Patent Grant
9575936
The present disclosure relates to the field of automated data processing, and more specifically to the application of ontology programming to process and analyze communication data and to efficiently convey that data to a user.
An ontology is a formal representation of a set of concepts, and the relationships between those concepts in a defined domain. The ontology models the specific meanings of terms as they apply to that domain, and may be devised to incorporate one or several different spoken and/or written languages. Communication data may exist in the form of an audio recording, streaming audio, a transcription of spoken content, or any written correspondence or communication. In the merely exemplary context of a customer service interaction, the communication data may be a transcript between a customer service agent or an interactive voice response (IVR) recording with a customer/caller. The interaction may be via phone, via email, via internet chat, via text messaging, etc. An ontology can be developed and applied across all types of communication data, for example, all types of customer interactions (which may include interactions in multiple languages), to develop a holistic tool for processing and interpreting such data.
The disclosed solution uses machine learning-based methods to improve the knowledge extraction process in a specific domain or business environment, and then provides that extracted knowledge in a word cloud user interface display capable of summarizing and conveying a vast amount of information to a user very quickly. By formulizing a specific company's internal knowledge and terminology, the ontology programming accounts for linguistic meaning to surface relevant and important content for analysis. For example, the disclosed ontology programming adapts to the language used in a specific domain, including linguistic patterns and properties, such as word order, relationships between terms, and syntactical variations. Based on the self-training mechanism developed by the inventors, the ontology programming automatically trains itself to understand the domain or environment of the communication data by processing and analyzing a defined corpus of communication data. Then, the developed ontology can be applied to process a dataset of communication information and to create a word cloud that can provide a quick view into the content of the dataset, including the information about the language used by the participants in the communications, such as identifying for a user key phrases and terms, the frequency of those phrases, the originator of the terms of phrases, and the confidence levels of such identifications.
The ontology is built on the premise that meaningful terms are detected in the corpus and then classified according to specific semantic concepts, or entities. Once the main terms are defined, direct relations or linkages can be formed between these terms and their associated entities. Then, the relations are grouped into themes, which are groups or abstracts that contain synonymous relations. Relations are detected in interactions and surfaced during the system's self-training process. A theme is essentially a single concept defined by its associated relations, which represent that same concept among multiple interactions in the corpus. Themes provide users with a compressed view of the characteristics of interactions throughout the corpus. Themes may be identified according to the exemplary methods described herein.
Themes provide a basis for analytic functions of the ontological software. Accordingly, themes may be provided names, or identifiers, that summarize or identify the content of a theme so that large amounts of theme data can be integrated and displayed in a user-friendly fashion—e.g. in a word cloud display.
In the context of customer service interactions, communication content may exist as various forms of data, including but not limited to audio recording, streaming audio, transcribed textual transcript, or documents containing written communications, such as email, physical mail, text messages, etc. While the present disclosure is exemplified herein by describing an embodiment involving the analysis of audio data, such as recorded audio transcripts, it is to be understood that in alternative embodiments of oral or written communications may be used or analyzed.
An ontology as disclosed is a formal representation of a set of concepts and the relationships between these concepts. In general, an ontology will focus on a specific domain or general context within which the individualized terms or classes as described herein are interpreted. As a non-limiting example, the ontologies described herein are with respect to customer service interactions. A particular ontology may be defined for a specific domain, such as financial services, consumer products, subscription services, or some other service interactions.
The presently disclosed ontology solution incorporates four main stages. As seen in
In the training phase 1, communication data is transformed into a usable format and then analyzed. For example, audio data from a customer interaction between a customer service agent/IVR and a customer/caller can be automatically transcribed into a textual file through speech recognition techniques. However, challenges exist in automatically interpreting the content and sentiments conveyed in a human communication, such as a customer service interaction. An ontology, which generally refers to a collection of entities and their relations, is one way in which an automated interpretation of a customer service interaction can be developed, organized, and presented as disclosed herein.
