
# PoC (Patents with One Citation) dataset

This dataset is useful for training or evaluating models that predict patent-to-patent similarity, such as those used for patent searching.

It was developed and used for the training of an ML model that powers the [PQAI](https://search.projectpq.ai/) search engine.


## Details

The dataset contains 90,013 samples.

Each sample contains:
- a subject patent (`sp`)
- its only citation (`cit`)
- its CPC code (`cpc`)
- a list of 10 patents (`sims`) that are similar to `sp` (in that they share the CPC code) and published before `sp`

Every line of the dataset is a JSON parsable string (`.jsonl` format), which upon parsing given an array of this format:

```
[pn, cit, cpc, [...sims]]
```

## Task

Given the subject patent `sp` the task is to assign a similarity score to each patent `[cit, ...sims]`. Ideally, the score should be maximum for `cit`.

## Metrics

It's a ranking task, so the following metrics make the most sense:

- DCG/NDCG
- Accuracy
license:mit region:us
{"license": "mit"}
TAGS
#license-mit #region-us 


# PoC (Patents with One Citation) dataset

This dataset is useful for training or evaluating models that predict patent-to-patent similarity, such as those used for patent searching.

It was developed and used for the training of an ML model that powers the PQAI search engine.


## Details

The dataset contains 90,013 samples.

Each sample contains:
- a subject patent ('sp')
- its only citation ('cit')
- its CPC code ('cpc')
- a list of 10 patents ('sims') that are similar to 'sp' (in that they share the CPC code) and published before 'sp'

Every line of the dataset is a JSON parsable string ('.jsonl' format), which upon parsing given an array of this format:



## Task

Given the subject patent 'sp' the task is to assign a similarity score to each patent '[cit, ...sims]'. Ideally, the score should be maximum for 'cit'.

## Metrics

It's a ranking task, so the following metrics make the most sense:

- DCG/NDCG
- Accuracy
# PoC (Patents with One Citation) dataset

This dataset is useful for training or evaluating models that predict patent-to-patent similarity, such as those used for patent searching.

It was developed and used for the training of an ML model that powers the PQAI search engine. ## Details

The dataset contains 90,013 samples.

Each sample contains:
- a subject patent ('sp')
- its only citation ('cit')
- its CPC code ('cpc')
- a list of 10 patents ('sims') that are similar to 'sp' (in that they share the CPC code) and published before 'sp'

Every line of the dataset is a JSON parsable string ('.jsonl' format), which upon parsing given an array of this format: ## Task

Given the subject patent 'sp' the task is to assign a similarity score to each patent '[cit, ...sims]'. Ideally, the score should be maximum for 'cit'. ## Metrics

It's a ranking task, so the following metrics make the most sense:

- DCG/NDCG
- Accuracy
TAGS
#license-mit #region-us 
 # PoC (Patents with One Citation) dataset

This dataset is useful for training or evaluating models that predict patent-to-patent similarity, such as those used for patent searching.

It was developed and used for the training of an ML model that powers the PQAI search engine. ## Details

The dataset contains 90,013 samples.

Each sample contains:
- a subject patent ('sp')
- its only citation ('cit')
- its CPC code ('cpc')
- a list of 10 patents ('sims') that are similar to 'sp' (in that they share the CPC code) and published before 'sp'

Every line of the dataset is a JSON parsable string ('.jsonl' format), which upon parsing given an array of this format: ## Task

Given the subject patent 'sp' the task is to assign a similarity score to each patent '[cit, ...sims]'. Ideally, the score should be maximum for 'cit'. ## Metrics

It's a ranking task, so the following metrics make the most sense:

- DCG/NDCG
- Accuracy
passage: TAGS
#license-mit #region-us 
# PoC (Patents with One Citation) dataset

This dataset is useful for training or evaluating models that predict patent-to-patent similarity, such as those used for patent searching.

It was developed and used for the training of an ML model that powers the PQAI search engine.## Details

The dataset contains 90,013 samples.

Each sample contains:
- a subject patent ('sp')
- its only citation ('cit')
- its CPC code ('cpc')
- a list of 10 patents ('sims') that are similar to 'sp' (in that they share the CPC code) and published before 'sp'

Every line of the dataset is a JSON parsable string ('.jsonl' format), which upon parsing given an array of this format:## Task

Given the subject patent 'sp' the task is to assign a similarity score to each patent '[cit, ...sims]'. Ideally, the score should be maximum for 'cit'.## Metrics

It's a ranking task, so the following metrics make the most sense:

- DCG/NDCG
- Accuracy