Disambiguation Step 2: Applying Similarity Metrics

1. Constructing Canopies | 2. Applying Similarity Metrics | 3. Clustering by Similarity Score| 4. Clustering Algorithms Evaluation

The second step in the PatentsView disambiguation process is to calculate similarity scores. Similarity scores are used to recluster the canopies described in Step 1. All mentions within the canopies are reclustered based on the similarity metrics described below. The comparison process is different for each entity— inventors, assignees, and locations—however, the method of calculation is the same where a numerical score represents the degree of similarity.

Assignee Similarity Score

Calculating assignee similarity combines various metrics that form a model used for computing a similarity score. Some of the metrics are based on the similarity of the names, while the slightly more complicated metrics are related to locations, inventors, and patent classifications.

The latter part of the calculation relies on the assumption that mentions with overlapping lists of these characteristics are more likely to represent the same assignee. This is done through what is called a bag-of-attributes representation. A bag-of-attributes representation creates a column for each inventor, location, or patent classification and a row for each mention being compared. Table 1 shows an example of this representation for assignees.

Table 1. Assignee Bag-of-Attributes Representation

 

One of the similarity metrics included in the model is the cosine similarity of these values for each mention to be compared. Cosine similarity is a numerical representation between 0 and 1 of the similarity of the two sets of values; in our case, the above bag-of-attributes representation. Table 2 contains the formal definition for all metrics in the model.

Table 2. Assignee Similarity Metrics

 

Each metric in the above model has its value determined based on the description and possible values. These values are totaled together to find the final similarity score. This score is used in the final clustering algorithm to sort mentions into their final clusters. Any value of “infinite” implies that the model believes the two refer to the same assignee, and other metrics will not change that.

Inventor Similarity Score

Calculating similarity scores for inventors uses a different model. Part of the inventor similarity score is based on name similarity, contained in a Name Match Score function. This function determines the likelihood that names from a cluster of first/middle names with the same last name refer to the same individual. The input of the function is a list of first or middle names that share a last name. The output is the final similarity score of the names.

The steps that this function takes are as follows:

1. Check for penalties, which may include a “no name” penalty (when the list is empty), “too many names” penalty (when the list is too large), and the “mismatch on common last name” penalty (when the list does not share last names).
2. Compute the difference for each pair by taking the following steps:
   1. Check if the initial first letter matches. Increase firstLetterMismatches by 1 if not.
   2. Compute how many changes need to be made to create an exact match (edit distance). Increase nameMismatches by this amount.
3. Multiply firstLetterMatches by initial_mismatch_weight, and subtract the result from the score.
4. Multiply nameMismatches by name_mismatch_weight, and subtract the result from the score.

After calculating Name Match similarity, it is combined with the rest of the metrics (including the cosine similarity mentioned in assignee rules) in the model to produce a final score.

Table 3 shows the rest of these metrics and the weight assigned to each portion.

Table 3. Inventor Similarity Metrics

Feature	Weight Name	Value	Description
First name	initial_mismatch_weight	20.0
First name	no_name_penalty	4.0
First name	name_mismatch_weight	6.0
First name	mismatch_on_common_last	–1000.0
First name	too_many_names_penalty	–1000.0
Middle name	initial_mismatch_weight	8.0
Middle name	no_name_penalty	0.35
Middle name	name_mismatch_weight	3.0
Middle name	mismatch_on_common_last	–1000
Middle name	too_many_names_penalty	–1000
Patent Title Embedding	cosine_similarity_weight	10.0	cosine_similarity_weight * cos_sim(fv1,fv2) + cosine_similarity_bias
Patent Title Embedding	cosine_similarity_bias	–0.25	cosine_similarity_weight * cos_sim(fv1,fv2) + cosine_similarity_bias
Coinventor	cosine_similarity_weight	9.5
Coinventor	cosine_similarity_bias	–0.2
Coinventor	entropy_weight	0.125
Coinventor	complexity_weight	0.5
Location	cosine_similarity_weight	9.5
Location	cosine_similarity_bias	–0.1
Location	entropy_weight	0.25
Location	complexity_weight	0.5
Assignee	cosine_similarity_weight	9.5
Assignee	cosine_similarity_bias	–0.1
Assignee	entropy_weight	0.125
Assignee	complexity_weight	0.5
Lawyers	cosine_similarity_weight	5.0
Lawyers	cosine_similarity_bias	0.0
CPC	cosine_similarity_weight	1.5
CPC	cosine_similarity_bias	0.0
IPCR	cosine_similarity_weight	1.5
IPCR	cosine_similarity_bias	0.0
NBER	cosine_similarity_weight	1.5
NBER	cosine_similarity_bias	0.0
USPC	cosine_similarity_weight	1.5
USPC	cosine_similarity_bias	0.0

Note: CPC = Cooperative Patent Classification; IPCR = International Patent Classification-Revised; NBER = National Bureau of Economic Research; USPC = U.S. Patent Classification System.

 

Location Similarity Score

Calculating location similarity is largely focused on name similarity between mentions but also uses the cosine similarity of the inventors/assignees associated with the city mentions being compared. State and/or country are included as attributes to aid in the comparison. The focus on name similarity arises from the fact that cities with the same name are likely to be distinguished by associated states/provinces and/or country. Part of the name similarity model checks if two cities have the same name, and one does not exist in the MaxMind database used for these comparisons. This is intended to accommodate for cases where the wrong state is entered, but the same inventor/assignee has patented in that city and entered the correct name on a different patent. Table 4 shows the model for computing location similarity scores.

Table 4. Location Similarity Metrics