In the United States, Tax Season descends upon the country every April, requiring millions of Americans to spend hours deciphering cryptic documents and performing complex math just to figure out what they owe. Wouldn’t it be grand if there was a way for a computer to take all the relevant documents and extract out exactly what the IRS is looking for? Lending Document AI from Google Cloud supports common document types used for Income Tax Filing, such as W-2s and 1099s. These advancements in machine learning technology now makes it possible to alleviate some anxiety leading up to April 15th.

Lending Document AI is a Document Understanding solution that allows for classification and parsing of documents commonly used in the mortgage lending industry. The data in these unstructured files is then converted into a structured format, which can be stored in a database or used for analysis and calculations. You can read more about the product in the announcement blog post. For this tax filing use case, we will focus on automatically classifying and parsing the 2020 editions of the following forms:

W-2

1099-DIV

1099-INT

1099-MISC

1099-NEC

This sample application creates an automated pipeline where the user can bulk upload a collection of PDFs, the Lending Document Splitter & Classifier will classify each document and send each PDF to the appropriate specialized parser to extract the data, which can then be used to calculate an individual tax return and fill out a 1040 Form.

Overview

Let’s explore how this application works. You can check out the sample code in this GitHub Repository.

Here is an outline of the architecture of this application. As you can see, it utilizes Cloud Run and Firestore in Native Mode for the web application in addition to Document AI.

The User uploads multiple PDF files to the web application, hosted on Cloud Run.

An API call is made to the Lending Document Splitter & Classifier for each PDF file.

The output of the classifier (e.g. W-2, 1099-MISC, etc.) is then mapped to an appropriate specialized parser in the Google Cloud Project.

Each document file is sent to the appropriate specialized parser that matches the document type.

The entities are extracted by the parser processor and the data is written to Firestore.

The raw data is now retrieved from Firestore and displayed to the User showing the file classification and extracted values from each form.

The data values from all the forms are used together to calculate an individual income tax return.

The Calculated Tax Rates/Incomes/Deductions are displayed to the User in a Tabular Format matching the IRS Form 1040. The app also displays which form data was used for each field. (Some output fields use values from multiple forms, such as line 25b.)

Step-by-Step directions

Want to try this out for yourself? Here’s how you can deploy and run this application using a Google Cloud Project. You can run this in Cloud Shell (Quickstart) or on your local machine.

NOTE: The Lending Processors in this Demo are in Limited GA as of March 2022. If you have a business use case for these processors, you can fill out and submit the Document AI limited access customer request form.

Install dependencies

1. Clone the GitHub Repository to get the sample code.

git clone https://github.com/GoogleCloudPlatform/document-ai-samples.git

2. Enter the directory for the tax pipeline demo

cd document-ai-samples/tax-processing-pipeline-python

3. Install Python and the Google Cloud SDK if they aren’t already installed.
4. Install the python libraries:

pip install -r requirements.txt

5. Create a new Google Cloud project, and enable billing if you don’t already have one.
6. Enable the Document AI API:

gcloud services enable documentai.googleapis.com

7. Setup application default credentials:
   gcloud auth application-default login

Deploy demo application

1. Edit the config.yaml file, adding your own Project Details docai_processor_location: us # Document AI Processor Location (us OR eu)
   docai_project_id: YOUR_PROJECT_ID # Project ID for Document AI Processors
   firestore:
   collection: tax_demo_documents # Set with your preferred Firestore Collection Name
   project_id: YOUR_PROJECT_ID # Project ID for Firestore Database
2. Run setup scripts to create the processors and Cloud Run app in your project. python3 setup.py
   gcloud run deploy tax-demo –source .
3. Visit the Deployed Web Page (You should get a link from the deployment command)

Calculate Tax Returns Homepage

4. Upload Documents. I created some sample documents you can download from the sample-docs folder of the repository.

This demo currently supports the following Document Types (2020 Editions)

W-2
1099-DIV
1099-INT
1099-MISC
1099-NEC

5. Click “Upload” Button, wait for processing to complete.
   The page will display the steps completed for each document file. These are also written to stdout for troubleshooting purposes.

6. View the extracted values from each file.

7. Click “Calculate Taxes” to see the tax calculation output

Conclusion

Warning: This is NOT financial advice, for educational purposes only.

