Determining a matching score between users of wearable camera systems

• US 10,341,544 B2
• Filed: 07/23/2015
• Issued: 07/02/2019
• Est. Priority Date: 07/23/2014
• Status: Active Grant

• Current Assignee: OrCam Technologies Ltd
• Original Assignee: OrCam Technologies Ltd
• Inventors: Wexler, Yonatan, Shashua, Amnon
• Primary Examiner(s): Perungavoor, Sathyanaraya V
• Assistant Examiner(s): Dang, Philip P.
• Application Number:
  US14/807,073
• Publication Number:
  US 20160026238A1
• Time in Patent Office: 1,440 Days
• CPC Class Codes: G01S 3/7864 T.V. type tracking systems
• CPC Class Codes: G02B 2027/0138 comprising image capture systems, e.g. camera
• CPC Class Codes: G02B 2027/014 comprising information/image processing systems
• CPC Class Codes: G02B 2027/0178 Eyeglass type eyeglass details G02C
• CPC Class Codes: G02B 2027/0187 slaved to motion of at least a part of the body of the user, e.g. head, eye
• CPC Class Codes: G02B 27/0093 with means for monitoring data relating to the user, e.g. head-tracking, eye-tracking
• CPC Class Codes: G02B 27/017 Head mounted
• CPC Class Codes: G06F 1/163 Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 1/1686 the I/O peripheral being an integrated camera
• CPC Class Codes: G06F 1/18 Packaging or power distribution
• CPC Class Codes: G06F 1/188 Mounting of power supply units
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/53 Querying
• CPC Class Codes: G06F 16/532 Query formulation, e.g. graphical querying
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 2203/011 Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns
• CPC Class Codes: G06F 3/005 Input arrangements through a video camera
• CPC Class Codes: G06F 3/011 Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/012 Head tracking input arrangements
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/0304 : Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• CPC Class Codes: G06F 3/038 : Control and interface arrangements therefor, e.g. drivers or device-embedded control circuitry
• CPC Class Codes: G06Q 30/0246 : Traffic
• CPC Class Codes: G06Q 30/0257 : User requested
• CPC Class Codes: G06Q 30/0267 : Wireless devices
• CPC Class Codes: G06Q 30/0269 : based on user profile or attribute
• CPC Class Codes: G06T 2207/10004 : Still image; Photographic image
• CPC Class Codes: G06T 2207/10016 : Video; Image sequence
• CPC Class Codes: G06T 2207/30232 : Surveillance
• CPC Class Codes: G06T 7/20 : Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06T 7/97 : Determining parameters from multiple pictures depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85
• CPC Class Codes: G06V 10/776 : Validation; Performance evaluation
• CPC Class Codes: G06V 20/20 : in augmented reality scenes
• CPC Class Codes: G06V 20/30 : in albums, collections or shared content, e.g. social network photos or video
• CPC Class Codes: G06V 20/41 : Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/64 : Three-dimensional objects
• CPC Class Codes: G06V 20/647 : by matching two-dimensional images to three-dimensional objects
• CPC Class Codes: G06V 2201/07 : Target detection
• CPC Class Codes: G06V 2201/09 : Recognition of logos
• CPC Class Codes: G06V 40/16 : Human faces, e.g. facial parts, sketches or expressions
• CPC Class Codes: G06V 40/172 : Classification, e.g. identification
• CPC Class Codes: G06V 40/20 : Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: H04N 1/2112 : using still video cameras
• CPC Class Codes: H04N 23/45 : for generating image signals from two or more image sensors being of different type or operating in different modes, e.g. with a CMOS sensor for moving images in combination with a charge-coupled device [CCD] for still images
• CPC Class Codes: H04N 23/50 : Constructional details
• CPC Class Codes: H04N 23/51 : Housings
• CPC Class Codes: H04N 23/57 : Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices
• CPC Class Codes: H04N 23/58 : Means for changing the camera field of view without moving the camera body, e.g. nutating or panning of optics or image sensors
• CPC Class Codes: H04N 23/61 : based on recognised objects
• CPC Class Codes: H04N 23/611 : where the recognised objects include parts of the human body
• CPC Class Codes: H04N 23/62 : Control of parameters via user interfaces
• CPC Class Codes: H04N 23/661 : Transmitting camera control signals through networks, e.g. control via the Internet
• CPC Class Codes: H04N 23/667 : Camera operation mode switching, e.g. between still and video, sport and normal or high- and low-resolution modes
• CPC Class Codes: H04N 23/6812 : based on additional sensors, e.g. acceleration sensors
• CPC Class Codes: H04N 23/73 : by influencing the exposure time
• CPC Class Codes: H04N 23/80 : Camera processing pipelines; Components thereof
• CPC Class Codes: H04N 5/44 : Receiver circuitry for the reception of television signals according to analogue transmission standards H04N5/14 takes precedence
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: H04N 7/185 : from a mobile camera, e.g. for remote control

