PATENT DOCUMENT

Publication Number: US-10997763-B2
Application Number: US-201916511820-A
Country: US
Kind Code: B2

Title: Face syncing in distributed computing environment

Abstract:
Embodiments are disclosed for face syncing in a distributed computing environment. In an embodiment, a method comprises: obtaining, by a processor, media assets that include faces of individuals; detecting, by the processor of a mobile device, the faces on the media assets; generating, by the processor, faceprints for the detected faces; clustering, by the processor, the faceprints into clusters; generating, by the processor, a face crop image for each cluster; and sending, by the processor, the face crop images to a network-based, distributed syncing service.

Claims:
What is claimed is: 
     
       1. A method, comprising:
 displaying, in a graphical user interface (GUI), a media asset comprising a first face indicated by a first window marking the first face; 
 determining a candidate person that belongs to the first face; 
 presenting, in the GUI, a query requesting user input about whether the first face belongs to the candidate person; 
 receiving user input indicating that the first face does not belong to the candidate person; and 
 associating metadata with the media asset, the metadata: a) describing that the first face does not belong to the candidate person, and b) not identifying another person to associate with the first face. 
 
     
     
       2. The method of  claim 1 , wherein the media asset is encrypted to protect user privacy, the method further comprising decrypting the media asset prior to determining the candidate person that belongs to the first face. 
     
     
       3. The method of  claim 1 , further comprising:
 generating a faceprint for the first face; 
 receiving additional user input identifying a person that belongs to the first face; and 
 clustering the faceprint into a cluster comprising one or more additional faceprints specific to the user-identified person. 
 
     
     
       4. The method of  claim 3 , further comprising disassociating the candidate person from any cluster to which the faceprint for the first face is assigned. 
     
     
       5. The method of  claim 3 , further comprising:
 determining a conflict due to at least one faceprint in the cluster representing a different person; 
 obtaining a user action to resolve the conflict; and 
 adjusting the cluster to resolve the conflict according to the user action. 
 
     
     
       6. The method of  claim 5 , wherein obtaining the user action to resolve the conflict further comprises:
 displaying one or more media assets associated with one or more clusters in the GUI; and 
 receiving second user input that names faces on the one or more displayed media assets. 
 
     
     
       7. The method of  claim 5 , wherein obtaining the user action to resolve the conflict further comprises:
 displaying one or more media assets associated with one or more clusters in the GUI; and 
 receiving second user input that merges one or more displayed media assets. 
 
     
     
       8. The method of  claim 7 , wherein the second user input includes merging two unverified media assets. 
     
     
       9. A system, comprising:
 one or more processors; 
 memory coupled to the one or more processors and configured to store instructions, which, when executed by the one or more processors, causes the one or more processors to perform operations comprising:
 displaying, in a graphical user interface (GUI), a media asset comprising a first face indicated by a first window marking the first face; 
 determining a candidate person that belongs to the first face; 
 presenting, in the GUI, a query requesting user input about whether the first face belongs to the candidate person; 
 receiving user input indicating that the first face does not belong to the candidate person; and 
 associating metadata with the media asset, the metadata: a) describing that the first face does not belong to the candidate person, and b) not identifying another person to associate with the first face. 
 
 
     
     
       10. The system of  claim 9 , wherein the media asset is encrypted to protect user privacy, the operations further comprising decrypting the media asset prior to determining the candidate person that belongs to the first face. 
     
     
       11. The system of  claim 9 , wherein the operations further comprise:
 generating a faceprint for the first face; 
 receiving additional user input identifying a person that belongs to the first face; and 
 clustering the faceprint into a cluster comprising one or more additional faceprints specific to the user-identified person. 
 
     
     
       12. The system of  claim 11 , wherein the operations further comprise disassociating the candidate person from any cluster to which the faceprint for the first face is assigned. 
     
     
       13. The system of  claim 11 , wherein the operations further comprise:
 determining a conflict due to at least one faceprint in the cluster representing a different person; 
 obtaining a user action to resolve the conflict; and 
 adjusting the cluster to resolve the conflict according to the user action. 
 
     
     
       14. The system of  claim 13 , wherein obtaining the user action to resolve the conflict further comprises:
 displaying one or more media assets associated with one or more clusters in the GUI; and 
 receiving second user input that names faces on the one or more displayed media assets. 
 
     
     
       15. The system of  claim 13 , wherein obtaining the user action to resolve the conflict further comprises:
 displaying one or more media assets associated with one or more clusters in the GUI; and 
 receiving second user input that merges one or more displayed media assets. 
 
