System and method for determining and processing user temperament

A system and method for determining user temperament. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code. The executable code causes the processor(s) to receive an incoming call from a user, receive audible language from the user, determine from the language properties such as: volume, content, inflection, and/or pitch, input the determined properties into an algorithm configured to rank temperament, and receive as output from the algorithm a ranking of the user's temperament. Additionally, the executable code may cause the processor to transfer the incoming call to a representative and relay the user's temperament ranking to the representative. The representative chosen for the user may be selected based on the user's temperament ranking. The system may furnish the representative with a script that correlates with the user's temperament ranking.

FIELD

This invention relates generally to the field of emotional assessment, and more particularly embodiments of the invention relate to utilizing machine learning to predict user temperament.

BACKGROUND

Customers, or users, may call or contact an entity and/or its representatives for many different reasons and with many different temperaments. When a representative gets to each user's call, they generally have no idea what reason the user has called or what the user's temperament may be. The representative may not be appropriately equipped to handle a particular temperament or a particular problem the user may have. To this end, it would be beneficial to have a system in place to know the user's temperament prior to the transferring the user to a representative.

BRIEF SUMMARY

Embodiments of the present invention address the above needs and/or achieve other advantages by providing systems, apparatuses, and methods that predict a calling user's temperament based on audible language from the user.

Embodiments of the invention include a system for determining user temperament. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code. The executable code causes the processor(s) to receive an incoming call from a user, receive audible language from the user, determine from the language properties such as: volume, content, inflection, and/or pitch, input the determined properties into an algorithm configured to rank temperament, and receive as output from the algorithm a ranking of the user's temperament. Additionally, the executable code may cause the processor to transfer the incoming call to a representative and relay the user's temperament ranking to the representative. The representative chosen for the user may be selected based on the user's temperament ranking. The system may furnish the representative with a script that correlates with the user's temperament ranking. Additionally, the call may be monitored and/or recorded. The algorithm may take into consideration the user's call history. The temperament ranking may be determined using a number scale.

In some embodiments, a system for processing user temperament is disclosed. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code. The executable code causes the processor(s) to receive an incoming call from a user, receive audible language from the user, determine from the language properties such as: volume, content, inflection, and/or pitch, input the determined properties into an algorithm configured to rank temperament, receive as output from the algorithm a ranking of the user's temperament, transfer the call to a representative, and relay the ranking of the user's temperament to the representative. The system may furnish the representative with a script that correlates with the user's temperament ranking. Additionally, the call may be monitored and/or recorded. The algorithm may take into consideration the user's call history. The temperament ranking may be determined using a number scale.

According to embodiments of the invention, a method for determining and processing a user's temperament is disclosed. The method includes: receiving a call from a user; receiving language from the user; determining parameters such as: volume, content, inflection, and/or pitch from the language; inputting the parameters into an algorithm to rank the user's temperament; and receiving as output from the algorithm a ranking of the user's temperament. In some embodiments, the method further includes transferring the call to a representative, and relaying the user's temperament to the representative. The representative may be chosen based on the user's temperament. The method may also include providing the representative with a script based on the user's temperament. The method may also include monitoring the transferred call with the representative.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments of the invention are shown. Indeed, the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like numbers refer to like elements throughout. Unless described or implied as exclusive alternatives, features throughout the drawings and descriptions should be taken as cumulative, such that features expressly associated with some particular embodiments can be combined with other embodiments. Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which the presently disclosed subject matter pertains.

The exemplary embodiments are provided so that this disclosure will be both thorough and complete, and will fully convey the scope of the invention and enable one of ordinary skill in the art to make, use, and practice the invention.

The systems and methods described herein are configured to collect information from a user's language in a call, input that information into a machine learning algorithm trained to predict/determine user's temperament, and based on the predicted/determined user temperament transfer the user to a representative equipped to speak with the user. This may include choosing a representative more experienced at handling a particular temperament. It may also include providing the representative with a script to be used for a particular temperament or situation. This system may also be used to balance a representative's call log such that a representative is not receiving an overwhelming amount of irate user calls.

The terms “coupled,” “fixed,” “attached to,” “communicatively coupled to,” “operatively coupled to,” and the like refer to both (i) direct connecting, coupling, fixing, attaching, communicatively coupling; and (ii) indirect connecting coupling, fixing, attaching, communicatively coupling via one or more intermediate components or features, unless otherwise specified herein. “Communicatively coupled to” and “operatively coupled to” can refer to physically and/or electrically related components.

FIG.1illustrates a system100and environment thereof, according to at least one embodiment, by which a user110benefits through use of services and products of an enterprise system200. The user110accesses services and products by use of one or more user devices, illustrated in separate examples as a computing device104and a mobile device106, which may be, as non-limiting examples, a smart phone, a portable digital assistant (PDA), a pager, a mobile television, a gaming device, a laptop computer, a camera, a video recorder, an audio/video player, radio, a GPS device, or any combination of the aforementioned, or other portable device with processing and communication capabilities. In the illustrated example, the mobile device106is illustrated inFIG.1as having exemplary elements, the below descriptions of which apply as well to the computing device104, which can be, as non-limiting examples, a desktop computer, a laptop computer, or other user-accessible computing device.

Furthermore, the user device, referring to either or both of the computing device104and the mobile device106, may be or include a workstation, a server, or any other suitable device, including a set of servers, a cloud-based application or system, or any other suitable system, adapted to execute, for example any suitable operating system, including Linux, UNIX, Windows, macOS, iOS, Android and any other known operating system used on personal computers, central computing systems, phones, and other devices.

The user110can be an individual, a group, or any entity in possession of or having access to the user device, referring to either or both of the mobile device104and computing device106, which may be personal or public items. Although the user110may be singly represented in some drawings, at least in some embodiments according to these descriptions the user110is one of many such that a market or community of users, consumers, customers, business entities, government entities, clubs, and groups of any size are all within the scope of these descriptions.

