{"id":289,"date":"2015-02-03T06:18:33","date_gmt":"2015-02-03T06:18:33","guid":{"rendered":"http:\/\/sushain.com\/blog\/?page_id=289"},"modified":"2017-09-05T20:38:55","modified_gmt":"2017-09-05T20:38:55","slug":"patents","status":"publish","type":"page","link":"https:\/\/sushain.com\/blog\/patents\/","title":{"rendered":"Patents"},"content":{"rendered":"
\n

If necessity is the mother of invention, perhaps intellectual curiosity to question the status quo is the father.<\/p>\n<\/blockquote>\n

Over the years, I’ve been fortunate to work and innovate alongside smart\/driven engineers. This, \"\"<\/a>while building products to solve real-world problems<\/a><\/span>, often leads to new ideas, inventions and a vision of how the future might look like. Though I’m not a devout believer in software patents, I do think that they are one way to protect ideas and reward the effort<\/a> <\/span>that goes in addressing a need, differentiating your product or simply, quenching intellectual curiosity. That said, I also think that we must not over-protect<\/span><\/a> and should share technical knowledge<\/a><\/span> at the right level of abstraction so that the industry can benefit in general. In line with this philosophy, I carefully tread the dotted line between protecting<\/em> and sharing <\/em>intellectual capital.<\/p>\n

Following is a representative list of my pending\/granted patents across data, analytics, AI\/ML and related areas. For a complete list, see Google Patents<\/a><\/span> . My publications are listed here<\/a><\/span>.<\/p><\/blockquote>\n

 <\/p>\n

1. US8849828<\/strong>, Refinement and calibration mechanism for improving classification of information assets.<\/a><\/span><\/p>\n

\"\"<\/a>Techniques are described for refining the manual classification of assets classified or categorized using the terms of a business glossary. A semantic refinement mechanism is used to refine the manual classification of such assets, as well as subsequently evaluate the refined asset classifications.<\/p>\n

Further, the refined asset classifications may be used as a training set for a machine learning classifier. That is, should the classification of an asset contributing to a refinement change, the refinement based on that classification may be undone, at least in some cases.<\/p>\n

 <\/p>\n

\n

2. US20140201116<\/strong>, <\/span><\/span>Optimally configuring an information landscape.<\/span><\/span><\/a><\/p>\n

\"\"According to an embodiment of the present invention, a system optimizes an information processing environment, and comprises at least one processor. The system collects information pertaining to operational behavior of the information processing environment and including a plurality of parameters. A neural network structure is established to associate the parameters to a desired operational performance characteristic for the information processing environment. The neural network structure is trained with the collected information from the information processing environment to produce a model for the information processing environment. The model is optimized to determine values for the parameters and the information processing environment is adjusted based on the determined parameter values to attain the desired operational performance of the information processing environment. Embodiments of the present invention further include a method and computer program product for optimizing an information processing environment in substantially the same manner described above.<\/p>\n

\n

3. US8583626<\/span><\/strong>, <\/span><\/a>Method to detect reference data tables in ETL processes<\/span><\/a>.<\/em><\/span><\/p>\n

A method, system and computer program product for identifying reference data tables in an Extract-Transform-Load (ETL) process, by identifying, by operation of one or more computer processors, at least a first reference data operator in the process, wherein the first reference data operator references one or more tables and evaluating at least a first table referenced by the reference data operator to determine whether the first table is a reference data table by assigning a score to the first table, wherein the score is indicative of the likelihood that the first table is a reference data table and wherein a reference data table contains a set of values that describes other data.<\/span><\/span><\/span>
\n\"\"<\/p>\n