View source for 18300238. EFFICIENT GENERATION OF REVIEW SUMMARIES simplified abstract (Microsoft Technology Licensing, LLC)

=EFFICIENT GENERATION OF REVIEW SUMMARIES=


==Organization Name==

[[:Category:Microsoft Technology Licensing, LLC|Microsoft Technology Licensing, LLC]]


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Edy Daniel Paulino of Bellevue WA (US)|Edy Daniel Paulino of Bellevue WA (US)]][[Category:Edy Daniel Paulino of Bellevue WA (US)]]

[[:Category:Kyle Matthew Unger of Seattle WA (US)|Kyle Matthew Unger of Seattle WA (US)]][[Category:Kyle Matthew Unger of Seattle WA (US)]]

[[:Category:Judah Gabriel Himango of Monroe WA (US)|Judah Gabriel Himango of Monroe WA (US)]][[Category:Judah Gabriel Himango of Monroe WA (US)]]

[[:Category:Wey Hsuan Low of Seattle WA (US)|Wey Hsuan Low of Seattle WA (US)]][[Category:Wey Hsuan Low of Seattle WA (US)]]

==EFFICIENT GENERATION OF REVIEW SUMMARIES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18300238 titled 'EFFICIENT GENERATION OF REVIEW SUMMARIES

==Simplified Explanation==
The patent application describes methods and systems for efficiently generating review summaries of items by using machine learning models.

* Reviews associated with an item are obtained.
* A set of reviews is selected based on specific attributes.
* A model prompt is generated for input into a trained machine learning model.
* The output is a review summary that summarizes the selected set of reviews.

==Key Features and Innovation==
* Obtaining and selecting reviews based on attributes.
* Generating a model prompt for machine learning models.
* Summarizing reviews efficiently using machine learning.

==Potential Applications==
This technology can be applied in e-commerce platforms, product review websites, and customer feedback analysis tools.

==Problems Solved==
This technology addresses the challenge of efficiently summarizing a large number of reviews associated with an item.

==Benefits==
* Saves time by automating the review summary generation process.
* Provides concise and informative summaries for users.
* Improves decision-making based on aggregated review information.

==Commercial Applications==
* "Efficient Review Summarization Technology for E-commerce Platforms"
* This technology can be licensed to online retailers, review websites, and market research firms for enhancing customer experience and product analysis.

==Prior Art==
Researchers can explore prior art related to machine learning models for text summarization and sentiment analysis in the context of review aggregation.

==Frequently Updated Research==
Stay updated on advancements in machine learning models for natural language processing and text summarization techniques.

==Questions about Efficient Review Summarization Technology==
===How does this technology improve the user experience on e-commerce platforms?===
This technology enhances user experience by providing concise and informative summaries of product reviews, aiding in decision-making.

===What are the potential limitations of using machine learning models for review summarization?===
One potential limitation could be the accuracy of the summarization process, as it heavily relies on the quality and relevance of the selected reviews.


==Original Abstract Submitted==

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating review summaries. In embodiments, reviews associated with an item are obtained. A set of the reviews are then determined or selected based on an attribute associated with the corresponding review. Thereafter, a model prompt to be input into a trained machine learning model is generated. The model prompt can include an indication of the item and the determined set of the reviews. As output from the trained machine learning model, a review summary that summarizes the set of the reviews associated with the item is obtained.



[[Category:G06F40/166]]
[[Category:G06F40/40]]
[[Category:G06N3/08]]
[[Category:CPC_G06F40/166]]