Automated response engine implementing a universal data space based on communication interactions via an omnichannel electronic data channel

Abstract

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to implement automated responses to data representing electronic messages, among other things, and, more specifically, to an automated predictive response computing system independent of electronic communication channel of an electronic message payload, the automated predictive response computing system being configured to, for example, implement a universal data space based on, at least in part, conversational data flows, which may be classified and used to provide a predictive response to assist resolution, such as assisting an agent among other things. In an example, a method may include augmenting at least a subset of one or more portions of communication data, implementing augmented communication portion data to determine a predicted response, and generating data to facilitate the predicted response based on the subset of inbound electronic messages.

Description

FIELD

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to implement automated responses to data representing electronic messages, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate implementation of an automated predictive response computing system independent of electronic communication channel or payload of an electronic message payload, the automated predictive response computing system being configured to implement, for example, an automated response engine configured to implement a universal data space based on, at least in part, conversational data flows, which may be classified and used to provide a predictive response to assist resolution, such as assisting an agent among other things.

BACKGROUND

Advances in computing hardware and software have fueled exponential growth in delivery of vast amounts of information due to increased improvements in computational and networking technologies. Also, advances in conventional data network technologies provide an ability to exchange increasing amounts of generated data via various electronic messaging platforms. Thus, improvements in computing hardware, software, network services, and storage have bolstered growth of Internet-based messaging applications, such as social networking platform-based messenger applications (or web-based chat data communications), especially in technological areas aimed at exchanging digital information concerning products and services expeditiously. As an example, various organizations and corporations (e.g., retailer sellers) may exchange information through any number of electronic messaging networks, including social media networks (e.g., Twitter®, Facebook Messenger™, Reddit™, etc.), as well as any user-generated communication (e.g., texting via SMS, or the like, or audio-based telephone calls, and the like), any of which may rely specific or proprietary channels of data communication whereby any of the channels may convey text data, voice data, image data, and any other data in disparate data formats. Such organizations and corporations aim generally to provide data and targeted content timely to users online to manage, for example, brand loyalty and reputation, and to enhance customer engagement.

Conventionally, some typical electronic messaging platforms are designed to implement “bot” or “chat bot” applications to provide quasi-computer-generated responses to on-line inquiries. However, traditional approaches are not well-suited to multiplex across different data protocols, different communication paths, different computing platforms, and the like. Hence, such applications generally are limited to communicate with a specific communication channel.

Also, traditional approaches to providing computer-generated responses to on-line inquiries may also implement a “bot” or “chat bot” application with limited functionality as to relevant responses. Consequently, traditional server architectures and processes that provide electronic messaging platforms may include redundancies that suboptimally may require redundant resources to create, implement, and deploy, among other things.

Thus, what is needed is a solution to overcome the deficiencies of the above-described approaches to generate responses predictively that may be configured to exchange conversational data automatically via any medium, such as voice data and text data, or may assist an agent in resolving an issue without the limitations of conventional techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) of the invention are disclosed in the following detailed description and the accompanying drawings:

FIG. 1 is a diagram depicting an automated predictive response computing system configured to identify inbound voice data relative to other data to provide optimize responses based on data conveyed via any disparate electronic communication channels, according to some embodiments;

FIG. 2 depicts an example of a user interface including a number of queues that may include conversations (e.g., exchanges of messages) requiring resolution, according to some examples;

FIG. 3 is a diagram depicting an example of a flow to resolve issues implementing one or more predictive responses, according to some examples;

FIG. 4 is a diagram depicting an example of a flow to respond to a digital conversational flow, according to some examples;

FIG. 5 depicts an example of a subset of functional elements of an automated predictive response computing system, according to some examples;

FIG. 6 depicts an example of another subset of functional elements of an automated predictive response computing system, according to some examples;

FIG. 7 depicts an example of an automated predictive response computing system configured to augment portions of communication data, according to some examples;

FIG. 8 depicts an example of a universal data management engine configured to correlate portions of communication data and other data generated in a universal dataspace, according to some examples;

FIG. 9 depicts integration of multiple data applications configured to extract data from multiple data sources, according to some examples;

FIG. 10 depicts an example of flow configured to implement a universal data management engine, according to some examples;

FIG. 11 illustrates an exemplary layered architecture for implementing an automated predictive response application, according to some examples;

FIG. 12 depicts an example of a system architecture to provide a computing platform to host an application to analyze electronic messages including data associated with electronic messages, according to an example; and

FIG. 13 illustrates examples of various computing platforms configured to provide various functionalities to components of an electronic message platform configured to analyze electronic message data and provide functionalities described herein.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways, including as a system, a process, an apparatus, a user interface, or a series of program instructions on a computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in any arbitrary order, unless otherwise provided in the claims.

A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples, but is not limited to any particular example. The scope is limited only by the claims, and numerous alternatives, modifications, and equivalents thereof. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description or providing unnecessary details that may be already known to those of ordinary skill in the art.

As used herein, “system” may refer to or include the description of a computer, network, or distributed computing system, topology, or architecture implementing hardware or software, or both, using various computing resources that are configured to provide computing features, functions, processes, elements, components, or parts, without any particular limitation as to the type, make, manufacturer, developer, provider, configuration, programming or formatting language, service, class, resource, specification, protocol, or other computing or network attributes. As used herein, “software” or “application” may also be used interchangeably or synonymously with, or refer to, a computer program, software, program, firmware, or any other term that may be used to describe, reference, or refer to a logical set of instructions that, when executed, performs a function or set of functions in association with a computing system or machine, regardless of whether physical, logical, or virtual and without restriction or limitation to any particular implementation, design, configuration, instance, or state. Further, “platform” may refer to any type of computer hardware (hereafter “hardware”) or software, or any combination thereof, that may use one or more local, remote, distributed, networked, or computing cloud (hereafter “cloud”)-based computing resources (e.g., computers, clients, servers, tablets, notebooks, smart phones, cell phones, mobile computing platforms or tablets, and the like) to provide an application, operating system, or other computing environment, such as those described herein, without restriction or limitation to any particular implementation, design, configuration, instance, or state. Distributed resources such as cloud computing networks (also referred to interchangeably as “computing clouds,” “storage clouds,” “cloud networks,” or, simply, “clouds,” without restriction or limitation to any particular implementation, design, configuration, instance, or state) may be used for processing and/or storage of varying quantities, types, structures, and formats of data, without restriction or limitation to any particular implementation, design, or configuration.

As used herein, data may be stored in various types of data structures including, but not limited to databases, data repositories, data warehouses, data stores, or other data structures or memory configured to store data in various computer programming languages and formats in accordance with various types of structured and unstructured database schemas such as SQL, MySQL, NoSQL, DynamoDB™, etc. Also applicable are computer programming languages and formats similar or equivalent to those developed by data facility and computing providers such as Amazon® Web Services, Inc. of Seattle, Wash., FMP, Oracle®, Salesforce.com, Inc., or others, without limitation or restriction to any particular instance or implementation. DynamoDB™, Amazon Elasticsearch Service, Amazon Kinesis Data Streams (“KDS”)™, Amazon Kinesis Data Analytics, and the like, are examples of suitable technologies provide by Amazon Web Services (“AWS”). Another example of cloud computing services include the Google® cloud platform that may implement a publisher-subscriber messaging service (e.g., Google® pub/sub architecture).

Further, references to databases, data structures, memory, or any type of data storage facility may include any embodiment as a local, remote, distributed, networked, cloud-based, or combined implementation thereof. For example, social networks and social media (e.g., “social media”) using different types of devices may generate (i.e., in the form of posts (which is to be distinguished from a POST request or call over HTTP) on social networks and social media) data in different forms, formats, layouts, data transfer protocols, and data storage schema for presentation on different types of devices that use, modify, or store data for purposes such as electronic messaging, audio or video rendering (e.g., user-generated content, such as deployed on YouTube®), content sharing, or like purposes. Data may be generated in various formats such as text, audio, video (including three dimensional, augmented reality (“AR”), and virtual reality (“VR”)), or others, without limitation, as electronic messages for use on social networks, social media, and social applications (e.g., “social media”) such as Twitter® of San Francisco, Calif., Snapchat® as developed by Snap® of Venice, Calif., Messenger as developed by Facebook®, WhatsApp®, or Instagram® of Menlo Park, Calif., Pinterest® of San Francisco, Calif., LinkedIn® of Mountain View, Calif., and others, without limitation or restriction. In various embodiments, the term “content” may refer to, for example, one or more of executable instructions (e.g., of an application, a program, or any other code compatible with a programming language), textual data, image data, video data, audio data, or any other data.