The user device, as illustrated with reference to the mobile device106, includes components such as, at least one of each of a processing device120, and a memory device122for processing use, such as random access memory (RAM), and read-only memory (ROM). The illustrated mobile device106further includes a storage device124including at least one of a non-transitory storage medium, such as a microdrive, for long-term, intermediate-term, and short-term storage of computer-readable instructions126for execution by the processing device120. For example, the instructions126can include instructions for an operating system and various applications or programs130, of which the application132is represented as a particular example. The storage device124can store various other data items134, which can include, as non-limiting examples, cached data, user files such as those for pictures, audio and/or video recordings, files downloaded or received from other devices, and other data items preferred by the user or required or related to any or all of the applications or programs130.

The memory device122is operatively coupled to the processing device120. As used herein, memory includes any computer readable medium to store data, code, or other information. The memory device122may include volatile memory, such as volatile Random Access Memory (RAM) including a cache area for the temporary storage of data. The memory device122may also include non-volatile memory, which can be embedded and/or may be removable. The non-volatile memory can additionally or alternatively include an electrically erasable programmable read-only memory (EEPROM), flash memory or the like.

The memory device122and storage device124can store any of a number of applications which comprise computer-executable instructions and code executed by the processing device120to implement the functions of the mobile device106described herein. For example, the memory device122may include such applications as a conventional web browser application and/or a mobile P2P payment system client application. These applications also typically provide a graphical user interface (GUI) on the display140that allows the user110to communicate with the mobile device106, and, for example a mobile banking system, and/or other devices or systems. In one embodiment, when the user110decides to enroll in a mobile banking program, the user110downloads or otherwise obtains the mobile banking system client application from a mobile banking system, for example enterprise system200, or from a distinct application server. In other embodiments, the user110interacts with a mobile banking system via a web browser application in addition to, or instead of, the mobile P2P payment system client application.

The processing device120, and other processors described herein, generally include circuitry for implementing communication and/or logic functions of the mobile device106. For example, the processing device120may include a digital signal processor, a microprocessor, and various analog to digital converters, digital to analog converters, and/or other support circuits. Control and signal processing functions of the mobile device106are allocated between these devices according to their respective capabilities. The processing device120thus may also include the functionality to encode and interleave messages and data prior to modulation and transmission. The processing device120can additionally include an internal data modem. Further, the processing device120may include functionality to operate one or more software programs, which may be stored in the memory device122, or in the storage device124. For example, the processing device120may be capable of operating a connectivity program, such as a web browser application. The web browser application may then allow the mobile device106to transmit and receive web content, such as, for example, location-based content and/or other web page content, according to a Wireless Application Protocol (WAP), Hypertext Transfer Protocol (HTTP), and/or the like.

The memory device122and storage device124can each also store any of a number of pieces of information, and data, used by the user device and the applications and devices that facilitate functions of the user device, or are in communication with the user device, to implement the functions described herein and others not expressly described. For example, the storage device may include such data as user authentication information, etc.

The processing device120, in various examples, can operatively perform calculations, can process instructions for execution, and can manipulate information. The processing device120can execute machine-executable instructions stored in the storage device124and/or memory device122to thereby perform methods and functions as described or implied herein, for example by one or more corresponding flow charts expressly provided or implied as would be understood by one of ordinary skill in the art to which the subject matters of these descriptions pertain. The processing device120can be or can include, as non-limiting examples, a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU), a microcontroller, an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a digital signal processor (DSP), a field programmable gate array (FPGA), a state machine, a controller, gated or transistor logic, discrete physical hardware components, and combinations thereof. In some embodiments, particular portions or steps of methods and functions described herein are performed in whole or in part by way of the processing device120, while in other embodiments methods and functions described herein include cloud-based computing in whole or in part such that the processing device120facilitates local operations including, as non-limiting examples, communication, data transfer, and user inputs and outputs such as receiving commands from and providing displays to the user.

The mobile device106, as illustrated, includes an input and output system136, referring to, including, or operatively coupled with, user input devices and user output devices, which are operatively coupled to the processing device120. The user output devices include a display140(e.g., a liquid crystal display or the like), which can be, as a non-limiting example, a touch screen of the mobile device106, which serves both as an output device, by providing graphical and text indicia and presentations for viewing by one or more user110, and as an input device, by providing virtual buttons, selectable options, a virtual keyboard, and other indicia that, when touched, control the mobile device106by user action. The user output devices include a speaker144or other audio device. The user input devices, which allow the mobile device106to receive data and actions such as button manipulations and touches from a user such as the user110, may include any of a number of devices allowing the mobile device106to receive data from a user, such as a keypad, keyboard, touch-screen, touchpad, microphone142, mouse, joystick, other pointer device, button, soft key, and/or other input device(s). The user interface may also include a camera146, such as a digital camera.

Further non-limiting examples include, one or more of each, any, and all of a wireless or wired keyboard, a mouse, a touchpad, a button, a switch, a light, an LED, a buzzer, a bell, a printer and/or other user input devices and output devices for use by or communication with the user110in accessing, using, and controlling, in whole or in part, the user device, referring to either or both of the computing device104and a mobile device106. Inputs by one or more user110can thus be made via voice, text or graphical indicia selections. For example, such inputs in some examples correspond to user-side actions and communications seeking services and products of the enterprise system200, and at least some outputs in such examples correspond to data representing enterprise-side actions and communications in two-way communications between a user110and an enterprise system200.

The mobile device106may also include a positioning device108, which can be for example a global positioning system device (GPS) configured to be used by a positioning system to determine a location of the mobile device106. For example, the positioning system device108may include a GPS transceiver. In some embodiments, the positioning system device108includes an antenna, transmitter, and receiver. For example, in one embodiment, triangulation of cellular signals may be used to identify the approximate location of the mobile device106. In other embodiments, the positioning device108includes a proximity sensor or transmitter, such as an RFID tag, that can sense or be sensed by devices known to be located proximate a merchant or other location to determine that the consumer mobile device106is located proximate these known devices.

