Abstract: The present disclosure relates to an intelligent quote-to-cash software agent (“the Agent”) that enables users to efficiently interface with a quote-to-cash system from external messaging applications. The Agent is able to communicate with users using natural language and to identify quote-to-cash system action requests and associated parameters from natural language communications. The user may communicate with the Agent from one of plurality of messaging applications that are not associated with the quote-to-cash system. In response to identifying a quote-to-cash action request and associated parameters in a communication session with a user, the Agent calls the quote-to-cash system and obtains the applicable quote-to-cash output requested by the user. The Agent forwards the quote-to-cash system output to the user via the external messaging application selected by the user. The Agent may initiate communications with the user to inform the user of an opportunity in the quote-to-cash process.

Abstract: The present disclosure relates to a large-scale and low-latency data retrieval and storage system for a multi-tenant, cloud-based application, such as a Quote-to-Cash application. Conventionally, such applications rely heavily on SQL databases, which have difficultly providing service and performance at scale. The system of the present disclosure uses a distributed blob storage for data records, wherein each tenant has their own partition within the blob storage. Blob storage is able to provide service and performance at scale. Blob storage alone, however, cannot solve the needs of a multi-tenant, cloud-based application in which customer inputs complex data queries to retrieve data records. The present disclosure describes a system that converts basic blob storage into a data store can manage complex data queries in an efficient and scalable way for multiple tenants.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure describes a system, method, and computer program for real-time and computationally efficient calculation of a recommended value range for a quote variable, such as price, discount, volume, or closing time. The system uses the highest-density interval (HDI) of probability density function (PDF) as a recommended or suggested value range for a quote variable. PDFs for the quote variable are precomputed for groups of related inputs, and each PDF is summarized as an array of discrete points. A dimension reduction technique is applied to the PDF inputs in both the training and real-time (non-training) phases to reduce the number of possible combinations of PDFs. During a quote-creation process, a PDF look-up table enables the system to efficiently identify an applicable PDF from the group of precomputed PDFs based on reduced input values.

Abstract: The present disclosure relates to an intelligent quote-to-cash software agent (“the Agent”) that enables users to efficiently interface with a quote-to-cash system from external messaging applications. The Agent is able to communicate with users using natural language and to identify quote-to-cash system action requests and associated parameters from natural language communications. The user may communicate with the Agent from one of plurality of messaging applications that are not associated with the quote-to-cash system. In response to identifying a quote-to-cash action request and associated parameters in a communication session with a user, the Agent calls the quote-to-cash system and obtains the applicable quote-to-cash output requested by the user. The Agent forwards the quote-to-cash system output to the user via the external messaging application selected by the user. The Agent may initiate communications with the user to inform the user of an opportunity in the quote-to-cash process.

Abstract: The present disclosure describes a system, method, and computer program for creating and deploying prepackaged, predictive and prescriptive analytics modules for use with a quote-to-cash application. When deployed, the prepackaged analytics modules execute seamlessly, from the user's perspective, with the quote-to-cash application to provide predictions, recommendations, or other data-driven insights at one or more places in the quote-to-cash process. The modules are created by leveraging a pre-defined transactional data model that is native to the quote-to-cash application. A separate instance of the module is created for each customer that deploys a module. A customer's instance of the module is automatically retrained with the customer's own quote-to-cash data during the deployment phase by retrieving customer data matching metadata definitions predefined by the quote-to-cash application.

Abstract: This disclosure relates to a system, method, and computer program that enables users to initiate quote-to-cash system actions and receive quote-to-cash system output via a virtual/augmented reality interface. A virtual reality client device provides a virtual/augmented reality user interface via which the user can initiate one of a plurality of quote-to-cash action requests, such as requesting a quote or configuring a product. In response to the user selecting a quote-to-cash action, the client device displays a virtual/augmented scene with graphical images that correspond to parameters of a quote-to-cash action. The user is able to specify parameters for the quote-to-cash action by interacting with these images in the virtual/augmented reality scene.

Abstract: The present disclosure describes a system, method, and computer program for real-time and computationally efficient calculation of a recommended value range for a quote variable, such as price, discount, volume, or closing time. The system uses the highest-density interval (HDI) of probability density function (PDF) as a recommended or suggested value range for a quote variable. PDFs for the quote variable are precomputed for groups of related inputs, and each PDF is summarized as an array of discrete points. A dimension reduction technique is applied to the PDF inputs in both the training and real-time (non-training) phases to reduce the number of possible combinations of PDFs. During a quote-creation process, a PDF look-up table enables the system to efficiently identify an applicable PDF from the group of precomputed PDFs based on reduced input values.