Intelligent communication message format automatic correction

Embodiments for intelligent communication message format automatic correction in a computing system by a processor. An appropriateness of the communication message formats is learned based on a plurality of factors for receiving communication messages from a communication system. A communication message, having one or more errors of a received communication message, may be automatically corrected according to the learned appropriateness of the communication messages.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to computing systems, and more particularly, to various embodiments for intelligent communication message format automatic correction by a processor.

Description of the Related Art

In today's society, consumers, businesspersons, educators, and others communicate over a wide variety of mediums in real time, across great distances, and many times without boundaries or borders. The advent of computers and networking technologies have made possible the intercommunication of people from one side of the world to the other. Smartphones and other sophisticated devices that rest in the palm of a person's hand allow for the sharing of information between users in an increasingly user friendly and simple manner. The increasing complexity of society, coupled with the evolution of technology continue to engender the sharing of a vast amount of information such as, for example, communication messages sent and received between different computing systems.

SUMMARY OF THE INVENTION

Various embodiments of a cognitive system for intelligent communication message format automatic correction in a computing system by a processor are provided. In one embodiment, by way of example only, a method for intelligent communication message format automatic correction in a computing system, again by a processor, is provided. An appropriateness of the communication message formats is learned based on a plurality of factors for receiving communication messages from a communication system. A communication message, having one or more errors of a received communication message, may be automatically corrected according to the learned appropriateness of the communication messages.

DETAILED DESCRIPTION OF THE DRAWINGS

As a preliminary matter, computing systems may include large scale computing called “cloud computing,” in which resources may interact and/or be accessed via a communications system, such as a computer network. Resources may be software-rendered simulations and/or emulations of computing devices, storage devices, applications, and/or other computer-related devices and/or services run on one or more computing devices, such as a server. For example, a plurality of servers may communicate and/or share information that may expand and/or contract across servers depending on an amount of processing power, storage space, and/or other computing resources needed to accomplish requested tasks. The word “cloud” alludes to the cloud-shaped appearance of a diagram of interconnectivity between computing devices, computer networks, and/or other computer related devices that interact in such an arrangement.

The Internet of Things (IoT) is an emerging concept of computing devices that may be embedded in objects, especially appliances, and connected through a network. An IoT network may include one or more IoT devices or “smart devices”, which are physical objects such as appliances with computing devices embedded therein. Examples of network-enabled appliances or devices may include computers, smartphones, laptops, wearable devices, sensor devices, voice-activated devices, face-activated devices, digital assistants, home appliances, audio systems, televisions, security cameras, security sensors, among countless other examples. Such IoT computing systems may be employed in a variety of settings.

Often times, however, communication failures may occur between separate systems and also inside micro-service architecture systems due to mal-format or invalid parameters against specific constraints. For example, a monetary organization may receive Society for Worldwide Interbank Financial Telecommunication (“SWIFT”) messages. Some messages may be received with format errors causing the receiving system to reject the messages and place the messages in a manual processing queue. An operation team member may consult with the business team on the appropriate decision. The decisions are either manual correction, re-insertion or, in case of large errors, rejection or acceptance and reporting the error to the sender. Many organizations use a specific service (whether in a micro-services architecture or a Service Oriented Architecture (“SOA”)) whereby some of the organizations may not use an up-to-date version of a service communication interface model, which may cause rejection of inputs. The solution may be one of two. The first solution may be updating the system interface models, which would require long down time and much effort that leads in turn to the organization postponing the update, and resorting to the manual corrections of some of the rejected inputs. The second solution may be an operation team does the same as in the previous case, which is manual corrections or permanent rejection.

Accordingly, a need exists for intelligent communication message format automatic correction. In one aspect, the present invention provides for use of machine learning to teach a cognitive component, on the message receiver side, to automatically modify received messages with faults and compensate for these faults, without operator or sender intervention. In one aspect, the present invention focuses on receiver-side only auto-correction, in a communication system, whereby the messages exchanged are of a known defined structure. The cognitive component may be attached to an input part of a computing system in question. The cognitive component may learn one or more decisions of the operation/business team in a supervised learning fashion. The cognitive component may perform the learning offline using historical data. Then, the cognitive component may be employed to use the learned decisions under the supervision (intervention and corrections in case of errors) of the operation/business team until reaching a high level of accuracy (e.g., a determined accuracy above a selected threshold), which may be an intermediate one phase between the training and production (e.g., “on-the-job training” phase). Also, the “on-the-job training” phase may be an optional phase as the training offline.

