Microsoft technology licensing, llc (20240135105). MEASURING PROBABILITY OF INFLUENCE USING MULTI-DIMENSIONAL STATISTICS ON DEEP LEARNING EMBEDDINGS simplified abstract
Contents
- 1 MEASURING PROBABILITY OF INFLUENCE USING MULTI-DIMENSIONAL STATISTICS ON DEEP LEARNING EMBEDDINGS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MEASURING PROBABILITY OF INFLUENCE USING MULTI-DIMENSIONAL STATISTICS ON DEEP LEARNING EMBEDDINGS - 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
MEASURING PROBABILITY OF INFLUENCE USING MULTI-DIMENSIONAL STATISTICS ON DEEP LEARNING EMBEDDINGS
Organization Name
microsoft technology licensing, llc
Inventor(s)
Aleksander Werpachowski of Oslo (NO)
Roman Werpachowski of Ameberg (NO)
Mohammadreza Bonyadi of Trondheim (NO)
Arun Shankar Iyer of Bangalore (IN)
MEASURING PROBABILITY OF INFLUENCE USING MULTI-DIMENSIONAL STATISTICS ON DEEP LEARNING EMBEDDINGS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135105 titled 'MEASURING PROBABILITY OF INFLUENCE USING MULTI-DIMENSIONAL STATISTICS ON DEEP LEARNING EMBEDDINGS
Simplified Explanation
The patent application describes a system for measuring the probability of influence in digital communications to determine if communication content originated in a person's own prior knowledge or new information obtained from interactions with others.
- The system uses multidimensional statistics on embeddings representing communications to estimate the probability that a new communication by a first user comes from the same distribution as their prior communications.
- It also estimates the probability that the new communication comes from the same distribution as communications of a second user that were accessible to the first user.
- If the second probability is greater than the first probability, the new communication is more likely influenced by exposure to the second user's communications.
- An influence attribution recommendation is generated based on these probabilities.
Potential Applications
This technology could be applied in social media platforms to identify the source of information shared by users and detect potential influence between users.
Problems Solved
This technology helps in determining the origin of communication content, distinguishing between a user's own knowledge and information obtained from interactions with others.
Benefits
- Enhances transparency in digital communications - Helps in understanding the influence of interactions on communication content
Potential Commercial Applications
"Enhancing Influence Attribution in Digital Communications" could be used in social media monitoring tools for brands to analyze the impact of influencer marketing campaigns.
Possible Prior Art
One possible prior art could be systems that analyze user behavior to personalize content recommendations, but not specifically focused on measuring influence in digital communications.
What are the limitations of this technology in real-world applications?
The technology may face challenges in accurately determining the influence of interactions on communication content, especially in cases where users have diverse sources of information.
How does this technology ensure user privacy and data protection?
The system would need to comply with data protection regulations and ensure that user data used for influence attribution is anonymized and secure to protect user privacy.
Original Abstract Submitted
the disclosure herein describes a system for measuring probability of influence in digital communications to determine if communication content originated in a person's own prior knowledge or new information more recently obtained from interaction with communications of others. an estimated probability a new communication by a first user comes from the same distribution as prior communications of the first user are generated using multidimensional statistics on embeddings representing the communications. a second estimated probability that the new communication comes from the same distribution as communication(s) of a second user that were accessible to the first user are generated. if the second probability is greater than the first probability, the new communication is more likely influenced by exposure of the first user to the second user's communications rather than the first user's own historical knowledge. an influence attribution recommendation is generated, including an influence attribution or other recommended action.
