Note: This blog post was originally written in Japanese for our Japanese website. We used our machine translation platforms to translate it and post-edit the content in English. The original Japanese post can be found here.
In intellectual property departments and patent firms, the use of generative AI is no longer anything out of the ordinary. Many of you have probably already seen clear benefits in terms of partial efficiency gains in areas such as summarizing patent publications and screening prior art.
At the same time, we at our translation agency have also been hearing comments like this: “We introduced AI tools, but for some reason, the amount of manual work has not decreased.”
There also seem to be quite a few workplaces where people feel this kind of disconnect. Why is it that even after adopting high-performance AI, true efficiency in intellectual property practice is not progressing as much as expected?
We believe that behind this are three gaps that tools alone cannot fully bridge.
The accuracy of generative AI is improving year by year. Summarization, classification, and extraction have all become far more capable than they were a few years ago. Even so, we still hear people say, “It’s not reducing our workload as much as we expected.” One reason for this is that AI is being introduced in isolation. AI can handle individual tasks. However, intellectual property work is not simply a series of one-off tasks, but a process of continuous decision-making.
Even if AI provides summaries, the number of items themselves does not decrease. In the end, the task of deciding which ones should be read still remains with humans.
How should AI output be evaluated? As long as those criteria remain dependent on individual judgment, the workload for final review will not decrease. It also becomes harder to accumulate knowledge.
AI can generate text, but that does not necessarily mean it is in a form that can be used as-is for analysis, sharing, or reporting. As a result, people end up having to reformat it.
The reason workloads are not decreasing despite AI’s high performance is that AI is still limited to only part of the work. The overall workflow itself remains unchanged from the conventional approach.
Intellectual property work is a continuous process involving stages such as acquisition, selection, summarization, classification, sharing, and analysis. Even if you improve efficiency in just one part, it will not lead to overall optimization if the steps before and after it remain manual work.
That is why what is needed is not introducing AI at isolated points but designing the entire workflow with automation in mind. AI should prepare everything up to the point just before human judgment is needed, then hand it off naturally to the next step. Only with that kind of design can meaningful workload reduction finally be achieved.
Kawamura International’s API system, LDX hub, can eliminate these operational bottlenecks.
LDX hub is not a standalone tool that simply provides generative AI or machine translation. It is an integrated platform that incorporates these technologies into operational workflows and enables them to function as AI agents. Specifically, it makes the following possible:
• Using our proprietary technology, we instantly tag tens of thousands of documents, greatly improving screening efficiency and creating an environment where engineers can focus on the patent publications they should read.
• Turn complex patent claims into three-line summaries that anyone can understand. This makes it easier even for engineers outside the specialty to grasp the content and speeds up internal sharing.
• Extract competitive trends from text as JSON data. We structure qualitative information into a format that is easy to use for analysis and reporting.
In other words, LDX hub is a platform that brings together disparate AI functions to automate the entire intellectual property workflow.
Move from simply using AI to a system in which AI keeps running. We aim to fill the less visible gaps in day-to-day operations and achieve both reduced workloads and faster decision-making.
The goal of digital transformation in IP management is not simply to implement AI. Rather, it is to free IP professionals to focus on higher-value activities, such as competitor analysis and IP strategy.
If you feel that AI is useful, but it does not fit well into your current workflow, why not take another look at the entire process?
We propose a practical approach to steadily reducing manual work one task at a time while leveraging LDX hub.
Kawamura International also offers StructFlow, an AI platform that converts unstructured data into structured data. It automatically transforms free-form text into easy-to-use data formats such as JSON, enabling smooth integration with downstream tasks such as classification, summarization, and analysis.
Through advanced contextual understanding and parallel processing powered by generative AI, we enable the fast and accurate processing of large volumes of data, helping automate and streamline entire operations. If manual work is still piling up despite your AI tools, let's talk. We'd love to help you find where the gaps are.