In one aspect, the present invention provides for intelligent communication message format automatic correction in a computing system, by a processor. An appropriateness of the communication message formats is cognitively learned based on a plurality of factors for receiving communication messages from a communication system. A communication message, having one or more errors of a received communication message, may be automatically corrected according to the learned appropriateness of the communication messages.

The so-called “appropriateness” of communication, such as a message, may be very subjective and context, format, and/or message type dependent. A learned decision for correcting a communication format by the machine learning operation may be appropriate for a one type of message. However, the same learned decision may be deemed to be inappropriate (e.g., not relevant or does not correct the error) for correcting another communication message format. Thus, use of the machine learning will learn the most appropriate decision and auto-correct function for automatically modifying/correcting one or more received messages with faults and compensate for these faults so as to achieve a correction accuracy greater than a selected threshold or percentage. Accordingly, the so-called “appropriateness” of a particular communication may depend greatly upon contextual factors, such as the type of message, and other contextual factors such as the type of service communication interface models.

It should be noted as described herein, the term “cognitive” (or “cognition”) may be relating to, being, or involving conscious intellectual activity such as, for example, thinking, reasoning, or remembering, that may be performed using a machine learning. In an additional aspect, cognitive or “cognition may be the mental process of knowing, including aspects such as awareness, perception, reasoning and judgment. A machine learning system may use artificial reasoning to interpret data from one or more data sources (e.g., sensor based devices or other computing systems) and learn topics, concepts, and/or processes that may be determined and/or derived by machine learning.

In an additional aspect, cognitive or “cognition” may refer to a mental action or process of acquiring knowledge and understanding through thought, experience, and one or more senses using machine learning (which may include using sensor based devices or other computing systems that include audio or video devices). Cognitive may also refer to identifying patterns of behavior, leading to a “learning” of one or more events, operations, or processes. Thus, the cognitive model may, over time, develop semantic labels to apply to observed behavior and use a knowledge domain or ontology to store the learned observed behavior. In one embodiment, the system provides for progressive levels of complexity in what may be learned from the one or more events, operations, or processes.

In additional aspect, the term cognitive may refer to a cognitive system. The cognitive system may be a specialized computer system, or set of computer systems, configured with hardware and/or software logic (in combination with hardware logic upon which the software executes) to emulate human cognitive functions. These cognitive systems apply human-like characteristics to convey and manipulate ideas which, when combined with the inherent strengths of digital computing, can solve problems with a high degree of accuracy (e.g., within a defined percentage range or above an accuracy threshold) and resilience on a large scale. A cognitive system may perform one or more computer-implemented cognitive operations that approximate a human thought process while enabling a user or a computing system to interact in a more natural manner. A cognitive system may comprise artificial intelligence logic, such as natural language processing (NLP) based logic, for example, and machine learning logic, which may be provided as specialized hardware, software executed on hardware, or any combination of specialized hardware and software executed on hardware. The logic of the cognitive system may implement the cognitive operation(s), examples of which include, but are not limited to, question answering, identification of related concepts within different portions of content in a corpus, and intelligent search algorithms, such as Internet web page searches.

In general, such cognitive systems are able to perform the following functions: 1) Navigate the complexities of human language and understanding; 2) Ingest and process vast amounts of structured and unstructured data; 3) Generate and evaluate hypotheses; 4) Weigh and evaluate responses that are based only on relevant evidence; 5) Provide situation-specific advice, insights, estimations, determinations, evaluations, calculations, and guidance; 6) Improve knowledge and learn with each iteration and interaction through machine learning processes; 7) Enable decision making at the point of impact (contextual guidance); 8) Scale in proportion to a task, process, or operation; 9) Extend and magnify human expertise and cognition; 10) Identify resonating, human-like attributes and traits from natural language; 11) Deduce various language specific or agnostic attributes from natural language; 12) Memorize and recall relevant data points (images, text, voice) (e.g., a high degree of relevant recollection from data points (images, text, voice) (memorization and recall)); and/or 13) Predict and sense with situational awareness operations that mimic human cognition based on experiences.

Other examples of various aspects of the illustrated embodiments, and corresponding benefits, will be described further herein.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

As previously mentioned, the mechanisms of the illustrated embodiments provide novel approaches for the monitoring and dissemination of communications to safeguard a user against submitting communication that the user may later regret to have submitted. These mechanisms include functionality that interprets the content of a particular message in terms of identified contextual factors, verifies an “appropriateness” of the message, and alerts the author or others providing the message when the content of the post in a certain setting could have potentially negative implications.

These mechanisms may use, in one embodiment, several identified contextual factors such as the author's profile, the type of audience, time and location of posting, and the like. The appropriateness checking functionality may be made against multifaceted factors such as country-specific laws, organizational policies, ethical and emotional impacts, determined negativity, and the like.