     
     
       16. The system of  claim 15 , wherein the second user input includes merging two unverified media assets. 
     
     
       17. A non-transitory computer-readable medium including one or more sequences of instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
 displaying, in a graphical user interface (GUI), a media asset comprising a first face indicated by a first window marking the first face; 
 determining a candidate person that belongs to the first face; 
 presenting, in the GUI, a query requesting user input about whether the first face belongs to the candidate person; 
 receiving user input indicating that the first face does not belong to the candidate person; and 
 associating metadata with the media asset, the metadata: a) describing that the first face does not belong to the candidate person, and b) not identifying another person to associate with the first face. 
 
     
     
       18. The non-transitory computer-readable medium of  claim 17 , wherein the media asset is encrypted to protect user privacy, the operations further comprising decrypting the media asset prior to determining the candidate person that belongs to the first face. 
     
     
       19. The non-transitory computer-readable medium of  claim 17 , wherein the operations further comprise:
 generating a faceprint for the first face; 
 receiving additional user input identifying a person that belongs to the first face; and 
 clustering the faceprint into a cluster comprising one or more additional faceprints specific to the user-identified person. 
 
     
     
       20. The non-transitory computer-readable medium of  claim 19 , wherein the operations further comprise:
 determining a conflict due to at least one faceprint in the cluster representing a different person; 
 obtaining a user action to resolve the conflict; and 
 adjusting the cluster to resolve the conflict according to the user action.

Description:
INCORPORATION BY REFERENCE; DISCLAIMER 
     The following application is hereby incorporated by reference: application Ser. No. 15/613,098 filed on Jun. 2, 2017. The Applicant hereby rescinds any disclaimer of claim scope in the parent application(s) or the prosecution history thereof and advises the USPTO that the claims in this application may be broader than any claim in the parent application(s). 
     TECHNICAL FIELD 
     This disclosure relates generally to face detection and syncing in distributed computing environments. 
     BACKGROUND 
     Modern computing devices often include an application to access, organize and share digital photos and videos (“media assets”). These media assets are typically stored in a local library on the computing device, and for some applications, also stored remotely on a network server. The local library can be synchronized with the remote library to ensure coherency between the libraries. If a user edits a media asset on a mobile computing device, the edited media asset can be “synced” to the remote library. The user can then access the edited photo using another computing device. 
     Some applications provide face detection where a media asset library is scanned to detect human faces. The detected faces are presented to the user in a graphical user interface (GUI) that allows the user to confirm or reject the detected faces, which are then tagged with metadata. Once a face is tagged, the face detection process searches the media asset library to find other media assets that include the tagged face. The user can create collections of media assets that include a specific individual whose face was tagged. 
     To protect user privacy, some applications encrypt the media assets before syncing the assets with an online library. Because the media assets are stored encrypted in the online library, face detection is performed on the user&#39;s computing device. In such applications, maintaining consistency of face detection results among multiple user computing devices is challenging. For example, the user&#39;s computing devices may be running different versions of the face detection technology which may provide inconsistent face detections. 
     SUMMARY 
     Embodiments are disclosed for face syncing m a distributed computing environment. 
     In an embodiment, a method comprises: obtaining, by a processor, media assets that include faces of individuals; detecting, by the processor of a mobile device, the faces on the media assets; generating, by the processor, faceprints for the detected faces; clustering, by the processor, the faceprints into clusters; generating, by the processor, a face crop image for each cluster; and sending, by the processor, the face crop images to a network-based, distributed syncing service. 
