Patent Publication Number: US-2022222481-A1

Title: Image analysis for problem resolution

Description:
COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 
     FIELD 
     The field relates generally to information processing systems, and more particularly to providing techniques for image analysis and problem resolution in such information processing systems. 
     BACKGROUND 
     Enterprises typically provide multiple channels (e.g., telephone, web-based, mobile applications, etc.) for their customers to interact with their support systems. For example, some enterprises provide search options to give customers access to knowledge through, for example, frequently asked questions (FAQs), guided steps for troubleshooting and articles in a knowledge base. These traditional support systems typically utilize text-based searches which are not very effective when it comes to supporting and/or troubleshooting complex problems or issues. Text-based searches also rely heavily on the use of keywords to understand semantics and return useful content. 
     In many problematic scenarios or outages, customers may be viewing certain images on the screens of their devices, and the images may include one or more messages about the problem. Using text-based searches in an attempt to describe an image and understand the root cause of a specific problem can be extremely difficult. For example, customers may not be able to write an accurate description identifying the exact problem (e.g., error code, etc.) that may be occurring. As a result, in response to the customer&#39;s description, a support system provides generic articles about the issue, requiring the customer to read, understand and apply heuristics to independently solve a problem. If customers are unable proceed further on their own, they will typically reach out to support agents for assisted support, thus minimizing the value of self-support systems. 
     SUMMARY 
     Illustrative embodiments provide image analysis techniques to identify and resolve issues and/or problems. 
     In one embodiment, a method comprises receiving an input of at least one image associated with an issue. In the method, text is extracted from the at least one image, and an intent is determined from the extracted text. The method further includes recommending a response to the issue based at least in part on the determined intent, and transmitting the recommended response to a user. The extracting, determining and recommending are performed at least in part using one or more machine learning models. 
     Further illustrative embodiments are provided in the form of a non-transitory computer-readable storage medium having embodied therein executable program code that when executed by a processor causes the processor to perform the above steps. Still further illustrative embodiments comprise an apparatus with a processor and a memory configured to perform the above steps. 
     These and other features and advantages of embodiments described herein will become more apparent from the accompanying drawings and the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts details of an information processing system with an image analysis and resolution platform for analyzing incoming image inputs and recommending appropriate resolutions according to an illustrative embodiment. 
         FIG. 2  depicts details of an operational flow for analyzing incoming image inputs and recommending appropriate resolutions according to an illustrative embodiment. 
         FIG. 3  depicts example pseudocode for text extraction from an image according to an illustrative embodiment. 
         FIG. 4  depicts example regions of interest in an image according to an illustrative embodiment. 
         FIG. 5 . depicts an architecture of a multi-task network model according to an illustrative embodiment. 
         FIG. 6  depicts details of an operational flow for intent classification according to an illustrative embodiment. 
         FIG. 7  depicts a table including details of intent training data according to an illustrative embodiment. 
         FIG. 8  depicts example pseudocode for intent analysis and classification according to an illustrative embodiment. 
         FIG. 9  depicts a table including details of intents and corresponding resolutions according to an illustrative embodiment. 
         FIG. 10  depicts details of an operational flow of image analysis in connection with predicting intent and recommending resolutions according to an illustrative embodiment. 
         FIG. 11  depicts a graph of a cosine distance algorithm for different intents according to an illustrative embodiment. 
         FIG. 12  depicts example pseudocode for providing resolution information to users according to an illustrative embodiment. 
         FIG. 13  depicts a process for analyzing incoming image inputs and recommending appropriate resolutions according to an illustrative embodiment. 
         FIGS. 14 and 15  show examples of processing platforms that may be utilized to implement at least a portion of an information processing system according to illustrative embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Illustrative embodiments will be described herein with reference to exemplary information processing systems and associated computers, servers, storage devices and other processing devices. It is to be appreciated, however, that embodiments are not restricted to use with the particular illustrative system and device configurations shown. Accordingly, the term “information processing system” as used herein is intended to be broadly construed, so as to encompass, for example, processing systems comprising cloud computing and storage systems, as well as other types of processing systems comprising various combinations of physical and virtual processing resources. An information processing system may therefore comprise, for example, at least one data center or other type of cloud-based system that includes one or more clouds hosting tenants that access cloud resources. Such systems are considered examples of what are more generally referred to herein as cloud-based computing environments. Some cloud infrastructures are within the exclusive control and management of a given enterprise, and therefore are considered “private clouds.” The term “enterprise” as used herein is intended to be broadly construed, and may comprise, for example, one or more businesses, one or more corporations or any other one or more entities, groups, or organizations. An “entity” as illustratively used herein may be a person or system. On the other hand, cloud infrastructures that are used by multiple enterprises, and not necessarily controlled or managed by any of the multiple enterprises but rather respectively controlled and managed by third-party cloud providers, are typically considered “public clouds.” Enterprises can choose to host their applications or services on private clouds, public clouds, and/or a combination of private and public clouds (hybrid clouds) with a vast array of computing resources attached to or otherwise a part of the infrastructure. Numerous other types of enterprise computing and storage systems are also encompassed by the term “information processing system” as that term is broadly used herein. 
     As used herein, “real-time” refers to output within strict time constraints. Real-time output can be understood to be instantaneous or on the order of milliseconds or microseconds. Real-time output can occur when the connections with a network are continuous and a user device receives messages without any significant time delay. Of course, it should be understood that depending on the particular temporal nature of the system in which an embodiment is implemented, other appropriate timescales that provide at least contemporaneous performance and output can be achieved. 
     As used herein, “natural language” is to be broadly construed to refer to any language that has evolved naturally in humans. Non-limiting examples of natural languages include, for example, English, Spanish, French and Hindi. 
     As used herein, “natural language processing (NLP)” is to be broadly construed to refer to interactions between computers and human (natural) languages, where computers are able to derive meaning from human or natural language input, and respond to requests and/or commands provided by a human using natural language. 
     As used herein, “natural language understanding (NLU)” is to be broadly construed to refer to a sub-category of natural language processing in artificial intelligence (AI) where natural language input is disassembled and parsed to determine appropriate syntactic and semantic schemes in order to comprehend and use languages. NLU may rely on computational models that draw from linguistics to understand how language works, and comprehend what is being said by a user. 