In addition to the social attributes of all parties involved in the communication in question, the parties' influence (e.g., number of followers/social eminence), current employment, etc., may also be considered to determine if the particular communications may have a negative impact to the user. In the event of an alert notification provided to the user, the mechanisms of the illustrated embodiments may provide the rationale behind the alert, enabling the user to either cancel the alert and proceed, or alter the communications accordingly. Should the user elect to proceed, the mechanisms may then require the user to provide feedback (e.g., reason, percentage of applicability/accuracy, etc.), which may be provided to a learning mechanism of the illustrated embodiments to adjust internal rules to improve the accuracy and enhance performance of the overall system.

The mechanisms of the illustrated embodiments provide, where applicable, alert notifications when the issuing of communications (e.g., social media post) may have a potential negative impact to the user, or to another party, which may result in loss of income, employment, legal implications, social implications, and so forth. The mechanisms are, among other aspects, rules driven, the development of these rules based on interpretation of the text of particular communication. In one embodiment, the rules may be adjusted towards sentiment analysis (e.g., opinion/negativity/emotional state/etc. of a particular communication) based on jurisdictional-specific rules or laws, geographic location, topic/thread, or other factors. A variety of cognitive, interpretive analysis in the context of a given communication may be utilized. Additionally, the mechanisms of the illustrated embodiments may consider still other factors such as social attributes/influence of all parties involved that may be used to determine the ultimate impact of a particular post in a social network, for example.

In view of the foregoing, the mechanisms of the illustrated embodiments provide, among other aspects, a cognitive mechanism to cognitively correct or modify one or more communication messages format/errors in a computing system. An appropriateness of the communication message formats is cognitively learned based on a plurality of factors for receiving communication messages from a communication system. A communication message, having one or more errors of a received communication message, may be automatically corrected according to the learned appropriateness of the communication messages.

Turning now toFIGS. 4A-4B, block diagrams depict exemplary functional components in computing systems400,425according to various mechanisms of the illustrated embodiments.FIG. 4Aillustrates a system400for a dual operation mode for intelligent communication message format automatic correction in a computing environment (e.g., a cloud computing environment), according to an example of the present technology.FIG. 4Billustrates a system425for a production operation mode for intelligent communication message format automatic correction in a computing environment (e.g., a cloud computing environment), according to an example of the present technology.

As will be seen, many of the functional blocks may also be considered “modules” or “components” of functionality, in the same descriptive sense as has been previously described inFIGS. 1-3. With the foregoing in mind, the module/component blocks400may also be incorporated into various hardware and software components of a system for cognitive data curation in accordance with the present invention. Many of the functional blocks400may execute as background processes on various components, either in distributed computing components, or on the user device, or elsewhere.

Turning now toFIG. 4A, computing system400may include a cognitive computing system420(e.g., cognitive sub-system) that may be attached to one or more different computing systems. For example, the cognitive computing system420may receive one or more inputs from other computing systems and output, via a main application402, data for further processing.

In one aspect, the cognitive computing system420may include a fault presenter406, a cognitive format corrector408, a message (“MSG”) assembler410, and a correction presenter412(in association with one or more manual decisions414), each of which may be described in more detail inFIG. 4B. The cognitive computing system420may be in communication with a rejection queue404and a main application402. The cognitive computing system420may learn how one or more decisions (e.g., decisions and/or operations performed by an administrator, business team, or predefined rules) correct one or more errors in the input message format according to a selected organization message model and version.

In one aspect, to prepare the cognitive computing system420for production and automatic correction, the following steps may be used. To illustrate, assume one or more inputs (e.g., received messages/inputs from other computing systems) are received by the main application402(e.g., input validation) and one or more errors are detected. The rejected messages may be placed in a rejection queue404. The cognitive computing system420may retrieve one or more rejected messages from the rejection queue404and the following steps may be used.

Step 1, the cognitive format corrector408may be trained offline first by preparing and using historical data of a system error log, one or more previously rejected messages, and any corresponding correction operations on the previously rejected messages. Step 2, the trained cognitive format corrector408may be employed in a dual-mode operation (e.g., manual correction and automatic correction (“auto-correction”)). That is, the automatic correction mode may be performed under complete review of an administrator or group of supervisors (e.g., operation/business team). In the event there is an error in the cognitive component output from the cognitive computing system420, one or more manual decisions414may be used to manually correct the cognitive component output and the cognitive computing system420may obtain a condensed offline training cycle that covers the error and the relevant versions of correction. Step 3, the trained cognitive format corrector408may be operated in a production mode (e.g., online) with one or more standardized reviews as any other component, as described inFIG. 4B. The dual-mode operation ofFIG. 4Amay also be a final or completed phase that permits human online manual corrections whenever needed. These manual corrections414may be followed by a training session of the cognitive format corrector408that may be automatically and dynamically initiated to teach the cognitive format corrector408the new correction.