Generally, an ontology as disclosed herein includes terms which are individual words or short phrases that represent the basic units or concepts that might come up in the customer service interaction. Non-limiting examples of terms, as used herein, include “device”, “iPhone”, “iPhone four”, “invoice”, “I”, “she”, “bill”, “cancel”, “upgrade”, “activate”, “broken”, or “cell phone”, “customer care”, or “credit card.” However, these are not intended to be limiting in any manner and are merely exemplary of basic units or concepts that may be found in a customer service interaction. All words in the corpus can only be associated with one term, and each term can only be counted once.
Classes are broader concepts that encapsulate or classify a set of terms. Classes describe semantic concepts to which classified terms are related. It is also to be understood that classes may also classify or encapsulate a set of subclasses in which the terms are classified. Non-limiting examples of classes, may be include “objects”, “actions”, “modifiers”, “documents”, “service”, “customers”, or “locations”. However, these are not intended to be limiting on the types of classes, particularly the types of classes that may appear in an ontology directed to a specific or specialized domain.
The classes, subclasses, and terms are connected by a plurality of relations which are defined binary directed relationships between terms and classes/subclasses or subclasses to classes. In a non-limiting example, the term “pay” is defined under the class “action” and the term “bill” is defined in the class “documents”. Still further binary directed relationships can be defined between these class/term pairs. The action/pay pair is related to the document/bill pair in that the payment action requires an underlying document, which may be a bill. In another non-limiting example, the term “broken” is defined in the class “problems” and the term “iPhone” is defined in the class “device”. The problem/broken pair can also have a directed relationship to the “devices” class in which the “iPhone” term is a specific example as represented by the devices/iPhone pair.
Although the computing system 1200 as depicted in
The processing system 1206 can comprise a microprocessor and other circuitry that retrieves and executes software 1202 from storage system 1204. Processing system 1206 can be implemented within a single processing device but can also be distributed across multiple processing devices or sub-systems that cooperate in existing program instructions. Examples of processing system 1206 include general purpose central processing units, applications specific processors, and logic devices, as well as any other type of processing device, combinations of processing devices, or variations thereof.
The storage system 1204 can comprise any storage media readable by processing system 1206, and capable of storing software 1202. The storage system 1204 can include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Storage system 1204 can be implemented as a single storage device but may also be implemented across multiple storage devices or sub-systems. Storage system 1204 can further include additional elements, such a controller capable, of communicating with the processing system 1206.
Examples of storage media include random access memory, read only memory, magnetic discs, optical discs, flash memory, virtual memory, and non-virtual memory, magnetic sets, magnetic tape, magnetic disc storage or other magnetic storage devices, or any other medium which can be used to storage the desired information and that may be accessed by an instruction execution system, as well as any combination or variation thereof, or any other type of storage medium. In some implementations, the store media can be a non-transitory storage media. In some implementations, at least a portion of the storage media may be transitory. It should be understood that in no case is the storage media a propagated signal.
User interface 1210 can include a mouse, a keyboard, a voice input device, a touch input device for receiving a gesture from a user, a motion input device for detecting non-touch gestures and other motions by a user, and other comparable input devices and associated processing elements capable of receiving user input from a user. Output devices such as a video display or graphical display can display an interface further associated with embodiments of the system and method as disclosed herein. Speakers, printers, haptic devices and other types of output devices may also be included in the user interface 1210. The user interface 1210 portion of the system 1200 may connect to a display 610, which may be any display known in the art.
As described in further detail herein, the computing system 1200 receives communication data 10. The communication data 10 may be, for example, an audio recording or a conversation, which may exemplarily be between two speakers, although the audio recording may be any of a variety of other audio records, including multiple speakers, a single speaker, or an automated or recorded auditory message. The audio file may exemplarily be a .WAV file, but may also be other types of audio files, exemplarily in a pulse code modulated (PCM) format and an example may include linear pulse code modulated (LPCM) audio data. Furthermore, the audio data is exemplarily mono audio data; however, it is recognized that embodiments of the method as disclosed herein may also be used with stereo audio data. In still further embodiments, the communication data 10 may be streaming audio data received in real time or near-real time by the computing system 1200.