In some examples, data may be formatted and transmitted via electronic messaging channels (i.e., transferred over one or more data communication protocols) between computing resources using various types of data communication and transfer protocols such as Hypertext Transfer Protocol (“HTTP”), Transmission Control Protocol (“TCP”)/Internet Protocol (“IP”), Internet Relay Chat (“IRC”), SMS, text messaging, instant messaging (“IM”), File Transfer Protocol (“FTP”), or others, without limitation. As described herein, disclosed processes implemented as software may be programmed using Java®, JavaScript®, Scala, Python™, XML, HTML, and other data formats and programs, without limitation. Disclosed processes herein may also implement software such as Streaming SQL applications, browser applications (e.g., Firefox™) and/or web applications, among others. In some example, a browser application may implement a JavaScript framework, such as Ember.js, Meteor.js, ExtJS, AngularJS, and the like. References to various layers of an application architecture (e.g., application layer or data layer) may refer to a stacked layer application architecture such as the Open Systems Interconnect (“OSI”) model or others. As described herein, a distributed data file may include executable instructions as described above (e.g., JavaScript® or the like) or any data constituting content (e.g., text data, video data, audio data, etc.), or both.

In some examples, systems, software, platforms, and computing clouds, or any combination thereof, may be implemented to facilitate online distribution of subsets of units of content, postings, electronic messages, and the like. In some cases, units of content, electronic postings, electronic messages, and the like may originate at social networks, social media, and social applications, or any other source of content.

FIG. 1 is a diagram depicting an automated predictive response computing system configured to identify inbound voice data relative to other data to provide optimize responses based on data conveyed via any disparate electronic communication channels, according to some embodiments. Diagram 100 depicts an example of an automated predictive response computing system 150 configured to predictively identify data representing a response as a function of data in an electronic message independent of an electronic communication channel and its functionalities, and independent of data representing voice data (e.g., vocalized speech), text data (e.g., digitized text or written symbols), image data (e.g., images that may be identified through image recognition algorithms), or any other data that may originate from any of users 108a and 108b via computing devices 103. For example, any of users 108a and 108b may transmit requests in any data medium, such as text, audio, imagery, and the like, whereby such requests may include data representing requests for information, requests to purchase a product or service, requests for customer service or troubleshooting information, and the like. Automated predictive response computing system 150 may be configured to identify a nature of an electronic message (e.g., a request regarding a certain topic or intent), and may be further configured to provide automated responses to any of users 108a and 108b, as well as agent-assisted responses.

In various examples, any of users 108a or 108b may generate and transmit a request for information or for an action to be performed in association with automated predictive response computing system 150. Responsive to such requests, automated predictive response computing system 150 may be configured to identify subject matter of a message 114, such as an “intent of an electronic message,” an “entity attribute of an electronic message,” a topic of an electronic message, a “sentiment” or “affinity” level, a linguistic language of an electronic message, and other attributes that may be implemented to characterize exchanges of data, such as a characterized data exchange depicted in message response interface 122, which, as shown in diagram 100, may represent a conversational flow of portions of communication data in interface portions 137 and 137a. Various successive or multiple user inquiries may be automatically received as electronic messages 114 at a user interface of an agent identified as “@EcoHaus Support” in user interface portion 136. In response to messages 114, automated predictive response computing system 150 may be configured to provide automated responses as electronic messages 116, or any other actions in furtherance of resolving an issue associated with any of messages 114. In various implementations, automated predictive response computing system 150 may be configured to present in message response interface 122 a number of optimized responses 199a, 199b, and 199c that may be configured to facilitate an optimized response to exchanges of data representing a conversational flow regardless of medium (e.g., regardless as to whether inbound or outbound data may include voice data, text data, image data, etc.). In some examples, automated predictive response computing system 150 may be implemented as an application, such as a customer care application (or any other application) developed and maintained by Khoros, LLC of Austin, Tex.

As shown in diagram 100, any of users 108a and 108b may communicate electronically via any of message computing systems 110a to 110n using any of communication devices 103. As an example, communication devices 103 may include a mobile computing device 105, a voice-based communication phone 106, an image generation device 107, such as a camera, a computing device 109, or any other electronic device configured to generate requests for information, actions, or any other outcome.

In various examples, message computing systems 110a to 110n may be configured to implement social networks, social media, and social applications (e.g., “social media”) such as Twitter® of San Francisco, Calif., Reddit® of San Francisco, Calif., Snapchat® as developed by Snap® of Venice, Calif., Messenger services as developed by Facebook®, WhatsApp®, or Instagram® of Menlo Park, Calif., Pinterest® of San Francisco, Calif., LinkedIn® of Mountain View, Calif., Telegram Messenger™ of Telegram Messenger Inc. of the United Kingdom, Slack™ of Slack Technologies, Inc., and others, without limitation or restriction. Message computing systems 110a to 110n may be configured to generate and host any other type of digital content, such as email, image curation, voice calls (e.g., Voice over IP, or “VOIP”), text messaging (e.g., via SMS messaging, Multimedia Messaging Service (“MIMS”), WhatsApp™, WeChat™, Apple® Business Chat™, Instagram™ Direct Messenger, etc.), Twilio® SMS, and web pages configured to implement web chat functionality (e.g., news websites, retailer websites, etc.). Google® voice, Twilio™ voice, and other voice or telephony technology may be accessed or implemented as any of message computing systems 110a to 110n. Further, message computing systems 110a to 110n may be configured to provide image data and/or audio data, such as voice data to facilitate telephone calls. As an example, message computing systems 110a to 110n may be configured to implement Twilio Voice® or any other voice or telephony application (including third party voice applications).

Any of message computing systems 110a to 110n may implement various corresponding electronic communication channels 111a to 111n to exchange data via one or more networks 112, such as the Internet or any other network. Each of electronic communication channels 111a to 111n may be configured to exchange data using different (e.g., proprietary) protocols, data formats, metadata (and types thereof), etc. As shown, automated predictive response computing system 150 may be configured to receive electronic messages 114 via omnichannel electronic communication channel 113, whereby any of messages 114 may originate any of the different electronic communication channels 111a to 111n.

In the example shown, automated predictive response computing system 150 may be configured to include an omnichannel transceiver 151, a feature extraction controller 156, a predictive intent controller 154, a linguistic language translator 159, and a universal data management engine 190, which may include a response generator 171 and an optimized response/action selector 173. Omnichannel transceiver 151 may be configured to receive electronic messages 114 from any disparate electronic communication channels 111a to 111n and data formats to convert data representing electronic messages 114 into data formats with which automated predictive response computing system 150 may analyze and generate automated responses thereto. Hence, omnichannel transceiver 151 may be configured to detect data representing one or more electronic messages 114 configured to generate a response associated with an electronic communication channel of any electronic communication channels 111a to 111n, any of which may be associated with multiple data sources (e.g., computing platforms including processors and memory configured to provide communicative functionalities).

Omnichannel transceiver 151 may be configured to include logic to implement any number of application programming interface (“APIs”) 151a and a channel converter 152. In some examples, which are non-limiting, omnichannel transceiver 151 may be configured to implement one or more APIs to exchange data with any of electronic communication channels 111a to 111n. As an example, APIs 151a may include an API configured to communicate electronically with Facebook® Messenger and the like, as well as any API configured to exchange voice data, text data, image data, or any other type of data. Channel converter 152 may be configured to detect a data format in which data of electronic message 114 is being conveyed, and may be further configured to convert a detected data format into a uniform or agnostic data format, such as a text data format or as graph-based data arrangement. As an example, image recognition software may be configured to detect an image and characterize its data elements, including an “intent” and associated “entity attributes.” As another example, channel converter 152 may be configured to detect and identify (e.g., via tagged data, computational derivation, etc.) that data associated with electronic message 114 include text-based data, including supplemental data (e.g., metadata) as available. In yet another example, channel converter 152 may be configured to detect voice data to convert to text data, and further configured to detect text data to convert to voice data. Further, omnichannel transceiver 151 may be configured to transmit messages 116 that conform with requirements of any of electronic communication channels 111a to 111n.

