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Mood Identification and Warning System

IP.com Disclosure Number: IPCOM000248610D
Publication Date: 2016-Dec-21
Document File: 3 page(s) / 22K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a method and system to define a user’s mood based on written and spoken content captured from a user's mobile device, and then periodically inform the user, through visual and contextual representations, of the defined mood. Additionally, the system sends the user recommended actions to take to alter or prevent a negative mood.

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Mood Identification and Warning System

Professional and personal situations demand people to produce and deliver more every day. An essential component that impacts human productivity is mood. There is a relationship between a person’s productivity level and the person’s current mood. People are less productive with a negative mood (e.g., anger, fear, sadness or disgust) and more productive when happy or joyous. Another significant benefit of being in a good and happy mood is better health. Happier people are more productive and healthier, and may live longer.

People who are very busy and always multitasking are not always self-aware of mood. A few are lucky enough to be in tuned to the subconscious and be aware of mood; however, even fewer have the proper training and skill to take proactive actions to improve a mood.

A method is needed to periodically, in real time, inform a person about moods and then offer ways to alter the mood from negative to positive. In addition, a method that can notify a person that a negative mood is pending based on analyzing the individual’s mood pattern, addresses another problem. A person might periodically be exposed to an event that causes the person to be angry; however, without a solution, the person is not able to identify the pattern. Further, a method that can alert the person to the possibility of a negative mood based on an upcoming event could be helpful.

The novel contribution is a method and system that define a user’s mood based on written and spoken content captured from a user's mobile device, and then periodically inform the user, through visual and contextual representations, of the defined mood. Additionally, the system sends the user recommended actions to take to alter or prevent a negative mood (e.g., anger, fear, sadness, or disgust).

The method and system do not depend on a user creating a predefined set of rules, keywords, or other information to be used to determine mood. With predefined rules, context of the content is not taken into account, so results may be inaccurate. This system uses Natural Language Processing (NLP) to fully understand the context of what was written or spoken and extract mood.

Some solutions require the user to periodically set a mood in an application. The accuracy of the information is dependent on the honesty level of the person. The novel system automatically derives the user’s mood. Performing analytics on the user’s historical mood data may identify patterns as well as keywords that are being used during instances of negative mood. Over time, the system can identify a negative mood pattern (e.g., on a certain day of the week and a period during that day, the user becomes angry). The application pre-emptively provides queues to the user to prevent the negative mood.

To determine the user’s mood, the system analyzes content created by the user using an associated mobile device. Content can include photographs, text messages, emails,

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