     In an embodiment, a system comprises: one or more processors; memory coupled to the one or more processors and configured to store instructions, which, when executed by the one or more processors, causes the one or more processors to perform operations comprising: obtaining media assets that include faces of individuals; detecting the faces on the media assets; generating faceprints for the detected faces; clustering the faceprints into clusters; generating a face crop image for each cluster; and sending the face crop images to a network-based, distributed syncing service. 
     Particular embodiments disclosed herein provide one or more of the following advantages. The disclosed embodiments allow synchronizing face detection data across multiple computing devices in a distributed computing environment where the media assets are stored online (in the cloud) in encrypted form. Face detection results can differ from device to device in a distributed computing environment. For example, a face detection algorithm operating on a first device may yield 10 faces and a face detection algorithm on a second device (e.g., running a different version of the algorithm, hardware variations) may yield 9 faces. The disclosed embodiments are robust to such inconsistent face detection results. Accordingly, the benefits of face syncing in a distributed computing environment can be realized without sacrificing user privacy. 
     The details of one or more implementations of the subject matter are set forth in the accompanying drawings and the description below. Other features, aspects and advantages of the subject matter will become apparent from the description, the drawings and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example distributed computing environment, according to an embodiment. 
         FIG. 2  is an example system for face syncing in a distributed computing environment, according to an embodiment. 
         FIG. 3  illustrates an example face crop image, according to an embodiment. 
         FIG. 4  illustrates an example GUI for adding names to faces, according to an embodiment. 
         FIG. 5A  illustrates an example GUI for allowing a user to confirm or reject a named face, according to an embodiment. 
         FIG. 5B  illustrates an example GUI for allowing a user to confirm or rejection an unnamed face, according to an embodiment. 
         FIG. 6  illustrates an example GUI for merging and adding faces, according to an embodiment. 
         FIG. 7  illustrates conflict resolution in response to a user action that names an unverified cluster, according to an embodiment. 
         FIG. 8  illustrates conflict resolution in response to a user action that names a verified cluster, according to an embodiment. 
         FIG. 9  illustrates conflict resolution in response to a user action that names a single unassigned face, according to an embodiment. 
         FIG. 10  illustrates conflict resolution in response to a user action that names a single assigned face, according to an embodiment. 
         FIG. 11  illustrates conflict resolution in response to a user action that rejects a single face, according to an embodiment. 
         FIG. 12  illustrates conflict resolution in response to a user action that merges an unverified cluster with a verified cluster, according to an embodiment. 
         FIG. 13  illustrates conflict resolution in response to a user action that merges an unverified cluster to an unverified cluster, according to an embodiment. 
         FIG. 14  illustrates conflict resolution in response to a user action that merges a verified cluster to a verified cluster, according to an embodiment. 
         FIG. 15  illustrates conflict resolution in response to a user action that confirms a face suggestion, according to an embodiment. 
         FIG. 16  illustrates conflict resolution in response to a user action that rejects a face suggestion, according to an embodiment. 
         FIG. 17  illustrates conflict resolution in response to a user action that confirms an unverified candidate face to a verified candidate face, according to an embodiment. 
         FIG. 18  illustrates conflict resolution in response to a user action that confirms a verified candidate face to a verified candidate face, according to an embodiment. 
         FIG. 19  illustrates conflict resolution in response to a user action that rejects an unverified candidate face to a verified candidate face, according to an embodiment. 
         FIG. 20  illustrates conflict, resolution in response to a user action that rejects a verified candidate face to a verified candidate face, according to an embodiment. 
         FIG. 21  is a flow diagram of an example process of face detection in a distributed computing environment, according to an embodiment. 
         FIG. 22  illustrates an example server architecture, implementing the service-side features and operations described in reference to  FIGS. 1-21 . 
         FIG. 23  illustrates an example device architecture of a mobile device implementing client-side features and operations described in reference to  FIGS. 1-21 . 
     