Feature extraction controller 156 may be configured to extract features from one or more portions of data of one or more electronic messages 114. In some examples, feature extraction controller 156 may be configured to identify and form data units, such as tokens, words, linguistic phrases, etc., using any predictive algorithm, including any machine learning algorithm, deep learning algorithm, and other natural language algorithmic function (e.g., natural language processing, or “NLP”), as well as any other predictive algorithm, probabilistic algorithm, and the like. In some examples, feature extraction controller 156 may be configured to generate data so that predictive intent controller 154 may identify from extracted data units an “intent” and/or “topic,” as well as one or more “entity attributes” (e.g., parameters, metrics, etc.) with which to generate an automated response. In some examples, feature extraction controller 156 may be configured to extract feature data that may include units of text (e.g., words or tokens), units of image data (e.g., an amount of pixels, or matched image data), units of audio or voice data, and the like.

Predictive intent controller 154 may be configured to receive data including extracted feature data from feature extraction controller 156 and other data, including, but not limited to, supplemental data, metadata, and other ancillary data. Further, predictive intent controller 154 may be configured to predict (e.g., statistically, probabilistically, etc.) an “intent” of subject matter associated with data of electronic message 114. In some examples, “intent” associated with data of an electronic message may be referred to as a “trigger,” and may be calculated to be a predicted topic of a subset (e.g., a step) of an electronic conversation between any of users 108a and 108b and automated predictive response computing system 150. For example, an electronic message 114 may include data stating or requesting “I want to travel now from Paris to Hong Kong. Are there any flights available?” In this example, predictive intent controller 154 may include logic configured to determine that a user is interested “TRAVEL” as an “intent.” Further, predictive intent controller 154 may be configured to determine entity attributes describing a “time” of travel (e.g., “now”), a destination (e.g., “Hong Kong”), and a point of origination (e.g., “Paris”). As such, predictive intent controller 154 may be configured to identify one or more subsets of intent-related data and one or more subsets of data representing one or more entity attributes (e.g., parameters with which to respond to an intent of electronic message 114), as well as data representing a degree or level of sentiment (e.g., affinity), a language associated with voice data and text data, a characterization of a message including profanity, and any other attribute. In some examples, predictive intent controller 154 may be configured to predict, identify, and monitor a “context” during which an intent or topic of electronic message 114 may be received and analyzed relative to other messages as part of an exchange of data constituting conversational flow.

Linguistic language translator 159 may be configured to receive data from feature extraction controller 156 that indicates a type of linguistic language (e.g., a spoken language) that may be defined by region and/or dialect, in at least some examples. In some examples, linguistic language translator 159 may be configured to determine a language based on text, a verbal utterance, or any other data input. Further, linguistic language translator 159 may be configured to translate or modify languages of received data in messages 114 and responses in messages 116, whereby subsets of messages 114 and 116 may vary in languages. For example, a multilingual speaker as user 108a or 108b may inadvertently vacillate among a number of languages. In this case, linguistic language translator 159 may be configured to detect messages 114, regardless of voice data or text data, by a specific user 108a in different languages, and may be further configured to correspond in reply messages 116 in corresponding languages. In various examples, linguistic language translator 159 may be configured to adapt any of portions of communication to provide an optimized response 199a to 199c in a specific language.

Further to diagram 100, automated predictive response computing system 150 or a universal data management engine 190, or both, may be configured to generate, analyze, implement, and store data related to exchanges of characterized data to generate or identify (e.g., predictively) optimized responses 199a to 199c. As shown, inbound communication data in interface portion 180 (e.g., interface portion 137) may include either voice data or text data as follows: “Hi, I bought a Galaxy 520 at Best Buy 2 months ago. Starting last week the battery drains. I get about 2 hours then it's dead.” In response, automated predictive response computing system 150 or universal data management engine 190, or both, may be configured to automatically (or with agent assistance) respond as follows: “Hi Catherine, sorry to hear you have an issue with your phone,” as shown in interface portion 137a Other exchanges of communication data portions may automatically address issues based on a portion of communication data (e.g., an “utterance” verbally or in text that may be segmented), whereby an agent may be involved (optionally) to resolve an issue.

Universal data management engine 190 may be configured to analyze each portion of communication data to identify intent of a conversation, or a topic of thereof, as well as a degree of sentiment (or affinity), entity attributes (e.g., parameters, etc.), and any other data or metadata that may characterize or describe any portion of communication data. Further to diagram 100, universal data management engine 190 may be configured to analyze each portion of communication data, and access other equivalent data in a universal dataspace (e.g., a universal data fabric) to characterize and augment each of portions of communication data with associations to data at response generator 171 to cause optimized response/action selector 173 to determine one or more optimized responses 199a to 199c. In some examples, an agent 172a may provide solutions via a message field 141, as implemented in a user interface of computing device 172b.

In view of the foregoing, structures and/or functionalities depicted in FIG. 1 as well as other figures herein, may be implemented as software, applications, executable code, application programming interfaces (“APIs”), processors, hardware, firmware, circuitry, or any combination thereof. In at least one example, automated predictive response computing system 150 may be implemented as a chatbot application. Note that elements depicted in diagram 100 of FIG. 1 may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings.

FIG. 2 depicts an example of a user interface including a number of queues that may include conversations (e.g., exchanges of messages) requiring resolution, according to some examples. Diagram 200 depicts an assigned queue 234 that may include exchanges of electronic messages and/or conversations 234a, 234b, and 234c that may be assigned specifically to an agent computing device into which a specific agent (e.g., agent-user) is logged for generating a response. As shown, each exchange of messages may be surfaced to display higher priorities first, such as depicted within encircle “P2” to represent a second highest level of priority 233. Available queue 236 may include conversations 236a that are available to any agent upon which to act. Supplemental queue 238 represents one or more additional queues that may include conversations that require supplemental activity or action, such as “needing approval” or “needing expert assistance,” or requiring other supplemental processing (not shown). Whether such conversations appear in supplemental queue 238 may depend on whether an agent has permissions or a role authorized to process conversations in those specialized queues, according to at least some examples.

In this example, user interface 201 depicts an active user interface (“UP”) portion 231 and another user interface portion depicting customer profile data 270. Active user interface portion 231 may be configured to interact and/or respond to customer-user “@Catherine Ramos” of conversation 234a. As shown, the associated originating message may include inbound data (e.g., either via voice data or text data) that states: “Hi, I bought a Galaxy 520 at Best Buy 2 months ago. Starting last week the battery drains. I get about 2 hours than its dead” has been assigned a conversation identifier (“1427371”) 232. This conversation is of a priority “2” out of 6 priority levels 233, has a status 234 of being assigned, and is assigned 235 to a particular agent (e.g., “you”). Further, an entity computing system associated with a brand “EcoHaus Support” may have an agent with an electronic identifier “@EcoHaus Support” 281a in interface portion 281. The text of the originating message may be displayed in interface portion 237. Further to the example in diagram 200, exchanges of portions of communication is depicted in user interface portions 237a and 237b. In user interface portion 237a, an agent @EcoHaus Support may respond “Hi Catherine, sorry to hear you have an issue with your phone,” regardless of whether the agent is a human or a specialized automated response application. In user interface portion 237b, a customer @Catherine Ramos may respond, again, to interject: “My sister had the same issue last year,” which may be accompanied by an image (e.g., an emoji) that depicts or conveys a sentiment of a customer-user about a particular product or situation.

Diagram 200 also depicts multiple tabs, as user inputs, that may be implemented to perform a variety of actions. Interactive tab 239a may be implemented to generate a response, tab 239b may be configured to cause a conversation, 239c may be configured to present a review of the history of a customer or conversation, and tab 204 may provide any other action. In the example shown, highlighted tab 239b indicates a response can be generated. Interface portion 285 enables generation of a public response message or a direct message (e.g., a secure message), whereby the message may be input into a message field 241. A secure message link may be attached to a response upon activation of an “invite” 242 user input, which, when activated, may cause an invite manager to generate an invite message.

Customer profile data 270 may include user-specific data 277 (e.g., name, purchased products, email address, address, phone number, etc.), brands data 277a indicating brands that a user has purchased or searched, source devices 277b may include a list of computing devices associated with user “Catherine Ramos,” one or more conversation identifiers 277c for specific conversation or exchange of messages (over multiple conversations), one or more social networks 277d the user may use, loyalty member number data 277e, length of time as customer 277f, and other data 277g. In some these examples, customer profile data 270 may be encrypted included along with messages and associated tokens used to generate a secure communication channel, as described herein.

