LawBot

Inspiration

As of 2013, An average of 256 billion USD revenue was generated by the legal Industry, yet thousands of people donot have the fortune to get one. Often times, people especially in developing countries are mislead due to various factors like corruption, power and position of the torturer or often the victims opts out, due to possible loss of "reputation" or unlimited harassment of time, money & reputation of following up on the case.

Currently, if a person has a legal question, she generally takes one of several actions: seek counsel from a lawyer, search up laws and regulations on the internet, or ask friends/family. This process can be inefficient and lead to misdirected time and energy. We want to bridge the gap between users, Law and Law makers & provide a revolutionary one-stop tool for all those law questions to quickly determine whether any inquiries have any legal backing. Additionally, lawyers on our platform will be notified of people who potentially require their specialized services and government officials can gain access to visualizations and heatmap of possible law cases in every area.

What it does

LawBot targets three types of users:

Those who have legal questions- People with legal inquiries can relate their situation in the form of text to the LawBot chatbox. IBM-Watson analyzes the text and checks it against a keyword database, and then provides the inquirer with information on whether the described scenarios have any legal basis. For users without significant knowledge of English to frame decent sentences to explain their situation or need to immediate attention, can use image submissions, where machine learning predictions is done on the Clarafai backend. Once the type of law is identified by LawBot, the inquirer is provided with information on relevant legal and social services, based on his or her location and is also provided with a list of lawyers who can be contacted. They would further be able to rate Lawyers as well as Law making places like Police stations based on the services offered.

Those who can provide legal and other services- Lawyers on our platform save time by not having to do their own screening for relevant cases; they are contacted only by those who require their specific expertise. Lawyers choose whether or not to respond to the requests. If they accept the cases, the lawyers are connected with the clients and the legal processes begin. Cases declined are sent back into the database queue and can be addressed by Potential Lawyers who have subscribed to our premium model.

Those in government who can influence and create policy- Lawbot's geolocation data on offenses, when combined with publicly available data, can be used to create stunning visualizations and analysis on the map that help government officials analyze trends, propose action to anomalies and concentrated areas of risk, and make more informed decisions of places which needs certain amount of immediate law attention.

How we built it

We used Pubnub backend and server-less architecture, used Watson APIs to do text analytics = sentiment analysis and Keyword extraction (Watson natural language APIs), Clarifai API to train an image classifier for various cases like Acid Attacks & Domestic Violence, ESRI to find potential lawyers and police stations in the area and Nexmo to text the ones, they are interested in & further text to speech IBM Watson API (which in future would be able to be hooked up to the translation API for people who want to get law services in regional languages.

Challenges we ran into

Integrating more than 6 APIs including IBM Watson API, Pubnub Backend, ESRI API, Clarifai AI within 20 hours of the Hackathon. Also learning

Accomplishments that we're proud of

Figuring out ways to incorporate the various APIs and using it to build an complete end-to-end product of the law determining process.

What we learned

Learning more about server-less architecture, use of Nesmo, ESRI and IBM Watson API. Training Image Classifier with deep neural nets and also various use-cases, problems and loop-holes in the law industry. Project management, delegating tasks and working in a team are few non-technical skills we picked up after staying up 24 hours at the Hackathon.

What's next for LawBot

Implementing more location-based APIs, integrating a directory for Lawyers and Victims to mark all their conversations and interactions from start to finish on our portal etc. Finding Lawyers with cheapest and best services etc.