    
    
     DETAILED DESCRIPTION 
     Example Face Detection System 
       FIG. 1  illustrates an example distributed computing environment  100 , according to an embodiment. Environment  100  includes server computers  102 , client devices  103 , network  104 , remote media asset library  105  and local media asset library  106 . Client devices  103  include any device that can run an application and communicate with servers  102 , including: desktop computers, notebook computers, smartphones, tablet computers and wearable computers (e.g., a smartwatch). Network  104  can be a wide area network (WAN), such as the Internet. Remote and local media asset libraries  105 ,  106  store media assets and face detection data in storage devices. Note that each client device  103  will have a local media asset library  106  but only one is shown in  FIG. 1 . 
     Server computers  102  can be part of a distributed computing system that uses parallel processing architecture. Server computers  102  are configured to provide client devices  103  access to remote media asset library  105 , and syncing services for syncing media assets and face detection data between local media asset library  106  and remote media asset library  105 . Encrypted copies of media assets and face detection data are stored in remote media asset library  105  to protect user privacy. The syncing service syncs the media assets across client devices  103  through network  104 . 
     Because the media assets are encrypted, face detection cannot be run on media assets stored in remote media asset library  105 . Instead, face detection is run on client devices  103  using local media asset library  106 . Although the media assets can be synced between remote and local media asset libraries  105 ,  106 , syncing face detection data presents an additional challenge. For example, different versions of the face detection technology may be running on different client devices  103  and provide different results. The results of face detection are typically used to create collections of media assets with a face or faces of a particular individual or individuals. If face detection results differ from device to device, then the media assets (e.g., media assets for a particular individual) that are included in these collections may also differ from device to device. For example, a first photo album on a first client device may have more, fewer or different photos than photos in a second photo album on a second client device. Because the user expects the photo albums to include the same photos, the user&#39;s expectations may not be met, resulting in a poor user experience. This problem of inconsistent face detection results on different client devices  103  is addressed by system  200 , as described in reference to  FIG. 2 . 
       FIG. 2  is an example system  200  for face syncing in a distributed computing environment, according to an embodiment. System  200  includes local media asset library  106 , face detector  201 , cluster module  202 , key media asset selector  203 , user action module  204  and face crop generator  205 . System  200  can be included in each client device  103 , which communicates with server computers  102  over network  104  to sync media assets and face detection data (e.g., a face crop and associated metadata as described below) with client devices  103  using a syncing service run by server computers  102 . 
     Media assets (e.g., photographs, videos) are stored in local media asset library  106 . The media assets can be captured on one of client devices  103  using an embedded camera or received in another manner (e.g., received from remote media asset library  105 ). Client devices  103  can include a client-side application that is used to manage local media asset library  106  and allows a user to perform various tasks with the media assets. For example, the client application may allow the user to edit media assets, create slideshows or movies with the media assets and create collections of media assets (e.g., create photo albums) that can be shared with friends and family. The collections can be based on themes (e.g., holidays), content (e.g., based on people) and user-defined metadata (e.g., photos tagged by the user as a “favorite” or photos that the user “liked”). 
     Face detector  201  applies face detection technology to the locally stored media assets. Any suitable face detection technology can be used, including but not limited to face detection technology that uses appearance-based methods that rely on statistical analysis, machine learning or supervised learning (e.g., neural networks) to find relevant characteristics of face and non-face images. The learned characteristics can be in the form of distribution models or discriminant functions that are used for face detection. 
     For example, a feature vector (hereinafter also referred to as a “faceprint”) derived from an image can be used as a random variable that is characterized for faces and non-faces using Bayesian classification, or maximum likelihood, to classify a candidate image location as face or non-face. In an embodiment, eigenfaces can be used for face detection using techniques described in Navarrete et al. “ Analysis and Comparison of Eigenspace - Based Face Recognition Approaches ,” International Journal of Pattern Recognition and Artificial Intelligence, 16 (7): 817-830 (Nov. 2002). In another embodiment, a distribution-based system for face detection can be used as described in Kah-Kay Sung and Tomaso Poggio, “ Example - Based Learning fir View - Based Human Face Detection ,” Technical Report AI Memo 1521, Massachusetts Inst. Of Technology AI Lab, 1994. 
     The output of face detector  201  is one or more faceprints for each media asset that includes one or more detected faces. For example, a photo that has two detected faces will generate two faceprints, one for each face detection. In an embodiment, a faceprint is a feature vector defined in n-space that uniquely identifies a face image but does not associate a name with the face. An additional step of face verification or rejection is performed by user actions, as described in reference to  FIGS. 4-6 . 
     Cluster module  202  receives the faceprints and applies a clustering process on the faceprints to generate one or more clusters of faceprints based-on statistical similarity between the faceprints. Any suitable clustering algorithm can be used. The clustering can be performed periodically or in response to a trigger event, such as in response to a new media asset being added to local media asset library  106 . Any suitable clustering technique can be used to cluster faceprints, including but not limited to k-means, hierarchical cluster analysis (HCA) and DBSCAN. 