     As used herein, “image” is to be broadly construed to refer to a visual representation which is, for example, produced on an electronic display such as a computer screen or other screen of a device. An image as used herein may include, but is not limited to, a screen shot, window, message box, error message or other visual representation that may be produced on a device. Images can be in the form of one or more files in formats including, but not necessarily limited to, Joint Photographic Experts Group (JPEG), Portable Network Graphics (PNG), Graphics Interchange Format (GIF), and Tagged Image File (TIFF). 
     In an illustrative embodiment, machine learning (ML) techniques are used to extract knowledge from images associated with system problems to predict the issues corresponding to the problems and provide users with targeted resolution steps. One or more embodiments leverage historical support case information comprising images and videos, and use the historical support case information as training data for one or more ML models. The trained ML models receive images corresponding to a problem from a user and determine matching images and support cases to automatically recommend resolutions which are specific to the problem. Although the embodiments herein are discussed in terms of images, the embodiments may alternatively apply to videos produced on a device in one or more formats such as, but not necessarily limited to, Moving Picture Experts Group (MPEG), Audio Video Interleave (AVI) and Windows Media Video (WMV). 
       FIG. 1  shows an information processing system  100  configured in accordance with an illustrative embodiment. The information processing system  100  comprises user devices  102 - 1 ,  102 - 2 , . . .  102 -M (collectively “user devices  102 ”). The user devices  102  communicate over a network  104  with an image analysis and resolution platform  110 . The information processing system further comprises an assisted support channel  170 , which may communicate over the network with the user devices  102  and the image analysis and resolution platform  110 . 
     The user devices  102  can comprise, for example, Internet of Things (IoT) devices, desktop, laptop or tablet computers, mobile telephones, or other types of processing devices capable of communicating with the image analysis and resolution platform  110  and/or the assisted support channel  170  over the network  104 . Such devices are examples of what are more generally referred to herein as “processing devices.” Some of these processing devices are also generally referred to herein as “computers.” The user devices  102  may also or alternately comprise virtualized computing resources, such as virtual machines (VMs), containers, etc. The user devices  102  in some embodiments comprise respective computers associated with a particular company, organization or other enterprise. The variable M and other similar index variables herein such as K and L are assumed to be arbitrary positive integers greater than or equal to two. 
     The assisted support channel  170  comprises an interface layer  171 , a customer relationship management (CRM) system  173  and a file store  175 . According to one or more embodiments, a CRM system  173  includes technical support personnel (e.g., agents) tasked with assisting users that experience issues with their devices, systems, software, firmware, etc. Users such as, for example, customers, may contact the technical support personnel when they have device and/or system problems and require technical assistance to solve the problems. Users may access the assisted support channel  170  through one or more interfaces supported by the interface layer  171 . The interfaces include multiple communication channels, for example, websites, email, live chat, social media, mobile application and telephone sources. Users can access the assisted support channel  170  through their user devices  102 . In response to user inquiries and/or requests for assistance, technical support personnel may create support tickets and/or cases summarizing the issues and the steps taken to resolve the issues. 
     As part of agent assisted support tickets and/or cases, screen shots and images related to the issues are collected along with any textual log files from the user (e.g., customer) and stored in the file store  175 . These images, as well as any textual log files, can be used as reference data for technical support personnel to help diagnose and fix that specific case. After the case is complete, this data and images remain in the file store  175  as historical records. 
     The terms “client,” “customer” or “user” herein are intended to be broadly construed so as to encompass numerous arrangements of human, hardware, software or firmware entities, as well as combinations of such entities. Image analysis and problem resolution services may be provided for users utilizing one or more machine learning models, although it is to be appreciated that other types of infrastructure arrangements could be used. At least a portion of the available services and functionalities provided by the image analysis and resolution platform  110  in some embodiments may be provided under Function-as-a-Service (“FaaS”), Containers-as-a-Service (“CaaS”) and/or Platform-as-a-Service (“PaaS”) models, including cloud-based FaaS, CaaS and PaaS environments. 
     Although not explicitly shown in  FIG. 1 , one or more input-output devices such as keyboards, displays or other types of input-output devices may be used to support one or more user interfaces to the image analysis and resolution platform  110 , as well as to support communication between the image analysis and resolution platform  110  and connected devices (e.g., user devices  102 ), between the assisted support channel  170  and connected devices and/or between other related systems and devices not explicitly shown. 
     In some embodiments, the user devices  102  are assumed to be associated with repair technicians, system administrators, information technology (IT) managers, software developers release management personnel or other authorized personnel configured to access and utilize the image analysis and resolution platform  110 . 
     The image analysis and resolution platform  110  and the assisted support channel  170  in the present embodiment are assumed to be accessible to the user devices  102 , and vice-versa, over the network  104 . In addition, the image analysis and resolution platform  110  is accessible to the assisted support channel  170 , and vice-versa, over the network  104 . The network  104  is assumed to comprise a portion of a global computer network such as the Internet, although other types of networks can be part of the network  104 , including a wide area network (WAN), a local area network (LAN), a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. The network  104  in some embodiments therefore comprises combinations of multiple different types of networks each comprising processing devices configured to communicate using Internet Protocol (IP) or other related communication protocols. 
     As a more particular example, some embodiments may utilize one or more high-speed local networks in which associated processing devices communicate with one another utilizing Peripheral Component Interconnect express (PCIe) cards of those devices, and networking protocols such as InfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternative networking arrangements are possible in a given embodiment, as will be appreciated by those skilled in the art. 
     The image analysis and resolution platform  110 , on behalf of respective infrastructure tenants each corresponding to one or more users associated with respective ones of the user devices  102 , provides a platform for analyzing incoming image inputs and recommending appropriate resolutions. 
     Referring to  FIG. 1 , the image analysis and resolution platform  110  comprises an interface layer  120 , an image analysis and annotation engine  130 , an image matching and recommendation engine  140 , a data repository  150  and a search engine  160 . The image analysis and annotation engine  130  includes a text extraction component  131  and an intent analysis component  133 . The image matching and recommendation engine  140  includes an image analysis component  141  and a recommendation component  143 . The search engine  160  includes a knowledge base  161 . 