Turning now toFIG. 4B, as stated previously, the cognitive computing system420may include the fault presenter406, the cognitive format corrector408, and the message assembler410. The fault presenter406may function and play a role in both the training phase (seeFIG. 4A) and the production phase (seeFIG. 4B). The fault presenter406may be responsible for creating the input to the cognitive format corrector408. In one aspect, the process of creating the input and obtaining the fully corrected message (for a rejected message having one or more errors) may be incrementally performed. After reading the message, the fault presenter406loops on the faults in the message that it has extracted from the corresponding part of the error log. That is, the fault presenter406reads the faults from the log file in a loop to present the fault to the cognitive format corrector. For each fault, the fault presenter406receives a fault code and converts both the fault code and the fault/error (e.g., a faulty statement of the message) to a predefined format (e.g., a learned format). The fault presenter406may also associate a formatted fault code with the faulty statement and forward the formatted fault code with the faulty statement as the input to the cognitive format corrector408component. The output of each input may be the corrected statement of the message. The corrected statements of the same message may be accumulated to form a complete corrected message before it is re-entered to the main business application via the message assembler410.

Turning back to the training phase ofFIG. 4A, it should be noted that the correction presenter412may be a component having functionality and a role similar to the role of the fault presenter406component, but only in the training phase (e.g.,FIG. 4A). The correction presenter412may be responsible for the presentation of a reference output and/or the correct output needed for the supervised learning process of the cognitive computing system420. As a main part of the correction presenter412, a user interface may be employed and used by a human corrector to do the manual correction. The user interface of the correction presenter412may display the faulty statement and the fault description and code to the human corrector. The correction presenter412may also provide the human corrector one or more options for the correction decisions and may then execute the selected decision. In addition to the execution of the manual correction, the correction presenter412component may synchronize one or more manual correction decisions with the corresponding faults extracted by the fault presenter406component and format the manual correction decisions in the predefined format. The correction presenter412may present the correction decisions as part of each iteration inputs.

For an offline training ofFIG. 4A, the correction presenter412component may obtain one or more manual correction decisions414, format the one or more manual correction decisions, associate the one or more manual correction decisions with the corresponding fault codes, and then generate a labeled message file that contains the faulty message with the formatted fault code and correction decision. The labeled files may be used as the only input to an offline training operation.

Returning back toFIG. 4B, the message (“MSG”) assembler410may be a component that, in conjunction with the other components (e.g., the fault presenter406, the cognitive format corrector408, and/or the correction presenter412), may accumulate the corrected statements to form a complete corrected message.

The cognitive format corrector408may be a component that is the primary operative component of the cognitive computing system420. The cognitive format corrector408may learn and model a format correction process via a machine learning operation that learns, during a training cycle, one or more decisions from a human corrector. That is, the cognitive format corrector408may employ one or more machine learning operations and/or one or more cognitive applications (e.g., NPL, artificial intelligence (AI), machine learning, IBM® Watson® Alchemy Language (IBM Watson and Alchemy are trademarks of International Business Machines Corporation)).

The learning may occur by the cognitive format corrector408executing a machine learning process in the training phase to train a cognitive auto-correct model. The cognitive format corrector408may use the trained cognitive auto-correct model in a production phase to correct the input faulty messages (e.g., received messages from one or more computing systems, which may be external to and/or internal with the cognitive computing system420).

In view of the operations of computing systems400,425ofFIGS. 4A-B, consider, as an illustration of exemplary use cases, the following. In use case 1, assume bank A sends a SWIFT message to bank B. Bank B has not upgraded the SWIFT version on the computing system of bank B. The receiving system at bank B rejects the message. The cognitive computing system420may receive/obtain the rejected message and any relevant error codes from an error log (e.g., system log). The cognitive computing system420may correct the message, as described inFIGS. 4A and/or 4B. The generated corrected message may be entered again to the receiving system at bank B. The message may be accepted by the computing system of bank B and passed for further processing at bank B.

Turning now to use case 2, assume system A receives an Extensible Markup Language (“XML”) message. Assume system A works under one or more security rules (e.g., Payment Card Industry “PCI”-compliance) that mandates encrypting a specific field value. System A rejects the message because system A detects the specific field value is not encrypted. The cognitive computing system420may receive/obtain the rejected message and any relevant error codes from an error log (e.g., system log). The output of the cognitive computing system420may, in this case, initiate a routing in the message assembler410component that sends a message field in question to be encrypted. The message assembler410may output the corrected message that has the encrypted field to be entered again to system A. System A accepts the message and may pass it further, as output, to the next step of the business process (e.g., pass for further processing).