     A key media asset is selected from the media assets in each cluster. The key media asset for a cluster is a media asset that has a faceprint in the cluster and that represents all the media assets in the cluster. The key media asset can be selected based on any suitable criteria. In an embodiment, the key media asset can be based on a centroid of the cluster. For example, the media asset in a cluster that has a faceprint that is nearest to the centroid of the cluster can be selected as the key media asset. Additionally, the key media asset can be based on specific attributes of the media asset, including but not limited to: whether a face in the media asset is smiling, whether the face is a frontal view or rotated, whether the face is in the background or foreground, the quality of the lighting, location, noise or blurriness, type of media asset (e.g., photo, video frame) and any other suitable criteria. Since the key media asset will be presented to the user in a GUI, it is desirable to select a key media asset that provides a flattering image of the user. 
     After the key media asset is selected, the key media asset is input to user action module  204 . In an embodiment, user action module  204  generates GUIs that display media assets in a cluster or a key media asset and requests the user take certain user actions with respect to the media assets. For example, various affordances are provided by the GUI that allow the user to add names to faces in a cluster, merge face clusters, confirm or reject names assigned to faces and confirm or reject suggested names for faces. 
     Once a user associates a name with a face, the other media assets in the cluster will also be associated with the name and the cluster will become a verified cluster. When a new media asset is added to local media asset library  106  that includes a face, a faceprint of the face is compared with the faceprints for the key media assets of all the clusters using a suitable distance metric, such as a Euclidian distance metric. The new media asset can then be assigned to the cluster having a key media asset faceprint that is closest to the faceprint for the new media asset based on the distance metric. 
     After a key media asset is associated with a name, the key media asset is input to face crop generator  205  and metadata describing the user action (e.g., user confirmation or rejection is created. Face crop generator  205  generates a face crop from the key media asset or another media asset, which is sent together with user action metadata, and optionally other metadata (e.g., name, contact ID, “favorites”, “likes”), collectively the “face detection data,” to server computers  102 . 
     During syncing, the face detection data is transferred to each of client devices  103 . The face crop image is processed like any other media asset and is included in the local clustering on client device  103  and processed as described above. The face crop will be included in a cluster and every faceprint in the same cluster will be associated with the person associated with the face crop image and associated metadata (e.g., name, contact ID, “favorites”, “likes”). The cluster becomes a verified cluster due to the user confirmed face crop image. Any conflicts that arise on the client device as a result of syncing can be resolved through user actions, as described in further detail below in reference to  FIGS. 7-20 . For example, if clustering was performed on a device while the device is offline, two face crop images may be present in a cluster. The user will be presented with a GUI to resolve the conflict by selecting one of the two face crop images to be the truth face. 
       FIG. 3  illustrates an example of face crop image  301 , according to an embodiment. Face crop generator  205  creates face crop image  301  of a face detected in key media asset  300  to be used as face truth data for face clustering and conflict resolution on client devices  103 . In an embodiment, face crop image  301  is encrypted. In another embodiment, face crop image  301  is generated such that the face cannot be recognized (e.g., no hair or outline of the face is captured in the face crop image). 
     In an embodiment, metadata associated with the face crop image  301  (e.g., user contact information) can also be used as a source of “truth” in addition to face crop image  301 . For example, if a face of John Doe is verified on a first device, a person profile is created for John Doe on the first device and associated with contact information for John Doe (e.g., provided by the user). The contact information is stored in remote database  105  with the face crop image. A person created on a second device that is associated with the contact information of John Doe is merged with a person profile of John Doe on the second device based on the assumption that the person created on the second device is the same John Doe created on the first device. 
     In an embodiment, face crop image  300  is cropped from a media asset other than a key media asset. For example, if a user action to resolve a conflict includes naming a single face that is assigned or unassigned to a cluster, or a single face that is rejected, the face crop image can be taken from the single face rather than the key media asset for the cluster, as described in reference to  FIGS. 9-11 . Face crop  300  can be encrypted before being sent to server computers  102 . 
     Example GUIs for User Actions 
       FIG. 4  illustrates an example GUI for adding names to detected faces, according to an embodiment. In this example, computer display  400  presents GUI  401  that includes two media assets, which in this example are photos. The photos can be, for example, photos assigned to the same cluster but not verified by the user. The user may click on the “+ add name” affordance which opens a text box that allows the user to type in a name for the media asset. Once the name is added the name will become metadata associated with the photo and the photo is assigned to a cluster of other photos associated with the name. Additionally, the user can use affordance  404  to add other photos from local media asset library  201 . 