     When there are system issues and/or outages, users may be presented with certain images on their devices (e.g., user devices  102 ). For example, in connection with a blue screen of death (“B SOD”) problem, a user may see an image similar to the image  400  in  FIG. 4 , but without the borders around the different textual portions. In another example, a new device may fail to function due to a missing device driver, and the user may encounter an image including one or more textual phrases about missing and/or uninstalled components. Although issues and/or outages may be visually captured as images by a user, there are no existing platforms to analyze the images and recommend resolutions to the issues and/or outages based on the analysis. 
     Referring to the system  100  in  FIG. 1  and to the operational flow  200  in  FIG. 2 , the image analysis and annotation engine  130 , and the image matching and recommendation engine  140  provide end-to-end capability for enhanced self-support. The image analysis and annotation engine  130  uses a mask region-based convolutional neural network (Mask-R-CNN) algorithm, and the image matching and recommendation engine  140  uses natural language processing (NLP) and distance similarity algorithms. 
     Referring to blocks  201  and  202  of operational flow  200 , following a start of the operational flow  200 , images and case resolution metadata are collected through assisted support channels (e.g., assisted support channel  170 ) from an assisted support channel file store  275  (or  175  in  FIG. 1 ) and passed to the image analysis and annotation engine  130 , which semantically analyzes the image and extracts text from the image. For example, referring to  FIGS. 1 and 2 , at block  203 , the images are analyzed by the text extraction component  131 , and at block  204 , the intent analysis component  133  analyzes the extracted text to determine semantic intent from an image. Many images include useful text that is typically present in the image to convey important information to a user. For example, important textual objects and entities found in the image text can be critical to understanding the context and semantic intent of an issue and may assist with providing automatic case resolution or guidance to a customer. For example, in keeping with the example that a device may fail to function due to a missing device driver, text in an image associated with that failure may convey to a user that a device driver is not installed. 
     Based on the identified intents, the extracted text is annotated with intent identifiers and, at block  205 , the identified intents and annotated extracted text are stored along with the image and case resolution data in platform data repository  250  (or  150  in  FIG. 1 ) that forms a foundation for an image-based search by users. The data repository  150 / 250  includes mapped relationships between images, extracted text, intents and case resolutions to be used in connection with image-based analysis performed by the image analysis and resolution platform  110  upon receipt of one or more images from a user/customer in connection with a scenario where a user/customer is accessing a self-support portal to resolve issues and/or problems without assistance from support personnel. 
     According to one or more embodiments, a self-support portal provides a user with access to the image analysis and resolution platform  110  via the interface layer  120 . Similar to the interface layer  171 , the interface layer  120  supports multiple communication channels, for example, websites, email, live chat, social media and mobile applications. Users can access the image analysis and resolution platform  110  through their user devices  102 . Referring to block  210  in  FIG. 2 , a user seeking to solve a problem or address an issue without help from technical support personnel can upload an image file (e.g., JPEG, GIF, TIFF, PNG or other type of image file) associated with the issue or problem via the interface layer  120  and the image is received by the image analysis and resolution platform  110 . As noted herein, the image file may correspond to an image that appears on the screen of a user device in the event of a problem or issue such as, for example, a BSOD or an image including a message about a missing or uninstalled component. Similar to blocks  203  and  204  discussed above, at blocks  211  and  212 , the image analysis component  141  of the image matching and recommendation engine  140  leverages the text extraction component  131  to extract text from the uploaded image (or images if a user uploads multiple images), and leverages the intent analysis component  133  to analyze the extracted text to determine semantic intent from the image. Referring to block  213 , based on the identified intent, the recommendation component  143  predicts a resolution to the problem by matching the identified intent with corresponding historical images and associated resolutions stored in the platform data repository  250  (or  150  in  FIG. 1 ). In addition, referring to block  214 , a search engine (e.g., search engine  160  in  FIG. 1 ) searches a knowledge base (e.g., knowledge base  161  in  FIG. 1 ) with data annotated with intent identifiers based on the identified intent. The knowledge base  161  includes, for example, articles, product manuals and guided flows for resolving the given problem, which can be returned a user as search results in connection with a recommended resolution. As per block  215 , the recommendation component  143  provides the predicted issue, recommended resolution and search results to a user. 
     The text extraction component  131  of the image analysis and annotation engine  130  extracts text and semantics from an uploaded customer image comprising structured and unstructured data. In order to parse through unstructured text, the embodiments utilize a combination of a Mask-R-CNN algorithm with optical character recognition (OCR), which accomplishes object detection, text object segmentation and text extraction for an inputted image. The pseudocode  300  in  FIG. 3  corresponds to utilization of OCR in connection with recognizing, reading and extracting text embedded in an image. The code includes, for example, Python® code. 
     Referring to the image  400  in  FIG. 4 , the combined Mask-R-CNN and OCR algorithm creates boundary boxes  401 - 1  and  401 - 2  around areas in the image that include text objects. Mask-R-CNN is an object detection model used in the embodiments to provide a flexible mechanism to identify regions of interest (RoIs) in the images. In the images, the model identifies two types of objects, text objects and non-text objects. The Mask-R-CNN model is trained with OCR related data to identify RoIs in an image that are highly likely to contain text. This identification of RoIs is referred to herein as text localization. In addition to text localization, the model reads and extracts the text in a process referred to herein as text recognition. 
     Referring to  FIG. 5 , the Mask-R-CNN model is part of a multi-task network  500  that predicts multiple outputs from a single input image to achieve both text localization and text recognition. The model comprises three heads  503 ,  504  and  506 , where a first head (bounding-box regression head  503 ) proposes boundary boxes that are likely to contain objects of interest, a second head (classification head  504 ) classifies which type of objects, for example, text and non-text (e.g., graphics) are contained in each box, and the third head (text recognition head  506 ) recognizes the text using, for example, OCR. The bounding-box regression head  503  implements a region proposal network followed by a bounding-box regression network. The output of the bounding-box regression head  503  predicts RoIs/locations  507  in the image that might contain text. The classification head  504  comprises a binary classification component that estimates a class of an object inside an RoI as text versus everything else (e.g., non-text). The text recognition head  506  receives feature maps as an input from a convolutional stack  502  and RoI coordinates generated from the bounding-box regression head  503 . Convolutional neural networks (CNNs) apply a filter to an input to generate a feature map summarizing the presence of detected features in the input. The stacking of convolutional layers in the convolutional stack  502  allows for a hierarchical decomposition of the input, components of which are input to the heads  503 ,  504  and  506 . 