In the illustrated example, a system intraconnect138, connects, for example electrically, the various described, illustrated, and implied components of the mobile device106. The intraconnect138, in various non-limiting examples, can include or represent, a system bus, a high-speed interface connecting the processing device120to the memory device122, individual electrical connections among the components, and electrical conductive traces on a motherboard common to some or all of the above-described components of the user device. As discussed herein, the system intraconnect138may operatively couple various components with one another, or in other words, electrically connects those components, either directly or indirectly—by way of intermediate component(s)—with one another.

The user device, referring to either or both of the computing device104and the mobile device106, with particular reference to the mobile device106for illustration purposes, includes a communication interface150, by which the mobile device106communicates and conducts transactions with other devices and systems. The communication interface150may include digital signal processing circuitry and may provide two-way communications and data exchanges, for example wirelessly via wireless communication device152, and for an additional or alternative example, via wired or docked communication by mechanical electrically conductive connector154. Communications may be conducted via various modes or protocols, of which GSM voice calls, SMS, EMS, MMS messaging, TDMA, CDMA, PDC, WCDMA, CDMA2000, and GPRS, are all non-limiting and non-exclusive examples. Thus, communications can be conducted, for example, via the wireless communication device152, which can be or include a radio-frequency transceiver, a Bluetooth device, Wi-Fi device, a Near-field communication device, and other transceivers. In addition, GPS (Global Positioning System) may be included for navigation and location-related data exchanges, ingoing and/or outgoing. Communications may also or alternatively be conducted via the connector154for wired connections such by USB, Ethernet, and other physically connected modes of data transfer.

The processing device120is configured to use the communication interface150as, for example, a network interface to communicate with one or more other devices on a network. In this regard, the communication interface150utilizes the wireless communication device152as an antenna operatively coupled to a transmitter and a receiver (together a “transceiver”) included with the communication interface150. The processing device120is configured to provide signals to and receive signals from the transmitter and receiver, respectively. The signals may include signaling information in accordance with the air interface standard of the applicable cellular system of a wireless telephone network. In this regard, the mobile device106may be configured to operate with one or more air interface standards, communication protocols, modulation types, and access types. By way of illustration, the mobile device106may be configured to operate in accordance with any of a number of first, second, third, fourth, fifth-generation communication protocols and/or the like. For example, the mobile device106may be configured to operate in accordance with second-generation (2G) wireless communication protocols IS-136 (time division multiple access (TDMA)), GSM (global system for mobile communication), and/or IS-95 (code division multiple access (CDMA)), or with third-generation (3G) wireless communication protocols, such as Universal Mobile Telecommunications System (UMTS), CDMA2000, wideband CDMA (WCDMA) and/or time division-synchronous CDMA (TD-SCDMA), with fourth-generation (4G) wireless communication protocols such as Long-Term Evolution (LTE), fifth-generation (5G) wireless communication protocols, Bluetooth Low Energy (BLE) communication protocols such as Bluetooth 5.0, ultra-wideband (UWB) communication protocols, and/or the like. The mobile device106may also be configured to operate in accordance with non-cellular communication mechanisms, such as via a wireless local area network (WLAN) or other communication/data networks.

The communication interface150may also include a payment network interface. The payment network interface may include software, such as encryption software, and hardware, such as a modem, for communicating information to and/or from one or more devices on a network. For example, the mobile device106may be configured so that it can be used as a credit or debit card by, for example, wirelessly communicating account numbers or other authentication information to a terminal of the network. Such communication could be performed via transmission over a wireless communication protocol such as the Near-field communication protocol.

The mobile device106further includes a power source128, such as a battery, for powering various circuits and other devices that are used to operate the mobile device106. Embodiments of the mobile device106may also include a clock or other timer configured to determine and, in some cases, communicate actual or relative time to the processing device120or one or more other devices. For further example, the clock may facilitate timestamping transmissions, receptions, and other data for security, authentication, logging, polling, data expiry, and forensic purposes.

System100as illustrated diagrammatically represents at least one example of a possible implementation, where alternatives, additions, and modifications are possible for performing some or all of the described methods, operations and functions. Although shown separately, in some embodiments, two or more systems, servers, or illustrated components may utilized. In some implementations, the functions of one or more systems, servers, or illustrated components may be provided by a single system or server. In some embodiments, the functions of one illustrated system or server may be provided by multiple systems, servers, or computing devices, including those physically located at a central facility, those logically local, and those located as remote with respect to each other.

The enterprise system200can offer any number or type of services and products to one or more users110. In some examples, an enterprise system200offers products. In some examples, an enterprise system200offers services. Use of “service(s)” or “product(s)” thus relates to either or both in these descriptions. With regard, for example, to online information and financial services, “service” and “product” are sometimes termed interchangeably. In non-limiting examples, services and products include retail services and products, information services and products, custom services and products, predefined or pre-offered services and products, consulting services and products, advising services and products, forecasting services and products, internet products and services, social media, and financial services and products, which may include, in non-limiting examples, services and products relating to banking, checking, savings, investments, credit cards, automatic-teller machines, debit cards, loans, mortgages, personal accounts, business accounts, account management, credit reporting, credit requests, and credit scores.

To provide access to, or information regarding, some or all the services and products of the enterprise system200, automated assistance may be provided by the enterprise system200. For example, automated access to user accounts and replies to inquiries may be provided by enterprise-side automated voice, text, and graphical display communications and interactions. In at least some examples, any number of human agents210, can be employed, utilized, authorized or referred by the enterprise system200. Such human agents210can be, as non-limiting examples, point of sale or point of service (POS) representatives, online customer service assistants available to users110, advisors, managers, sales team members, and referral agents ready to route user requests and communications to preferred or particular other agents, human or virtual.

