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Chain custom Amazon SageMaker Ground Truth jobs for image processing

Amazon SageMaker Ground Truth supports many different types of labeling jobs, including several image-based labeling workflows like image-level labels, bounding box-specific labels, or pixel-level labeling. For situations not covered by these standard approaches, Ground Truth also supports custom image-based labeling, which allows you to create a labeling workflow with a completely unique UI and associated…

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Amazon SageMaker Ground Truth supports many different types of labeling jobs, including several image-based labeling workflows like image-level labels, bounding box-specific labels, or pixel-level labeling. For situations not covered by these standard approaches, Ground Truth also supports custom image-based labeling, which allows you to create a labeling workflow with a completely unique UI and associated processing. Beyond that, you can chain different Ground Truth labeling jobs together so that the output of one job acts as the input to another job, to allow even more flexibility in a labeling workflow by breaking the job into multiple stages.

In this post, we show how to chain two custom Ground Truth jobs together to perform advanced image manipulations, including isolating portions of images, and de-skewing images that were photographed from an angle. Additionally, we demonstrate several techniques for augmenting source images, which are helpful for situations where you have a limited number of source images.

Extracting regions of an image

Suppose we’re tasked with creating a machine learning (ML) model that processes an image of a shelving unit and determines whether any of the bins in that shelving unit need restocking. Due to the size of the storage room, a single camera is used to capture images of several shelving units, each from a different angle. The following image is an example of such a shelving unit.

Figure 1: A shelving unit with many bins full, photographed from an angle

Figure 1: A shelving unit with many bins full, photographed from an angle

For training or inference, we need images of individual bins, rather than the overall shelving unit. The model we’re developing takes an image of a single bin, and return a classification of Empty or Full. This classification feeds into an automated restocking system, allowing us to maintain stock levels at the bin level without the trouble of someone physically checking the levels.

Unfortunately, because the shelf images are taken at an angle, each bin is skewed and has a different size and shape. Because any bin images extracted from the main image are rectangular, the extracted images include undesirable content, as shown in the following image of two adjoining bins.

Figure 2: A closeup of a single bin which shows two adjoining bins

Figure 2: A closeup of a single bin, which shows two adjoining bins

In this example, we’ve isolated a rectangular region that bounds a given bin, but because the image was taken from an angle, portions of the bins on the left and right are also partially included. Because a rectangular section includes information from other bins, an image like this performs poorly when used for training or for inference.

To solve this, we can select a non-rectangular section of the original image and warp it to create a new image. The following image demonstrates the results of a warp transformation applied to the original image.

Figure 3: Original shelving unit with just the bins isolated, and the image warped to make it orthogonal

Figure 3: Original shelving unit with just the bins isolated, and the image warped to make it orthogonal

This warping accomplishes two tasks. First, we’ve selected just the shelving unit, cropping out the nearby walls, floor, and any other irrelevant areas near the edges of the shelves. Second, the warping of the image results in each bin being more rectangular than the original version.

This warped image doesn’t have any new content—it’s just a distortion of the original image. But by performing this warping, each bin can be selected using a rectangular bounding box, which provides needed consistency, no matter what position a bin is in. Compare the following two bin images: the image on the left is extracted from the original image, and the image on the right is the same bin, extracted from the de-skewed image.

Figure 4: A single bin from the original image (left) compared with the bin from the warped image (right)

Figure 4: A single bin from the original image (left) compared with the bin from the warped image (right)

The bottom opening of the bin was originally at an angle, and now it’s horizontal. Overall, we’ve reduced the amount of the bin shown, and increased the proportion of the contents of the bin within the image. This improves our ML training process, because each bin image has less superfluous content.

Ground Truth jobs

Each custom Ground Truth labeling job is defined with a web-based user interface and two associated AWS Lambda functions (for more information, see Processing with AWS Lambda). One function runs prior to each image displayed by the UI, and the other runs after the user finishes the labeling job for all the images. Ground Truth offers several pre-made user interfaces (like bounding box-based selection), but you can also create your own custom UI if needed, as we do for this example.

When Ground Truth jobs are chained together, the output of one job is used as the input of another job. For this task, we use two chained jobs to process our images, as illustrated in the following diagram.

Figure 5: Architecture diagram showing two chained Ground Truth jobs, each with a Pre- and Post- UI Lambda function

Figure 5: Architecture diagram showing two chained Ground Truth jobs, each with a Pre- and Post- UI Lambda function

Images that need to be labeled are stored in Amazon Simple Storage Solution (Amazon S3). The first Ground Truth job retrieves images from Amazon S3 and displays them one at a time, waiting for the user to specify the four corners of the shelving unit within the image, using a custom UI. When that step is complete, the post-UI Lambda function uses the corner coordinates to warp or de-skew each image, which is then saved to the same S3 bucket that the original image resides in. Note that it’s not necessary to do this during inference—for a situation where the camera is in a fixed location, you can save those corner coordinates for later use during inference.

