18517509. Identification and Issuance of Repeatable Queries simplified abstract (Google LLC)
Contents
- 1 Identification and Issuance of Repeatable Queries
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Identification and Issuance of Repeatable Queries - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Identification and Issuance of Repeatable Queries
Organization Name
Inventor(s)
Yew Jin Lim of Saratoga CA (US)
David Adam Faden of Mountain View CA (US)
Mario Tanev of San Francisco CA (US)
Lauren Ashley Koepnick of Capitola CA (US)
Sagar Gandhi of Seattle WA (US)
William Ming Zhang of Los Angeles CA (US)
Identification and Issuance of Repeatable Queries - A simplified explanation of the abstract
This abstract first appeared for US patent application 18517509 titled 'Identification and Issuance of Repeatable Queries
Simplified Explanation
The patent application describes methods, systems, and apparatus for identifying and issuing search queries expected to be issued in the future. Here is a simplified explanation of the abstract:
- Obtain a set of search queries issued by multiple user devices.
- Obtain contextual data for each query instance.
- Input a query and its contextual data into a model to predict its likelihood of being issued in the future.
- Train the model using contextual data and corresponding labels for training queries.
- Store a query as a repeatable query if its likelihood meets a repeatability threshold.
- Issue a stored repeatable query upon user selection and provide search results.
Potential Applications
This technology could be applied in personalized search engines, predictive text input systems, and targeted advertising platforms.
Problems Solved
This technology helps in predicting user search behavior, improving search engine efficiency, and enhancing user experience by providing relevant search results.
Benefits
The benefits of this technology include improved search accuracy, increased user engagement, and more efficient use of search engine resources.
Potential Commercial Applications
The potential commercial applications of this technology include search engine optimization tools, targeted advertising platforms, and personalized recommendation systems.
Possible Prior Art
One possible prior art for this technology could be predictive text input systems used in mobile devices to suggest search queries or text based on user behavior and context.
Unanswered Questions
How does the model handle new or previously unseen queries?
The article does not provide information on how the model deals with queries that have not been encountered before.
What is the impact of user privacy and data security in this system?
The article does not address the potential implications for user privacy and data security in collecting and analyzing contextual data for predicting future search queries.
Original Abstract Submitted
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that identify and issue search queries expected to be issued in the future. A set of search queries that have been issued by multiple user devices can be obtained. For each query instance, contextual data can be obtained. A first query and its contextual data can be input to a model that outputs the query's likelihood of being issued in the future. The model can be trained using contextual data for training queries and a corresponding labels for the training queries. The learning model outputs the first query's likelihood of being issued in future, and this query is stored as a repeatable query if the likelihood satisfying a repeatability threshold. Subsequently, a stored repeatable query is issued upon a selection of a user selectable interface component and the search engine provides search results for the query.