Human agents210may utilize agent devices212to serve users in their interactions to communicate and take action. The agent devices212can be, as non-limiting examples, computing devices, kiosks, terminals, smart devices such as phones, and devices and tools at customer service counters and windows at POS locations. In at least one example, the diagrammatic representation of the components of the user device106inFIG.1applies as well to one or both of the computing device104and the agent devices212.

Agent devices212individually or collectively include input devices and output devices, including, as non-limiting examples, a touch screen, which serves both as an output device by providing graphical and text indicia and presentations for viewing by one or more agent210, and as an input device by providing virtual buttons, selectable options, a virtual keyboard, and other indicia that, when touched or activated, control or prompt the agent device212by action of the attendant agent210. Further non-limiting examples include, one or more of each, any, and all of a keyboard, a mouse, a touchpad, a joystick, a button, a switch, a light, an LED, a microphone serving as input device for example for voice input by a human agent210, a speaker serving as an output device, a camera serving as an input device, a buzzer, a bell, a printer and/or other user input devices and output devices for use by or communication with a human agent210in accessing, using, and controlling, in whole or in part, the agent device212.

Inputs by one or more human agents210can thus be made via voice, text or graphical indicia selections. For example, some inputs received by an agent device212in some examples correspond to, control, or prompt enterprise-side actions and communications offering services and products of the enterprise system200, information thereof, or access thereto. At least some outputs by an agent device212in some examples correspond to, or are prompted by, user-side actions and communications in two-way communications between a user110and an enterprise-side human agent210.

From a user perspective experience, an interaction in some examples within the scope of these descriptions begins with direct or first access to one or more human agents210in person, by phone, or online for example via a chat session or website function or feature. In other examples, a user is first assisted by a virtual agent214of the enterprise system200, which may satisfy user requests or prompts by voice, text, or online functions, and may refer users to one or more human agents210once preliminary determinations or conditions are made or met.

A computing system206of the enterprise system200may include components such as, at least one of each of a processing device220, and a memory device222for processing use, such as random access memory (RAM), and read-only memory (ROM). The illustrated computing system206further includes a storage device224including at least one non-transitory storage medium, such as a microdrive, for long-term, intermediate-term, and short-term storage of computer-readable instructions226for execution by the processing device220. For example, the instructions226can include instructions for an operating system and various applications or programs230, of which the application232is represented as a particular example. The storage device224can store various other data234, which can include, as non-limiting examples, cached data, and files such as those for user accounts, user profiles, account balances, and transaction histories, files downloaded or received from other devices, and other data items preferred by the user or required or related to any or all of the applications or programs230.

The computing system206, in the illustrated example, includes an input/output system236, referring to, including, or operatively coupled with input devices and output devices such as, in a non-limiting example, agent devices212, which have both input and output capabilities.

In the illustrated example, a system intraconnect238electrically connects the various above-described components of the computing system206. In some cases, the intraconnect238operatively couples components to one another, which indicates that the components may be directly or indirectly connected, such as by way of one or more intermediate components. The intraconnect238, in various non-limiting examples, can include or represent, a system bus, a high-speed interface connecting the processing device220to the memory device222, individual electrical connections among the components, and electrical conductive traces on a motherboard common to some or all of the above-described components of the user device.

The computing system206, in the illustrated example, includes a communication interface250, by which the computing system206communicates and conducts transactions with other devices and systems. The communication interface250may include digital signal processing circuitry and may provide two-way communications and data exchanges, for example wirelessly via wireless device252, and for an additional or alternative example, via wired or docked communication by mechanical electrically conductive connector254. Communications may be conducted via various modes or protocols, of which GSM voice calls, SMS, EMS, MMS messaging, TDMA, CDMA, PDC, WCDMA, CDMA2000, and GPRS, are all non-limiting and non-exclusive examples. Thus, communications can be conducted, for example, via the wireless device252, which can be or include a radio-frequency transceiver, a Bluetooth device, Wi-Fi device, Near-field communication device, and other transceivers. In addition, GPS (Global Positioning System) may be included for navigation and location-related data exchanges, ingoing and/or outgoing. Communications may also or alternatively be conducted via the connector254for wired connections such as by USB, Ethernet, and other physically connected modes of data transfer.

The processing device220, in various examples, can operatively perform calculations, can process instructions for execution, and can manipulate information. The processing device220can execute machine-executable instructions stored in the storage device224and/or memory device222to thereby perform methods and functions as described or implied herein, for example by one or more corresponding flow charts expressly provided or implied as would be understood by one of ordinary skill in the art to which the subjects matters of these descriptions pertain. The processing device220can be or can include, as non-limiting examples, a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU), a microcontroller, an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a digital signal processor (DSP), a field programmable gate array (FPGA), a state machine, a controller, gated or transistor logic, discrete physical hardware components, and combinations thereof.

Furthermore, the computing device206, may be or include a workstation, a server, or any other suitable device, including a set of servers, a cloud-based application or system, or any other suitable system, adapted to execute, for example any suitable operating system, including Linux, UNIX, Windows, macOS, iOS, Android, and any known other operating system used on personal computer, central computing systems, phones, and other devices.

The user devices, referring to either or both of the mobile device104and computing device106, the agent devices212, and the enterprise computing system206, which may be one or any number centrally located or distributed, are in communication through one or more networks, referenced as network258inFIG.1.

Network258provides wireless or wired communications among the components of the system100and the environment thereof, including other devices local or remote to those illustrated, such as additional mobile devices, servers, and other devices communicatively coupled to network258, including those not illustrated inFIG.1. The network258is singly depicted for illustrative convenience, but may include more than one network without departing from the scope of these descriptions. In some embodiments, the network258may be or provide one or more cloud-based services or operations. The network258may be or include an enterprise or secured network, or may be implemented, at least in part, through one or more connections to the Internet. A portion of the network258may be a virtual private network (VPN) or an Intranet. The network258can include wired and wireless links, including, as non-limiting examples, 802.11a/b/g/n/ac, 802.20, WiMax, LTE, and/or any other wireless link. The network258may include any internal or external network, networks, sub-network, and combinations of such operable to implement communications between various computing components within and beyond the illustrated environment100. The network258may communicate, for example, Internet Protocol (IP) packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells, voice, video, data, and other suitable information between network addresses. The network258may also include one or more local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANs), wide area networks (WANs), all or a portion of the internet and/or any other communication system or systems at one or more locations.

