period can be used to refine the research when appropriate.

The assistant can then search the corresponding Docket Navigator decision data and review the annotations associated with the returned decisions to determine which ones actually address the problem you described.

Ask why a decision matches

Fact-pattern research is especially useful when you want more than a list of authorities.

For example, if the research returned Wirtgen America v. Caterpillar, you could ask:

Why is the Wirtgen decision in here? Walk me through what the expert did wrong.

Your assistant can use the Docket Navigator annotation associated with that decision to explain the relevant reasoning and direct you back to the source document.

This makes the returned decision set something you can inspect and evaluate, rather than a list of results you have to accept on faith.

Include decisions that cut the other way

A useful research result is not limited to decisions favoring the position you hope to take.

You can ask for decisions where courts rejected the same argument or found the expert’s methodology sufficient:

Show me the decisions in this set where the court allowed the expert’s opinion, and explain what those experts did differently.

This can help identify both favorable authority and the arguments opposing counsel may rely on.

Refine your research conversationally

Once you have a useful result set, you do not need to start over each time your question becomes more specific.

You can continue with instructions such as:

Show me the more recent decisions where the court actually excluded or limited the opinion.

Within these results, focus on cases where the expert used the value of the whole product as the royalty base.

Now show me just the District of Delaware decisions.

Which of these cases is closest to my fact pattern?

What distinction did the courts draw between methodologies they excluded and methodologies they allowed?

Your AI assistant can use those follow-up instructions to refine the Docket Navigator research while preserving the context of what you were originally trying to find.

Save the research to a binder

When the result set is useful, ask your assistant to save it:

Save this research as a Docket Navigator binder called “Apportionment Daubert Rulings.”

You can then use the binder as the working artifact for the research.

Additional refinements can be organized as separate binder tabs so that you can preserve the broader research while examining narrower questions such as recent decisions, a particular factual distinction, or decisions from your forum.

Tips for better fact-pattern research

Describe the facts that make the issue important

Instead of asking only for “apportionment decisions,” explain what happened:

The expert used total product revenue even though the patent covers only one component.

The factual description gives your AI assistant more information about what you actually consider responsive.

Tell the assistant what kind of decision you need

If procedural posture matters, include it naturally:

I want cases excluding the expert.

Find summary judgment decisions on this issue.

Show me preliminary injunction rulings.

You do not need to know the formal Docket Navigator category.

Add court, judge, or timing when those distinctions matter

You can include those constraints in the original question or add them as a follow-up:

Focus on Delaware.

What has Judge X done with this issue?

Limit this to decisions from the last five years.

Ask the assistant to explain relevance

For an important result, ask:

Why does this case match my facts?

What exactly did the court say was wrong with the methodology?

Is this really the same issue, or just adjacent to it?

This encourages the assistant to evaluate the Docket Navigator annotations instead of relying only on the result’s labels.

A useful way to think about this workflow

Traditional Docket Navigator research often begins with deciding which criteria will retrieve the decisions you need.

Fact-pattern decision research lets you begin one step earlier:

Describe the decision you need, not the filters you think will find it.

Your AI assistant handles the translation into Docket Navigator’s structured litigation data. You remain in control of the legal question, the relevance of the results, and how the research is refined.