Congratulations! You now have a fully functional tax processing application that can also be modified for use with other workflows that require data from multiple specialized documents.

The Document AI API is flexible and modular enough that most of the code in this example can be reused for any specialized processor.

Now tax returns can be filed with minimal manual effort!

If you want to learn more about Document AI, check out the Cloud Documentation and these videos:

• Getting started with the Document AI platform
• Process billions of pages and cut operational costs with DocAI

And if you want more hands-on experience, I recommend following these step-by-step codelabs to get started with the key features of Document AI:

• Optical Character Recognition (OCR) with Document AI (Python)
• Form Parsing with Document AI (Python)
• Specialized Processors with Document AI (Python)
• Managing Document AI processors with Python

Digital technology offers increasing flexibility and choice to consumers. As a result, the retail industry is dramatically shifting toward more tailored and personalized experiences for shoppers, and businesses are rethinking how they deliver value to customers.

This couldn’t be more true for the beauty retailing industry where leading companies are turning to digital technology to create customized shopping experiences.

At Google Cloud, we’re particularly excited about our work with Ulta Beauty, the largest beauty retailer in the United States with more than 1196 stores in all 50 states, and how the company is using Google Cloud technology solutions to power personalization and redefine beauty retailing.

Established in 1990, Ulta Beauty has had incredible success as a company, and as customers become more discerning and curious about their purchases, the company is finding new ways to meet their changing needs.

Recently, leaders at Ulta Beauty recognized a huge opportunity to complement and enhance the shopping experience by helping beauty enthusiasts navigate through more than 500 brands and 25,000 products carried in their stores and online channel.

They decided to leverage the data from Ulta Beauty’s successful Ultamate Rewards loyalty program to create and offer more unique and personalized user experiences.

With more than 30 million members generating data through sales, transactions, product reviews, and social media engagement, Ulta Beauty’s Loyalty Program creates a comprehensive data set, and the company sought the right technology partner to help organize, analyze and transform that data into valuable insights for its customers.

Ulta Beauty’s leaders knew they had an opportunity to leverage data analytics and machine learning to reach customers in new ways, enhance the guest experience, and continue to grow their active loyalty member base. After considering a number of cloud providers, they chose to expand their existing partnership with Google Cloud.

“Google Cloud listened to our needs and worked in tandem with our engineering team to address our challenges,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “The ease of working with the Google Cloud team and their breadth of experience made the decision a no-brainer, laying the foundation for a great partnership.”  

In 2019, Ulta Beauty announced it was working with Google Cloud Platform to unify and organize its data, using:

• BigQuery to perform data analysis and generate dynamic content, personalized product recommendations, and event-based messages for customers.
• Cloud Storage to provide highly available, secure, resilient and cost-effective access to data across the entire enterprise.
• Compute Engine for the high-performance scalability needed to grow with customer demand while painlessly migrating existing applications to the cloud.
• Anthos to build a hybrid cloud foundation that allows their applications to take advantage of all this data, combining the power and flexibility of GKE with the ability to leverage their existing investment in secure infrastructure on-premises.

Our partnership with Ulta Beauty has enabled increased engagement with customers in store and online, and the creation of new tools and capabilities, including a new Virtual Beauty Advisor tool to deliver tailored recommendations and help shoppers choose the right products, and a Customer Conversation Platform that’s enabling deeper connections with guests, ultimately driving customer loyalty.

“It’s been a really efficient process so far due in part to the ease of working with the Google team,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “They’re experienced, approachable, and their can-do style makes for a great partnership. They listened to our needs and worked in tandem with our engineering team, figuring things out, and getting it done.”

The game of baseball has no shortage of statistics — from batting average to exit velocity, strikeouts to wins above replacement. Among all sports, Major League Baseball (MLB) arguably contains the most analytical and data-driven participants and fan base. Subconsciously or viscerally, players and managers on the field and those following from anywhere are constantly assessing and making decisions based off of game play trends and expectations — whether a batter will come through with a hit in an important situation, when a pitcher should be pulled. Less analyzed, however, is what leads fans to become engaged with certain players or teams, and what factors drive their love of the game. This is the motivation behind the problem being posed by Major League Baseball in their Kaggle competition for Player Digital Engagement Forecasting. Can you use machine learning to deconstruct baseball fandom?