     The multi-task network  500  uses the identified RoI/locations  507  and fetches the relevant representations for each region from the convolutional stack  502 . According to an illustrative embodiment, a convolutional machine learning method with short-range kernel width is utilized. At each spatial step, the output of convolutions is used to predict an output letter and the overall sequence is collapsed through a Connectionist Temporal Classification (CTC) layer to output a final sequence for an RoI. The embodiments differ from conventional Mask-R-CNN algorithms at least by introducing text localization and recognition. Leveraging the Mask-R-CNN and OCR model and using labeled (annotated) images for training permits text extraction with higher performance and efficiency than conventional OCR based mechanisms. 
     Referring to  FIGS. 1 and 6 , the intent analysis component  133 / 633  uses NLU to analyze text and classify intent. The embodiments base a text message, where the words come one after another over a period of time, on a time series model, and leverage a Recurrent Neural Network (RNN). In order to efficiently analyze a message, the embodiments use a bi-directional RNN, which uses two separate processing sequences, one from left to right and another from right to left. In order to address RNNs having exploding or vanishing gradient issues for longer and complex dialogs or messages, the embodiments utilize a bi-directional RNN with long short-term memory (LSTM) for the NLU. 
     The machine learning model used by the ML layer  637  is a bi-directional RNN with LSTM model. Unlike a traditional neural network, where input and output are independent, in an RNN the output from a previous step feeds into the input of a current step. As a result, when performing language processing, previous words are taken into account when predicting subsequent words of a sentence. An RNN includes a hidden state which remembers one or more words in the sentence. The bi-directional RNN of the embodiments performs bi-directional processing of a sentence (from past and from future in two directions in parallel). A bi-directional RNN addresses problems where sentences are too long, and some previous words in the sentence are not available due to limited hidden states. In addition, LSTM utilized by the embodiments introduces advanced memory units and gates to an RNN to improve accuracy and performance of the machine learning model. 
     Referring to the operational flow  600  in  FIG. 6 , intent analysis by the intent classification component  633  (which is the same or similar to the intent classification component  133 ) uses intent corpus data  636  to train the machine learning model. This corpus data contains words and/or phrases and the corresponding intent associated with each of the words and/or phrases. A small sample of the intent corpus data used to train the machine learning model is shown in the table  700  in  FIG. 7 . Referring to the table  700 , textual message samples are shown as corresponding to semantic intents corresponding to drivers (“System driver; Device driver installation”), blue screen of death (“B SOD”) and corruption (“File corruption; Registry file corruption; Hard drive failure”). The training data is input to the training component  638  of the ML layer  637  to train the machine learning model. 
     Referring to  FIG. 6 , according to an embodiment, a pre-processing component  634  cleans any unwanted characters and stop words from the corpus data. The pre-processing further comprises stemming and lemmatization, as well as changing text to lower case, removing punctuation, and removing incorrect or unnecessary characters. Once pre-processing and data cleanup is performed, the feature engineering component  635  tokenizes the input list of words in the sentences and/or phrases. Tokenization can be performed using, for example, a Keras library or a natural language toolkit (NLTK) library. A Keras tokenizer class can be used to index the tokens. After tokenization is performed, the resulting words are padded to make the words have equal lengths so that the words can be used in the machine learning model. For output encoding, tokenization and padding are performed on the intent list in the corpus  636 . 
     After tokenization and padding, a list of intents is indexed and fed into the machine learning model for training. The intents may be one-hot encoded before being input to the model. Some features and/or parameters used in connection with the creation of the bi-directional RNN with LSTM model include an Adam optimizer, Softmax activation function, batch size and a number of epochs. These parameters or features are tuned to get the best performance and accuracy of the model. After the model is trained with the intent corpus training data, the model is used to predict the intent for incoming dialogs and/or messages. The accuracy of the model is calculated for hyperparameter tuning. 
     Referring to the operational flow  600  in  FIG. 6 , extracted text  631  (e.g., from an uploaded image) is pre-processed and engineered by the pre-processing and feature engineering components  634  and  635 , and then input to the ML layer  637  so that semantic intent can be classified (e.g., by the classification component  639 ) using the trained machine learning model. Predicted intents  680 - 1 ,  680 - 2  and  680 - 3  which may be outputs to the operational flow  600  include, for example, driver installation, BSOD and hard drive failure. 
       FIG. 8  depicts example pseudocode  800  for intent analysis and classification according to an illustrative embodiment. For the implementation of the intent analysis component  133 / 633 , Python language and NumPy, Pandas, Keras and NLTK libraries can be used. 
     The semantic intent classified from the extracted text of an image is stored in the data repository  150  along with associated case resolution information for future scenarios when a customer uploads an image in connection with a request for resolution of a problem, outage or other issue.  FIG. 9  depicts a table  900  including semantic intent and associated case resolution information that can be stored in the data repository  150 . As shown in the table  900 , semantic intent 1 (“System driver; Device driver installation”) corresponds to a resolution of “Installed driver by uploading from www.company.com/drivers/system”, semantic intent 2 (“BSOD”) corresponds to a resolution of “Bootstrap was corrupt; system was re-imaged” and semantic intent 3 (“File corruption; Registry file corruption; Hard drive failure”) corresponds to a resolution of “Dispatch for replacing hard drive at customer site”. 
