Salesforce has been granted a patent for a system that optimizes indexes for accessing database tables. The system uses machine learning to determine frequently accessed column values and creates optimized indexes for them. When a specific column value is specified in a database operation, the system references the matching optimized index to access the associated record in the database table. GlobalData’s report on Salesforce gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Salesforce, Social media analytics was a key innovation area identified from patents. Salesforce's grant share as of September 2023 was 67%. Grant share is based on the ratio of number of grants to total number of patents.
Optimizing indexes for accessing database tables

A recently granted patent (Publication Number: US11775519B2) describes a system for optimizing indexes for accessing database tables. The system includes one or more processors and a computer-readable medium storing instructions. These instructions, when executed, train a machine-learning model to determine counts of instances of column values specified in database operations on a table. The model also determines a threshold for these column values. The system identifies frequently accessed column values that meet the threshold and creates an index for each column of the table that stores these frequently accessed values. It then inserts the values into the corresponding index.
When a database operation specifies a specific column value, the system determines if the created index matches the specific column and stores the specific column value. If there is a match, the system accesses the record associated with the specific column and value by referencing the matching frequently accessed column value in the index.
The system can execute a historical set of database operations to determine counts and thresholds. It can also retrain the machine-learning model based on these counts and execute a production set of database operations. This allows the system to identify additional frequently accessed column values and create optimized indexes for them. It can also identify frequently accessed column values that historically met a threshold but currently fail to meet a threshold. In such cases, the system can delete the optimized index for the corresponding column and remove the frequently accessed column value from the index.
The threshold for determining frequently accessed column values can be determined by either the machine-learning model or a system administrator. Additionally, if none of the optimized indexes match the specific column or store the specific column value, the system can still access the record associated with the specific column and value without referencing any frequently accessed column value in any index.
Overall, this patented system offers a method for optimizing indexes in database tables based on frequently accessed column values. By using machine learning and historical data, the system can dynamically create and update indexes to improve database performance and access efficiency.
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