This competition asks you to predict measures of digital engagement for each active player on a daily basis during the MLB season. So, how large was the surge in fan interest after Joe Musgrove threw the first no-hitter in Padres history? Is Shohei Ohtani’s engagement higher when he pitches well, when he hits a monster home run…or when he does both? You’re provided a wealth of game, team and player information – detailed stats, awards, rosters, and transaction information – as well as social and digital engagement data as your inputs. Data scientists will recognize this as an exciting forecasting problem with both traditional regression and time series components, where having this input data just prior to the prediction date is critical to determining which players will receive the most engagement.

With so many variables in the game, there are an endless number of vectors which could possibly influence fan engagement. Eleven-time All-Star Miguel Cabrera delighted fans by hitting the first home run of the season – in the snow! Occasionally a lesser-known player like Musgrove or Carlos Rodón “wins the day” with an unlikely no-hitter. And sometimes just getting traded to an iconic franchise like the Yankees generates a ton of fan interest, like it did for Rougned Odor in early April.

As these examples show, a player’s digital engagement can be pretty dynamic during the season, with many different potential contributors to who is “trending” on a given day. How can you use data to uncover which factors are the most influential of engagement with each player’s digital content?

Ready to play ball? Check out the competition on Kaggle for all the details. $50,000 in prizes is up for grabs in two prize categories. The code competition puts your machine learning skills to the test, to see who can build the most accurate forecasting models to predict daily digital engagement for every active player. You’ll have until July 31st to build your models and then be evaluated on a future time frame, which will determine the winners. For data visualization and exploration experts out there, the explainability prizes give you an opportunity to analyze more broadly which factors, even those outside of what we’re providing directly, most influence digital engagement. You’ll be evaluated on how well you can use what the data is telling you to support your findings.

And if you’re looking to get started, we’ve provided an introductory video and some notebook tutorials, including a starting point for harnessing the power of Vertex AI through tools including Cloud Notebooks, Explainable AI, and Vizier.

With the second half of the season upon us, it’s an exciting time to be an MLB fan. With this Kaggle competition, it’s also a perfect opportunity to use data science to help understand baseball fandom and potentially earn some of your own accolades in the process. Step up to the plate!

Major League Baseball trademarks and copyrights are used with permission of Major League Baseball. Visit MLB.com.

In recent years, the grocery industry has had to shift to facilitate a wider variety of checkout journeys for customers. This has meant ensuring a richer transaction mix, including mobile shopping, online shopping, in-store checkout, cashierless checkout or any combination thereof like buy online, pickup in store (BOPIS).  

What’s more, in the past year and a half alone grocers have had to enable consumers new ways to shop for essentials. This has included needing to rapidly integrate or build on-demand delivery apps, offer curbside pickup with near-instant fulfillment as well as support touchless and cashless checkout experiences. Searches on Google Maps for retailers in the US with curbside pickup options have increased by 9000% since March 2020, and we believe these trends from 2020 will continue to define the future of grocery shopping.

The future of grocery will require agility and openness

Firstly, the need to rapidly adapt to changing consumer habits will be the new normal. Grocers will increasingly look to digitally transform legacy retail systems and modernize point of sale (POS) platforms to deliver and scale omnichannel experiences as quickly as possible. This necessitates a more agile and open architectural approach to technology – one built on microservices and leverages APIs so that new applications and experiences can be built, integrated and delivered faster.

Automation and data-driven retailing will be table stakes

In order for retailers to blend what they’re offering in the store with digital experiences more efficiently, they will also need to automate more. For example, with automation and business intelligence, grocers can take labor that might have been tied up with tender operations and checkout and redistribute those resources to restocking shelves, curbside pick-up or improving customer experiences. 

Automation and access to real-time in-store inventory & supply chain data can also help grocers avoid the supply chain challenges seen in the early days of COVID-19. Grocers will need to find ways to leverage automation to ingest, organize, and analyze data from physical store networks, digital channels, distribution centers to better forecast demand and manage future fluctuations.

How NCR and Google Cloud are helping grocers adapt to disruption with operational agility

Helping grocers improve operational agility to address changing consumer shopping habits and to thrive during times of disruption is something that NCR and Google Cloud have teamed up to do. NCR has over 135 years of experience in retail, having invented the cash register and are continuing to help grocers innovate. NCR Emerald builds upon the company’s leadership in POS software and has turned it into a unified platform that helps grocers operate the entire store from front to back. The solution supports cashier-led checkout, self-checkout, integrated payments, merchandising, and enables regional managers and corporate employees access to the analytics and tools needed to optimize loyalty programs and promotions.