As exemplified in
Developing the sample data set 201 involves accumulating a good and varied range of data for each planned ontology. The data required for this purpose preferably originates from different time periods, for example, within about a month previous to the date of implementing the training step. The data is validated and gathered from different types of defined sources. Preferably, the ontology training process 1 is not executed until a certain, predefined amount of data is gathered for the training. For example, a configured scheduler may monitor the data gathering process and count the number of records or amount of data added. When the number of records or amount of data in the sample data set 201 reaches that predetermined amount, the scheduler may execute the ontology training process 1.
Once the sample data set 201 is fully developed, the training may begin. At step 301, the sampled data set 201 for each planned ontology 200 is fed into the training module 300. The module 300 then identifies scripts 302 within the sample data set 201. Scripts are lengthy, repeated patterns that appear in the data set 201. For example, a standard phrase spoken by a customer service agent, such as “Hello, how can I help you?” may comprise a script. Next, the module 300 executes a zoning process 303 that segments conversations within a defined corpus, or data set, into meaning units. Meaning units 68 are sequences of words that express an idea, such as may be the equivalent of sentences. An example of a meaning unit in a customer service context would be the customer statement “I would like to buy a phone.”
After the zoning process has been completed for the sample data set 201, the module 300 executes term extraction 304. Term extraction 304 is a process that reviews all meaning units and extracts the terms that are meaningful in a corpus. A term is a short list of words (e.g. between 1 and 5 words) that has a precise meaning, or a meaning that stands out in its context. For example, “credit card” and “your account number” could both be appropriate terms. Next, the module 300 executes a pre-ontology step 305 that tags all overlapping terms in a non-overlapping way. Longer terms are generally preferred over shorter ones. For example, the term “my phone number” is counted as one term, rather than two—i.e. “my phone” and “my phone number.”
Following the pre-ontology process step 305, the module 300 processes the sample data set 201 to identify context vectors 306. Context vectors 306 are linkages between defined terms in the corpus, or data set, and the words that appear before or after each term. For example, the term “account” could be preceded by any of several terms, such as “have an,” “to your.” “a prepaid,” “cancel my,” or “my husband's.” Likewise, the term “account” could be followed by any number of terms, such as “holder's”, “receivable”, or “been canceled.” These so called left and right context vectors contain the predictive words for a main term, also referred to as a pivot term.
Identifying context vectors 306 forms the basis for the next step, building dendrograms 307, which is building a hierarchical clustering of terms. The training system uses the premise that terms that share contextual similarity and have similar linguistic characteristics share the same general meaning. In other words, terms with similar context vectors may be synonyms (e.g., purchase/buy), conceptually interchangeable (e.g., days, dates, locations), ontologically similar (e.g., financial transactions). Terms that share these characteristics are good candidates to be inserted in the ontology as a group of terms with similar meanings. In order to accomplish that, the system scans the context vectors of all terms created in the previous phase and clusters together terms with similar context vectors. A dendrogram, for example, may center around the term “purchase.” The left context vector (preceding term) may be “want to”, “need to”, etc. While the right context vector (following term) may be “an i-phone”, “the service”, “high-speed internet.” Initially, all detected terms in the corpus are located with similar terms in clusters on the dendrogram. Then, the dendrogram is transformed into a contextual similarity tree that only contains the stronger similarity clusters of the original dendrogram based on a similarity score algorithm that scores the similarity of the terms in the associated context vectors. During this transformation process, some terms are eliminated and some are grouped or merged with other meaningful terms. Preferably, the minimum number of terms in a dendrogram cluster is four, in order to provide a meaningful analysis of the grouped terms. By way of example, the following terms “purchase” and “buy” have similar context vectors:
After the dendrogram 307 development, relations 308 are developed within the sample data set 201. Relations 308 are linkages or relationships between the defined terms in the corpus. For example, “cancel>account,” “speak with>supervisor,” and “buy>new iPhone” are exemplary relations 308. The system defines a concise number of strong, meaningful relations according to certain pre-defined policies or rules. Those strong relations are given a higher score, and thus are given preference over other, lower-scoring relations.