Two external systems202and204are expressly illustrated inFIG.1, representing any number and variety of data sources, users, consumers, customers, business entities, banking systems, government entities, clubs, and groups of any size are all within the scope of the descriptions. In at least one example, the external systems202and204represent automatic teller machines (ATMs) utilized by the enterprise system200in serving users110. In another example, the external systems202and204represent payment clearinghouse or payment rail systems for processing payment transactions, and in another example, the external systems202and204represent third party systems such as merchant systems configured to interact with the user device106during transactions and also configured to interact with the enterprise system200in back-end transactions clearing processes.

In certain embodiments, one or more of the systems such as the user device106, the enterprise system200, and/or the external systems202and204are, include, or utilize virtual resources. In some cases, such virtual resources are considered cloud resources or virtual machines. Such virtual resources may be available for shared use among multiple distinct resource consumers and in certain implementations, virtual resources do not necessarily correspond to one or more specific pieces of hardware, but rather to a collection of pieces of hardware operatively coupled within a cloud computing configuration so that the resources may be shared as needed.

As used herein, an artificial intelligence system, artificial intelligence algorithm, artificial intelligence module, program, and the like, generally refer to computer implemented programs that are suitable to simulate intelligent behavior (i.e., intelligent human behavior) and/or computer systems and associated programs suitable to perform tasks that typically require a human to perform, such as tasks requiring visual perception, speech recognition, decision-making, translation, and the like. An artificial intelligence system may include, for example, at least one of a series of associated if-then logic statements, a statistical model suitable to map raw sensory data into symbolic categories and the like, or a machine learning program. A machine learning program, machine learning algorithm, or machine learning module, as used herein, is generally a type of artificial intelligence including one or more algorithms that can learn and/or adjust parameters based on input data provided to the algorithm. In some instances, machine learning programs, algorithms, and modules are used at least in part in implementing artificial intelligence (AI) functions, systems, and methods.

Artificial Intelligence and/or machine learning programs may be associated with or conducted by one or more processors, memory devices, and/or storage devices of a computing system or device. It should be appreciated that the AI algorithm or program may be incorporated within the existing system architecture or be configured as a standalone modular component, controller, or the like communicatively coupled to the system. An AI program and/or machine learning program may generally be configured to perform methods and functions as described or implied herein, for example by one or more corresponding flow charts expressly provided or implied as would be understood by one of ordinary skill in the art to which the subjects matters of these descriptions pertain.

A machine learning program may be configured to implement stored processing, such as decision tree learning, association rule learning, artificial neural networks, recurrent artificial neural networks, long short term memory networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, genetic algorithms, k-nearest neighbor (KNN), and the like. In some embodiments, the machine learning algorithm may include one or more image recognition algorithms suitable to determine one or more categories to which an input, such as data communicated from a visual sensor or a file in JPEG, PNG or other format, representing an image or portion thereof, belongs. Additionally or alternatively, the machine learning algorithm may include one or more regression algorithms configured to output a numerical value given an input. Further, the machine learning may include one or more pattern recognition algorithms, e.g., a module, subroutine or the like capable of translating text or string characters and/or a speech recognition module or subroutine. In various embodiments, the machine learning module may include a machine learning acceleration logic, e.g., a fixed function matrix multiplication logic, in order to implement the stored processes and/or optimize the machine learning logic training and interface.

One type of algorithm suitable for use in machine learning modules as described herein is an artificial neural network or neural network, taking inspiration from biological neural networks. An artificial neural network can, in a sense, learn to perform tasks by processing examples, without being programmed with any task-specific rules. A neural network generally includes connected units, neurons, or nodes (e.g., connected by synapses) and may allow for the machine learning program to improve performance. A neural network may define a network of functions, which have a graphical relationship. As an example, a feedforward network may be utilized, e.g., an acyclic graph with nodes arranged in layers.

A feedforward network (see, e.g., feedforward network260referenced inFIG.2A) may include a topography with a hidden layer264between an input layer262and an output layer266. The input layer262, having nodes commonly referenced inFIG.2Aas input nodes272for convenience, communicates input data, variables, matrices, or the like to the hidden layer264, having nodes274. The hidden layer264generates a representation and/or transformation of the input data into a form that is suitable for generating output data. Adjacent layers of the topography are connected at the edges of the nodes of the respective layers, but nodes within a layer typically are not separated by an edge. In at least one embodiment of such a feedforward network, data is communicated to the nodes272of the input layer, which then communicates the data to the hidden layer264. The hidden layer264may be configured to determine the state of the nodes in the respective layers and assign weight coefficients or parameters of the nodes based on the edges separating each of the layers, e.g., an activation function implemented between the input data communicated from the input layer262and the output data communicated to the nodes276of the output layer266. It should be appreciated that the form of the output from the neural network may generally depend on the type of model represented by the algorithm. Although the feedforward network260ofFIG.2Aexpressly includes a single hidden layer264, other embodiments of feedforward networks within the scope of the descriptions can include any number of hidden layers. The hidden layers are intermediate the input and output layers and are generally where all or most of the computation is done.