NCR has invested in a comprehensive, agile, and API-led retail architecture that lets grocers continually innovate and design new experiences as customers and the industry evolve. By running Emerald on Google Cloud, NCR can offer the solution on a subscription basis, helping grocers lower upfront capital expenditures and ensuring scalability. What’s more, NCR can tap into Google Cloud’s strength in data, analytics, and openness to deliver three key imperatives. Let’s take a look at each of these below.

Run the way grocers need to while leveraging Google Cloud as a single source of logic

Traditionally the POS system lived in the store. If disaster strikes, people still need access to food and essentials so the grocery store still needs to operate. It hardly gets more mission-critical than that. NCR Emerald is built on microservices, leveraging Kubernetes for front-of-house compute, and VMs (See graphic 1 below). This makes it easy to support lightweight clients accessible by store employees via any range of mobile devices, computer terminals, self-service kiosks, peripheral devices like receipt printers as well as legacy applications.

What’s unique is that because Emerald runs on Google Cloud, it supports all those in-store and digital touchpoints mentioned above, but also allows grocers to run lean. Emerald leverages Google Cloud as a single source of truth and operates a lot of what it does out of logic. Every sales transaction coming from every channel, including e-commerce, can be logged via NCR’s Hosted Service and centralized in BigQuery and Bigtable as a transaction data master. This enables the grocer to manage any transactional use case very consistently, whether it e supporting customers who want to purchase in one store and return in another, offering digital receipts or the ability to exchange online purchases in store. Emerald on Google Cloud can help retailers extend capabilities through the power of the cloud but not need to live exclusively in the cloud. In other words, the solution allows grocers the ability to run the way they need to.

Enable data-driven and real-time decision making for grocers

Store managers, regional managers, category managers, and others all require different cuts of the data to do their jobs effectively. However, data silos persist and how data is formatted and arranged can still remain pretty static. Therefore allowing users with different roles the ability to view and analyze that data quickly and in different ways continues to be a challenge. 

As mentioned above, Emerald leverages Google Cloud data management solutions as the central repository for transactional, behavioral, and merchandising data. Every transaction from every store and every channel can be stored via NCR Hosted Service on BigQuery and Bigtable. NCR Analytics then harnesses the advanced analytical and data visualization capabilities of Looker to help grocers get a consolidated view of their business across all channels and then allow employees to slice and dice the data they way they need to. NCR Analytics also leverages the power of Google Cloud AI and machine learning to add another level of intelligence to the retailer’s data. For example, store managers can visualize how well they’re using their real estate and see how productive lanes 1-3 are compared with 7-10 or compare self-service versus manned lanes. By mapping to the retailer’s own catalog, they can also break down category-level performance and trends.

NCR Analytics takes advantage of Google Cloud’s data pipeline to reduce processing time, with scaling and resource management provided out of the box. By letting the cloud store and process the data, NCR is providing the ability for retailers to analyze their data in near real-time across all platforms – a real game changer in the grocery business.

Open APIs let grocers continually enrich the retail experience

Finally, Emerald is built on an API-first architecture managed through Apigee. It uses the power of Apigee as an open API platform to expose how Emerald can work with other NCR applications like loyalty and promotions, and third party applications like mobile ordering and order delivery to enrich the grocery experience for employees and customers. Every API that Emerald uses is available on Apigee, allowing them to share code samples and giving developers the ability to run scripts. This approach can allow retailers the ability to innovate in a fraction of the time and cost, speeding up 3rd party integrations up front and as businesses grow. 

Take, for example, Northgate Market, a chain of 40 stores in California, that were able to transform its digital operations and enable experiences that set it apart from competitors – quickly and simply with Emerald. It took less than 6 months to go from contract to live deployment in the first store. Since then, Northgate Market has been able to extend their intelligence by leveraging the power of Looker and NCR Analytics.

Learn more about how NCR has been able to leverage an open, cloud-enabled architecture to help customers innovate across the retail, hospitality, and banking industries on the webinar “Role of APIs in Digital Transformation”. You can also learn more about how Northgate uses e-commerce to transform customer experience and gain consumer insights.