In my career I have a few years of experience in Finance and Trading industry and recently I decided to look back at that industry again and define the new goals in my career :). In these days, in spare time, I’m working on the trading “pet project” and came out with one interesting conception of finance modelling and prediction (an algorithm actually) that I’m seriously considering to patent.
The patenting was quite new for me, especially what relates to patenting of the algorithms :), and after a few weeks of investigation I’ve found out the following stuff that I reckon will be interesting for everybody.
An algorithm must possess five essential features – definition, finiteness, input/ output, effectiveness and order
- The feature of definition relates to the certainty of description of an algorithm’s steps
- The feature of finiteness relates to the need for an algorithm to specify a route to a final step in all possible cases to be handled by it
- The need for an algorithm to have input and output follows from the fact that it is designed to be useful.
- An algorithm is said to be effective when if each of the steps are simple enough to be performed manually, even if such performance would be tedious and inefficient
- An algorithm would be less than serviceable if its steps were described in no particular order, even if an experienced operator could work out the correct sequence for him or herself.
2) Process of Patenting – WHAT
Before patenting you need to find if the similar patent already exists. There is a patent database that allows to search across all existing patents http://www.uspto.gov/patents/process/search/index.jsp. The goal of this step is to find if your patent already exists, and if not –what’s the most related one
3) Process of Patenting - HOW
I hope that I will have enough time to finish my algorithm this year (after I learn this bloody MatLab) and apply for the provisional patent.
Last few month I’m playing with how FAST Search is working and found interesting information about the problems in searching the right information.
Studies estimate we spend 10%-15% of out time to search necessary information (what correlates to another research that employees spend 50mins per working day to find information)
The relevance of the search consist from 3 parts: recall, precision and ranking
The “catch-22” situation of the search is in “Precision vs recall” battle. High precision means that the exact document found, when high recall means that no document are missed. In other words “recall” measure how well system finds your items, when “precision” filters our what you don’t want. Precision and recall can be calculated by result set.
Thus, returning more documents (encreasing recall) we loose the precision and vice versa. The choice in up to you.
Recently found a checklist for the SharePoint Farm design template I described previously , which I reckon quite good to be used across my projects
- An infrastructure design to support your solution
- A detailed document that describes how you will implement the solution
- A plan for testing and validating the solution
- A site and solution architecture
- An understanding of the monitoring and sustained engineering requirements to support the solution
- A record of how the solution will be governed
- An understanding of how the solution will be messaged to the consumer to drive adoption of the solution