Bridging Patents to Academic Validation
- Kasturi Murthy
- 6 days ago
- 2 min read
Searching is Science, but Finding is an Art
This phrase is often repeated in the halls of Information Science, and for good reason. Searching is a mechanical act—the "Science." It is the algorithm traversing billions of rows in a database, the cold logic of indexing, and the raw compute power that retrieves a list of results. Anyone can search; you simply type a keyword and hit "Enter."
Finding, however, is the "Art." It is the human intuition that knows *how* to ask the question. It is the ability to filter out the noise to reveal the signal. It is knowing that to find the most relevant innovation, you don't just look for keywords—you look for context, timeframes, and specific technical classifications.
When we use tools like SerpApi [1] to scrape Google Patents, we aren't just writing code; we are painting a query. We are using parameters as our brushstrokes to turn a generic search into a targeted discovery.
The Canvas: Your Search Parameters
Let's look at a Python dictionary [2] that represents this philosophy. This isn't just a configuration object; it is a set of filters designed to eliminate the irrelevant.
```python
params = {
"engine": "google_patents",
"q": "visual language models", # The Subject
"cpc": "G06T", # The Classification (The Art)
"after": "filing:20200101", # The Timeframe (The Strategy)
"num": "100", # The Scale
"api_key": apiKey
}
"q": "visual language models" - the query
This is your broad stroke. You are telling the engine what you are interested in. But if you stop here, the "Science" of the search engine will bury you in results— irrelevant keyword matches, and outdated tech. To "Find," we need to go deeper.
The Classification (`cpc`)
"cpc": "G06T"
Note: This filters by Cooperative Patent Classification
This is the most "artistic" line in your code. You aren't relying on text matches anymore; you are relying on the universal language of patent examiners.
The Science: `G06T` is the specific code for "Image Data Processing or Generation."
The Art: By adding this, you strictly limit your results to patents that focus on the *image processing* aspect of Visual Language Models. You automatically filter out patents that might just be about linguistics (text-only). You are forcing the engine to "think" like an engineer, not a keyword scanner.
The Efficiency (`num`)
"num": "100"
This is about respect for your resources. Instead of making 10 separate requests for 10 results each (which is slow and burns API credits), you are requesting a "bulk" delivery. It maximizes the value of every interaction.
Bridging the Gap: From Patents to Papers
The final piece of the "Art" is connecting distinct worlds. Innovation doesn't happen in a vacuum; it usually starts in a lab (Google Scholar) before it reaches the patent office (Google Patents).
With SerpApi, you can bridge this gap programmatically. Here is a conceptual workflow of how to take a patent you found and check if the academic world is citing it. An excerpt how this can be done is given below
References
Comments