System and Method For Detecting The Phase Of A Female Menstrual Cycle

• US 20090306537A1
• Filed: 06/09/2009
• Published: 12/10/2009
• Est. Priority Date: 06/09/2008
• Status: Abandoned Application

• Current Assignee: Andy Ofer Goren
• Original Assignee: Andy Ofer Goren
• Inventors: Goren, Andy Ofer
• Application Number:
  US12/481,520
• Publication Number:
  US 20090306537A1
• Time in Patent Office: Days
• US Class Current: 600/551
• CPC Class Codes: A61B 10/0012 Ovulation-period determination for animals A61D17/002

Abstract

A system for predicting a woman'"'"'s current position in her menstrual or ovulation cycle includes a baseline voice characteristics storage module in which first set of characteristics of the woman'"'"'s voice at a known time relative to her menstrual or ovulation cycle is stored, an analysis module for receiving for analyzing a voice sample from the woman and generating a second set of characteristics of the woman'"'"'s voice, a comparison module for comparing the first and second sets of characteristics, and a results module for providing a prediction of the woman'"'"'s current position in her menstrual or ovulation cycle based on the comparison.

Claims

1. A system for predicting a woman'"'"'s current position in her menstrual or ovulation cycle, comprising:

a baseline voice characteristics storage module in which first set of characteristics of the woman'"'"'s voice at a known time relative to her menstrual or ovulation cycle is stored;

an analysis module for receiving for analyzing a voice sample from the woman and generating a second set of characteristics of the woman'"'"'s voice;

a comparison module for comparing the first and second sets of characteristics; and

a results module for providing a prediction of the woman'"'"'s current position in her menstrual or ovulation cycle based on the comparison.

2. The system of claim 1, wherein the first and second sets relate frequency and or pitch.

3. The system of claim 1, wherein the comparison module further compares the difference between the first and second sets of characteristics with pre-stored population differences.

4. The system of claim 1, wherein a neural network is employed.