Neural networks may perform a supervised learning process where known inputs and known outputs are utilized to categorize, classify, or predict a quality of a future input. However, additional or alternative embodiments of the machine learning program may be trained utilizing unsupervised or semi-supervised training, where none of the outputs or some of the outputs are unknown, respectively. Typically, a machine learning algorithm is trained (e.g., utilizing a training data set) prior to modeling the problem with which the algorithm is associated. Supervised training of the neural network may include choosing a network topology suitable for the problem being modeled by the network and providing a set of training data representative of the problem. Generally, the machine learning algorithm may adjust the weight coefficients until any error in the output data generated by the algorithm is less than a predetermined, acceptable level. For instance, the training process may include comparing the generated output produced by the network in response to the training data with a desired or correct output. An associated error amount may then be determined for the generated output data, such as for each output data point generated in the output layer. The associated error amount may be communicated back through the system as an error signal, where the weight coefficients assigned in the hidden layer are adjusted based on the error signal. For instance, the associated error amount (e.g., a value between −1 and 1) may be used to modify the previous coefficient, e.g., a propagated value. The machine learning algorithm may be considered sufficiently trained when the associated error amount for the output data is less than the predetermined, acceptable level (e.g., each data point within the output layer includes an error amount less than the predetermined, acceptable level). Thus, the parameters determined from the training process can be utilized with new input data to categorize, classify, and/or predict other values based on the new input data.

An additional or alternative type of neural network suitable for use in the machine learning program and/or module is a Convolutional Neural Network (CNN). A CNN is a type of feedforward neural network that may be utilized to model data associated with input data having a grid-like topology. In some embodiments, at least one layer of a CNN may include a sparsely connected layer, in which each output of a first hidden layer does not interact with each input of the next hidden layer. For example, the output of the convolution in the first hidden layer may be an input of the next hidden layer, rather than a respective state of each node of the first layer. CNNs are typically trained for pattern recognition, such as speech processing, language processing, and visual processing. As such, CNNs may be particularly useful for implementing optical and pattern recognition programs required from the machine learning program. A CNN includes an input layer, a hidden layer, and an output layer, typical of feedforward networks, but the nodes of a CNN input layer are generally organized into a set of categories via feature detectors and based on the receptive fields of the sensor, retina, input layer, etc. Each filter may then output data from its respective nodes to corresponding nodes of a subsequent layer of the network. A CNN may be configured to apply the convolution mathematical operation to the respective nodes of each filter and communicate the same to the corresponding node of the next subsequent layer. As an example, the input to the convolution layer may be a multidimensional array of data. The convolution layer, or hidden layer, may be a multidimensional array of parameters determined while training the model.

An exemplary convolutional neural network CNN is depicted and referenced as280inFIG.2B. As in the basic feedforward network260ofFIG.2A, the illustrated example ofFIG.2Bhas an input layer282and an output layer286. However where a single hidden layer264is represented inFIG.2A, multiple consecutive hidden layers284A,284B, and284C are represented inFIG.2B. The edge neurons represented by white-filled arrows highlight that hidden layer nodes can be connected locally, such that not all nodes of succeeding layers are connected by neurons.FIG.2C, representing a portion of the convolutional neural network280ofFIG.2B, specifically portions of the input layer282and the first hidden layer284A, illustrates that connections can be weighted. In the illustrated example, labels W1and W2refer to respective assigned weights for the referenced connections. Two hidden nodes283and285share the same set of weights W1and W2when connecting to two local patches.

Weight defines the impact a node in any given layer has on computations by a connected node in the next layer.FIG.3represents a particular node300in a hidden layer. The node300is connected to several nodes in the previous layer representing inputs to the node300. The input nodes301,302,303and304are each assigned a respective weight W01, W02, W03, and W04in the computation at the node300, which in this example is a weighted sum.

An additional or alternative type of feedforward neural network suitable for use in the machine learning program and/or module is a Recurrent Neural Network (RNN). An RNN may allow for analysis of sequences of inputs rather than only considering the current input data set. RNNs typically include feedback loops/connections between layers of the topography, thus allowing parameter data to be communicated between different parts of the neural network. RNNs typically have an architecture including cycles, where past values of a parameter influence the current calculation of the parameter, e.g., at least a portion of the output data from the RNN may be used as feedback/input in calculating subsequent output data. In some embodiments, the machine learning module may include an RNN configured for language processing, e.g., an RNN configured to perform statistical language modeling to predict the next word in a string based on the previous words. The RNN(s) of the machine learning program may include a feedback system suitable to provide the connection(s) between subsequent and previous layers of the network.

An example for a Recurrent Neural Network RNN is referenced as400inFIG.4. As in the basic feedforward network260ofFIG.2A, the illustrated example ofFIG.4has an input layer410(with nodes412) and an output layer440(with nodes442). However, where a single hidden layer264is represented inFIG.2A, multiple consecutive hidden layers420and430are represented inFIG.4(with nodes422and nodes432, respectively). As shown, the RNN400includes a feedback connector404configured to communicate parameter data from at least one node432from the second hidden layer430to at least one node422of the first hidden layer420. It should be appreciated that two or more and up to all of the nodes of a subsequent layer may provide or communicate a parameter or other data to a previous layer of the RNN network400. Moreover and in some embodiments, the RNN400may include multiple feedback connectors404(e.g., connectors404suitable to communicatively couple pairs of nodes and/or connector systems404configured to provide communication between three or more nodes). Additionally or alternatively, the feedback connector404may communicatively couple two or more nodes having at least one hidden layer between them, i.e., nodes of nonsequential layers of the RNN400.

In an additional or alternative embodiment, the machine learning program may include one or more support vector machines. A support vector machine may be configured to determine a category to which input data belongs. For example, the machine learning program may be configured to define a margin using a combination of two or more of the input variables and/or data points as support vectors to maximize the determined margin. Such a margin may generally correspond to a distance between the closest vectors that are classified differently. The machine learning program may be configured to utilize a plurality of support vector machines to perform a single classification. For example, the machine learning program may determine the category to which input data belongs using a first support vector determined from first and second data points/variables, and the machine learning program may independently categorize the input data using a second support vector determined from third and fourth data points/variables. The support vector machine(s) may be trained similarly to the training of neural networks, e.g., by providing a known input vector (including values for the input variables) and a known output classification. The support vector machine is trained by selecting the support vectors and/or a portion of the input vectors that maximize the determined margin.