Abstract

A server and method are provided for determining a matching score related to users of wearable camera systems. In one implementation, a server determines a matching score related to users of wearable camera systems. Each wearable camera system is configured to capture images from an environment of a corresponding user and produce image data from the captured images. At least one processing device associated with the server may be programmed to receive image data from the wearable camera systems. The processing device may further determine a value of a matching score related to at least two users of the wearable camera systems. The value of the matching score may be based on the image data received from the wearable camera systems. The value of the matching score may indicate a level of exposure of the two users to similar visual details in their environments.

Claims

1. A server for determining a matching score between users of wearable camera systems, the server comprising:

a memory for storing image data received from the wearable camera systems, wherein each wearable camera system is configured to capture images from an environment of a corresponding user and produce image data from the captured images; and

at least one processing device associated with the server and programmed to;

receive the image data from the wearable camera systems, wherein the image data includes first image data from a first wearable camera system of a first user and second image data from a second wearable camera system of a second user; and

determine a value of the matching score between the first user and the second user of the wearable camera systems based on;

the first image data received from the wearable camera system of the first user and the second image data received from the wearable camera system of the second user, and

information related to the first user and the second user, including profile information for each of the first user and the second user, wherein the matching score between the first user and the second user indicates a level of commonality between the first user and the second user according to one or more visual details of objects, people, or features in an environment of the first user and one or more visual details of objects, people, or features in an environment of the second user.

2. The server of claim 1, wherein the image data includes images or video streams captured by the wearable camera systems.

3. The server of claim 1, wherein the image data includes information derived from at least one of images and video streams captured by the wearable camera systems.

4. The server of claim 1, wherein the at least one processing device is further programmed to:

determine, based on the value of the matching score, a recommendation for at least one of the first and second users; and

provide the recommendation to the at least one of the first and second users.

5. The server of claim 4, wherein the recommendation includes a product that was used by at least one of the first and second users in at least one of the captured images.

6. The server of claim 1, wherein the at least one processing device is further programmed to use information related to the image data to determine the value of the matching score of the first and second users.

7. The server of claim 6, wherein the information related to the image data includes a location where the captured images were captured, a time when the captured images were captured, and identified content in the captured images.

8. The server of claim 1, wherein the profile information related to the first and second users includes at least one of gender, age, weight, height, hair color, eye color, physical attributes of at least one of the first and second users, income level, education level, a home address, a work address, marital status, and a postal code.

9. The server of claim 1, wherein the matching score is used to suggest a possible match between the first and second users.

10. The server of claim 1, wherein the matching score is used to identify a common preference of the first and second users.

11. The server of claim 1, wherein the matching score is used to build a social graph of the first and second users.

12. A computer-implemented method for determining a matching score between users of wearable camera systems, the method comprising:

receiving, by at least one processing device, image data received from the wearable camera systems, wherein each wearable camera system is configured to capture images from an environment of a corresponding user and produce image data from the captured images, wherein the image data includes first image data from a first wearable camera system of a first user and second image data from a second wearable camera system of a second user; and

determining, by the at least one processing device, a value of the matching score between the first user and the second user of the wearable camera systems based on;

the first image data received from the wearable camera system of the first user and the second image data received from the wearable camera system of the second user, and

information related to the first user and the second user, including profile information for each of the first user and the second user, wherein the matching score between the first user and the second user indicates a level of commonality between the first user and the second user according to one or more visual details of objects, people, or features in an environment of the first user and one or more visual details of objects, people, or features in an environment of the second user.

13. The computer-implemented method of claim 12 further comprising:

determining, based on the value of the matching score, a recommendation for at least one of the first and second users.

14. The computer-implemented method of claim 12, wherein the matching score is used to suggest a possible match between the first and second users.

15. The computer-implemented method of claim 12, wherein the matching score is used to identify a common trait of the first and second users.

16. The computer-implemented method of claim 12, wherein the matching score is used to build a social graph of the first and second users.

17. A non-transitory computer readable storing computer implementable instructions for carrying out the method of claim 12.