After the first Ground Truth job has de-skewed the source image, the second job uses simple bounding boxes to label each bin within the de-skewed image. The post-UI Lambda function then extracts the individual bin images, augments them with rotations, flipping, and color and brightness alterations, and writes the resulting data to Amazon S3, where it can be used for model training or other purposes.

You can find example code and deployment instructions in the GitHub repo.

Custom user interface

From a labeler’s perspective, after they log in and select a job, they use the custom UI to select the four corners of a bin.

Figure 6: The custom Ground Truth UI for the first labeling job

Figure 6: The custom Ground Truth UI for the first labeling job

For custom Ground Truth user interfaces, a set of custom tags is available, known as Crowd tags. These tags include bounding boxes, lines, points, and other user interface elements that you can use to build a labeling UI. In this case, we use the crowd-polygon tag, which is displayed as a yellow polygon.

After the labeler draws a polygon with four corners on the UI for all source images, they exit the UI by choosing Done. At this point, the post-UI Lambda function is run and each de-skewed image is saved to Amazon S3. When the function is complete, control is passed to the next chained Ground Truth job.

Generally, chained Ground Truth jobs reuse an output manifest file as the input manifest file for the next (chained) labeling job. In this case, we created a new image, so we modify the pre-UI Lambda function so it passes in the correct (de-skewed) file name, rather than the original, skewed image file name.

The second job in the chain uses the bounding box-based labeling functionality that is built in to Ground Truth. The bounding boxes don’t cover the entire contents of each bin, but they do cover the openings of the bins. This provides enough data to create a model to detect whether a bin is full or empty.

Figure 7: De-skewed image with bounding boxes from the second chained Ground Truth labeling job

Figure 7: De-skewed image with bounding boxes from the second chained Ground Truth labeling job

After the labeler selects all the bins, they exit the UI by choosing Done. At this point, the post-UI Lambda function runs and crops out each bin image, makes variations of it for image augmentation purposes, and saves the variations into a folder structure in Amazon S3 based on classification. The top level of the folder structure is named training_data, with two subfolders: empty and full. Each subfolder contains images of bins that are either empty or full, suitable for use in model training.

Image augmentation

Image augmentation is a technique sometimes used in image-based ML workloads. It’s especially helpful when the number of source images is low, or limited in the number of variants. Typically, image augmentation is performed by taking a source image and creating multiple variants of it, altering factors like brightness and contrast, coloring, and even cropping or rotating images. These variations help the resulting model be more robust and capable of handling images that are dissimilar to the original training images.

In this example, we use image augmentation methods in the post-UI Lambda function of the second Ground Truth job. The labeler has specified the bounding boxes for each bin image in the Ground Truth UI, and that data is used to extract portions of the overall image. Those extracted portions are of the individual bins, and these smaller images are used as input into our image augmentation process.

In our case, we create 14 variants of each bin image, with variations of brightness, contrast, and sharpness, as well horizontal flipping combined with these variations. With this approach, a single source image of a shelving unit with 24 bins generates 14 variants for each bin image, for a total of 336 images that can be used for training a model. The following shows an original bin image (upper left) and each of its variants.

Conclusion

Custom Ground Truth jobs provide a great deal of flexibility, and using them with images allows advanced functionality like cropping and de-skewing images, as well as performing custom image augmentation. The supplied Crowd HTML tags support many different labeling approaches like polygons, lines, text boxes, modal alerts, key point placement, and others. Combined with the power of pre-UI and post-UI Lambda functions, a custom Ground Truth job allows you to construct complex labeling jobs to support a wide variety of use cases, and combining different custom jobs by chaining them together provides even more options.

You can use the GitHub repo associated with this post as a starting point for your own chained image labeling jobs. You can also extend the code to support additional image augmentation methods (like cropping or rotating the source images), or modify it to fit your particular use case.

To learn more about chained Ground Truth jobs, see Chaining Labeling Jobs.

For more information about the Crowd tags you can use in the Ground Truth UI, see Crowd HTML Elements Reference.

About the Author

Greg Sommerville is a Senior Prototyping Architect on the AWS Envision Engineering Americas Prototyping team, where he helps AWS customers implement innovative solutions to challenging problems with machine learning, IoT and serverless technologies. He lives in Ann Arbor, Michigan and enjoys practicing yoga, catering to his dogs, and playing poker.



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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…

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This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia



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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…

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A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.

Prerequisites

For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!

Cleanup

If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.

Conclusion

In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.



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Enhance the caller experience with hints in Amazon Lex

We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up…

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We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up such clues or hints based on their domain knowledge and access to business intelligence dashboards. Amazon Lex now supports a hints capability to enhance the recognition of relevant phrases in a conversation. You can programmatically provide phrases as hints during a live interaction to influence the transcription of spoken input. Better recognition drives efficient conversations, reduces agent handling time, and ultimately increases customer satisfaction.

In this post, we review the runtime hints capability and use it to implement verification of callers based on their mother’s maiden name.