     Referring to the operational flow  1000  in  FIG. 10 , when a user attempts to look for help and/or support via a customer support portal with an image  1001  captured by the user, the image analysis component  1041  (same or similar to the image analysis component  141  in  FIG. 1 ) performs a reverse image search. This search is achieved by leveraging the text extraction component  1031  (same or similar to the text extraction component  131  in  FIG. 1 ) and intent analysis component  1033  (same or similar to the intent analysis component  133  in  FIG. 1  or the intent analysis component  633  in  FIG. 6 ) to extract and analyze text from the user-provided image  1001  and determine the intent associated with the image  1001 . Then, using the predicted intent, the recommendation component  1043  (same or similar to the recommendation component  143  in  FIG. 1 ) attempts to find a match with historical image and resolution data from the platform data repository (e.g., repository  150  in  FIG. 1 or 250  in  FIG. 2 ) to recommend a resolution. 
     In some use cases, images comprising error messages can have very similar features, but the error messages or error codes may be different. For example, an error dialog image for a “device driver not found” message may look very similar to an error dialog image for a hard drive failure message from an image feature perspective. However, the textual messages are different, and returning case resolution information based on the images features alone may not give a user an adequate solution to the problem or issue. Accordingly, instead of matching image features, the embodiments leverage the text extraction component  1031  to extract text and/or messages from an image, and the intent analysis component  1033  to determine the intent(s) of the text and/or messages in an image. The ML layer  1032  of the text extraction component applies the combined Mask-R-CNN and OCR algorithm as described herein above to extract the text and/or messages from an image  1001 . 
     The identified intent(s) associated with the image can then be passed to the recommendation component  1043  for recommending a resolution based on the text and/or messages in the image and not the visual image features. Advantageously, recommending a support solution based on intent, not the image itself, avoids returning irrelevant resolution information associated with similar visual images. In addition, the techniques of the embodiments are useful in situations where there are no existing historical images with which to form a match, but support tickets, case descriptions and/or notes from past resolutions are available to be compared with the determined intent to find a historical match to a current scenario. As an additional advantage, the embodiments create a foundation for potentially analyzing user textual searches and providing resolution recommendations from historical data by matching intents from the textual searches. 
     The recommendation component  1043  provides a support resolution recommendation based on a comparison of historical case data with a user&#39;s problem or issue derived from a conclusion about the intent of the user-provided image  1001 . An ML layer  1044  of the recommendation component  1043  recommends a resolution to a given issue or problem by using NLP and distance similarity algorithms (e.g., Cosine or Euclidian) to identify similar intents from historical data in a repository (e.g., repository  150  or  250 ) and recommend a resolution from the historical data. The repositories  150  and  250  store historical case data and metadata with corresponding resolutions and intents as obtained from previously uploaded and/or analyzed images and/or from support case descriptions and notes. The historical data is also used to train an ML model used by the ML layer  1044  that will receive intents (e.g., predicted intents  1080 - 1 ,  1080 - 2  and/or  1080 - 3 ), match the received intents to similar intents in the historical case data and metadata, and recommend the most appropriate resolution based on the matching intents and their resolutions. The predicted intents  1080 - 1 ,  1080 - 2  and  1080 - 3  can be the same or similar to the predicted intents  680 - 1 ,  680 - 2  and  680 - 3  discussed in connection with  FIG. 6 . 
     According to one or more embodiments, the ML layer  1044  of the recommendation component  1043  creates a term frequency-inverse document frequency (TF-IDF) vectorizer of the semantic intents of historical training data. At least part of (or all) the intent entries in the data repository  150  or  250  may comprise the historical training data for the intent analysis component  1033  (or  633  in  FIGS. 6 and 133  in  FIG. 1 ). 
     TF-IDF is a numerical statistic in NLP that reflects how important a word is to a document in a collection. In general, the TF-IDF algorithm is used to weigh a keyword in any document and assign an importance to that keyword based on the number of times the keyword appears in the document. Each word or term has its respective TF and IDF score. The product of the TF and IDF scores of a term is referred to as the TF-IDF weight of that term. The higher the TF-IDF weight (also referred to herein as “TF-IDF score”), the rarer and more important the term, and vice versa. It is to be understood that the embodiments are not limited to the use of TF-IDF, and there are alternative methodologies for text vectorization. 
     In illustrative embodiments, the TF-IDF vectorizer is generated and used to build a TF-IDF matrix, which includes each word and its TF-IDF score for each intent entry in the historical training data (e.g., each intent entry in the data repository  150  or  250 ). According to an embodiment, a TfidfVectorizer function from a SciKitLearn library is used to build the vectorizer. 
     When a user provides an image in connection with an issue or problem (e.g., image  1001 ), the intent(s) determined from that image will be used to build another TF-IDF matrix based on the determined intents. The TF-IDF matrices ignore stop words. The recommendation component  1043  uses a similarity distance algorithm between the two generated TF-IDF matrices to find matching intents between the uploaded image and the historical data. The similarity functions are as follows:
         tf=TfidfVectorizer(analyzer=‘word’, ngram_range=(1, 3), min_df=0, stop_words=‘english’)   tfidf_matrix_history=tf.fit_transform(ds[‘intents’])       

     In a similarity distance algorithm approach, using a vector space model where intents are stored as vectors of their attributes in an n-dimensional space, the angles between the vectors are calculated to determine the similarities between the vectors. The embodiments may utilize different distance algorithms such as, for example, Cosine and Euclidian distance. The application of a Cosine distance algorithm for different intents (e.g., system driver, device driver installation and BSOD error) is illustrated by the graph  1100  in  FIG. 11 , where angles between the vectors are represented as θ 1  and θ 2 . According to one or more embodiments, an algorithm such as K-Nearest Neighbor (KNN) may be utilized to compute the distance similarity using Cosine distance by passing a parameter as a metric and a value as “cosine.” 
     Once the intent associated with the uploaded image  1001  is matched with the intent(s) in the historical data, the recommendation component  1043  (or  143  in  FIG. 1 ) returns the associated resolution information to the user as a recommendation. As noted above, in addition to the recommendation, the intent is also passed to a textual search engine (e.g., search engine  160 ) to return additional information from a knowledge base (e.g., knowledge base  161 ) to the user. The additional information comprises, but is not necessarily limited to, articles, manuals and guided flows.  FIG. 12  depicts example pseudocode  1200  for using the similarity distance algorithm between the two generated TF-IDF matrices to find matching intents between the uploaded image and the historical data and for providing resolution information to users. 