Then, based upon the established relations 308, the system identifies, or surfaces, themes 309 appearing within the dataset. Themes 309 are groups or categories of relations that are similar in meaning. A theme 309 represents a concept and is defined by its associated relations. A theme encapsulates the same concept among several interactions. Themes 309 allow users to easily and efficiently understand the characteristics of interactions throughout the corpus. For example, the theme “got an email” might correspond to several relations, including “got the email,” “got confirmation,” “received an email,” “received an email confirmation,” etc. In a call center data set, for example, one theme may represent a concept expressed in several different calls. In that way, a theme can provide a summary, or a compressed view, of the characteristics of the interactions in a communications data set. Preferably, a relation is assigned to only a single theme. Additionally, preferably only relations are tagged in the tagging phase 3 of a corpus. Themes are used in the analytics phase 4, and act as building blocks employed by analytics applications or modules.
Specifically, in one embodiment, themes can be identified using the following algorithm, or method. First, the term pairs, or relations, in a corpus are scored according to the following algorithm:
In the above algorithm, “joint count” represents the number of times the terms appear together in the specified order in the dataset (or a designated subset of the dataset), the “length in letters” represents the length of the words (letters or characters), taken together, in the term set (or relation). Those numbers are multiplied together and divided by the “average distance” between the terms plus 1. The average distance may be calculated as the average number of words that appear between the two terms. Alternatively, the average distance could be calculated as the average number of letters or characters between the two terms. Strong, or high scoring, term sets are those that are long (have many letters) with high appearance count and that appear close together. Low scoring term sets are short, appear infrequently, and are far apart in the data set (indicating loose context). High scoring term pairs, or relations, are valued over low scoring pairs.
After the relations, or term pairs, are scored, the relations are listed in descending order based on that score. That list of scored relations is then truncated so that only a certain number of top scoring relations are maintained. For example, the list may be truncated to retain a pre-defined constant number of relations. Alternatively, a predefined percentage of the relations may be kept. Before or after the list is truncated, the scores for each of the relations in the list may be normalized by assigning them a new score according to their rank in the list. Preferably the score is normalized in descending order, with the best pair (highest scoring term pair) receiving the highest normalized score and the worst pair (lowest scoring term pair) receiving the lowest normalized score.
Then, for each term of each relation in the list, the corresponding dendrogram cluster, or parent node, if it exists, is identified. If found, the term pair is assigned to the identified dendrogram nodes pair, and a list of nodes is developed. Relations, or terms pairs, belonging to the same nodes pair can be grouped together. For example, as seen in
As is also illustrated in
In this way, the letter strings in each term are compared and their similarity is determined. Terms that have sufficiently similar letter strings in them are grouped together, for example in the same dendrogram cluster, or node. Thereby, previously unassociated terms can be placed into a group.
