Invented by Sahoo; Dibya Prakash, Gupta; Sumit, Mishra; Lipsa, Choudhary; Manish Kumar
Modern businesses depend on clear and fast communication, especially when it comes to getting paid. This new patent application introduces a machine learning system that helps workers send emails for accounts receivable tasks. Let’s break down what this means, why it matters, and what makes this invention special.
Background and Market Context
Every business needs a healthy cash flow to keep running. That means making sure customers pay for what they buy, on time. Many companies use emails to remind customers about payments, send invoices, and answer questions. Emails are cheap, fast, and easy to keep track of. They also create a record of what was said, which is important if there is a disagreement or a legal issue.
But there’s a problem. The people who manage accounts receivable—like collection analysts and cash managers—often handle hundreds or even thousands of emails every week. They have to send reminders, answer questions, attach documents, and follow up on unpaid invoices. Sometimes, customers ask for account statements or copies of invoices, and these requests pile up quickly. The time window to respond is short, often just 48 to 72 hours. If these emails are not handled quickly and correctly, payments can be delayed, leading to cash flow problems for the business.
Other ways to reach customers—like phone calls or letters—take more time and cost more money. That’s why email is the preferred method. But even with email, the workload can overwhelm the staff. Manually writing each email, attaching documents, and making sure the right tone is used for each customer is not easy. Mistakes can happen, and important emails can slip through the cracks. That’s where technology can help.
Over the last few years, businesses have tried to use templates, auto-responders, and simple automation to help with emails. These tools can save time, but they don’t always create the right message. Customers want emails that are personal, polite, and relevant to their situation. They also want quick responses. If a message sounds like it was written by a robot, or if it misses important details, the customer might ignore it or get frustrated.
So, there is a real need for a smarter system that helps teams handle emails faster, but also makes each message feel personal and accurate. That’s what this machine learning-based system tries to solve. By using new technology, it aims to make email management for accounts receivable smoother, faster, and friendlier—so companies get paid on time and keep their customers happy.
Scientific Rationale and Prior Art
To understand why this new system is important, it helps to look at what has already been tried in the world of automated emails and accounts receivable. For years, companies have used templates—pre-written pieces of text that can be copied and pasted into an email. These templates save time, but they are not flexible. If a customer’s situation doesn’t fit the template exactly, the message can feel off or even confusing.
Some businesses have used simple rules-based automation. For example, if an invoice is 30 days overdue, the system sends a reminder. If a customer replies, a human has to take over. These systems work for basic cases but struggle with anything complex. They can’t adjust the message based on the customer’s past behavior or specific needs.
Recently, machine learning and artificial intelligence have begun to change the game. Large language models like GPT and BERT can generate text that sounds human and is tailored to the situation. These models learn from large amounts of data, so they can understand context, pick the right tone, and even add personal touches. But most existing AI email tools are still quite general—they are not built for the special needs of accounts receivable teams.
In accounts receivable, every customer is different. Some pay on time, others need reminders. Some need documents sent, others have disputes or questions. The tone of an email matters a lot—sometimes it needs to be friendly, other times firm or even urgent. There are many moving parts: invoices, payment status, customer notes, and much more.
Some recent advances in AI have made it possible to create systems that learn from past emails, understand the intent of a message, and generate new emails that are both accurate and personal. These systems use “embedding vectors” to measure how similar two texts are, so they can find the best past example to copy or adapt. They can also be retrained over time, learning from new data as it comes in.
Despite these advances, most solutions so far have been either too simple or too hard to set up for the average business. They don’t pull together all the data needed—like payment status, past emails, or personal notes. They don’t adjust the message based on intent or customer type. And they can’t be easily retrained to get better as needs change.
This patent application builds on the latest science in language models and data extraction, but applies it in a focused way to solve the real-world problems of accounts receivable teams. By combining data from many sources, using smart prompts, and allowing for ongoing learning, it promises to do what older systems cannot—make email management both faster and smarter.
Invention Description and Key Innovations
Let’s look closely at what this new invention does and why it stands out.
This system is a smart email helper for people who work in accounts receivable. It sits between the team and their customers, helping to write, organize, and send emails that feel personal and accurate. Here’s how it works in simple terms:
First, the system receives input from workers. This could be a request to send a new email to a customer, a reply to a message from a customer, or even just a prompt about what kind of tone or message is needed. The worker can also add personal notes, like wishing a customer a happy birthday or asking about their vacation.
Next, the system pulls in all the data it needs. It looks up information about the customer—like their payment status, any open invoices, notes from past conversations, or whether there are any unresolved questions. It grabs documents if needed, like invoices or account statements, so they can be attached to the email. All this information comes from company databases and records.
Then, the system searches its email repository. It looks for past emails that are similar to what needs to be sent now. It uses machine learning models to score how close each old email is to the new situation, using “embedding vectors” to measure similarity. If the text of the email is long, it breaks it into chunks—like paragraphs or sentences—so the meaning isn’t lost. It compares these chunks and picks the best match.
Now comes the smart part: the system generates “augmented prompts.” These are special instructions for the language model (like GPT or BERT) that tell it exactly what to say. The prompt includes not just the message, but also the tone (friendly, professional, strict, or urgent), customer details, and any personal notes added by the worker. It can even include the intent of the email—like if it’s a payment reminder, a response to a dispute, or just a regular update.
After the prompt is ready, the machine learning model writes the email. It uses all the data and instructions to create a message that is accurate, polite, and tailored to the customer. The email can be reviewed by the worker, who can make changes if needed before sending it out.
One of the biggest innovations here is the feedback loop. After emails are sent, the system tracks how customers respond. If a message works well—getting a quick payment or a good reply—the system learns from that. If it doesn’t, it can adjust its prompts or try a new approach next time. Over time, the system gets smarter, learning what works best for each customer and each situation.
This invention also stands out because it’s very flexible. It can handle many types of users—analysts, collectors, managers—and many types of customers—individuals, companies, or even legal entities. It works with lots of data formats (like CSV, JSON, or spreadsheets), and can connect to different databases and language models.
The system is built for security, too. Only authorized workers can see or send emails. Documents are attached automatically, so nothing important gets missed, but everything stays confidential.
From a technical point of view, the system is made up of several subsystems, each with a clear job:
– Receiving input from workers
– Extracting customer data from databases
– Retrieving similar past emails from the repository
– Building smart prompts with tone, intent, and personal notes
– Generating the email text using a language model
– Sending the email and attaching documents
– Retraining itself over time to get better
All of this happens in a way that’s easy for workers to use, with a simple interface that lets them focus on what matters—communicating with customers and getting paid.
In summary, what makes this invention special is how it brings together data, machine learning, and user input to create emails that are fast to send, personal in tone, and effective in getting results. It’s not just automation; it’s smart, ongoing help for a key business process.
Conclusion
Getting paid on time is vital for every business, and good communication is the key. This patent application describes a smart system that uses machine learning to help accounts receivable teams manage emails quickly and effectively. By combining data from many sources, using advanced language models, and learning from every interaction, this invention promises to make email management faster, more personal, and more reliable. It solves real problems for real businesses, helping them save time, build better customer relationships, and keep cash flowing smoothly. In a world where every minute counts, this kind of help can make all the difference.
Click here https://ppubs.uspto.gov/pubwebapp/ and search 20250217598.