Apttus Corporation

Koti R Nandyala

Venkatraman Naganathan

Manjula Devi Malairasan

Stanley Bryce Bochenek

Ramananda Gubbi Ramachandra

Sriranga Srinivasa Reddy Meeniga

Balu Jaidev Sreekumar

Maulik Patel

The present disclosure relates to a system, method, and computer program for providing a pricing platform for performing pricing calculations for a plurality of different customers with different types of pricing calculations. An instance of the price-calculation pipeline is created for each of a plurality of customers of the pricing platform. The instances of the price-calculation pipeline are executed to perform pricing calculations for a plurality of different customers with different types of pricing calculations. For each instance of the price-calculation pipeline, a performance metric is generated for each of the modular pricing stages within the instance. The performance metrics are displayed in a user dashboard that provides user controls that enable the user to adjust the computational resources allocated to each of the modular pricing stages executing on the platform. The computational resources allocated to one or more modular pricing stages are adjusted in accordance with received user input.

System, method, and computer program for providing a pricing platform for performing different types of pricing calculations for different customers

The present disclosure describes a system, method, and computer program for converting natural language queries to structured database queries, including nested database queries. In response to receiving a natural language query for a database, an NLU model is applied to the query to identify an intent and entities associated with the query. The entities are tagged with an entity type that enables the system to identify any database object names, candidate query fields, operands, and contextual entities in the query. From the tagged entities, the system identifies one or more valid explicit, implicit, and indirect references to database objects in the user query. If there is only one valid reference to a database object in the user's query, the system proceeds with steps to create a single-object query. If there are valid references to two or more database objects in the query, the system proceeds with steps to create a nested database query. This includes grouping candidate query fields, operands, and contextual entities by independent object name, and evaluating each group separately to identify subject fields, conditional parameters, order/sort criteria, and record count limits for each group.

System, method, and computer program for converting a natural language query to a nested database query

The present disclosure describes a system, method, and computer program for converting a natural language query to a structured database query. In response to receiving a natural language query for a database, an NLU model is applied to the query to identify an intent and entities associated with the query. The intent is mapped to a database object, and candidate query fields and operands are identified from the entities. The candidate query fields and operands are evaluated to identify any subject fields, conditional expressions, record count limit, and ordering/sorting criteria for the query. This including matching certain query fields and operands based on query parameters, operand types, and locations of operands relative to query fields. A query plan is created based on the evaluation of the candidate query fields and operands, and a database query is generated from the query plan.

System, method, and computer program for converting a natural language query to a structured database query

The present disclosure describes a system, method, and computer program for converting a natural language update instruction to a structured update database statement. In response to receiving a natural language query for a database, an NLU model is applied to the query to identify an intent and entities associated with the query. If the intent is to update a data object, the system evaluates the entities to identify update fields and update values. Update fields are matched to update values based on update parameters, operand type of the update value, and location of the update fields and values. For each update field and value pair, an update context is calculated to determine whether the update value is absolute or relative to an existing field value. An update plan is created with the update field and value pairs and corresponding update contexts, and a database update statement is generated from the update plan.

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Fremont, CA, United States of America

Koti R Nandyala