In some examples, a response generator 271 may be implemented to provide optimized response or actions 299a, 299b, and 299c to assist an agent to provide customers optimized solutions and information that are configured to resolve issues about a customer may be communicating. For example, optimized response/action selector 273 may select optimized responses, such as responses 299a, 299b, and 299c that may assist an agent to resolve pending issues or questions. For example, response generator 271 may generate, based on analysis and application of predictive logic regarding data exchanged in conversation 232, a voice-text (“V-T”) response 299a, which may be conveyed to a customer via an automated response system. Also, response generator 271 may select and present a workflow response 299b to assist in agent or a customer to troubleshoot an issue with a particular product. Further, response generator 271 may select a solution derived as a “community response” 299c, which may provide a solution based on aggregated knowledge of an online community. Note that other automated responses other than 299a to 299c may be provided by response generator 271.

FIG. 3 is a diagram depicting an example of a flow to resolve issues implementing one or more predictive responses, according to some examples. Flow 300 initiates at 302, at which electronic message data may be received via any electronic communication channel. In some examples, electronic message data may be received and transmitted over any communication channel implementing an omnichannel transceiver or any other electronic device. At 304, features from portions of communication data may be extracted, such as features identifying entities and/or entity attributes. At 306, flow 300 may be configured to identify data representing intent-related data and data representing one or more entity attributes as the features, which may be extracted at 304.

At 308, a subset of any number of portions of communication data may be augmented (e.g. associated or linked to metadata) to determine a predicted response that may be optimized to resolve or address an issue or a question that may be predicted based on associated data. At 310, augmented portions of communication data may be implemented to determine a predicted response. In some examples, a universal data management engine, as described herein, may be configured to determine a predicted response. Further, one or more predicted responses may be presented in a user interface for activation to provide assistance automatically or in combination with an agent's assistance. At 312, data representing a subset of inbound electronic messages may be identified. In some examples, identified inbound electronic message data may be analyzed to identify the context, intent, sentiment, and other characteristics with which to derive a predicted response. At 314, data to facilitate a predicted response may be generated based on the subset of inbound electronic messages. Further, one or more predictive responses may be presented to an agent via the user interface, such as depicted in FIG. 2. For example, augmented communication portion data may be analyzed to determine, implement, or present one or more of: (1.) a predicted voice or data response as an outbound electronic message, (2.) a workflow response as an outbound message, and (3.) a community-derived response as an outbound electronic message, among other types of predictive responses.

FIG. 4 is a diagram depicting an example of a flow to respond to a digital conversational flow, according to some examples. At 402, flow 400 is initiated to identify derived data as well as profile data for analysis to determine an optimized response based on data across various datasets in a universal data space, as described herein. At 404, data representing various aspects of derived data and profile data, as well as other data, may be converted into a universal data format (e.g., a graph-based data arrangement, or any other type of data arrangement). At 406, a subset of data may be linked to equivalent or similar types of data or data values in any one of a number of various datasets, whereby any of the datasets may be formed using different applications (e.g., in an enterprise or any other organization).

At 408, data representing an inbound communication data portion may be received. At 410, a response (e.g., predictive response) may be generated as a function of analyzed subsets of data that may be linked together across various datasets. In some cases, machine learning, deep learning, and other types of predictive algorithms may be implemented, as described herein. At 412, data representing a response may be configured to be presented for selection (e.g., via a user interface provided to an agent). At 414, a response may be transmitted, either automatically (by a specialized automated application or bot) or in response to a user input detected at a user interface configured to assist in agent.

FIG. 5 depicts an example of a subset of functional elements of an automated predictive response computing system, according to some examples. Diagram 200 includes an omnichannel transceiver 541, which is shown to include a channel converter 521, and a feature extraction controller 522. Note that elements depicted in diagram 500 of FIG. 5 may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings.

In some examples, omnichannel transceiver 541 may be configured to receive electronic message data 501 from any electronic communication channel, and may further configured to generate or transmit session identifier (“ID”) data 502, text-based data 505, and supplemental data 504. Session ID data 502, which may be optional, may include data referring to an originating communication device 103 of FIG. 1 (e.g., an IP or MAC address, etc.) or any other identifying information associated with a particular user 108a. Session ID data 502 may be used to monitor exchanges of data constituting conversation data for establishing a “context” with which to enhance accuracy of generating automated responses to electronic message data 501. Session ID data 502 may be implemented to identify profile data of a particular user or computing device, where profile data may be stored in a profile data repository 511. Examples of profile data are depicted or described herein. For example, profile data associated with session ID data 502 may include a name of a user (e.g., a customer) and customer contact information, such as an email, a residential address, a telephone number, etc. Further, profile data may include data representing past interactions with automated bots and/or agents, data representing any number of social networks with which a customer is affiliated, data representing a loyalty member number, and any other data, such as past product purchases, searches for products, inquiries, and the like.

In some examples, omnichannel transceiver 541 may be configured to identify and transmit supplemental data 504, which may include any metadata that be identified (e.g., in association with a particular electronic communication channel). For example, supplemental data 504 may include metadata specifying a particular language (and/or geographic region) that a particular user desires to communicate linguistically.

Channel converter 521 and feature extraction controller 522 may include any number of feature extraction processes to, for example, extract feature data to analyze electron message data 501 and supplemental data. Channel converter 521 and feature extraction controller 522 may be further configured to generate a number of feature vectors to perform pattern recognition, predictive or probabilistic data analysis, machine learning, deep learning, or any other algorithm (e.g., heuristic-based algorithms) to identify at least a subset of features that may constitute an event (as derived from data from various data sources).

Channel converter 521 may include any number of image recognition processor algorithms 521d to 521f, any number of audio recognition processor algorithms 521g to 521i, or any other set of algorithms. Image recognition processor algorithms 521d to 521f may be configured to perform character recognition (e.g., optical character recognition, or “OCR”), facial recognition, or implement any computer vision-related operation to determine image-related features, which may be interpreted into text-based data 505. Audio recognition processor algorithms 521g to 521i may be configured to perform voice and speech recognition, sound recognition, or implement any audio-related operation to determine audio-related features, which may be converted into text-based data 505.

Feature extraction controller 522 may include any number of natural language processor algorithms 521a to 521c that may be configured, for example, to tokenize sentences and words, perform word stemming, filter out stop or irrelevant words, or implement any other natural language processing operation to determine text-related features. In some examples, feature extraction controller 522 may include any number of predictive data modeling algorithms 590a to 590c that may be configured to perform pattern recognition and probabilistic data computations. For example, predictive data modeling algorithms 590a to 590c may apply “k-means clustering,” or any other clustering data identification techniques to form clustered sets of data that may be analyzed to determine or learn optimal classifications of “intent” data and associated outputs and supplemental data related thereto, as well as “entity attribute” data. In some examples, feature extraction controller 522 maybe configured to detect patterns or classifications among datasets through the use of Bayesian networks, clustering analysis, as well as other known machine learning techniques or deep-learning techniques (e.g., including any known artificial intelligence techniques, or any of k-NN algorithms, linear support vector machine (“SVM”) algorithm, regression and variants thereof (e.g., linear regression, non-linear regression, etc.), “Zero-shot” learning techniques and algorithms, Bayesian inferences and the like, including classification algorithms, such as Naïve Bayes classifiers, or any other statistical, empirical, or heuristic technique). In other examples, predictive data modeling algorithms 590a to 590c may include any algorithm configured to extract features and/or attributes based on classifying data or identifying patterns of data, as well as any other process to characterize subsets of data, regardless of whether supervised or unsupervised.

In the example shown, feature extraction controller 522 may be configured to implement any number of statistical analytic programs, machine-learning applications, deep-learning applications, and the like. Feature extraction controller 522 is shown to have access to any number of predictive models, such as predictive model 590a, 590b, and 590c, among others. As shown, predictive data model 590a may be configured to implement one of any type of neuronal networks to predict an action or disposition of an electronic message, or any output representing an extracted feature for determining either an event or supplemental data to determine compatibility, or both. A neural network model 590a includes a set of inputs 591 and any number of “hidden” or intermediate computational nodes 592, whereby one or more weights 597 may be implemented and adjusted (e.g., in response to training). Also shown is a set of predicted outputs 593, such as text terms defining a predicted “intent” 555a or “entity attributes” 555b (e.g., parameters, characteristics, etc.), among any other types of outputs.

