Latest from IPRally

Last week, Sakari Arvela spoke at IP Service World in Munich, sharing his insights on the impact of generative AI in the patent industry. Drawing from a recent survey of IPRally’s admin users, he explored the current state of AI adoption within IP teams, typical use cases, and future expectations surrounding AI's impact on patent processes. In this blog post, Sakari summarizes his presentation. We're moving past the initial generative AI hype and into practical implementation, he concludes in his key takeaways.

We’re thrilled to announce that IPRally has been recognized as one of the fastest-growing technology companies in Finland, securing #16 on Deloitte’s Technology Fast 50 list! This award highlights the rapid growth we’ve achieved in the past few years, and we couldn’t be prouder to be counted among Finland’s tech leaders.

Last week's Image Search webinar, hosted by Sakari Arvela and myself, was a great success with nearly 300 attendees from 27 countries. We had an incredibly engaged audience who asked about 60 questions throughout the session. While we addressed many questions during the webinar, we couldn't get to all of them. So we have compiled this Q&A to share comprehensive answers with everyone. Thank you to all who joined and made the session so interactive!

IPRally’s new feature Image Search is the first tool to combine image search with Graph AI. This practical solution allows you to find patents by simply uploading a photograph, a drawing or a product sheet. Unlike traditional image searches that rely on visual similarity, IPRally’s Image Search decodes the technical content in the image. Antti Tarvainen, Generative AI Product Manager, explains how Image Search works, its key benefits, and how it can transform how you conduct patent searches.

From streamlining tedious tasks to accelerating decision-making, we are getting closer to realizing the vision of patent work automation. Rather than removing humans from the loop entirely, the focus is on boosting work productivity, quality, and consistency of patent professionals. CPO and co-founder Sakari Arvela reflects on IPRally's achievements and future direction: “Gradually, IPRally will add AI assistants that solve parts of the process, and eventually, we’ll connect them to perform complete tasks in the value chain”, he shares in a blog post outlining IPRally’s vision.

From targeted patent summarization to detail extraction and ad hoc classification – never before has it been possible to review patents with such flexibility and efficiency. CPO and Co-Founder Sakari Arvela explores ten innovative ways to leverage IPRally's Ask AI for efficient patent review, illustrated with informative video clips. Join him as he showcases real-world applications and demonstrates how Ask AI lets you extract complex patent insights on the fly in a way that would have seemed like science fiction just a few years ago.

Over the past year, the progress with IPRally's search model has been tremendous, and the trend continues with the latest Graph Transformer 3.0. Recall metrics have improved to the point where today’s recall at 10 documents matches what it was at 20 documents a year ago. If this pace of development continues, Boolean searches might soon be history. Juho Kallio, CTO and Co-founder, provides context for this progress and explains why he wouldn't teach his son to write cumbersome Boolean queries.

IPRally adds an experienced CEO to the team to better support the delivery of leading AI solutions for the patent industry. As Sakari Arvela hands over the CEO responsibilities to Dennis van der Veeke, he will focus on product strategy in his new role as the Chief Product Officer (CPO).

The rise of artificial intelligence in the patent space brings both promises and challenges. With its potential to enhance drafting and examination processes, AI offers the prospect of improving patent quality. Join Sakari Arvela as he navigates through the opportunities, challenges, and ethical considerations inherent in this evolving landscape, while also presenting ideas for a healthier patent system.

A few days ago, the USPTO released additional guidelines about the use of AI in interactions with the USPTO. What do they mean in relation to using AI for patent research and analysis? Andreas Cehlinder gives a summary.

Amidst economic constraints, industry convergence, and the need for improved patent quality, patent workflows demand a paradigm shift to meet the evolving landscape. The current macro environment combined with the increasing maturity of AI initiatives opens up a unique window of opportunity to explore and take advantage of these innovative solutions. IPRally's CCO Andreas Cehlinder explains why the time for change is now.

Traditionally, patent review has been enhanced through approaches like data presentation and highlighting. Yet, remarkably few efforts have delved into the core of the challenge: the understanding of the technical substance of patents. The launch of Ask AI in September served as a brilliant testament to the viability of this effort. Now, we're thrilled to introduce Multi-patent Ask AI, designed to tackle large data sets and portfolios. Join us as Sakari Arvela, CEO and Co-founder, provides an insightful introduction.

In a short time, AI has integrated into numerous workflows, presenting a multitude of opportunities to revolutionize patent processes. Which ones of them will have the most impact? Sakari Arvela delves into the transformative impact of AI on IP management and introduces you to your new favorite IP channel.

Sakari Arvela, CEO and co-founder of IPRally, sets out his forecast for 2024. He predicts that AI will continue to shape the future of IP search with most progress being made in the intersection between generalist LLMs for text analysis and generation and specialist search models like Graph AI.

One more feather in IPRally’s security cap: Our Cyber Essentials certificate accompanies the ISO 27001 certification we achieved last year.

IPRally is converging the best from past and present technologies to help you do your patent research more efficiently. Bit by bit, we eliminate the manual tasks you want to get rid of.

We are delighted to introduce Graph Transformer 2.0, the latest advancement in our AI model, designed to elevate your patent search experience. In this blog post, we'll delve into the upgrades in our new model and how it significantly enhances patent search.