As depicted, and in some embodiments, the machine learning program may include a neural network topography having more than one hidden layer. In such embodiments, one or more of the hidden layers may have a different number of nodes and/or the connections defined between layers. In some embodiments, each hidden layer may be configured to perform a different function. As an example, a first layer of the neural network may be configured to reduce a dimensionality of the input data, and a second layer of the neural network may be configured to perform statistical programs on the data communicated from the first layer. In various embodiments, each node of the previous layer of the network may be connected to an associated node of the subsequent layer (dense layers). Generally, the neural network(s) of the machine learning program may include a relatively large number of layers, e.g., three or more layers, and are referred to as deep neural networks. For example, the node of each hidden layer of a neural network may be associated with an activation function utilized by the machine learning program to generate an output received by a corresponding node in the subsequent layer. The last hidden layer of the neural network communicates a data set (e.g., the result of data processed within the respective layer) to the output layer. Deep neural networks may require more computational time and power to train, but the additional hidden layers provide multistep pattern recognition capability and/or reduced output error relative to simple or shallow machine learning architectures (e.g., including only one or two hidden layers).

Referring now toFIG.5and some embodiments, an AI program502may include a front-end algorithm504and a back-end algorithm506. The artificial intelligence program502may be implemented on an AI processor520, such as the processing device120, the processing device220, and/or a dedicated processing device. The instructions associated with the front-end algorithm504and the back-end algorithm506may be stored in an associated memory device and/or storage device of the system (e.g., memory device124and/or memory device224) communicatively coupled to the AI processor520, as shown. Additionally or alternatively, the system may include one or more memory devices and/or storage devices (represented by memory524inFIG.5) for processing use and/or including one or more instructions necessary for operation of the AI program502. In some embodiments, the AI program502may include a deep neural network (e.g., a front-end network504configured to perform pre-processing, such as feature recognition, and a back-end network506configured to perform an operation on the data set communicated directly or indirectly to the back-end network506). For instance, the front-end program506can include at least one CNN508communicatively coupled to send output data to the back-end network506.

Additionally or alternatively, the front-end program504can include one or more AI algorithms510,512(e.g., statistical models or machine learning programs such as decision tree learning, associate rule learning, recurrent artificial neural networks, support vector machines, and the like). In various embodiments, the front-end program504may be configured to include built in training and inference logic or suitable software to train the neural network prior to use (e.g., machine learning logic including, but not limited to, image recognition, mapping and localization, autonomous navigation, speech synthesis, document imaging, or language translation). For example, a CNN508and/or AI algorithm510may be used for image recognition, input categorization, and/or support vector training. In some embodiments and within the front-end program504, an output from an AI algorithm510may be communicated to a CNN508or509, which processes the data before communicating an output from the CNN508,509and/or the front-end program504to the back-end program506. In various embodiments, the back-end network506may be configured to implement input and/or model classification, speech recognition, translation, and the like. For instance, the back-end network506may include one or more CNNs (e.g., CNN514) or dense networks (e.g., dense networks516), as described herein.

For instance and in some embodiments of the AI program502, the program may be configured to perform unsupervised learning, in which the machine learning program performs the training process using unlabeled data, e.g., without known output data with which to compare. During such unsupervised learning, the neural network may be configured to generate groupings of the input data and/or determine how individual input data points are related to the complete input data set (e.g., via the front-end program504). For example, unsupervised training may be used to configure a neural network to generate a self-organizing map, reduce the dimensionally of the input data set, and/or to perform outlier/anomaly determinations to identify data points in the data set that falls outside the normal pattern of the data. In some embodiments, the AI program502may be trained using a semi-supervised learning process in which some but not all of the output data is known, e.g., a mix of labeled and unlabeled data having the same distribution.

In some embodiments, the AI program502may be accelerated via a machine learning framework520(e.g., hardware). The machine learning framework may include an index of basic operations, subroutines, and the like (primitives) typically implemented by AI and/or machine learning algorithms. Thus, the AI program502may be configured to utilize the primitives of the framework520to perform some or all of the calculations required by the AI program502. Primitives suitable for inclusion in the machine learning framework520include operations associated with training a convolutional neural network (e.g., pools), tensor convolutions, activation functions, basic algebraic subroutines and programs (e.g., matrix operations, vector operations), numerical method subroutines and programs, and the like.

It should be appreciated that the machine learning program may include variations, adaptations, and alternatives suitable to perform the operations necessary for the system, and the present disclosure is equally applicable to such suitably configured machine learning and/or artificial intelligence programs, modules, etc. For instance, the machine learning program may include one or more long short-term memory (LSTM) RNNs, convolutional deep belief networks, deep belief networks DBNs, and the like. DBNs, for instance, may be utilized to pre-train the weighted characteristics and/or parameters using an unsupervised learning process. Further, the machine learning module may include one or more other machine learning tools (e.g., Logistic Regression (LR), Naive-Bayes, Random Forest (RF), matrix factorization, and support vector machines) in addition to, or as an alternative to, one or more neural networks, as described herein.

FIG.6is a flow chart representing a method600, according to at least one embodiment, of model development and deployment by machine learning. The method600represents at least one example of a machine learning workflow in which steps are implemented in a machine learning project.

In step602, a user authorizes, requests, manages, or initiates the machine-learning workflow. This may represent a user such as human agent, or customer, requesting machine-learning assistance or AI functionality to simulate intelligent behavior (such as a virtual agent) or other machine-assisted or computerized tasks that may, for example, entail visual perception, speech recognition, decision-making, translation, forecasting, predictive modelling, and/or suggestions as non-limiting examples. In a first iteration from the user perspective, step602can represent a starting point. However, with regard to continuing or improving an ongoing machine learning workflow, step602can represent an opportunity for further user input or oversight via a feedback loop.