Feature extraction controller 522 may include a neural network data model configured to predict (e.g., extract) contextual or related text terms based on generation of vectors (e.g., word vectors) with which to determine degrees of similarity (e.g., magnitudes of cosine similarity) to, for example, establish “contextual” compatibility, at least in some examples. Output data 593 as contextual or related text terms may be used to identify intent data (e.g., as an event or a trigger). In at least one example, feature extraction controller 522 may be configured to implement a “word2vec” natural language processing algorithm or any other natural language process that may or may not transform, for example, text data into numerical data (e.g., data representing a vector space). According to various other examples, feature extraction controller 522 may be configured to implement any natural language processing algorithm.

In view of the foregoing, channel converter 521 and feature extraction controller 522 may be configured to implement various feature extraction functions to extract features that can identify one or more groups of data units 571 to 574 as extracted feature data 503, whereby each group of data units 571 to 574 may be associated with an electronic message data 501. As an example, electronic message data 501 may include text data requesting “I need to book a flight now from Paris to Amsterdam.” Further to this example, data unit 571 may represent extracted text term “TRAVEL” as a predicted “intent” data value 555a. Data unit 572 may represent extracted text term “now” as an entity attribute (or parameter) that describes timing of a “traveling” event. Data unit 573 may represent extracted text term “Paris,” which may describe a point of embarkation and data unit 574 may represent extracted text term “Hong Kong” as a destination. Data units 572, 573, and 574 may be entity attributes 555b (or parameters, or as entities). Note further that extracted text term “TRAVEL” may be determined as a predicted “intent” data value 555a by feature extraction controller 522 or by predictive intent controller 654 of FIG. 6, or by both.

FIG. 6 depicts an example of another subset of functional elements of an automated predictive response computing system, according to some examples. Diagram 600 includes a predictive intent controller 654, which is shown to include a one or more context state classifiers 644, and a flow controller 658. Predictive intent controller 654 may be configured to receive one or more of session ID data 502, extracted feature data 503, and supplemental data 504 of FIG. 5. In some examples, predictive intent controller 654 may be configured to determine (or confirm) that one or more extracted data units (e.g., one or more extracted text terms) specify a topic of electronic conversation, or an intent of an electronic message. Predictive intent controller 654 may generate predictive intent data 606 specifying an “intent” of an electronic message. Note that elements depicted in diagram 600 of FIG. 6 may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings.

In some examples, state classifiers 644a and 644b may be configured to implement any number of statistical analytic programs, machine-learning applications, deep-learning applications, and the like. State classifier 644a may include any number of predictive models, such as predictive models 690a, 690b, and 690c, and state classifier 644b may include one or more predictive models, such as predictive models 691a, 691b, and 691c. Predictive models 690 and 691 may be implemented similar to, or equivalent to, predictive models described in FIG. 5.

In one example, predictive intent controller 654 and/or state classifier 644a may receive inputs of any combination of session ID data 502, extracted feature data 503, and supplemental data 504 to compute predictive context data 608. For example, inputs to state classifier 644a may generate predictive context data 608 to indicate a predicted state of a flow of conversational data to provide context to determine an optimal reply or response. According to some examples, predictive context data 608 may include data describing an intent, a topic, a summary of a group of text (including text data converted from voice data), or any other data. In some examples, predictive logic (e.g., a neural network model may include a set of inputs 681 and any number of “hidden” or intermediate computational nodes 682 and 683, whereby one or more weights 687 may be implemented and adjusted (e.g., in response to training) to provide output data at 684.

As another example, inputs into state classifier 644b may determine affinity data 607 that may indicate sentiment state data, such as whether a distributed data file may be associated with a positive affinity state, a neutral affinity state, or a negative affinity state (or any degree or level of positive or negative affinity or sentiment). In accordance with at least some examples, affinity data 607 (e.g., sentiment state data or other like data) may include a range of data values that can include data values ranging from a maximal value of a positive affinity state to a maximal negative affinity state, the range including at least a subset of one or more data values representing a neutral affinity state. Thus, affinity data 607 may include a range of affinity (e.g., sentiment values).

Other state classifiers, such as state classifier 644n, may generate other electronic message state data characterizing an electronic message to determine a voice-text response flow with which to respond. As shown, one example of a state classifier 644n may be configured to implement a linguistic language translator 659 to determine a language associated with an exchange of data. In yet another example, state classifier 644n may be configured to classify voice and text data as being inappropriate or profane to, for example, exclude or mask such language from public display.

In the example shown, flow controller 658 may include a communication portion augmentation engine 658a and a response generator 658b. Further, flow controller 658 may be configured to analyze data representing an “intent” (e.g., a predicted topic or intended result of an electronic message), one or more entity attributes (e.g., data representing one or more entities), context data, etc., to calculate an optimal response. Flow controller 658 may be configured to receive predictive intent data 606 and other data from predictive intent controller 654, including affinity data 607 and predictive context data 608, as well as session ID data 502, extracted feature data 503, and supplemental data 504, both as described relative to FIG. 5. Referring back to FIG. 6, flow controller 658 may also be configured to receive universal dataspace data 699 that may include data or derived data that may originate over multiple datasets each associated with a specific application (e.g., a marketing application, an on-line community application, a customer care application, and the like). Universal dataspace data 699 may be used to provide an optimized response via response message data 650, as generated by response generator 658b.

As example, an optimized response in response message data 650 may be determined or originate in a popular or validated posting to an on-line community that resolves a specific issue, based on a community of users having specialized knowledge. As another example, an optimized response in response message data 650 may include a workflow, such as a step-by-step guided trouble-shooting flow to present to either a customer user or an agent user, whereby the workflow may be selected by response generator 658b as a function of one or more of predictive intent data 606, affinity data 607, predictive context data 608, session ID data 502, and extracted feature data 503, as well as other data described herein. Examples of universal dataspace data 699 may be described in FIGS. 7 and 8 herein, as well as in relation to any other figure.

Communication portion augmentation engine 658a may include logic (e.g., hardware or software, or any combination thereof) configured to associate (e.g., “tag”) portions of communication data, such as an “utterance,” with contextual data for further analysis and optimization of either automatic responses (e.g., via specialized bots) or responses provided by agents, or any hybrid or combination of a blended flow including automatic responses and responses by agents via voice data or text data (or other data). Further, communication portion augmentation engine 658a may be configured to tag various types of data to augment one or more of a unit of conversation, a portion of communication data, and/or data representing an utterance. In some examples, data associated with a portion of conversation data may represent metadata or any other data derived at an automated predictive response computer system or supplemented as profile data associated with a particular user or group of users.

Flow controller 658 may be coupled electronically to a rule model data repository 632 and an augmented communication portion data repository 634. In some examples, data representing rule model data 632 may be configured to provide one or more rules with which to select and route responses to adaptively and flexibly provide replies and responses to corresponding incoming electronic messages. Augmented communication portion data repository 634, according to some examples, may be configured to store and manages one or more of predictive intent data 606, affinity data 607, predictive context data 608, session ID data 502, and extracted feature data 503, as well as other data described herein, such as profile data. Data stored in augmented communication portion data repository 634 may be accessible to facilitate analysis with which to determine optimized responses based on aforementioned types of data.

Response generator 658b may be configured to generate response message data 650 based on a selected omnichannel voice-text response flow. Response message data 650 may include one or more of a response message, an action to be performed, a “hand-off” to an agent, a deflected voice call to text message, or any other action. Hence, flow controller 658 may be further configured to control a flow of an electronic conversation by, for example, identifying successive “intents” associated with successive incoming electronic messages, and by routing a flow of an electronic conversation based on predicted intents and responses, regardless of communication data channel (e.g., either voice or text) and whether an automated response or an agent-generated response is provided.

In view of the foregoing, structures and/or functionalities depicted in FIGS. 5 and 6 as well as other figures herein, may be implemented as software, applications, executable code, application programming interfaces (“APIs”), processors, hardware, firmware, circuitry, or any combination thereof, any of which may constitute at least a portion of data fabric.

FIG. 7 depicts an example of an automated predictive response computing system configured to augment portions of communication data, according to some examples. Diagram 700 includes an automated predictive response computing system 750, which may include a predictive intent controller 754 and a flow controller 758 configured to associated (e.g., tag) a communication data portion 761 with augmented data (e.g., a metadata). Hence, communication data portion 761 may be referred to as an augmented communication data portion 761. Further, diagram 700 includes an automated voice-text response engine 771 configured to facilitate exchanges of communication data portions via a user interface 701. Note that elements depicted in diagram 700 of FIG. 7 may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings or described herein.