After a public procurement process with rigorous performance and security audits, the solution has been made available to the office's 110 patent examiners as an online tool.

We’re excited to announce that IPRally has achieved ISO 27001 certification, making us one of the few companies in our field to receive this designation. Great news for IPRally, but even more so for our customers.

We're thrilled to announce a major update to our core neural network architecture, which improves your patent search experience.

Defining class filters in Boolean searching is a challenging task due to the complicated classification systems. Graph-AI can mitigate the effect of human errors and biases and widen the horizon of prior art search.

We are witnessing the largest worldwide AI experiment ever, but how will AI change the patent industry?

Sakari Arvela and Jan C. Scholtes focus their attention on Large Language Models, the limitations of GPT, and how to improve today's models.

The A round is led by Endeit Capital together with previous investors Join Capital, Spintop Ventures and Icebreaker.vc.

We have received a lot of questions relating to the custom patent classifier. As many of them could be of general interest, we wanted to attempt to answer some of the more common ones here.

With the launch, IPRally adds additional strategic capabilities to their product, suitable for projects like portfolio segmentation, benchmarking and competitive intelligence.

In September, IPRally will launch an AI-based patent classifier. Sakari Arvela and Andreas Cehlinder shed some light on the new module that will allow users to create their own custom version of IPRally’s cutting-edge technology.

Tired of flipping back and forth between the patent drawings and the descriptions? IPRally now displays the technical part names in the drawings.

We were delighted to sit down with Kent Häll to discuss innovation workshops, knowledge graphs and the art of bridging the communication gap between lawyers and engineers.

What were some of the most impactful improvements in our patent search engine and what are our near-future plans?

We were very fortunate to have the opportunity to sit down with Jeremy Kriegel, Partner at Marshall IP, and learn how IPRally has changed the way this firm’s professionals support their clients.

Patent search engines are traditionally optimized for metrics like recall. But recall is a problematic metric. We are about to take a leap towards a new metric that more directly measures understanding.

After having been caught in the virtual world for almost two years, IPRally is enjoying the opportunity to meet with the IP community again.

The AI market is expected to grow 40% annually during the 2020s, and AI continues to play an increasing role in intellectual property management. Today we launch a blog series to shed some light on why and where to consider artificial intelligence for patent related tasks and how to evaluate if a tool fits your organization.

Are your patent workflows cluttered with repetitive tasks and bottlenecks? What are you spending too much time on? The second part of our AI tool evaluation blog series discusses the topic of how to recognize the need for a patent AI tool.

The latest addition to the IPRally board is a tech entrepreneur, angel investor and IP attorney.

Clojure is a functional programming language, written for those who seek simplicity. The combination of a better way to build and hire top talent can help a company to move faster.

The round is led by Join Capital and Spintop Ventures, with the participation of previous investor Icebreaker.vc.

The company starts a new sprint in AI development and global operations following a Seed investment round

An independent study confirms that IPRally significantly improves the quality of patent searches. Being the best performing AI platform tested, an average improvement of 29% was reported.

The amount of non-English patent documents is rapidly growing. To tackle the searchability problem, we are using machine translations to create English knowledge graphs from non-English texts.

The dynamic highlighting of the search results loads faster from now on. This is the first time we brought GPUs to the frontline, to speed up your user experience.

We used our model trained for prior art searches to classify patents into CPC classes. The performance we achieved is on par with the performance reported for state of the art classifiers in scientific articles.

Upon updating our database with millions of new documents, we expanded the filtering options with the new data sources so that you find faster the patent literature you are looking for. The database covers by now (May 2020) 75 million patents and patent applications.

Artificial intelligence (AI) opens up new opportunities to examine the patentability of inventions. The combination of AI and specialist work increases efficiency also in a telecommuting environment.

While working from home, our new UX designer Maggie connects with our customers and seeks to understand them better.

Google Cloud Pub/Sub is simple to use from Python, but what about Clojure? There is no official support and the Java interop is not straightforward. There are few libraries but none is active. We ended up building our internal mini-library jonotin, and today we have published the code.

Researchers have improved the widely used deep learning optimizer Adam, decreasing the time needed for training models and improving model generalization. We made a small contribution of our own by open sourcing our PyTorch implementation of QHAdamW.

The story of our first steps towards explainable patent search. In the end of the post you get to test our unique approach, which is one of the first few serious attempts to provide real world value from explainable NLP deep learning models.

Knowledge graphs are a great fit for patents. The benefits of a graph based patent search will be increasingly hard to compete with the traditional approaches.

IPRally won the most innovative AI startup award in North Star AI conference. The event had some star speakers like Estonian president Kersti Kaljulaid and DeepMind's Samuel Ritter.

The most notable IPR innovation award of Finland, granted by the IPR University Center, was given to IPRally.

Our story about utilising neural nets with tree like data. Can a Tree-LSTM model be used for real world applications? Turns out the answer is yes, but only after the performance is improved by 7000%.

The story of IPRally in a nutshell. From initial questions to validation of ideas and a working product.

Moving towards efficient patent searches requires better understanding of both the target of the search and the reason for the search. This post discusses this problematic and sligthly opens our approach to solving it.