Abstract: A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot.

Abstract: The present disclosure relates to automated chatbot generation for different domains from available human-to-human chat logs. The systems and methods may be configured to cluster user utterances as well as agent utterances from the human chat logs. A data miner mines intents and entities from the user utterance clustering and mines actions from agent utterances. The intents, entities and actions mined are used to generate a set of stories or flows which are further used by a machine learning engine to train the chatbot. The stories or flows are also generated automatically by mapping the intents with the actions.

Abstract: A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot.

Abstract: The present disclosure relates to automated chatbot generation for different domains from available human-to-human chat logs. The systems and methods may be configured to cluster user utterances as well as agent utterances from the human chat logs. A data miner mines intents and entities from the user utterance clustering and mines actions from agent utterances. The intents, entities and actions mined are used to generate a set of stories or flows which are further used by a machine learning engine to train the chatbot. The stories or flows are also generated automatically by mapping the intents with the actions.

Abstract: Transformation configuration data is set for a consumer replication set on a consumer instance to replicate on the consumer instance data of a source table included in a producer replication set on a producer instance. The transformation configuration data includes configuration data of at least one of: (i) a target table from among a plurality of tables on the consumer instance that is specified in the consumer replication set as a table for loading on the consumer instance, incoming data from the source table; and (ii) a specified mapping of incoming fields of the source table with respective fields of the target table. Replication event data of a data modification event associated with a record on the source table is received. The received replication event data is transformed based on the set transformation configuration data, and loaded on the target table.

Abstract: A system for categorizing words into clusters includes a receiver to receive a set of sentences formed by a plurality of words. The set of sentences is indicative of interaction of a user with a virtual assistant. A categorizer categorizes the plurality of words into a first set of clusters by using a first clustering technique, and categorizes the plurality of words into a second set of clusters by using a second clustering technique. A detector detects words that appear in similar clusters after categorization by the first clustering technique and the second clustering technique. Similarity of clusters is based on a nature of words forming the clusters. A generator generates a confidence score for each of the plurality of words based on the detection. The confidence score of a word is indicative of accuracy of the categorization of the word.

Abstract: Transformation configuration data is set for a consumer replication set on a consumer instance to replicate on the consumer instance data of a source table included in a producer replication set on a producer instance. The transformation configuration data includes configuration data of at least one of: (i) a target table from among a plurality of tables on the consumer instance that is specified in the consumer replication set as a table for loading on the consumer instance, incoming data from the source table; and (ii) a specified mapping of incoming fields of the source table with respective fields of the target table. Replication event data of a data modification event associated with a record on the source table is received. The received replication event data is transformed based on the set transformation configuration data, and loaded on the target table.

Abstract: A system for categorizing words into clusters includes a receiver to receive a set of sentences formed by a plurality of words. The set of sentences is indicative of interaction of a user with a virtual assistant. A categorizer categorizes the plurality of words into a first set of clusters by using a first clustering technique, and categorizes the plurality of words into a second set of clusters by using a second clustering technique. A detector detects words that appear in similar clusters after categorization by the first clustering technique and the second clustering technique. Similarity of clusters is based on a nature of words forming the clusters. A generator generates a confidence score for each of the plurality of words based on the detection. The confidence score of a word is indicative of accuracy of the categorization of the word.