     According to one or more embodiments, databases (e.g., knowledge base  161 ), repositories (e.g., repositories  150  and  250 ), stores (e.g., file stores  175  and  275 ) and/or corpuses (e.g., corpus  636 ) used by the image analysis and resolution platform  110  and/or assisted support channel  170  can be configured according to a relational database management system (RDBMS) (e.g., PostgreSQL). Databases, repositories, stores and/or corpuses in some embodiments are implemented using one or more storage systems or devices associated with the image analysis and resolution platform  110  and/or assisted support channel  170 . In some embodiments, one or more of the storage systems utilized to implement the databases comprise a scale-out all-flash content addressable storage array or other type of storage array. 
     The term “storage system” as used herein is therefore intended to be broadly construed, and should not be viewed as being limited to content addressable storage systems or flash-based storage systems. A given storage system as the term is broadly used herein can comprise, for example, network-attached storage (NAS), storage area networks (SANs), direct-attached storage (DAS) and distributed DAS, as well as combinations of these and other storage types, including software-defined storage. 
     Other particular types of storage products that can be used in implementing storage systems in illustrative embodiments include all-flash and hybrid flash storage arrays, software-defined storage products, cloud storage products, object-based storage products, and scale-out NAS clusters. Combinations of multiple ones of these and other storage products can also be used in implementing a given storage system in an illustrative embodiment. 
     Although shown as elements of the image analysis and resolution platform  110 , the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150  and the search engine  160  in other embodiments can be implemented at least in part externally to the image analysis and resolution platform  110 , for example, as stand-alone servers, sets of servers or other types of systems coupled to the network  104 . For example, the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150  and the search engine  160  may be provided as cloud services accessible by the image analysis and resolution platform  110 . 
     The interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150  and the search engine  160  in the  FIG. 1  embodiment are each assumed to be implemented using at least one processing device. Each such processing device generally comprises at least one processor and an associated memory, and implements one or more functional modules for controlling certain features of the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150  and/or the search engine  160 . 
     At least portions of the image analysis and resolution platform  110  and the components thereof may be implemented at least in part in the form of software that is stored in memory and executed by a processor. The image analysis and resolution platform  110  and the components thereof comprise further hardware and software required for running the image analysis and resolution platform  110 , including, but not necessarily limited to, on-premises or cloud-based centralized hardware, graphics processing unit (GPU) hardware, virtualization infrastructure software and hardware, Docker containers, networking software and hardware, and cloud infrastructure software and hardware. 
     Although the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150 , the search engine  160  and other components of the image analysis and resolution platform  110  in the present embodiment are shown as part of the image analysis and resolution platform  110 , at least a portion of the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150 , the search engine  160  and other components of the image analysis and resolution platform  110  in other embodiments may be implemented on one or more other processing platforms that are accessible to the image analysis and resolution platform  110  over one or more networks. Such components can each be implemented at least in part within another system element or at least in part utilizing one or more stand-alone components coupled to the network  104 . 
     It is assumed that the image analysis and resolution platform  110  in the  FIG. 1  embodiment and other processing platforms referred to herein are each implemented using a plurality of processing devices each having a processor coupled to a memory. Such processing devices can illustratively include particular arrangements of compute, storage and network resources. For example, processing devices in some embodiments are implemented at least in part utilizing virtual resources such as virtual machines (VMs) or Linux containers (LXCs), or combinations of both as in an arrangement in which Docker containers or other types of LXCs are configured to run on VMs. 
     The term “processing platform” as used herein is intended to be broadly construed so as to encompass, by way of illustration and without limitation, multiple sets of processing devices and one or more associated storage systems that are configured to communicate over one or more networks. 
     As a more particular example, the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150 , the search engine  160  and other components of the image analysis and resolution platform  110 , and the elements thereof can each be implemented in the form of one or more LXCs running on one or more VMs. Other arrangements of one or more processing devices of a processing platform can be used to implement the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150  and the search engine  160 , as well as other components of the image analysis and resolution platform  110 . Other portions of the system  100  can similarly be implemented using one or more processing devices of at least one processing platform. 
     Distributed implementations of the system  100  are possible, in which certain components of the system reside in one datacenter in a first geographic location while other components of the system reside in one or more other data centers in one or more other geographic locations that are potentially remote from the first geographic location. Thus, it is possible in some implementations of the system  100  for different portions of the image analysis and resolution platform  110  to reside in different data centers. Numerous other distributed implementations of the image analysis and resolution platform  110  are possible. 
     Accordingly, one or each of the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150 , the search engine  160  and other components of the image analysis and resolution platform  110  can each be implemented in a distributed manner so as to comprise a plurality of distributed components implemented on respective ones of a plurality of compute nodes of the image analysis and resolution platform  110 . 
     It is to be appreciated that these and other features of illustrative embodiments are presented by way of example only, and should not be construed as limiting in any way. 
     Accordingly, different numbers, types and arrangements of system components such as the interface layer  120 , the image analysis and annotation engine  130 , the image matching and recommendation engine  140 , the data repository  150 , the search engine  160  and other components of the image analysis and resolution platform  110 , and the elements thereof can be used in other embodiments. 
     It should be understood that the particular sets of modules and other components implemented in the system  100  as illustrated in  FIG. 1  are presented by way of example only. In other embodiments, only subsets of these components, or additional or alternative sets of components, may be used, and such components may exhibit alternative functionality and configurations. 
     For example, as indicated previously, in some illustrative embodiments, functionality for the image analysis and resolution platform can be offered to cloud infrastructure customers or other users as part of FaaS, CaaS and/or PaaS offerings. 
     The operation of the information processing system  100  will now be described in further detail with reference to the flow diagram of  FIG. 13 . With reference to  FIG. 13 , a process  1300  for analyzing incoming image inputs and recommending appropriate resolutions as shown includes steps  1302  through  1310 , and is suitable for use in the system  100  but is more generally applicable to other types of information processing systems comprising an image analysis and resolution platform configured for analyzing incoming image inputs and recommending appropriate resolutions. 