       FIG. 5A  illustrates an example GUI  501  for allowing a user to confirm or reject a named face, according to an embodiment. In this example, computer display  500  presents GUI  501  which includes media asset  502  with a face that is highlighted by window  503 . Window  503  can be any desired shape (e.g., circle, oval, rectangle). Window  503  calls to the user&#39;s attention a particular face in media asset  502 . The user is presented with the question “Is this Jane Do?” The user responds by selecting, touching or clicking the “No” or “Yes” affordance (e.g., virtual buttons), which rejects or confirms, respectively, the association of the name Jane Doe with the face in window  503 . Whether confirmed or rejected by the user, metadata describing the confirmation or rejection is associated with media asset  502 . If the user confirms the face as belonging to Jane Doe, then the highlighted face from media asset  502  will be assigned to the cluster that includes faceprints of faces of Jane Doe and the user verification will become part of the metadata associated with the face detection on media asset  502 . If the user rejects the face as belonging to Jane Doe, then a rejected face crop will be generated along with metadata indicating that the face detected on media asset  502  is not Jane Doe. Subject to conflicts, any cluster that the rejected face crop lands in will not be assigned to Jane Doe. 
       FIG. 5B  illustrates an example GUI  504  for allowing a user to confirm or reject an unnamed face, according to an embodiment. In this example, computer display  500  presents GUI  504  that includes media asset  502  and media asset  505  with faces that are highlighted by windows  506  and  507 , respectively. The user is presented with the question “Is this the same person?” The user responds by selecting, touching or clicking the “No” or “Yes” affordance (e.g., virtual buttons), which rejects or confirms, respectively, the suggestion that the faces in media assets  502  and  505  are the same person. Whether confirmed or rejected by the user, metadata that includes the confirmation or rejection is associated with faces detected on media assets  502 ,  505 . If the user confirms the faces in media assets  502  and  505  as belonging to the same person, then media assets  502 ,  505  will be associated with each other so that they will be assigned to the same cluster when one is generated. If the user rejects the faces as both belonging to the same person, media assets  502 ,  505  will not be assigned to the same cluster. It is important to note it is not the entire media asset that gets rejected or confirmed for a person. It is only the face presented to the user. 
       FIG. 6  illustrates an example GUI  600  for merging and adding face detections, according to an embodiment. In this example, computer display  600  presents GUI  601  that includes four media assets, which in this example are photos. The user can use GUI  601  to merge clusters of media assets. For example, the user can drag n′ drop photo cluster  602  represented by its key media asset onto photo cluster  603  which is also represented by its key media asset. Each The result is that the first and second clusters are assigned to the same person. In an embodiment, as an alternative to drag n′ drop, the user can select each photo cluster individual by click on the key media asset and then pressing a merge and add affordance displayed by the GUI  601  (not shown). 
     Examples of Resolution Based on User Actions 
     Due to the nature of the clustering process and variances in the media assets due to different client device hardware (e.g., different graphical processing units), described above, there may be situations where certain media assets are not assigned to clusters or assigned to the wrong cluster or assigned to multiple clusters. Solicited user actions can be used to resolve the conflicts on the client device. Each figure below illustrates how the conflicts are resolved. The letter “K” stands for key media asset. The key media asset is also shown in the figure with a striped fill pattern. The letter “C” stands for face crop image, which is created from a key media asset in response to user confirmation. The letter “R F ” stands for a face crop image created from a key media asset in response to user rejection. The letter “C F ” stands for a face crop image made from single face rather than a key media asset. The letter “P V ” stands for verified person. The letter “P U ” stands for unverified person. The superscripts on any letter represent different key media assets, different face crop images, different clusters or different verified or unverified persons depending on the letter superscripted. For example, K 1  means a key media asset for cluster  1 , K 2  means a key media asset for cluster  2  and so forth. Similarly, P U   1  means unverified person  1 , and P U   2  means unverified person  2 , P V   1  means verified person  1  and P V   2  means verified person  2 . Each cluster of media assets associated with a person is considered unverified until verified through a user action, as described below. Face crops are generated from verified clusters. 
       FIG. 7  illustrates conflict resolution in response to a user action that names an unverified cluster, according to an embodiment. Cluster  701  is associated with unverified person P U  and includes a plurality of media assets, including key media asset K. The user names cluster  701  creating verified person P V . Face crop C is created from key media asset K, face crop C becomes a P V  user confirmation (truth face) and key media asset K becomes the key media asset for verified person P V . All media assets in cluster  701  are associated with verified person P V . 
       FIG. 8  illustrates conflict resolution in response to a user action that names a verified cluster, according to an embodiment. Cluster  801  is associated with verified person P V   1  and includes a plurality of media assets, including key media asset K and face crop image C associated with verified person P V   1 . The user renames cluster  801 , creating a second verified person P V   2 . P V   1  becomes P V   2 , face crop C becomes P V   2  user confirmation, K becomes key media asset for P V   2  and P V   1  is deleted. 
       FIG. 9  illustrates conflict resolution in response to a user action that names a single unassigned face, according to an embodiment. Cluster  901  is associated with unverified person P U  and includes a plurality of media assets, including key media asset K. The user names a single unassigned face  902  in cluster  901 . Face crop C F  is created from face  902 , face crop C F  becomes P V  user confirmation and face  902  is added to P V  confirmation list. 
       FIG. 10  illustrates conflict resolution in response to a user action that names a single assigned face, according to an embodiment. Cluster  1001  is associated with verified person P V   1  and includes a plurality of media assets, including key media asset K and face crop C. The user names single assigned face  1002  in cluster  1001 . Face crops C F  and R F  are created from face  1002 , face crop C F  becomes P V   2  user confirmation, face crop RF becomes P V   1  user rejection and face  1002  is added to P V   2  confirmation list. 
