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US 12,762,420 B2Granted
Auto-generation of actionable information for business process instances using large language models (LLMs)
AssigneeSap SE
InventorPrashant Gautam
Life of this patent
Estimating the term…
Abstract
Techniques for automatically generating actionable information pertaining to business process instances using large language models (LLMs) are provided. In certain embodiments the information generated via these techniques can support participants of the business process instances, such as task owners and process managers, in taking actions and making decisions on the instances in accordance with their respective responsibilities.
Claims20
A method performed by one or more computer systems, the method comprising:
- collecting static information pertaining to a business process management (BPM) application;
- converting the static information into a first set of embeddings;
- storing the static information and the first set of embeddings in a static information vector store;
- detecting an error or a process visibility threshold violation with respect to a business process instance that is executed by the BPM application, the process visibility threshold violation being a violation of a key performance indicator (KPI) threshold defined for the business process instance in a process visibility application;
- and
- in response to detecting the error or the process visibility threshold violation:
- collecting instance-specific information that is specific to the business process instance;
- converting the instance-specific information into a second set of embeddings;
- storing the instance-specific information and the second set of embeddings in an instance-specific vector store;
- performing a semantic search of textual content of the error or the process visibility threshold violation on the static information vector store and the instance-specific vector store, the semantic search resulting in identification of one or more portions of the static information and/or the instance-specific information that are semantically relevant to the error or the process visibility threshold violation;
- building a large language model (LLM) prompt that includes the textual content of the error or the process visibility threshold violation and the identified one or more portions;
- providing the LLM prompt as input to an LLM, resulting in a natural language output from the LLM that is responsive to the error or the process visibility threshold violation;
- and
- causing the natural language output to be presented to a participant of the business process instance.
11 more elements
A non-transitory computer readable storage medium having stored thereon instructions executable by one or more processors, the instructions causing the one or more processors to:
- collect static information pertaining to a business process management (BPM) application;
- convert the static information into a first set of embeddings;
- store the static information and the first set of embeddings in a static information vector store;
- detect an error or a process visibility threshold violation with respect to a business process instance that is executed by the BPM application, the process visibility threshold violation being a violation of a key performance indicator (KPI) threshold defined for the business process instance in a process visibility application;
- and
- in response to detecting the error or the process visibility threshold violation:
- collect instance-specific information that is specific to the business process instance;
- convert the instance-specific information into a second set of embeddings;
- store the instance-specific information and the second set of embeddings in an instance-specific vector store;
- perform a semantic search of textual content of the error or the process visibility threshold violation on the static information vector store and the instance-specific vector store, the semantic search resulting in identification of one or more portions of the static information and/or the instance-specific information that are semantically relevant to the error or the process visibility threshold violation;
- build a large language model (LLM) prompt that includes the textual content of the error or the process visibility threshold violation and the identified one or more portions;
- provide the LLM prompt as input to an LLM, resulting in a natural language output from the LLM that is responsive to the error or the process visibility threshold violation;
- and
- cause the natural language output to be presented to a participant of the business process instance.
11 more elements
A computer system comprising:
- one or more processors;
- and
- a computer readable storage medium having stored thereon program code that, when executed by the one or more processors, cause the one or more processors to:
- collect static information pertaining to a business process management (BPM) application;
- convert the static information into a first set of embeddings;
- store the static information and the first set of embeddings in a static information vector store;
- detect an error or a process visibility threshold violation with respect to a business process instance that is executed by the BPM application, the process visibility threshold violation being a violation of a key performance indicator (KPI) threshold defined for the business process instance in a process visibility application;
- and
- in response to detecting the error or the process visibility threshold violation:
- collect instance-specific information that is specific to the business process instance;
- convert the instance-specific information into a second set of embeddings;
- store the instance-specific information and the second set of embeddings in an instance-specific vector store;
- perform a semantic search of textual content of the error or the process visibility threshold violation on the static information vector store and the instance-specific vector store, the semantic search resulting in identification of one or more portions of the static information and/or the instance-specific information that are semantically relevant to the error or the process visibility threshold violation;
- build a large language model (LLM) prompt that includes the textual content of the error or the process visibility threshold violation and the identified one or more portions;
- provide the LLM prompt as input to an LLM, resulting in a natural language output from the LLM that is responsive to the error or the process visibility threshold violation;
- and
- cause the natural language output to be presented to a participant of the business process instance.
14 more elements
Dependencies are inferred from the claim text.