     In step  1302 , an input of at least one image associated with an issue is received, and in step  1304 , text from the at least one image is extracted. According to one or more embodiments, the extracting comprises identifying a plurality of regions in the at least one image comprising one or more text objects, and generating respective boundaries around respective ones of the plurality of regions. The extracting may also comprise classifying objects in respective ones of the plurality of regions as one of text objects and non-text objects, and recognizing text in the one or more text objects of each of the plurality of regions. 
     In step  1306 , an intent is determined from the extracted text, and in step  1308 , a response to the issue is recommended based at least in part on the determined intent. In step  1310 , the recommended response is transmitted to a user. 
     The extracting, determining and recommending are performed at least in part using one or more machine learning models. In accordance with an embodiment, the one or more machine learning models comprises a Mask-R-CNN and/or bi-directional RNN with LSTM for NLU. 
     The method may comprise training the one or more machine learning models with training data comprising a plurality of text entries and a plurality of intents corresponding to the plurality of text entries. 
     In an illustrative embodiment, the method comprises training the one or more machine learning models with training data comprising a plurality of intents and a plurality of issue resolutions corresponding to the plurality of intents. A TF-IDF vectorizer of the plurality of intents is created from the training data, and a TF-IDF matrix is built, the TF-IDF matrix comprising a plurality of words corresponding to the plurality of intents and a plurality of TF-IDF scores for the plurality of words. 
     According to an embodiment, recommending the response to the issue comprises building an additional TF-IDF matrix comprising a plurality of words corresponding to the determined intent and a plurality of TF-IDF scores for the plurality of words corresponding to the determined intent, comparing the TF-IDF matrix with the additional TF-IDF matrix to determine a matching intent from the plurality of intents to the determined intent, and recommending the issue resolution corresponding to the matching intent. 
     In an illustrative embodiment, a data repository is maintained, the data repository comprising respective ones of a plurality of intents associated with respective ones of a plurality of images and respective ones of a plurality of issue resolutions corresponding to the respective ones of the plurality of intents. Recommending the response to the issue comprises comparing the determined intent with the plurality of intents, identifying a matching intent of the plurality of intents to the determined intent based on the comparing, and recommending the issue resolution corresponding to the matching intent. 
     It is to be appreciated that the  FIG. 13  process and other features and functionality described above can be adapted for use with other types of information systems configured to execute image analysis and problem resolution services in an image analysis and resolution platform or other type of platform. 
     The particular processing operations and other system functionality described in conjunction with the flow diagram of  FIG. 13  is therefore presented by way of illustrative example only, and should not be construed as limiting the scope of the disclosure in any way. Alternative embodiments can use other types of processing operations. For example, the ordering of the process steps may be varied in other embodiments, or certain steps may be performed at least in part concurrently with one another rather than serially. Also, one or more of the process steps may be repeated periodically, or multiple instances of the process can be performed in parallel with one another. 
     Functionality such as that described in conjunction with the flow diagram of  FIG. 13  can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device such as a computer or server. As will be described below, a memory or other storage device having executable program code of one or more software programs embodied therein is an example of what is more generally referred to herein as a “processor-readable storage medium.” 
     Illustrative embodiments of systems with an image analysis and resolution platform as disclosed herein can provide a number of significant advantages relative to conventional arrangements. For example, unlike conventional techniques, the embodiments advantageously provide techniques to perform image-based searches in a self-help portal that leverage existing support system knowledge and historical case data. The embodiments provide functionality for extracting text and/or messages from uploaded images and analyzing the extracted text to determine the intent associated with an image. Advantageously, instead of comparing visual image features to find matching images from historical data, the embodiments provide a framework to compare the determined intent with intents from images in the historical data, and to recommend corresponding resolutions associated with the matching intents to users. 
     Conventional approaches typically use text-based searches which are not very effective when supporting and/or troubleshooting complex problem or issues. Text-based searches also undesirably rely on the use of keywords that must be entered to understand semantics and return useful content to a user. The embodiments advantageously provide intelligent programmatic analysis of customer image data for object detection, segmentation, text detection and text extraction. The embodiments combine computer vision techniques and machine learning techniques such as, for example, OCR and neural networks to semantically analyze images associated with cases in assisted support channels and to build a repository with the images, annotations, description and case resolution metadata for addressing the issues. The data in the repository is used to train an intent classification component that utilizes deep learning to identify intents of customer provided images. The identified intents are matched with intents of corresponding images in the repository, so that a recommendation component can predict a customer issue and recommend a resolution based on historical case information. 
     Advantageously, the embodiments provide an optimized machine learning framework that combines select machine learning and image analysis techniques to extract the nuances of different images that may be visually similar, but convey different messages and intents. The image analysis and resolution platform performs a combination of image text extraction, intent analysis, intent comparison to historical support data and resolution recommendation to ensure that user attempts to resolve problems and/or issues are effectively and efficiently handled without need for agent intervention. The use of the text extraction, intent analysis and intent comparison techniques of the embodiments allows for the generation of accurate and useful case resolution information that is not achieved by visual image feature analysis, and is not provided by conventional text-based searches. 
     It is to be appreciated that the particular advantages described above and elsewhere herein are associated with particular illustrative embodiments and need not be present in other embodiments. Also, the particular types of information processing system features and functionality as illustrated in the drawings and described above are exemplary only, and numerous other arrangements may be used in other embodiments. 
     As noted above, at least portions of the information processing system  100  may be implemented using one or more processing platforms. A given such processing platform comprises at least one processing device comprising a processor coupled to a memory. The processor and memory in some embodiments comprise respective processor and memory elements of a virtual machine or container provided using one or more underlying physical machines. The term “processing device” as used herein is intended to be broadly construed so as to encompass a wide variety of different arrangements of physical processors, memories and other device components as well as virtual instances of such components. For example, a “processing device” in some embodiments can comprise or be executed across one or more virtual processors. Processing devices can therefore be physical or virtual and can be executed across one or more physical or virtual processors. It should also be noted that a given virtual device can be mapped to a portion of a physical one. 
