Google's Two Recent Patent Updates
A new article on Search Engine Journal reports on new Google search patents over the last few weeks.
The first patent got filed on March 13, 2013, and after seven years was awarded on October 20, 2020. It relates to: Querying a data graph using natural language queries. A summary of this patent can be seen below:
There are some notable points of interests in this patent:
"(...) in a data graph, entities, such as people, places, things, concepts, etc., may be stored as nodes and the edges between nodes may indicate the relationship between the nodes. In such a data graph, the nodes "Maryland" and "United States" may be linked by the edges of "in country" and/or "has state. The knowledge extracted from the text and the data graph is used as input to train a machine-learning algorithm to predict tuples for the data graph. The trained machine learning algorithm may produce multiple weighted features for a given relationship, each feature representing an inference about how two entities might be related. Some implementations allow natural language queries to be answered from the data graph. In such implementations, the machine learning module can be trained to map features to queries, and the features being used to provide possible query results. The training may involve using positive examples from search records or from query results obtained from a document-based search engine. The trained machine learning module may produce multiple weighted features, where each feature represents one possible query answer, represented by a path in the data graph. "
Refer to the image below:
To check out the other patent, feel free to visit the original article!
The first patent got filed on March 13, 2013, and after seven years was awarded on October 20, 2020. It relates to: Querying a data graph using natural language queries. A summary of this patent can be seen below:
| "Implementations include systems and methods for querying a data graph. An example method includes receiving a machine learning module trained to produce a model with multiple features for a query, each feature representing a path in a data graph. The method also includes receiving a search query that includes a first search term, mapping the search query to the query, and mapping the first search term to a first entity in the data graph. The method may also include identifying a second entity in the data graph using the first entity and at least one of the multiple weighted features, and providing information relating to the second entity in a response to the search query. Some implementations may also include training the machine learning module by, for example, generating positive and negative training examples from an answer to a query." |
"(...) in a data graph, entities, such as people, places, things, concepts, etc., may be stored as nodes and the edges between nodes may indicate the relationship between the nodes. In such a data graph, the nodes "Maryland" and "United States" may be linked by the edges of "in country" and/or "has state. The knowledge extracted from the text and the data graph is used as input to train a machine-learning algorithm to predict tuples for the data graph. The trained machine learning algorithm may produce multiple weighted features for a given relationship, each feature representing an inference about how two entities might be related. Some implementations allow natural language queries to be answered from the data graph. In such implementations, the machine learning module can be trained to map features to queries, and the features being used to provide possible query results. The training may involve using positive examples from search records or from query results obtained from a document-based search engine. The trained machine learning module may produce multiple weighted features, where each feature represents one possible query answer, represented by a path in the data graph. "
Refer to the image below:
To check out the other patent, feel free to visit the original article!