Overview of the runtime hints capability

You can provide a list of phrases or words to help your bot with the transcription of speech input. You can use these hints with built-in slot types such as first and last names, street names, city, state, and country. You can also configure these for your custom slot types.

You can use the capability to transcribe names that may be difficult to pronounce or understand. For example, in the following sample conversation, we use it to transcribe the name “Loreck.”

Conversation 1

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 1111 2222 3333 4444

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Loreck

IVR: Thank you. The balance on your checking account is 123 dollars.

Words provided as hints are preferred over other similar words. For example, in the second sample conversation, the runtime hint (“Smythe”) is selected over a more common transcription (“Smith”).

Conversation 2

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Smythe

IVR: Thank you. The balance on your checking account is 456 dollars.

If the name doesn’t match the runtime hint, you can fail the verification and route the call to an agent.

Conversation 3

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Savings

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Jane

IVR: There is an issue with your account. For support, you will be forwarded to an agent.

Solution overview

Let’s review the overall architecture for the solution (see the following diagram):

  • We use an Amazon Lex bot integrated with an Amazon Connect contact flow to deliver the conversational experience.
  • We use a dialog codehook in the Amazon Lex bot to invoke an AWS Lambda function that provides the runtime hint at the previous turn of the conversation.
  • For the purposes of this post, the mother’s maiden name data used for authentication is stored in an Amazon DynamoDB table.
  • After the caller is authenticated, the control is passed to the bot to perform transactions (for example, check balance)

In addition to the Lambda function, you can also send runtime hints to Amazon Lex V2 using the PutSession, RecognizeText, RecognizeUtterance, or StartConversation operations. The runtime hints can be set at any point in the conversation and are persisted at every turn until cleared.

Deploy the sample Amazon Lex bot

To create the sample bot and configure the runtime phrase hints, perform the following steps. This creates an Amazon Lex bot called BankingBot, and one slot type (accountNumber).

  1. Download the Amazon Lex bot.
  2. On the Amazon Lex console, choose Actions, Import.
  3. Choose the file BankingBot.zip that you downloaded, and choose Import.
  4. Choose the bot BankingBot on the Amazon Lex console.
  5. Choose the language English (GB).
  6. Choose Build.
  7. Download the supporting Lambda code.
  8. On the Lambda console, create a new function and select Author from scratch.
  9. For Function name, enter BankingBotEnglish.
  10. For Runtime, choose Python 3.8.
  11. Choose Create function.
  12. In the Code source section, open lambda_function.py and delete the existing code.
  13. Download the function code and open it in a text editor.
  14. Copy the code and enter it into the empty function code field.
  15. Choose deploy.
  16. On the Amazon Lex console, select the bot BankingBot.
  17. Choose Deployment and then Aliases, then choose the alias TestBotAlias.
  18. On the Aliases page, choose Languages and choose English (GB).
  19. For Source, select the bot BankingBotEnglish.
  20. For Lambda version or alias, enter $LATEST.
  21. On the DynamoDB console, choose Create table.
  22. Provide the name as customerDatabase.
  23. Provide the partition key as accountNumber.
  24. Add an item with accountNumber: “1111222233334444” and mothersMaidenName “Loreck”.
  25. Add item with accountNumber: “5555666677778888” and mothersMaidenName “Smythe”.
  26. Make sure the Lambda function has permissions to read from the DynamoDB table customerDatabase.
  27. On the Amazon Connect console, choose Contact flows.
  28. In the Amazon Lex section, select your Amazon Lex bot and make it available for use in the Amazon Connect contact flow.
  29. Download the contact flow to integrate with the Amazon Lex bot.
  30. Choose the contact flow to load it into the application.
  31. Make sure the right bot is configured in the “Get Customer Input” block.
  32. Choose a queue in the “Set working queue” block.
  33. Add a phone number to the contact flow.
  34. Test the IVR flow by calling in to the phone number.

Test the solution

You can now call in to the Amazon Connect phone number and interact with the bot.

Conclusion

Runtime hints allow you to influence the transcription of words or phrases dynamically in the conversation. You can use business logic to identify the hints as the conversation evolves. Better recognition of the user input allows you to deliver an enhanced experience. You can configure runtime hints via the Lex V2 SDK. The capability is available in all AWS Regions where Amazon Lex operates in the English (Australia), English (UK), and English (US) locales.

To learn more, refer to runtime hints.

About the Authors

Kai Loreck is a professional services Amazon Connect consultant. He works on designing and implementing scalable customer experience solutions. In his spare time, he can be found playing sports, snowboarding, or hiking in the mountains.

Anubhav Mishra is a Product Manager with AWS. He spends his time understanding customers and designing product experiences to address their business challenges.

Sravan Bodapati is an Applied Science Manager at AWS Lex. He focuses on building cutting edge Artificial Intelligence and Machine Learning solutions for AWS customers in ASR and NLP space. In his spare time, he enjoys hiking, learning economics, watching TV shows and spending time with his family.



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