Google llc (20240104108). Granular Signals for Offline-to-Online Modeling simplified abstract
Contents
- 1 Granular Signals for Offline-to-Online Modeling
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Granular Signals for Offline-to-Online Modeling - 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
Granular Signals for Offline-to-Online Modeling
Organization Name
Inventor(s)
Granular Signals for Offline-to-Online Modeling - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104108 titled 'Granular Signals for Offline-to-Online Modeling
Simplified Explanation
The abstract describes a computer-implemented method that involves receiving source activity data, executing a query for target activity related to the source activity data, determining predicted target activity using machine-learned models, generating a predicted temporal distribution of target activity, and outputting query results based on the predicted target activity and temporal distribution.
- Receiving source activity data
- Executing a query for target activity
- Determining predicted target activity using machine-learned models
- Generating a predicted temporal distribution of target activity
- Outputting query results based on predicted target activity and temporal distribution
Potential Applications
This technology could be applied in various fields such as predictive analytics, recommendation systems, and personalized marketing strategies.
Problems Solved
This technology helps in predicting target activities based on source data, which can assist in making informed decisions and improving user experiences.
Benefits
The benefits of this technology include improved accuracy in predicting target activities, enhanced efficiency in decision-making processes, and personalized user experiences.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of advanced recommendation systems for e-commerce platforms, leading to increased sales and customer satisfaction.
Possible Prior Art
One possible prior art could be the use of machine learning models in predictive analytics and recommendation systems in various industries.
Unanswered Questions
How does this technology handle data privacy concerns?
This article does not address how the technology ensures the privacy and security of the data used in predicting target activities.
What are the limitations of using machine-learned models in this context?
The article does not discuss any potential limitations or challenges associated with using machine-learned models for predicting target activities.
Original Abstract Submitted
example aspects of embodiments of the present disclosure provide an example computer-implemented method. the example method includes receiving source activity data. the example method includes executing a query for target activity related to the source activity data. in the example method, executing the query includes determining, using a first machine-learned model of a machine-learned model framework, predicted target activity related to the source activity data. in the example method, executing the query includes generating, using a second machine-learned model of the machine-learned model framework, a predicted temporal distribution of target activity. the example method includes outputting, in response to the query, query results based at least in part on the predicted target activity and the predicted temporal distribution of target activity.
