Business language processing using LoQoS and rb-LSTM

A method comprising:

• storing a plurality of incident-solution pairs;
• generating latent scoring values for the plurality of incident-solution pairs based on a predetermined number of domain specific latent features identified within the plurality of incident-solution pairs;
• building a data structure with incident-solution data from the plurality of incident-solution pairs stored therein, where each row in the data structure corresponds to a different incident-solution pair, and the data structure comprises one or more of a column for incident data, a column for solution data, or a column for the latent scoring values, wherein the latent scoring values are latent-space-oriented quality of solution (LoQoS) scores, wherein an incident-solution pair of the plurality of incident-solution pairs is assigned a lower LoQoS score based on a determination that a respective solution has a length less than a threshold value and lacks one or more predefined domain-specific patterns;
• removing a subset, of the plurality of incident-solution pairs, which have a LoQoS score that is below a predetermined threshold value from the data structure;
• inputting the data structure, comprising incident-solution pairs of the plurality of incident-solution pairs having LoQoS scores above the predetermined threshold value, into a machine learning model to train the machine learning model to identify solutions from incidents, wherein the trained machine learning model comprises a plurality of long short-term memory (LSTM) cells, wherein each LSTM cell of the plurality of LSTM cells comprises a respective rectified linear unit (ReLU) function connected to an output of a respective selective write gate;
• and
• deploying an instance of the trained machine learning model in a runtime environment on a cloud platform to predict a solution text sequence based on an input incident text sequence received via an application programming interface.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• store a plurality of incident-solution pairs;
• generate latent scoring values for the plurality of incident-solution pairs based on a predetermined number of domain specific latent features identified within the plurality of incident-solution pairs;
• build a data structure with incident-solution data from the plurality of incident-solution pairs stored therein, where each row in the data structure corresponds to a different incident-solution pair, and the data structure comprises one or more of a column for incident data, a column for solution data, or a column for the latent scoring values, wherein the latent scoring values are latent-space-oriented quality of solution (LoQoS) scores, wherein an incident-solution pair of the plurality of incident-solution pairs is assigned a lower LoQoS score based on a determination that a respective solution has a length less than a threshold value and lacks one or more predefined domain-specific patterns;
• remove a subset, of the plurality of incident-solution pairs, which have a LoQoS score that is below a predetermined threshold value from the data structure;
• input the data structure, comprising incident-solution pairs of the plurality of incident-solution pairs having LoQoS scores above the predetermined threshold value, into a machine learning model to train the machine learning model to identify solutions from incidents, wherein the trained machine learning model comprising a plurality of long short-term memory (LSTM) cells, wherein each LSTM cell of the plurality of LSTM cells comprises a respective rectified linear unit (ReLU) function connected to an output of a respective selective write gate;
• and
• deploy an instance of the trained machine learning model in a runtime environment on a cloud platform to predict a solution text sequence based on an input incident text sequence received via an application programming interface.