Automated systems for communications analysis according to recording restrictions

A computer-implemented method, comprising:

• dynamically processing, in real-time, communications exchanged during a communications session between a user and an agent to isolate user communications associated with the user and agent communications associated with the agent;
• continuously generating a recording that includes the agent communications and that is devoid of the user communications, wherein the recording is devoid of the user communications as a result of one or more recording restrictions associated with the user;
• processing the agent communications from the recording through a machine learning algorithm to generate a set of inferences corresponding to user sentiment associated with the communications session, wherein the machine learning algorithm is trained using historic recordings corresponding to historic communications sessions between users and agents, and feedback corresponding to the historic communications sessions;
• processing the agent communications from the recording and the set of inferences through a language processing algorithm to generate a sentiment score corresponding to the user sentiment, wherein the language processing algorithm is trained using a dataset of input communications and corresponding sentiments;
• and
• automatically transferring the communications session from the agent to another agent based on the sentiment score.

A system, comprising

• one or more processors;
• and
• memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
• dynamically process, in real-time, communications exchanged during a communications session between a user and an agent to isolate user communications associated with the user and agent communications associated with the agent;
• continuously generate a recording that includes the agent communications and that is devoid of the user communications, wherein the recording is devoid of the user communications as a result of one or more recording restrictions associated with the user;
• process the agent communications from the recording through a machine learning algorithm to generate a set of inferences corresponding to user sentiment associated with the communications session, wherein the machine learning algorithm is trained using historic recordings corresponding to historic communications sessions between users and agents, and feedback corresponding to the historic communications sessions;
• process the agent communications from the recording and the set of inferences through a language processing algorithm to generate a sentiment score corresponding to the user sentiment, wherein the language processing algorithm is trained using a dataset of input communications and corresponding sentiments;
• and
• automatically transfer the communications session from the agent to another agent based on the sentiment score.

A non-transitory computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:

• dynamically process, in real-time, communications exchanged during a communications session between a user and an agent to isolate user communications associated with the user and agent communications associated with the agent;
• continuously generate a recording that includes the agent communications and that is devoid of the user communications, wherein the recording is devoid of the user communications as a result of one or more recording restrictions associated with the user;
• process the agent communications from the recording through a machine learning algorithm to generate a set of inferences corresponding to user sentiment associated with the communications session, wherein the machine learning algorithm is trained using historic recordings corresponding to historic communications sessions between users and agents, and feedback corresponding to the historic communications sessions;
• process the agent communications from the recording and the set of inferences through a language processing algorithm to generate a sentiment score corresponding to the user sentiment, wherein the language processing algorithm is trained using a dataset of input communications and corresponding sentiments;
• and
• automatically transfer the communications session from the agent to another agent based on the sentiment score.