Aspects of the subject disclosure may include, for example, a method in which a processing system collects information associated with trouble tickets each including a problem abstract and a log text. The method includes analyzing the log text to obtain a problem resolution for that ticket; defining ticket clusters according to the problem abstracts, and labeling the clusters. The processing system creates a library of the labeled clusters, each entry including a cluster label, a problem abstract for that cluster, and a resolution summary for that problem abstract, indicating a mapping of the problem abstract to the resolution summary for that cluster. The method includes training, based on the mapping, machine-learning applications for a predicted resolution summary for each cluster and for classifying a new ticket. The method includes assigning the new ticket to a cluster according to the classifying. Other embodiments are disclosed.

Disclosed are methods, systems, and non-transitory computer-readable medium for context development. For instance, a first method may include obtaining first micro-application actor information associated with a first micro-application actor. The first micro-application actor information may include information for workflow rules, and the workflow rules may include data set rules, extract, transform, load (ETL) rules, and functional expressions. The first method may further include obtaining data from data sources based on the data set rules; applying the ETL rules to the obtained data to generate processed data; applying the functional expressions to the processed data to obtain an output; and performing at least one processing action based on the output. A second method may include generating a system component corresponding to a blueprint based on a user request; associating the system component with a domain of a user account; and performing processes associated the system component.

A method, apparatus and product for chat-based application interface for automation. Using a natural language interface, receiving user input. Based on the user input, determining an automation process of a computer program having a user interface (UI), to be executed. The automation process is executed by utilizing the UI to input data thereto or execute functionality thereof. Additionally or alternatively, a conversation to be implemented by a natural language interface may be defined. The conversation is configured to obtain from the user one or more values corresponding to one or more parameters. The conversation is associated with a parameterized automation process depending on the one or more parameters. The parameterized automation process is invoked automatically by a natural language interface and using one or more values provided by the user to the natural language interface for the one or more parameters.

A method, apparatus and product for chat-based application interface for automation. Using a natural language interface, receiving user input. Based on the user input, determining an automation process of a computer program having a user interface (UI), to be executed. The automation process is executed by utilizing the UI to input data thereto or execute functionality thereof. Additionally or alternatively, a conversation to be implemented by a natural language interface may be defined. The conversation is configured to obtain from the user one or more values corresponding to one or more parameters. The conversation is associated with a parameterized automation process depending on the one or more parameters. The parameterized automation process is invoked automatically by a natural language interface and using one or more values provided by the user to the natural language interface for the one or more parameters.

Stepwise relationship cadence management can include generating a discourse cadence and confidence (DCC) measure based on a response message. The response message is made in replying to an originating message during a multi-party discourse over an electronic communication channel. The DCC measure indicates a likelihood of improving cadence and confidence with respect to an originator of the originating message and is based on a stepwise relational confidence model (SRCM) generated from an analysis of a plurality of prior multi-party discourses. Stepwise relationship cadence management can also include prompting a user to provide a follow-on message in response to determining that the response message made in replying to the originating message is not likely to improve cadence and confidence.

A computer-implement method and computer system may be configured to facilitate policyholder setup in connection with estate handling. An audible or visible chatbot avator or doppelgänger may lead a trustee, beneficiary, or family member through the estate of an impaired or deceased user. A computer system may have been provided with, or gather, sample voice and visual recordings associated with a user that are used to build the chatbot avatar that simulates the user audibly and/or visually. The computer system may have previously prompted the user for necessary items to properly handle their estate, such as information related to financial accounts, loans, insurance policies, etc. and user names and passwords to various electronic accounts.

A computer-implement method and computer system may be configured to facilitate policyholder setup in connection with estate handling. An audible or visible chatbot avator or doppelgänger may lead a trustee, beneficiary, or family member through the estate of an impaired or deceased user. A computer system may have been provided with, or gather, sample voice and visual recordings associated with a user that are used to build the chatbot avatar that simulates the user audibly and/or visually. The computer system may have previously prompted the user for necessary items to properly handle their estate, such as information related to financial accounts, loans, insurance policies, etc. and user names and passwords to various electronic accounts.

A system comprises a communications module; at least one processor coupled with the communications module; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to provide, via the communications module, a first encryption key of an encryption key pair to a client device; receive, via the communications module and from a conversation agent server, a fulfillment request based on a natural language input transmitted from the client device to the conversation agent server; determine that the fulfillment request includes a request for personal data; obtain the requested personal data; encrypt the personal data with a second encryption key of the encryption key pair; and provide, via the communications module and to the conversation agent server, the encrypted personal data for transmission to the client device.

Apparatus and methods for sharing bookmarks are provided. Bookmarks may include queries to a chatbot or other interactive application. A server may include a shared bookmark controller and a personal computing device may include a user bookmark controller. A user may save a bookmark and share the bookmark with a chosen recipient. The user bookmark controller may send the bookmark and the identity of the recipient to the shared bookmark controller. The shared bookmark controller may then send the bookmark to the recipient and record whether the recipient saves or rejects the bookmark. Bookmarks may be collated and categorized into groups, and entire groups may be shared.

Apparatus and methods for sharing bookmarks are provided. Bookmarks may include queries to a chatbot or other interactive application. A server may include a shared bookmark controller and a personal computing device may include a user bookmark controller. A user may save a bookmark and share the bookmark with a chosen recipient. The user bookmark controller may send the bookmark and the identity of the recipient to the shared bookmark controller. The shared bookmark controller may then send the bookmark to the recipient and record whether the recipient saves or rejects the bookmark. Bookmarks may be collated and categorized into groups, and entire groups may be shared.