Automated voice-text response engine 771 may be configured to facilitate exchanges of inbound and outbound communication (“comm”) data portions 721, 722, and 723, among others, whereby one or more of inbound and outbound communication data portions 721, 722, and 723 may be communicated as voice data (e.g., indicated as a voice call 702) or as text data (e.g., indicated as text-based electronic message communicated via messenger data channel 704). Any of outbound communication data portions 722 may be derived as responses (e.g., optimize responses) is described herein, and may be automatically generated (e.g., as a “bot” implementing predictive data analysis such as provided by predictive intent controller 754 and flow controller 758. In at least one example, automated voice-text response engine 771 may be included as a portion of a “customer care” application configured to exchange communication data with any number of customers to resolve an issue and to provide information.

In the example shown, inbound communication data portions 721 and 723 may be received and analyzed at automated predictive response computing system 750 to detect entities or entity attributes 710a (“Galaxy” phone), 710b (“Best Buy” retailer), and 710c (“battery”), as well as an image 711 (e.g., a crying, sad emoji) that may be analyzed to determine a degree of sentiment (e.g., a negative sentiment associated with an exchange of communication data portions).

Automated predictive response computing system 750 may be configured to associate or otherwise “tag” one or more communication data portions 721, 722, and 723 with data representing augmenting data (e.g., metadata), such as derived data 762 (e.g., as derived by automated predictive response computing system 750) and profile data 764. Derived data 762 may include data representing a channel 731a (e.g., Facebook messenger, or FB messenger), a time zone 731b (e.g., GMT+1), a browser language 731c (e.g., EN, or English), a date and time 731d (e.g., 2021-05-10, 12:01:01), one or more identified “intents” or topics 731e (e.g., “Service,” “Product,” “Problem”), a degree of sentiment 731f (e.g., negative), one or more linguistic languages 731g (e.g., EN, or English) in which communication data portions are exchanged, a degree of profanity detected 731h (e.g., 0%), one or more entities or entity attributes 731i (e.g., “Galaxy,” “battery,” “Best Buy”), among other data representing derived data 762. Profile data 764 may include data representing user data 732a (e.g., a name, contact data, location, etc., associated with the user or customer), brand-related data 732b (e.g., “Samsung,” “iPhone,” “Android,” etc.), source electronic communication devices 732c (e.g., mobile phone, laptop, tablet, etc.), one or more conversation identifiers, or IDs, 732d (e.g., ZX1219, YY1238, etc.), one or more social networks associated with a user 732e (e.g., Twitter, WhatsApp, Facebook, etc.), a loyalty member number 732f (e.g., no. BBROYGBVGW), an amount of time during which a user is been a loyal customer 732g (e.g., 4 years and 3 months), and other data 732h (e.g., refund history, purchase history, interaction history, and any other data), among other data representing profile data 764.

Diagram 700 also depicts a universal data converter 770 that may be configured to convert derived data 762 and profile 764 into any data format with which to correlate or link to other subsets of data in datasets associated with other applications. As shown, each of data 731a to 731i and 732a to 732h may be converted into corresponding data unit 780, which may include nodes of a graph-based data arrangement. Note, however, that data arrangements described herein are not limited to graph-based data arrangements and may be implemented in any type of data arrangement (e.g., relational data base structures, structured data, unstructured data, etc.) As such, universal data converter 770 may be configured to facilitate normalization of data with which to associate across multiple data sources (e.g., multiple applications datasets of an enterprise) to identify related data to generate optimized responses, whether automatically generated or implemented by an agent.

FIG. 8 depicts an example of a universal data management engine configured to correlate portions of communication data and other data generated in a universal dataspace, according to some examples. Diagram 800 includes a universal data management engine 850, which may include a universal data integrator 852 configured to integrate data, such as data units 780 of FIG. 7 with other data within universal dataspace 802, a response generator 854 (e.g., including an optimized response selector 854a) configured to generate a response (e.g., an optimized response) a function of data analyzed within universal dataspace 802, an automated summary generator 856 to generate a cogent summary of an exchange of data between one or more users or customers, and universal data analyzer logic 858 configured to implement various predictive algorithms, such as described herein (e.g., machine learning, deep learning, etc.) to discover patterns and insights among data in universal dataspace 802 that may relate to data units 780 of FIG. 7.

Referring back to FIG. 8, universal data management engine 850 may electronically communicate with an exchange data repository 894 that may include an archive of exchanged communication data portions and conversations (e.g., voice-based data, text-based data, image-based data, etc.). Further, universal data management engine 850 may also electronically communicate with an analytically-derived data repository 890, which may include analytic data and insight-related data derived by universal data analyzer logic 858. As such, logic implementing an insights application 892 may be configured to provide analytic insights into any portion of data in universal dataspace 802. In one example, insights application 892 may include logic configured to provide “customer experience” or “CX” analytic data. Note that elements depicted in diagram 800 of FIG. 8 may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings or described herein.

Universal data integrator 852 may be configured to integrate data among any number of datasets, such as voice-text response dataset 820, on-line community dataset 822, marketing dataset 824, and any other dataset, to form a universal dataspace 802, which may represent a portion of a data fabric, at least in some examples. As shown, a voice-text response application 810 (e.g., a “customer care” application) may be configured to generate data associated with customer engagement and issue resolution in dataset 820, an on-line community application 812 may be configured to provide digital spaces where customers can provide or receive answers, connect with peers or other similarly-situated users, and share new ideas, whereby community-related data may be stored as dataset 822, and a marketing application may be configured to implement social media and engagement tools to generate marketing-related data in dataset 824. Note that data associated with universal dataspace 802 may be distributed among any number of data storages, such as various cloud storage devices or any number of on-location storage devices, and may be electronically coupled via any number APIs 801 to any number of data sources (e.g., including data-creation sources).

In the example shown, universal data integrator 852 may be configured to identify equivalent or similar data, and to generate links or association with correlated data over any number of datasets 820, 822, and 824. For example, universal data integrator 852 may be configured to identify equivalent or similar product type data 830 of data units 780 in dataset 820 (e.g., graph node 830a or any other data format), as well as in dataset 822 (e.g., node 830b), and node 830c of dataset 824. Further, universal data integrator 852 may be configured to identify equivalent or similar refund type data 840 of data units 780 in dataset 820 (e.g., graph node 840a or any other data format), as well as in dataset 822 (e.g., node 840b), and node 840c of dataset 824.

Further to the example above, universal data analyzer logic 858 may be configured to analyze product type data 830 and refund type data 840 over an entire universal dataspace 802 to identify, for example, extraordinary amount of refunds related to a particular product type. For example, an unusual number of refund requires for a certain product (e.g., in a particular geographic location) may suggest defective shipment or lot number of the product. Insights application 892 may be configured to identify such an anomaly and facilitate expeditious resolution than otherwise may be the case. Further, such an anomaly or issue with a particular product type may be identified by response generator 854, which, in turn, may be configured to enable optimized response selector 854a to communicate automatically to customers (e.g., via specialized automated bots or through agent assistance) an up-to-date response to resolve predicted issues based on data analyzed in universal dataspace 802, which may represent data associated with multiple data applications of an enterprise. In some examples, optimizer response selector 854a may be configured to present a “next best action” to an agent to provide assistance via voice data or text data, and may also provide a “next best action” by way of an automated bot.

Automated summary generator 856 may be configured to access exchange data repository 894 to generate auto-summary data 896 to provide for data representing a synopsis or summary of exchanges between a customer and automated bots or an agent, as well as summaries of communication exchanges among a group of customers and automated bots or an agent.

In view of the foregoing, structures and/or functionalities depicted in FIG. 8, as well as other figures herein, may be implemented as software, applications, executable code, application programming interfaces (“APIs”), processors, hardware, firmware, circuitry, or any combination thereof.

FIG. 9 depicts integration of multiple data applications configured to extract data from multiple data sources, according to some examples. As shown, platform 902 may include a customer care application 910, a marketing application 912, a community application 914, a flow-related application 916 (e.g., to automate messaging of specialized chatbots and voice-implemented applications via any type of API, such as a REST API), and an insights application 918. Applications 910 to 918 may generate any amount of data via an omnichannel configured to access multiple data sources 930 via APIs 950 and in response to computational processing of predictive logic and applications 940. An example of platform 902 is developed and maintained by Khoros, LLC, of Austin, Tex., USA.

