US-20260129120-A1 - AUTOMATED CHARACTERISTIC IDENTIFICATION

Abstract

Apparatuses, systems, and methods relate to technology to classify a communication session into a first category of a plurality of categories. The technology identifies a selected model from a plurality of models based on the communication session being classified into the first category, where the selected model includes neurons that include adjustable weight parameters, and the selected model is configured to process communication data of the communication session with the neurons. The weight parameters modify values of the neurons. The technology determines, with the selected model, a characteristic of the communication session.

Inventors

• Shivam Desai
• Mahipal Singareddy
• Anirban CHOWDHURY
• Paul Jerchaflie
• Michael Nassar
• Nikhil Dinesh Yajaman
• Venkata K. Potturi

Assignees

• EXPRESS SCRIPTS STRATEGIC DEVELOPMENT, INC.

Dates

Publication Date

20260507

Application Date

20251106

Claims (20)

1. 1 . A computing system comprising: a processor; and a memory having a set of instructions, which when executed by the processor, cause the computing system to: classify a communication session into a first category of a plurality of categories; identify a selected model from a plurality of models based on the communication session being classified into the first category, wherein the selected model includes neurons that include adjustable weight parameters, and the selected model is configured to process communication data of the communication session with the neurons, wherein the weight parameters modify values of the neurons; and determine, with the selected model, a characteristic of the communication session.
2. 2 . The computing system of claim 1 , wherein the instructions of the memory, when executed, cause the computing system to: adjust a parameter of the communication session based on the characteristic; and present the characteristic on a graphical user interface.
3. 3 . The computing system of claim 1 , wherein the instructions of the memory, when executed, cause the computing system to: schedule a remediation action based on the characteristic.
4. 4 . The computing system of claim 1 , wherein the plurality of models include: a first plurality of machine learning models that are dedicated to determining chat characteristics of chat conversations; a second plurality of machine learning models that are dedicated to determining call characteristics of call conversations; and a third plurality of machine learning models that are dedicated to determining email characteristics of emails, wherein the selected model includes the first plurality of machine learning models, the second plurality of machine learning models or the third plurality of machine learning models.
5. 5 . The computing system of claim 1 , wherein: the first category is a chat category; and the characteristic includes one or more of a topic, a sub-topic, a sentiment, whether a chat of the communication session is dropped, a reason for the chat being dropped if the chat is dropped, whether a chat agent contacted a third-party, whether a callback number was provided, or a name.
6. 6 . The computing system of claim 1 , wherein: the first category is a call category; and the characteristic includes one or more of a topic, a sentiment, a sub-topic, a name, whether a call of the communication session was transferred, whether the call was resolved, whether the call is associated with multiple users, or a name.
7. 7 . The computing system of claim 1 , wherein: the first category is an email category; and the characteristic includes one or more of a topic, a sentiment, a sub-topic, a name, or a signature.
8. 8 . At least one non-transitory computer readable storage medium comprising a set of instructions, which when executed by a computing system, cause the computing system to: classify a communication session into a first category of a plurality of categories; identify a selected model from a plurality of models based on the communication session being classified into the first category, wherein the selected model includes neurons that include adjustable weight parameters, and the selected model is configured to process communication data of the communication session with the neurons, wherein the weight parameters modify values of the neurons; and determine, with the selected model, a characteristic of the communication session.
9. 9 . The at least one non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, cause the computing system to: adjust a parameter of the communication session based on the characteristic; and present the characteristic on a graphical user interface.
10. 10 . The at least one non-transitory computer readable storage medium of claim 8 , wherein the instructions, when executed, cause the computing system to: schedule a remediation action based on the characteristic.
11. 11 . The at least one non-transitory computer readable storage medium of claim 8 , wherein the plurality of models include: a first plurality of machine learning models that are dedicated to determining chat characteristics of chat conversations; a second plurality of machine learning models that are dedicated to determining call characteristics of call conversations; and a third plurality of machine learning models that are dedicated to determining email characteristics of emails, wherein the selected model includes the first plurality of machine learning models, the second plurality of machine learning models or the third plurality of machine learning models.
12. 12 . The at least one non-transitory computer readable storage medium of claim 8 , wherein: the first category is a chat category; and the characteristic includes one or more of a topic, a sub-topic, a sentiment, whether a chat of the communication session is dropped, a reason for the chat being dropped if the chat is dropped, whether a chat agent contacted a third-party, whether a callback number was provided, or a name.
13. 13 . The at least one non-transitory computer readable storage medium of claim 8 , wherein: the first category is a call category; and the characteristic includes one or more of a topic, a sentiment, a sub-topic, a name, whether a call of the communication session was transferred, whether the call was resolved, whether the call is associated with multiple users, or a name.
14. 14 . The at least one non-transitory computer readable storage medium of claim 8 , wherein: the first category is an email category; and the characteristic includes one or more of a topic, a sentiment, a sub-topic, a name, or a signature.
