Machine Learning applied to the management of court notifications


Rocío Ramirez,

Independent Legal Tech Consultant



After 20 years of legal practicing as a lawyer, mainly processing court files, I can clearly state that

the management of court notifications is one of the main workloads of a lawyer's day-to-day work.


While it is true that the procedure the court notification requires, in many cases, will need

the expertise of a legal technician, such as drafting an answer to the claim, or the preparation of an appeal, the management of the notification itself, in most cases, is a repetitive task of little value and with a predictable and controllable result.


It also happens that judicial proceedings are completely standardized by the procedural laws

themselves. The stages of the process are fully determined, one part of them being manifested through judicial notifications and the other from our own writings. So the possible solutions, actions or responses to each of these stages are perfectly defined.


By knowing the possible actions or decisions to be taken after receiving a certain notification, we

can state that they are fully automated tasks.


Basically, the management exercise that a judicial notice requires is always the same:


- Reading the notification and categorization of it (determine whether it is a judgment,

declaration of firmness, hearing date, approval of cost appraisal, etc.).


- Extraction of the hearing date or expiry that the notification includes where appropriate.


- Schedule of the hearing date or expiry.


- File the notification.


Law firms invest a lot of time and resources in this item, but machine learning technology means

a before and after in the management of this task. Processing in bulk all the notifications received, and that through this technology replicates the above-mentioned process, will significantly lighten the document management of the files.


And this implementation is also extensible to the offices of prosecutors, whose majority of work

is the daily management of the court notifications received.


Imagine that in the day a law firm receives an average of 50-100 notifications, at the stroke of a

single click they are uploaded in bulk to the machine learning system, which also returns in bulk the results obtained once the documents are processed, and at the stroke of another click, they are turned into the management software.


On the contrary, the manual management of each of them, requires opening the court notification

one by one, reading and categorizing it, extracting the expiration and scheduling it, and filing the

notification in its file. And imagine that the average management time for each of them is between 1-2minutes. We could be saving 2 to 4 hours of a person's effective work on this task daily.


Automated management through machine learning allows you to manage the same task in a

much faster and more efficient way. The same can be done in much less time, without the quality being diminished. This means a considerable optimization of management processes and the use of resources.


In appearance, these types of technologies and implementations seem to be exclusive only to

large firms and legal operators, in which these types of tools become a real necessity given by the massive document management involved in the processing of its large volumes.


But nothing further from reality.


Any user can access these services. And the results obtained after processing the documents will

be load into the client's own ERP.


Impressive are the results that Matilda (EMC Legal Software) offers. They also provide this

technology Atomian or Taiger. While there are many more that we can find such as Luminance, Big ML or Kira System, their systems focus more on contracts rather than court notifications.


There is also legal software that allows the use of this technology in an integrated way, which is a

plus.


From my point of view, these types of technologies promise an unprecedented revolution in the

management of court files.


Digital tools and artificial intelligence will significantly lighten the workload of lawyers and

attorneys, allowing professionals to spend more time on tasks that require the most value. The technology will automate repetitive and routine tasks, and artificial intelligence will add great value to document management processes, making them more efficient and agile in terms of information and documentation exchange between parties, with significant optimization of the times and resources used in the documentary processing of files, and the consequent impact on profit margins.


Task automation will completely transform the way legal services are understood and delivered.


Obviously, there will always be such particular and specific issues that will require a legal exercise

according to the nature of the matter in question, but digital tools will also play their role in the

management of items of work in this type of matter.



Rocío Ramirez,

Independent Legal Tech Consultant



#MachineLearning #LegalTech

#DigitalTransformation


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