FIG. 10 depicts an example of flow configured to implement a universal data management engine, according to some examples. Flow 1000 may begin at 1002, at which a portion of communication data may be classified and identified as a function of received voice data or text data, or both, whereby the classification be indicative of an intent, a topic, an entity attribute, a sentiment, or any other data associated with one or more portions of communication data. At 1004, entity or entity attribute data may be associated with a portion of communication data. At 1006, data representing a portion of communication data may be linked to one or more datasets. Each dataset may be associated with a different application configured to generate the dataset. In some examples, different applications may generate different datasets in context of an enterprise or any other organization. At 1008, a determination is made as to whether to select a response automatically, such as by implementing predictive logic or specialized automated bots. Otherwise, and optimize response may be provided to an agent to assist a customer-user via any number of channels. At 1010, an auto-summary (e.g., summary of one or more conversations or exchanges of communication data portions) may be generated to include at least a portion of communication data. Again, a portion of communication data may include or may be referenced as an utterance. At 1012, link data in a universal dataspace may be analyzed to derive insights and to predict optimize responses and actions that may be implemented automatically or with assistance of an agent. At 1014, a response or an action may be selected (e.g., automatically) based on generated insight, which may be determined as a function of linked data across multiple datasets.

FIG. 11 illustrates an exemplary layered architecture for implementing an automated predictive response application, according to some examples. Diagram 1100 depicts application stack (“stack”) 1101, which is neither a comprehensive nor a fully inclusive layered architecture for detecting intent and entity attributes in electronic messages, and in response, automatically generating one or more compatible electronic messages as one or more responses or actions. One or more elements depicted in diagram 1100 of FIG. 11 may include structures and/or functions as similarly-named or similarly-numbered elements depicted in other drawings, or as otherwise described herein, in accordance with one or more examples, such as described relative to FIGS. 1-10 or any other figure or description provided herein.

Application stack 1101 may include an automated predictive response application layer 1150 upon application layer 1140, which, in turn, may be disposed upon any number of lower layers (e.g., layers 1103a to 1103d). Automated predictive response application layer 1150 may be configured to provide functionality and/or structure to implement an automated predictive response application and/or system, as described herein. Further, automated predictive response application 1150 and application layer 1140 may be disposed on data exchange layer 1103d, which may implemented using any programming language, such as HTML, JSON, XML, etc., or any other format to effect generation and communication of requests and responses among computing devices and computational resources constituting an enterprise or an entity and a planning application and/or platform configured to disseminate information expeditiously, such as information regarding products or services aligned with data in targeted data sources compatible with data integration. Data exchange layer 1103d may be disposed on a service layer 1103c, which may provide a transfer protocol or architecture for exchanging data among networked applications. For example, service layer 1103c may provide for a RESTful-compliant architecture and attendant web services to facilitate GET, PUT, POST, DELETE, and other methods or operations. In other examples, service layer 1103c may provide, as an example, SOAP web services based on remote procedure calls (“RPCs”), or any other like services or protocols (e.g., APIs). Service layer 1103c may be disposed on a transport layer 1103b, which may include protocols to provide host-to-host communications for applications via an HTTP or HTTPS protocol, in at least this example. Transport layer 303b may be disposed on a network layer 1103a, which, in at least this example, may include TCP/IP protocols and the like.

As shown, automated predictive response application layer 1150 may include (or may be layered upon) an application layer 1140 that includes logic constituting an omnichannel transceiver layer 1124, a universal data management engine layer 1120, a feature extraction controller layer 1126, and a response generator layer 1122, which may be implemented as a portion of universal data management engine layer 1122. In various examples, layers 1120, 1122, 1124, and 1126 may include logic to implement the various functionalities described herein.

Any of the described layers of FIG. 11 or any other processes described herein in relation to other figures may be implemented as software, hardware, firmware, circuitry, or a combination thereof. If implemented as software, the described techniques may be implemented using various types of programming, development, scripting, or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques, including, but not limited to, Python™, ASP, ASP.net, .Net framework, Ruby, Ruby on Rails, C, Objective C, C++, C#, Adobe® Integrated Runtime™ (Adobe® AIR™) ActionScript™, Flex™, Lingo™, Java™, JSON, Javascript™, Ajax, Perl, COBOL, Fortran, ADA, XML, MXML, HTML, DHTML, XHTML, HTTP, XMPP, PHP, and others, including SQL™, SPARQL™, Turtle™, etc., as well as any proprietary application and software provided or developed by Khoros, LLC, Flow.ai B.V., or the like. The above-described techniques may be varied and are not limited to the embodiments, examples or descriptions provided.

FIG. 12 depicts an example of a system architecture to provide a computing platform to host an application to analyze electronic messages including data associated with electronic messages, according to various examples. Data constituting executable instructions (e.g., remote applications) and other content, such as text, video, audio, etc. may be stored in (or exchanged with) various communication channels or storage devices. For example, various units of message data or content may be stored using one or more of a web application 1224 (e.g., a public data source, such as a new aggregation web site), an email application service 1226, an electronic messaging application 1228 (e.g., a texting or messenger application), social networking services 1230 and a services platform and repository 1232 (e.g., cloud computing services provided by Google® cloud platform, an AWS® directory service provided by Amazon Web Services, Inc., or any other platform service). A server 1215 may implement an automated predictive response application 1250 to perform various functionalities as described herein. As an example, server 1215 may be a web server providing the applications 1250 and 1252 via networks 1210. As an example, a client computing device may be implemented and/or embodied in a computer device 1205, a mobile computing device 1206 (e.g., a smart phone), a wearable computing device 1207, or any other computing device. Any of these client computing devices 1205 to 1207 may be configured to transmit electronic messages and content (e.g., as electronic text or documents, video content, audio content, or the like) from the store 1216, and may be configured to receive content (e.g., other electronic content).

FIG. 13 illustrates examples of various computing platforms configured to provide various functionalities to components of an electronic message platform 1300 configured to analyze electronic message data and provide functionalities described herein. Computing platform 1300 may be used to implement computer programs, applications, methods, processes, algorithms, or other software, as well as any hardware implementation thereof, to perform the above-described techniques.

In some cases, computing platform 1300 or any portion (e.g., any structural or functional portion) can be disposed in any device, such as a computing device 1390a, mobile computing device 1390b, and/or a processing circuit in association with initiating any of the functionalities described herein, via user interfaces and user interface elements, according to various examples.

Computing platform 1300 includes a bus 1302 or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor 1304, system memory 1306 (e.g., RAM, etc.), storage device 1308 (e.g., ROM, etc.), an in-memory cache (which may be implemented in RAM 1306 or other portions of computing platform 1300), a communication interface 1313 (e.g., an Ethernet or wireless controller, a Bluetooth controller, NFC logic, etc.) to facilitate communications via a port on communication link 1321 to communicate, for example, with a computing device, including mobile computing and/or communication devices with processors, including database devices (e.g., storage devices configured to store atomized datasets, including, but not limited to triplestores, etc.). Processor 1304 can be implemented as one or more graphics processing units (“GPUs”), as one or more central processing units (“CPUs”), such as those manufactured by Intel® Corporation, or as one or more virtual processors, as well as any combination of CPUs and virtual processors. Computing platform 1300 exchanges data representing inputs and outputs via input-and-output devices 1301, including, but not limited to, keyboards, mice, audio inputs (e.g., speech-to-text driven devices), user interfaces, displays, monitors, cursors, touch-sensitive displays, touch-sensitive input and outputs (e.g., touch pads), LCD or LED displays, and other I/O-related devices.

Note that in some examples, input-and-output devices 1301 may be implemented as, or otherwise substituted with, a user interface in a computing device associated with, for example, a user account identifier in accordance with the various examples described herein.

According to some examples, computing platform 1300 performs specific operations by processor 1304 executing one or more sequences of one or more instructions stored in system memory 1306, and computing platform 1300 can be implemented in a client-server arrangement, peer-to-peer arrangement, or as any mobile computing device, including smart phones and the like. Such instructions or data may be read into system memory 1306 from another computer readable medium, such as storage device 1308. In some examples, hard-wired circuitry may be used in place of or in combination with software instructions for implementation. Instructions may be embedded in software or firmware. The term “computer readable medium” refers to any tangible medium that participates in providing instructions to processor 1304 for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks and the like. Volatile media includes dynamic memory, such as system memory 1306.