15. 15 . A method comprising: classifying a communication session into a first category of a plurality of categories; identifying a selected model from a plurality of models based on the communication session being classified into the first category, wherein the selected model includes neurons that include adjustable weight parameters, and the selected model is configured to process communication data of the communication session with the neurons, wherein the weight parameters modify values of the neurons; and determining, with the selected model, a characteristic of the communication session.
16. 16 . The method of claim 15 , further comprising: adjusting a parameter of the communication session based on the characteristic; and presenting the characteristic on a graphical user interface.
17. 17 . The method of claim 15 , further comprising: scheduling a remediation action based on the characteristic.
18. 18 . The method of claim 15 , wherein the plurality of models include: a first plurality of machine learning models that are dedicated to determining chat characteristics of chat conversations; a second plurality of machine learning models that are dedicated to determining call characteristics of call conversations; and a third plurality of machine learning models that are dedicated to determining email characteristics of emails, wherein the selected model includes the first plurality of machine learning models, the second plurality of machine learning models or the third plurality of machine learning models.
19. 19 . The method of claim 15 , wherein: the first category is a chat category; and the characteristic includes one or more of a topic, a sub-topic, a sentiment, whether a chat of the communication session is dropped, a reason for the chat being dropped if the chat is dropped, whether a chat agent contacted a third-party, whether a callback number was provided, or a name.
20. 20 . The method of claim 15 , wherein: the first category is a call category; and the characteristic includes one or more of a topic, a sentiment, a sub-topic, a name, whether a call of the communication session was transferred, whether the call was resolved, whether the call is associated with multiple users, or a name.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present application claims the benefit of priority to U.S. Patent Application No. 63/717,626 (filed on Nov. 7, 2024), which is hereby incorporated by reference in its entirety. TECHNICAL FIELD The present disclosure relates to an enhanced electronic system to identify characteristics of a communication session. Specifically, examples herein provide a versatile and efficient tool for identifying characteristics from thousands of interactions and presenting the characteristics on a new and functionally enhanced graphical user interface. Examples facilitate the display of and the rapid placement of the characteristics within a graphical user interface that can be presented in real time. BACKGROUND Telecommunications can include an electronic transmission of information over distances for different purposes. Voice telephone calls, text messaging, emailing image sharing, video teleconferences, and/or video sharing can occur over telecommunication networks. Telecommunications are used to organize computer systems into telecommunications networks. These networks themselves can be operated by computers. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS The various advantages of the embodiments of the present disclosure will become apparent to one skilled in the art by reading the following specification and appended claims, and by referencing the following drawings, in which: FIG. 1 is a diagram of an example of a communication session analysis architecture according to an embodiment; FIGS. 2A, 2B and 2C are graphical user interfaces that display characteristics of a communication session according to an embodiment; FIG. 3 is an open search architecture diagram according to an embodiment; FIG. 4 is a block diagram of an example of a computing system according to an embodiment; FIG. 5 is a flowchart of an example of a method of determining characteristics according to an embodiment; FIG. 6 is a block diagram of an example characteristic identification engine that may be deployed within the system of FIG. 1, according to some examples; FIG. 7 is a functional block diagram of an example neural network that can be used for the inference engine or other functions (e.g., engines) as described herein to produce a predictive model; FIG. 8 is a functional block diagram of an example pharmacy fulfillment device, which may be deployed within the system of FIG. 1; and FIG. 9 is a functional block diagram of an example order processing device, which may be deployed within the system of FIG. 1. DETAILED DESCRIPTION Examples relate to enhanced and automated communication session analysis processes that can determine characteristics of a telecommunication system. Doing so can facilitate electronic or mechanical granular analysis and the implementation of specific actions to enhance the effectiveness of communications. Examples are based on cutting-edge telecommunication technologies and machine learning models implemented in computing devices. Several different communication technologies may implement remote communication. For example, electronic mail (email), video conferencing, telephony, and chat have rapidly increased in size, scope and magnitude as commensurate infrastructure has developed and due to increased demand. Remote communication is widely adopted and facilitates communication between customers and entities (e.g., companies, corporations, and the like) for example. Such communication technologies are often convenient but can suffer from imperfect implementations leading to increased length of communications, lower customer satisfaction and increased labor to remediate the imperfect implementations. Furthermore, such imperfect implementations lack actionable insight that guide future decision making. Moreover, manually identifying such issues and insight in real-time is impossible given the bulk of information. That is, it would be impossible to manually classify and identify issues among thousands of telecommunication sessions (e.g., phone, video conferencing, chats and emails), etc., as doing so would take months to analyze the volume of data for any issues, at which time the issues may no longer be present and/or are unable to be remediated. In some cases, determining characteristics of a communication session between at least two devices including a first device of a customer and a second device of an entity may result in opportunities for remediation via future automated actions. The characteristics can indicate whether the communication session is progressing smoothly or whether the communication session is unsatisfactory (e.g., from the customer and/or the entity perspective). The characteristics can dictate future and/or current actions (e.g., intervention and/or adjustment). For example, entities (e.g., business devices and operations partner devices) who support a digital experience for customers (e.g., via provider devices and/or patient/member devices) are c