Spherical Systems

Idea

Introduction

The central idea is to produce a recommendation engine based on Natural Language Processing (NLP) methods. The recommendation engine would operate behind a website or a user service and feedback to the site details of what users are saying. This could be done automatically using a computer system.

The system would learn what was said using empirical methods and control system methods. The engine would then be able to extract for meaning which could be used for commercial use or public use (for clients in the public sector).

The engine would be usable behind different types of websites giving rise to wide applications.

This opportunity has arisen now because we now have Big Data and a large volume of data would make a recommendation engine more effective as training sets can be produced.

Intended impact

The impact of Senym will be to help commercialise the heavy volume of Internet communication traffic and to provide a cost effective means of assisting security.

In essence, Senym is a means of interpreting language and specifically finding the object of a sentence. Since 2011, the growth in data exchange has been so extensive that a new name called Big Data has been given. This data now accounts for 90% of all the data held (offline and online) and that data was created in just two years.

This huge repository of data takes of form of billions of emails, tweets, facebook messages, web pages, forums messages, blogs, as well as other forms. There are 144 billion emails alone every day and 2.7 billion Facebook likes.

This data currently is being commercialised but there is plenty of scope for improvement. Senym would allow social media platforms to understand when recommendations are made, when certain facts are exchanged or requested (eg recipes), and when people are seeking recommendations.

Senym could also be applied as a spam filter or in security systems which read language (eg if someone is being monitored).

Other applications could include processing of transcripts, understanding ebook books, assisting in video search (once video have been transcribed).

If an application uses language, then Senym could be used.

Substantial accomplishments to date

To date a working demo has been built and implemented at Senym.com.

This demo was designed only to show the technologies could in principle handle the data. I will outline what was done, and its importance. A C#., .NET solution was implemented to collect Twitter data for testing.

This data was passed to a Python Natural Language Toolkit (NLTK) which identified 600 sentence objects in a sample of 3000 tweets. The data was then loaded into a Neo4j graph and visually shown using Arbor which renders graphs in Javascript. The system uses Linux. Aside from C#. and .NET all the software is free for development.

The NLTK is known to be slow and would be replaced by Java in a full prototype. The NLTK now writes files and that would be replaced to a key value store such as Hadoop for a full prototype.

The technologies demonstrated that data could be extracted from social media, classified, identified, and then loaded into a graph. This is an essential step in proof of concept.

Monetisation strategy

The fundamental business model is to sell the IP rights. The 10K requested is only to build a prototype and confirm proof of concept. Angel Funding and VC Funding would be needed to build a full application. This is estimated to be 700K. However, the potential value of an NLP system would be many times 700K.

Supporting evidence for the potential monetisation of a Natural Language Processing system may be found in the announcement that 1.4 Million Dollars was invested in Idibon which employs 6 people which "creates scalable languages technologies, supporting the natural language processing of enterprise organizations”. The distinction between Senym and other NLP system providers is that Senym focuses on recommendations. Therefore, Senym systems when developed would lend themselves more to applications linking products and comments. If IP rights could not be sold, then we think the recommendation engine could be licensed. But this is seen as a second choice in terms of moving forward.

Subsequent investment would be raised in two rounds with the bulk of the funding in the final and VC funding round (500K).

It is estimated after the proof of concept is established, that 200K of Angel Funding would be required to build a Big Data prototype which could handle a million records.

These sums are needed to hire the required PhD. qualified staff to work on this venture.

Use of proceeds

The 10K is required to establish proof of concept and to build a prototype which could work on 10,000 records and correctly identify 50% of them in terms of an object.

A new PC would be needed and two developers would work on the venture for 3 months. At the end of this period, the system would have the following solution: data collection -> NLTK --> Key Value Store --> Graph.

Depending on the level of difficulty, there could some progress in replacing the NLTK with Java but that would really part of the next phase of the venture related to a Big Data prototype.