       FIG. 11  illustrates conflict resolution in response to a user action that rejects a single face, according to an embodiment. Cluster  1101  is associated with verified person P V  and includes a plurality of media assets, including key media asset K and face crop C. The user rejects a single face  1102  in cluster  1101 . Face crop R F  is created from rejected face  1102 , face crop R F  becomes P V  user rejection and rejected face  1102  is added to the P V  rejection list. 
       FIG. 12  illustrates conflict resolution in response to a user action that merges an unverified cluster with a verified cluster, according to an embodiment. Cluster  1201  is associated with unverified person P U  and includes a plurality of media assets, including key media asset K 2 . Cluster  1202  is associated with verified person P V  and includes a plurality of media assets, including key media asset K 1  and face crop C 1 . The user merges cluster  1201  with cluster  1202 . Face crop C 2  is created from key media asset K 2  and face crop C 2  becomes P V  user confirmation. 
       FIG. 13  illustrates conflict resolution in response to a user action that merges an unverified cluster to an unverified cluster, according to an embodiment. Cluster  1301  is associated with unverified person P U   2  and includes a plurality of media assets, including key media asset K 2 . Cluster  1302  is associated with unverified person P U   1  and includes a plurality of media assets, including key media asset K 1 . The user merges unverified cluster  1301  with unverified cluster  1302  and creates verified person P V . Face crops C 1 , C 2  are created from key media assets K 1 , K 2 , respectively, and face crops C 1 , C 2  becomes P V  user confirmations. K 1  becomes P V  key media asset. 
       FIG. 14  illustrates conflict resolution in response to a user action that merges a verified cluster to a verified cluster, according to an embodiment. Cluster  1401  is associated with verified person P V   2  and includes a plurality of media assets, including key media asset K 2  and face crop C 2 . Cluster  1402  is associated with verified person P V   1  and includes a plurality of media assets, including key media asset K 1  and face crop C 1 . The user merges cluster  1401  with cluster  1402 . Face crop C 2  becomes P V   1  user confirmation and P V   2  is deleted. 
       FIG. 15  illustrates conflict resolution in response to a user action that confirms a face suggestion, according to an embodiment. Cluster  1501  is associated with unverified person P U  and includes a plurality of media assets, including key media asset K 2 . Cluster  1502  is associated with verified person P V  and includes a plurality of media assets, including key media asset K 1  and face crop C 1 . The user confirms a face in cluster  1501 . Face crop C 2  is created from key media asset K 2  and face crop C 2  becomes P V  user confirmation. 
       FIG. 16  illustrates conflict resolution in response to a user action that rejects a face suggestion, according to an embodiment. Cluster  1601  is associated with unverified person P U  and includes a plurality of media assets, including key media asset K 2 . Cluster  1602  is associated with verified person P V  and includes a plurality of media assets, including key media asset K 1  and face crop C 1 . The user rejects a face in cluster  1601 . Face crop R 2  is created from key media asset K 2  and face crop R 2  becomes P V  user rejection. 
       FIG. 17  illustrates conflict resolution in response to a user action that confirms an unverified candidate face compared to a verified candidate face, according to an embodiment. Cluster  1701  is associated with unverified person P U  and includes a plurality of media assets, including key media asset K 2 . Cluster  1702  is associated with verified person P V  and includes a plurality of media assets, including key media asset K 2 . The user confirms a candidate face in cluster  1701 . Face crop C 2  is created from key media asset K 2 , face crop C 2  becomes P V  user rejection and the candidate is deleted. 
       FIG. 18  illustrates conflict resolution in response to a user action that confirms a verified candidate face compared to a verified candidate face, according to an embodiment. Cluster  1801  is associated with verified person P V   2  and includes a plurality of media assets, including key media asset K 2  and face crop C 2 . Cluster  1802  is associated with verified person P V   1  and includes a plurality of media assets, including key media asset K 1  and face crop C 1 . The user confirms a candidate face in cluster  1801 . Face crop C 2  becomes P V   1  user confirmation, P V   2  is deleted and the candidate is deleted. 
       FIG. 19  illustrates conflict resolution in response to a user action that rejects an unverified candidate face compared to a verified candidate face, according to an embodiment. Cluster  1901  is associated with unverified person P U  and includes a plurality of media assets, including key media asset K 2 . Cluster  1902  is associated with verified person P V  and includes a plurality of media assets, including key media asset K 1  and face crop C 1 . The user rejects a candidate face in cluster  1901 . Face crop R 2  is created from key media asset K 2 , face crop R 2  becomes P V  user confirmation, the candidate is deleted and a candidate rejection between P U , P V  is created. 
       FIG. 20  illustrates conflict resolution in response to a user action that rejects a verified candidate face to a verified candidate face, according to an embodiment. Cluster  2001  is associated with verified person P V   2  and includes a plurality of media assets, including key media asset K 2  and face crop C 2 . Cluster  2002  is associated with verified person P V   1  and includes a plurality of media assets, including key media asset K 1  and face crop C 1 . The user rejects a verified candidate face in cluster  2001 . Face crops R 1 , R 2  are created from key media assets K 1 , K 2 , respectively, face crop R 1  becomes P V   2  user rejection, face crop R 2  becomes PV1 user rejection, the candidate is deleted and a candidate rejection between P U , P V  is created. 
     Additional conflict problems may arise that can be resolved using the conflict resolution rules described in Table I below. In the first column of Table I, the initial state of a cluster is described, and in the second column, the action taken to resolve the conflict is described. The same nomenclature is used as before for  FIGS. 7-20 . Note that L0 refers to a sub structure in the cluster. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Conflict Resolution Rules 
               