In step604, data is received, collected, accessed, or otherwise acquired and entered as can be termed data ingestion. In step606the data ingested in step604is pre-processed, for example, by cleaning, and/or transformation such as into a format that the following components can digest. The incoming data may be versioned to connect a data snapshot with the particularly resulting trained model. As newly trained models are tied to a set of versioned data, preprocessing steps are tied to the developed model. If new data is subsequently collected and entered, a new model will be generated. If the preprocessing step606is updated with newly ingested data, an updated model will be generated. Step606can include data validation, which focuses on confirming that the statistics of the ingested data are as expected, such as that data values are within expected numerical ranges, that data sets are within any expected or required categories, and that data comply with any needed distributions such as within those categories. Step606can proceed to step608to automatically alert the initiating user, other human or virtual agents, and/or other systems, if any anomalies are detected in the data, thereby pausing or terminating the process flow until corrective action is taken.

In step610, training test data such as a target variable value is inserted into an iterative training and testing loop. In step612, model training, a core step of the machine learning work flow, is implemented. A model architecture is trained in the iterative training and testing loop. For example, features in the training test data are used to train the model based on weights and iterative calculations in which the target variable may be incorrectly predicted in an early iteration as determined by comparison in step614, where the model is tested. Subsequent iterations of the model training, in step612, may be conducted with updated weights in the calculations.

When compliance and/or success in the model testing in step614is achieved, process flow proceeds to step616, where model deployment is triggered. The model may be utilized in AI functions and programming, for example to simulate intelligent behavior, to perform machine-assisted or computerized tasks, of which visual perception, speech recognition, decision-making, translation, forecasting, predictive modelling, and/or automated suggestion generation serve as non-limiting examples.

FIG.7is a flow diagram700illustrating a process of determining a user's temperament and ultimately processing the determined temperament. At box705, the system will receive an incoming call from a user. The user may call in from any communication device including but not limited to the mobile device104and computing device106. The user may be calling in for many reasons, including but not limited to: start a new service or open a new account, update information, report fraudulent activity, report lost or stolen devices, make a transfer and/or transaction, dispute a charge, request account-related information, apply for a loan, find an entity physical location, and/or ask a general question. Once the call has been received, the system will receive some language from the user at block710. The system may be equipped to handle verbal language in any language, as long as the algorithm is trained in that language. The languages may include but are not limited to: English, French, Spanish, German, Chinese (Mandarin, Cantonese, Yue, etc), Japanese, Hindi, Korean, Arabic, Russian, and Portuguese. From the language received, the system will determine parameters from the language at block715. These parameters include, but are not necessarily limited to, the content of the received language, pitch (or tone) of the received language, inflection in the received language, and volume of the received language. In a normal conversation, these parameters may give indication to the speaker's temperament and their intentions. The content of the received language may provide the system with the users intention, for example if the user is saying the words “fraud” or “stolen” this is an indication that the user's intention for the call is related to security such as reporting a fraudulent charge or a stolen credit card. The pitch of the user's language will give an indication as to their emotional state. For example, a low pitch tends to indicate anger or unhappiness while a high pitch may indicate excitement or happiness. The volume may also provide an indication to the user's emotional state, for example if the language is spoken at a high volume it is likely that the user is angry. Similarly, the inflection in the user's spoken language will give insight into the user's emotional state.

Once the system receives the information from the language, the method proceeds to box720, where the information will be used as input into an algorithm previously trained to predict user temperament using said information. The algorithm will have been previously trained using the desired parameters (at least one of volume, content, inflection, and pitch) and in the desired language (English, Spanish, French, etc). The output of the algorithm will be a ranking of the user's temperament, received at box725. The ranking may be a numerical ranking on a scale, for example on a scale of 1-10 where 1 is joyous and excited and 10 is unhappy and angry. The scale may be a word scale for example: joy and appreciation on one end and fear and despair on the other. The ranking may be a color scale with green being happy and red being angry. The ranking is intended to give the entity representative with an indication of how the user is feeling so that they are best equipped to handle the user's problem in the most efficient way possible.

The call will be transferred to the entity representative at box730. The temperament ranking is also relayed to the entity representative at box735. The call transfer and the relay of the ranking may occur simultaneously. But more preferably, the entity representative will get the ranking with a little time to prepare for the call. In the case the representative needs to locate a script or other information to help them assist the user. Optionally, at box740, the system may provide the entity representative with a script for speaking with the user based on the ranking of the user's temperament.

The call may continue to be monitored and recorded. The recorded call may be used to further train the algorithm as well as used for training material for the entity representatives. Details about the call, including the user's ranked temperament and the general issue of the call may be stored in a user profile as the user's call history. This call history could also be used as input into the algorithm when the user calls again to better predict their temperament. For example, if the user is frequently angry when they call in, the algorithm may be more likely to predict that the user is angry and provide them with the appropriate ranking.

After the call is over, the system may prompt the entity representative to confirm that the temperament ranking was correct. This information can be used to improve the algorithm. Additionally, this information may be stored in the user's call history and used as input into the algorithm the next time the user calls.

This system may be used to guide certain calls to certain entity representatives. For example, if a particular representative is very good at calming down irate users they may get a higher percentage of calls with a temperament ranking that indicates the user is angry. As another example, the system may want to spread the more angry customers out so as not to overwhelm the entity representatives; in that case they may alternate the higher ranked user calls amongst the representatives. Additionally, if the system was trained based on the content of the input language, the system may guide the calls to a certain representative based on their expertise in a certain area. For example, if a particular entity representative is experienced with mortgages, they may get the calls from user's who mentioned mortgages with the received language.

Particular embodiments and features have been described with reference to the drawings. It is to be understood that these descriptions are not limited to any single embodiment or any particular set of features. Similar embodiments and features may arise or modifications and additions may be made without departing from the scope of these descriptions and the spirit of the appended claims.