     Some illustrative embodiments of a processing platform that may be used to implement at least a portion of an information processing system comprise cloud infrastructure including virtual machines and/or container sets implemented using a virtualization infrastructure that runs on a physical infrastructure. The cloud infrastructure further comprises sets of applications running on respective ones of the virtual machines and/or container sets. 
     These and other types of cloud infrastructure can be used to provide what is also referred to herein as a multi-tenant environment. One or more system components such as the image analysis and resolution platform  110  or portions thereof are illustratively implemented for use by tenants of such a multi-tenant environment. 
     As mentioned previously, cloud infrastructure as disclosed herein can include cloud-based systems. Virtual machines provided in such systems can be used to implement at least portions of one or more of a computer system and an image analysis and resolution platform in illustrative embodiments. These and other cloud-based systems in illustrative embodiments can include object stores. 
     Illustrative embodiments of processing platforms will now be described in greater detail with reference to  FIGS. 14 and 15 . Although described in the context of system  100 , these platforms may also be used to implement at least portions of other information processing systems in other embodiments. 
       FIG. 14  shows an example processing platform comprising cloud infrastructure  1400 . The cloud infrastructure  1400  comprises a combination of physical and virtual processing resources that may be utilized to implement at least a portion of the information processing system  100 . The cloud infrastructure  1400  comprises multiple virtual machines (VMs) and/or container sets  1402 - 1 ,  1402 - 2 , . . .  1402 -L implemented using virtualization infrastructure  1404 . The virtualization infrastructure  1404  runs on physical infrastructure  1405 , and illustratively comprises one or more hypervisors and/or operating system level virtualization infrastructure. The operating system level virtualization infrastructure illustratively comprises kernel control groups of a Linux operating system or other type of operating system. 
     The cloud infrastructure  1400  further comprises sets of applications  1410 - 1 ,  1410 - 2 , . . .  1410 -L running on respective ones of the VMs/container sets  1402 - 1 ,  1402 - 2 , . . .  1402 -L under the control of the virtualization infrastructure  1404 . The VMs/container sets  1402  may comprise respective VMs, respective sets of one or more containers, or respective sets of one or more containers running in VMs. 
     In some implementations of the  FIG. 14  embodiment, the VMs/container sets  1402  comprise respective VMs implemented using virtualization infrastructure  1404  that comprises at least one hypervisor. A hypervisor platform may be used to implement a hypervisor within the virtualization infrastructure  1404 , where the hypervisor platform has an associated virtual infrastructure management system. The underlying physical machines may comprise one or more distributed processing platforms that include one or more storage systems. 
     In other implementations of the  FIG. 14  embodiment, the VMs/container sets  1402  comprise respective containers implemented using virtualization infrastructure  1404  that provides operating system level virtualization functionality, such as support for Docker containers running on bare metal hosts, or Docker containers running on VMs. The containers are illustratively implemented using respective kernel control groups of the operating system. 
     As is apparent from the above, one or more of the processing modules or other components of system  100  may each run on a computer, server, storage device or other processing platform element. A given such element may be viewed as an example of what is more generally referred to herein as a “processing device.” The cloud infrastructure  1400  shown in  FIG. 14  may represent at least a portion of one processing platform. Another example of such a processing platform is processing platform  1500  shown in  FIG. 15 . 
     The processing platform  1500  in this embodiment comprises a portion of system  100  and includes a plurality of processing devices, denoted  1502 - 1 ,  1502 - 2 ,  1502 - 3 , . . .  1502 -P, which communicate with one another over a network  1504 . 
     The network  1504  may comprise any type of network, including by way of example a global computer network such as the Internet, a WAN, a LAN, a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. 
     The processing device  1502 - 1  in the processing platform  1500  comprises a processor  1510  coupled to a memory  1512 . The processor  1510  may comprise a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a central processing unit (CPU), a graphical processing unit (GPU), a tensor processing unit (TPU), a video processing unit (VPU) or other type of processing circuitry, as well as portions or combinations of such circuitry elements. 
     The memory  1512  may comprise random access memory (RAM), read-only memory (ROM), flash memory or other types of memory, in any combination. The memory  1512  and other memories disclosed herein should be viewed as illustrative examples of what are more generally referred to as “processor-readable storage media” storing executable program code of one or more software programs. 
     Articles of manufacture comprising such processor-readable storage media are considered illustrative embodiments. A given such article of manufacture may comprise, for example, a storage array, a storage disk or an integrated circuit containing RAM, ROM, flash memory or other electronic memory, or any of a wide variety of other types of computer program products. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals. Numerous other types of computer program products comprising processor-readable storage media can be used. 
     Also included in the processing device  1502 - 1  is network interface circuitry  1514 , which is used to interface the processing device with the network  1504  and other system components, and may comprise conventional transceivers. 
     The other processing devices  1502  of the processing platform  1500  are assumed to be configured in a manner similar to that shown for processing device  1502 - 1  in the figure. 
     Again, the particular processing platform  1500  shown in the figure is presented by way of example only, and system  100  may include additional or alternative processing platforms, as well as numerous distinct processing platforms in any combination, with each such platform comprising one or more computers, servers, storage devices or other processing devices. 
     For example, other processing platforms used to implement illustrative embodiments can comprise converged infrastructure. 
     It should therefore be understood that in other embodiments different arrangements of additional or alternative elements may be used. At least a subset of these elements may be collectively implemented on a common processing platform, or each such element may be implemented on a separate processing platform. 
     As indicated previously, components of an information processing system as disclosed herein can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device. For example, at least portions of the functionality of one or more components of the image analysis and resolution platform  110  as disclosed herein are illustratively implemented in the form of software running on one or more processing devices. 
     It should again be emphasized that the above-described embodiments are presented for purposes of illustration only. Many variations and other alternative embodiments may be used. For example, the disclosed techniques are applicable to a wide variety of other types of information processing systems and image analysis and resolution platforms. Also, the particular configurations of system and device elements and associated processing operations illustratively shown in the drawings can be varied in other embodiments. Moreover, the various assumptions made above in the course of describing the illustrative embodiments should also be viewed as exemplary rather than as requirements or limitations of the disclosure. Numerous other alternative embodiments within the scope of the appended claims will be readily apparent to those skilled in the art.