Known forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can access data. Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus 1302 for transmitting a computer data signal.

In some examples, execution of the sequences of instructions may be performed by computing platform 1300. According to some examples, computing platform 1300 can be coupled by communication link 1321 (e.g., a wired network, such as LAN, PSTN, or any wireless network, including WiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.) to any other processor to perform the sequence of instructions in coordination with (or asynchronous to) one another. Computing platform 1300 may transmit and receive messages, data, and instructions, including program code (e.g., application code) through communication link 1321 and communication interface 1313. Received program code may be executed by processor 1304 as it is received, and/or stored in memory 1306 or other non-volatile storage for later execution.

In the example shown, system memory 1306 can include various modules that include executable instructions to implement functionalities described herein. System memory 1306 may include an operating system (“O/S”) 1332, as well as an application 1336 and/or logic module(s) 1359. In the example shown in FIG. 13, system memory 1306 may include any number of modules 1359, any of which, or one or more portions of which, can be configured to facilitate any one or more components of a computing system (e.g., a client computing system, a server computing system, etc.) by implementing one or more functions described herein.

The structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or a combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. These can be varied and are not limited to the examples or descriptions provided.

In some embodiments, modules 1359 of FIG. 13, or one or more of their components, or any process or device described herein, can be in communication (e.g., wired or wirelessly) with a mobile device, such as a mobile phone or computing device, or can be disposed therein.

In some cases, a mobile device, or any networked computing device (not shown) in communication with one or more modules 1359 or one or more of its/their components (or any process or device described herein), can provide at least some of the structures and/or functions of any of the features described herein. As depicted in the above-described figures, the structures and/or functions of any of the above-described features can be implemented in software, hardware, firmware, circuitry, or any combination thereof. Note that the structures and constituent elements above, as well as their functionality, may be aggregated or combined with one or more other structures or elements. Alternatively, the elements and their functionality may be subdivided into constituent sub-elements, if any. As software, at least some of the above-described techniques may be implemented using various types of programming or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques. For example, at least one of the elements depicted in any of the figures can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities.

For example, modules 1359 or one or more of its/their components, or any process or device described herein, can be implemented in one or more computing devices (i.e., any mobile computing device, such as a wearable device, such as a hat or headband, or mobile phone, whether worn or carried) that include one or more processors configured to execute one or more algorithms in memory. Thus, at least some of the elements in the above-described figures can represent one or more algorithms. Or, at least one of the elements can represent a portion of logic including a portion of hardware configured to provide constituent structures and/or functionalities. These can be varied and are not limited to the examples or descriptions provided.

As hardware and/or firmware, the above-described structures and techniques can be implemented using various types of programming or integrated circuit design languages, including hardware description languages, such as any register transfer language (“RTL”) configured to design field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”), multi-chip modules, or any other type of integrated circuit. For example, modules 1359 or one or more of its/their components, or any process or device described herein, can be implemented in one or more computing devices that include one or more circuits. Thus, at least one of the elements in the above-described figures can represent one or more components of hardware. Or, at least one of the elements can represent a portion of logic including a portion of a circuit configured to provide constituent structures and/or functionalities.

According to some embodiments, the term “circuit” can refer, for example, to any system including a number of components through which current flows to perform one or more functions, the components including discrete and complex components. Examples of discrete components include transistors, resistors, capacitors, inductors, diodes, and the like, and examples of complex components include memory, processors, analog circuits, digital circuits, and the like, including field-programmable gate arrays (“FPGAs”), application-specific integrated circuits (“ASICs”). Therefore, a circuit can include a system of electronic components and logic components (e.g., logic configured to execute instructions, such that a group of executable instructions of an algorithm, for example, and, thus, is a component of a circuit). According to some embodiments, the term “module” can refer, for example, to an algorithm or a portion thereof, and/or logic implemented in either hardware circuitry or software, or a combination thereof (i.e., a module can be implemented as a circuit). In some embodiments, algorithms and/or the memory in which the algorithms are stored are “components” of a circuit. Thus, the term “circuit” can also refer, for example, to a system of components, including algorithms. These can be varied and are not limited to the examples or descriptions provided.

Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described inventive techniques are not limited to the details provided. There are many alternative ways of implementing the above-described invention techniques. The disclosed examples are illustrative and not restrictive.

Claims

1. A method comprising: exchanging via an omnichannel transceiver data representing electronic messages associated with a plurality of electronic communication channels associated with disparate data sources, each of which is associated with a processor and memory;extracting features including a unit of text data, a unit of image data, and a unit of audio data, the unit of text data, the unit of image data, and the unit of audio data included in the electronic messages from portions of communication data associated with the electronic messages;identifying data representing intent-related data and data representing one or more entity attributes as the features;augmenting and associating at least a subset of the portions of communication data to associate with contextual data at an augmented communication portion data repository to form augmented communication portion data;storing extracted feature data derived from the portions of the communication data in the data repository,implementing the augmented communication portion data to determine a predicted response;identifying data representing a subset of inbound electronic messages; andaccessing stored data from the data repository and generating data to facilitate the predicted response based on the subset of inbound electronic messages and the stored data from the data repository.

2. The method of claim 1 further comprising: presenting a user input in a user interface configured to activate the predicted response.

3. The method of claim 2 wherein presenting the user input in the user interface comprises: presenting the user input configured for selection of the predicted response to provide assistance at the user interface to transmit a response.

4. The method of claim 1 wherein implementing the augmented communication portion data to determine the predicted response comprises: implementing a predicted voice or data response as an outbound electronic message.

5. The method of claim 1 wherein implementing the augmented communication portion data to determine the predicted response comprises: implementing a workflow response as an outbound electronic message.

6. The method of claim 1 wherein implementing the augmented communication portion data to determine the predicted response comprises: implementing a community-derived response as an outbound electronic message.

7. The method of claim 1 wherein augmenting at least the subset of the portions of communication data further comprises: augmenting the subset of the portions of communication data to associate with data representing sentiment data.

8. The method of claim 1 wherein augmenting at least the subset of the portions of communication data comprises: associating profile data to a portion of communication data.

9. The method of claim 1 wherein exchanging the data representing the electronic messages comprises: exchanging the data in association with a first application and a first dataset.

10. The method of claim 9 wherein exchanging the first application comprises: a community care response application.

11. The method of claim 1 wherein exchanging the data representing the electronic messages comprises: exchanging the data in association with multiple applications and multiple datasets.

12. The method of claim 11 wherein exchanging the data in association with multiple applications and multiple datasets comprises: implementing an on-line community application and a marketing application.

13. The method of claim 1 wherein implementing the augmented communication portion data to determine the predicted response comprises: accessing a data arrangement in a universal data space.

14. The method of claim 1 further comprising: linking data among equivalent data in a universal data space to identify one or more patterns; andanalyzing the one or more patterns to derive the predicted response.

15. A system comprising: a data store including executable instructions and configured to store data and to receive streams of data via a network into an application computing platform; anda processor configured to execute instructions to implement an application configured to: exchange via an omnichannel transceiver data representing electronic messages associated with a plurality of electronic communication channels associated with disparate data sources, each of which is associated with a processor and memory;extract features including a unit of text data, a unit of image data, and a unit of audio data, the unit of text data, the unit of image data, and the unit of audio data included in the electronic messages from portions of communication data associated with the electronic messages;identify data representing intent-related data and data representing one or more entity attributes as the features;augment and associate at least a subset of the portions of communication data to associate with contextual data at an augmented communication portion data repository to form augmented communication portion data;store extracted feature data derived from the portions of the communication data in the data repository;implement the augmented communication portion data to determine a predicted response;identify data representing a subset of inbound electronic messages; andaccess stored data from the data repository and generate data to facilitate the predicted response based on the subset of inbound electronic messages and the stored data from the data repository.

16. The system of claim 15 wherein the processor is further configured to: present a user input in a user interface configured to activate the predicted response.

17. The system of claim 16 wherein the processor configured to present the user input in the user interface is further configured to: present the user input configured for selection of the predicted response to provide assistance at the user interface to transmit a response.

18. The system of claim 15 wherein the processor configured to implement the augmented communication portion data to determine the predicted response is further configured to: implement a predicted voice or data response as an outbound electronic message.

19. The system of claim 15 wherein the processor configured to exchange the data representing the electronic messages is further configured to: exchange the data in association with a first application and a first dataset.