It should be noted that, the cognitive computing system420, using a machine learning operation, may apply one or more heuristics and machine learning based models using a wide variety of combinations of methods, such as supervised learning, unsupervised learning, temporal difference learning, reinforcement learning and so forth. Some non-limiting examples of supervised learning which may be used with the present technology include AODE (averaged one-dependence estimators), artificial neural networks, Bayesian statistics, naive Bayes classifier, Bayesian network, case-based reasoning, decision trees, inductive logic programming, Gaussian process regression, gene expression programming, group method of data handling (GMIDH), learning automata, learning vector quantization, minimum message length (decision trees, decision graphs, etc.), lazy learning, instance-based learning, nearest neighbor algorithm, analogical modeling, probably approximately correct (PAC) learning, ripple down rules, a knowledge acquisition methodology, symbolic machine learning algorithms, sub symbolic machine learning algorithms, support vector machines, random forests, ensembles of classifiers, bootstrap aggregating (bagging), boosting (meta-algorithm), ordinal classification, regression analysis, information fuzzy networks (IFN), statistical classification, linear classifiers, fisher's linear discriminant, logistic regression, perceptron, support vector machines, quadratic classifiers, k-nearest neighbor, hidden Markov models and boosting. Some non-limiting examples of unsupervised learning which may be used with the present technology include artificial neural network, data clustering, expectation-maximization, self-organizing map, radial basis function network, vector quantization, generative topographic map, information bottleneck method, IBSEAD (distributed autonomous entity systems based interaction), association rule learning, apriori algorithm, eclat algorithm, FP-growth algorithm, hierarchical clustering, single-linkage clustering, conceptual clustering, partitional clustering, k-means algorithm, fuzzy clustering, and reinforcement learning. Some non-limiting examples of temporal difference learning may include Q-learning and learning automata. Specific details regarding any of the examples of supervised, unsupervised, temporal difference or other machine learning described in this paragraph are known and are considered to be within the scope of this disclosure.

Turning now toFIG. 5, a method500for intelligent communication message format automatic correction by a processor is depicted, in which various aspects of the illustrated embodiments may be implemented. The functionality500may be implemented as a method executed as instructions on a machine, where the instructions are included on at least one computer readable medium or one non-transitory machine-readable storage medium. As one of ordinary skill in the art will appreciate, the various steps depicted in method500may be completed in an order or version differing from the depicted embodiment to suit a particular scenario.

The functionality500may start in block502. An appropriateness of the communication message formats is cognitively learned based on a plurality of factors for receiving communication messages from a communication system, as in block504. A communication message, having one or more errors of a received communication message, may be automatically corrected according to the learned appropriateness of the communication messages, as in block506. The functionality500may end, as in block508.

Turning now toFIG. 6, a method600for intelligent communication message format automatic correction by a processor is depicted, in which various aspects of the illustrated embodiments may be implemented. The functionality600may be implemented as a method executed as instructions on a machine, where the instructions are included on at least one computer readable medium or one non-transitory machine-readable storage medium. As one of ordinary skill in the art will appreciate, the various steps depicted in method600may be completed in an order or version differing from the depicted embodiment to suit a particular scenario.

The functionality600may start in block602. One or more message formats may be cognitively learned, using a machine learning operation, for communication messages based on a plurality of factors, as in block604. A message (e.g., communication message) may be received from one or more computing systems, as in block606. One or more message errors may be detected in the message according to the learning, as in block608. The one or more message errors may be extracted from the message, as in block610. A fault code may be assigned to the one or more message errors extracted from the message, as in block612. The fault code and the one or more message errors may be converted (e.g., corrected) to a predefined communication format to correct the one or more message errors according to the learning, as in block614. The one or more corrected message errors may be accumulated to complete correcting the message, as in block616. The functionality600may end in block618.

In one aspect, in conjunction with and/or as part of at least one block ofFIGS. 5-6, the operations of methods500and600may include each of the following. The operations of methods500and600may learn a plurality of decisions relating to correcting each communication message using a machine learning operation. Historical data may be used during a training cycle for the learning using a machine learning operation. The operations of methods500and600may learn, via a machine learning operation, one or more correction operations for correcting one or more rejected messages using the historical data according to one or more message models and message versions, wherein the historical data includes one or more system error logs, the rejected messages, and one or more correction actions to correct the rejected messages.