Preferably, the themes are expanded to incorporate as many of the identified terms and relations as possible. Since data sets may commonly be derived from speech-to-text translation algorithms, and because those algorithms are imperfect and often make slight mistranscriptions, it is desirable to use algorithms that can associate textually similar terms together—e.g., managers and manager, Sunday and Monday. Thus, as described above, unassociated relations can be assimilated into the established node groupings by comparing them with the already-grouped relations, for example using character trigram similarity. For relations that remain unassociated after such a comparison with the already-grouped relations, additional associations can be made by comparing the unassociated relations with one another. For example, the character trigram similarity algorithm can be used to compare and group the unassociated relations with one another. In some embodiments, the threshold for clustering or grouping these previously unassociated relations may be higher than the threshold for grouping the unassociated relations with the already-grouped relations. After all comparisons are completed, relations whose terms do not have any similarity linkages to other terms and thus cannot be clustered with other relations, are discarded as unimportant
Once all of the terms are placed into node number pairs or are discarded as unimportant, the remaining list of node number pairs indicates groups of term pairs. This list could appropriately be termed a list of “theme candidates” because it contains groups of relations that could potentially be identified as themes. The list of theme candidates can be paired down using any number of techniques. For example, the theme candidates can be scored by averaging the scores (or normalized scores) of its original term pair members. Alternatively or additionally, the list of theme candidates can be compared to a pre-created, or “canned”, list of important terms or themes. The “canned” list can be one that is created based on similar datasets, for example based on datasets belonging to another user in the same or similar industry. The theme candidates that appear on the “canned” list of important themes or terms could then be elevated as important, or high scoring, themes. Likewise, the list of theme candidates could be compared to a “canned” list of unimportant terms. The theme candidates that appear on the list can be removed, and purged because they are insignificant and do not add anything to the analysis.
Additionally, the theme candidates could be scored based on their number of members, with the candidates having the most members receiving the highest score. The theme candidates can also be scored according to their entity consensus, where themes having terms that belong to the same entity or groups of entities are scored higher than those with terms belonging to disparate entities. Another scoring means is by diversity, where themes with a greater number of unique terms on either side of the relations receive a higher score. Further, the list of theme candidates can also be refined by a user, for example, at the ontology administration stage 2. In one embodiment, the theme candidates are scored according to a number of different metrics, such as those listed above, and the then the scores are added together or averaged to calculate a final score. The theme candidates with the highest final scores can then be classified or identified as themes and used as a foundation for the analytics structure.
Themes can be displayed by displaying all of the relations comprising that theme and providing statistics about the appearance of such relations and/or the terms therein. In order to display a theme, or to create useful user interfaces displaying and conveying information about themes and about a group of themes in a dataset, each theme should be given a unique identifier, or theme name. For example, as seen in
The theme name 340 is an identifier for the theme 309 that may be used, for example, in user interfaces as a shortcut for conveying information about the theme 309 using only a short string of words and/or characters. For example, as seen in
Turning back to
Additionally, multiple themes can be viewed at one time. For example, all of the themes represented in a corpus may be displayed. Alternatively, a portion of the themes may be displayed, such as the most common themes or the themes most related to a particular term or subject, such as a term or subject identified by a user. As exemplified in
In the example of
Many other words and phrases appear in the word cloud 410, and their relative size indicates the dominance, or importance, of those words or phrases to the meaning, or subject matter, of the communication information contained in the dataset. Thereby, the word cloud 410 provides a quick and easily digested summary of the content of a dataset. Various embodiments of the word cloud 410 may be employed to identify key phrases or terms in a dataset. The analytics applications 4 may be employed to identify important terms or phrases by identifying terms that are used frequently in a dataset. Further, the analytics application 4 may attach higher weight to terms that are more unique in a dataset compared with general language. The analytics application 4 may also identify the speaker, or originator of each phrase.
Moreover, the analytics application may also identify a confidence level for each identified word or phrase indicating how likely it is that the word or phrase was identified and classified correctly. In some embodiments, the confidence level may also be visually indicated in the word cloud 410, such as by color, shade, font type, size, or other appearance quality of the word or phrase in the word cloud. For instance, in one embodiment the words and/or phrases associated with higher confidence levels may appear in a bold color shade, whereas those words and phrases associated with lesser confidence levels may appear in a relatively lighter color shade.
Turning to
In still other embodiments, the important words or phrases and metadata may be included in a word cloud based on their correlations with another word, phrase, or theme. In the exemplary word cloud of
In some embodiments, the word cloud 410 may be interactive to a user. For example, a user may be able to click on an item in a word cloud 410, such as a particular word or phrase, and information about that word or phrase may appear in the display. In the example of
This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to make and use the invention. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.
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