Oracle International Corporation

Teradata US, Inc.

Vivek Kumar

Meeten Bhavsar

Catherine You Francis

Kamyar Seradjfar

Jedidja Lubbers

Sanjib Kumar Mishra

Robert Crews

Edgar S Gilchrist

Arumugam Thiravallur Palani

Phaneendra Reddy

Ravinder Pal Singh Sidhu

Antonio Romero

Kishore Chilagani

Prabhakara Reddy Munnangi

Theresa M Robertson Bandy

Naveen Jaiswal

Techniques for filtering queries to a large language model (LLM) based on their relevance to an enterprise domain associated with the LLM involve training a machine learning model using historical LLM query data and associated relevance scores. These scores indicate how closely a query relates to the enterprise's operations. The trained model is then applied to new input queries, generating relevance scores for the input queries. Queries meeting a predetermined relevance threshold are passed to the LLM for processing. For queries falling below this threshold, remedial actions are taken instead of processing by the LLM. The techniques optimize computational resource allocation by prioritizing queries relevant to the enterprise while filtering out less pertinent ones. The techniques create a relevance-based gatekeeping mechanism for LLM query processing, enhancing efficiency and focusing the LLM's capabilities on enterprise-specific tasks.

Large language machine learning model query management

Techniques for runtime customization and management of user interfaces for enterprise applications are disclosed. In some embodiments, the techniques include tools, systems, and processes for building frontend, technology-independent user interfaces. A system may consume user input files that are language dependent and generate a metadata file that is language independent. The metadata file may be updated to customize or otherwise modify a user interface of an application at runtime. An event signal may be raised to notify the application when a new user interface is available. In response to detecting the signal, the application may refresh the user interface, including the application page being currently viewed, to modify the set of user interface components that are rendered and displayed to the end user. The system may store metadata for multiple versions of the user interface to allow users to quickly roll back to prior versions of a user interface.

Enterprise application runtime customization and release management

Techniques for sales orchestration using iterative machine learning are disclosed, including: obtaining, by a customer relationship management (CRM) system, opportunity-specific CRM data; generating, by the CRM system, one or more suggested sales actions applicable to the opportunity-specific CRM data, at least by applying a machine learning model to the opportunity-specific CRM data, wherein the machine learning model is configured to generate suggested sales actions based on CRM data; presenting, in a graphical user interface (GUI) of the CRM system, the one or more suggested sales actions; receiving, via the GUI of the CRM system, user input that indicates a chosen sales action associated with the opportunity-specific CRM data; obtaining, by the CRM system, data that indicates an outcome of the chosen sales action; updating the machine learning model based on the outcome of the chosen sales action.

Sales orchestration using iterative machine learning

Systems, methods, and other embodiments associated with a bot communication system for communicating between channels and bots are described. In one embodiment, a system includes a bot manager configured to provide communication between an individual bot from a plurality of bots and a target channel of a plurality of channels. The bot manager is configured to process a bot response from the individual bot to the target channel by at least: identifying channel configuration metadata from a channel metadata repository that defines a channel dependent format for communicating data with the target channel; converting the bot response to the channel dependent format associated with the target channel including formatting data components from the bot response according to the channel configuration metadata of the target channel; and transmitting, via a network connection, the converted bot response to the target channel in the channel dependent format of the target channel.

Bot omni channel communication system

Techniques for extracting data from conversations across different types of communication channels are disclosed. A system applies a set of rules to extract data from conversations based, at least in part, on a type of communication channel used for conducting the conversation. The system applies a machine learning model to recognize semantic content in conversations. The system divides conversations into conversation segments and classifies the conversation segments based on the semantic content. The system selects conversation segments to be extracted based on the semantic content and the type of communication channel over which a conversation is conducted. The system maps conversation segments from different conversations conducted on different types of communication channels to a same set of transactions.

Multi-channel conversation processing

Techniques for enhancing actionable opportunities through machine learning are disclosed. In some embodiments, a system includes an event listener for detecting an opportunity to perform an action using one or more computing resources. When a new opportunity is detected, the system generates a set of search criteria, which is used to search a set of external web resources for current events. The system may then generate, using a machine learning model as a function of one or more features extracted from current events satisfying the search criteria, a score representing a likelihood of success that the action leads to an optimal result. The system may tune the machine learning model based on feedback received that is indicative of how the current events affected the likelihood of success for the action.

Automated enhancement of opportunity insights

Systems, methods, and other embodiments associated with automatically expanding channels for bots are described. In one embodiment, a system includes a bot repository configured to identify each bot of a plurality of bots available in the computing system. A channel metadata repository is configured with channel configuration metadata for each channel of a plurality of channels available to the plurality of bots. The channel configuration metadata includes metadata that defines a specified format for communicating data with a target channel. A bot manager is configured to provide communication between each bot of the plurality of bots and each channel of the plurality of channels by at least converting output responses from each bot based on the channel configuration metadata associated with a target channel.

Bot omni channel auto expansion

In some examples, a database system identifies a plurality of query portions in a database query that contain references to a first external table, the first external table being based on data from a remote data store coupled to the database system over a network. The database system creates a common spool portion that includes projections and selections of the plurality of query portions, and rewrites the plurality of query portions into rewritten query portions that refer to a spool containing an output of the common spool portion. For execution of the database query, the database system determines, as part of optimizer planning, whether to use the plurality of query portions or the common spool portion and the rewritten query portions.

Database query processing for data in a remote data store

A centralized system may collect and aggregate assessments from multiple websites. An aggregate score may be calculated for the resource that cumulatively considers assessments from a plurality of different websites from which assessments are received from users. Text descriptions associated with each of the assessments may be provided to a machine-learning system that uses a trained model to assign identifiers to the assessments as they are received. These identifiers may include common words or text that are descriptive of different facets of user experiences related to receiving and using the resource. After selecting one or more identifiers, assessments associated with that identifier may be included or excluded from the display. Additionally, the overall aggregate score for the resource may be recalculated by removing components of that score that are based on assessments with identifiers that have been selected for exclusion.

Machine-learning model for resource assessments

Techniques are included for intelligently testing a software product using a limited number of test cases that are configured to test portions of the software product that have changed since a prior testing. To determine which test cases are best used for testing the implemented changes, a test case-to-programmatic flow mapping may be generated that relates each test case available for testing the software product with each programmatic flow that is tested by the test case. In addition, a programmatic flow-to-artifact mapping may be created that relates each programmatic flow discovered in the software product to each artifact that is accessed, created, changed, or dependent on the programmatic flow. Using these mappings, specific test cases may be determined for testing only those portions of the software product that have changed since the prior testing.

Intelligent automation test workflow

Systems and methods are provided that build and publish online documents. Source files of the online documents are automatically built into a book and published on a web site for review. A report regarding the build is provided to the author of the documents. The author can use the report to determine whether the build was successful, and whether manual intervention is necessary. The books are also archived.

Growing community of inventors

Redwood City, CA, United States of America

Vivek Kumar