            
           
           
               
               
               
            
               
                 Rule# 
                 Initial 
                 Action 
               
               
                   
               
            
           
           
               
               
               
            
               
                 1 
                 All faces assigned to PU person; no training data available 
                 No operation performed (NOP) 
               
               
                 2 
                 All faces assigned to PU; user confirmation to PV 
                 Assign all faces to PV; user confirmed 
               
               
                   
                   
                 faces K remain 
               
               
                 3 
                 Some faces assigned to PV person; some faces assigned to PU; 
                 Assign all faces to PV; user confirmed 
               
               
                   
                 user confirmation to PV 
                 faces K remain 
               
               
                 4 
                 Some faces assigned to PV person; some faces assigned to PU 
                 Assign all faces to PU; create 
               
               
                   
                 person; no user confirmation 
                 candidate between PU and PV; user 
               
               
                   
                   
                 confirmed faces K remain 
               
               
                 5 
                 All faces assigned to PU; candidate between PU and PV1; user 
                 Assign L0 faces to PV2; create 
               
               
                   
                 confirmation to PV2 
                 candidate between (PV1, PV2) and 
               
               
                   
                   
                 (PU, PV2); user confirmed faces K 
               
               
                   
                   
                 remain 
               
               
                 6 
                 Some faces assigned to PV1 and PV2; some faces assigned to 
                 Assign all faces to PU; create 
               
               
                   
                 PU; no user confirmation 
                 candidate between PU and PV1; 
               
               
                   
                   
                 create candidate between PU and 
               
               
                   
                   
                 PV2; create candidate between PV1 
               
               
                   
                   
                 and PV2; user confirmed faces K 
               
               
                   
                   
                 remain 
               
               
                 7 
                 Some faces assigned to PU; some faces assigned to PV2; user 
                 Assign PV2 faces to PU; assign all 
               
               
                   
                 confirmation to PV1 only 
                 other faces to PV1; create candidate 
               
               
                   
                   
                 between (PV1, PV2), (PU, PV1) and 
               
               
                   
                   
                 (PU, PV2); user confirmed faces K 
               
               
                   
                   
                 remain 
               
               
                 8 
                 Some faces assigned to PU; some faces assigned to verified 
                 Assign all faces in confirmed L0 to 
               
               
                   
                 person PV1; user confirmation to PV1 only; mixed verified and 
                 PV1; ignore other faces assigned to 
               
               
                   
                 unverified L0 pending candidates 
                 PU; user confirmed faces K remain 
               
               
                 9 
                 Some faces assigned to PU; some faces assigned to PV2 and 
                 Assign PV2 and PV3 faces to PU; 
               
               
                   
                 PV3; user confirmation to PV1 
                 assign all other faces to PV1; create 
               
               
                   
                   
                 candidate between (PV1, PV2), (PU, 
               
               
                   
                   
                 PV2), (PV1, PV3), (PU, PV1) and 
               
               
                   
                   
                 (PU, PV3); user confirmed K faces 
               
               
                   
                   
                 remain 
               
               
                 10 
                 Some faces assigned to PU; some faces assigned to PV1 and 
                 Assign L0 to respective PV1 and 
               
               
                   
                 PV2; some faces unverified in L0 with user confirmations; user 
                 PV2; assign all other faces to PU; 
               
               
                   
                 confirmations to PV1 and PV2 
                 create candidate between (PU, PV1), 
               
               
                   
                   
                 (PU, PV2) and (PV1, PV2); user 
               
               
                   
                   
                 confirmed faces K remain; keep all 
               
               
                   
                   
                 other faces to PV2 for precedence 
               
               
                 11 
                 All faces assigned to PU; user confirmations for PV1 and PV2 
                 Assign L0 to respective PV1 and 
               
               
                   
                   
                 PV2; assign all other faces to PU; 
               
               
                   
                   
                 create candidate between (PU, PV1), 
               
               
                   
                   
                 (PU, PV2) and (PV1, PV2); user 
               
               
                   
                   
                 confirmed faces K remain. 
               
               
                 12 
                 All faces assigned to PV1; some faces assigned to PU; user 
                 Assign L0 faces to PV2; keep all 
               
               
                   
                 confirmation to PV1 and PV2 
                 other faces to PV1 for precedence; 
               
               
                   
                   
                 create candidate between (PV1, 
               
               
                   
                   
                 PV2), (PU, PV1) and (PU, PV2); user 
               
               
                   
                   
                 confirmed faces K remain 
               
               
                 13 
                 All faces assigned to PU; user confirmations for PV1 and PV2 
                 Assigned L0 to PV2; assign 
               
               
                   
                 and PV3; some faces assigned to PV1 
                 respective single faces to PV1 and 
               
               
                   
                   
                 PV3; assign all other faces to PU; 
               
               
                   
                   
                 create candidate between (PU, PV1), 
               
               
                   
                   
                 (PU, PV2) and (PU, PV3); create 
               
               
                   
                   
                 candidate between (PV1, PV2), (PV1, 
               
               
                   
                   
                 PV3) and (PV2, PV3); user 
               
               
                   
                   
                 confirmed faces K remain; keep all 
               
               
                   
                   
                 other faces to PV1 for precedence 
               
               
                 14 
                 All faces assigned to PU; user confirmations for PV1 and PV2; 
                 All faces assigned to PU; user 
               
               
                   
                 user rejections for PV1 and PV2; candidate between (PU, PV1); 
                 confirmation for PV1 and PV2; user 
               
               
                   
                 rejected candidates between (PU, PV2) and (PV1, PV2) 
                 rejection for PV1 and PV2; candidate 
               
               
                   
                   
                 between (PU, PV1); rejected 
               
               
                   
                   
                 candidates between (PU, PV2) and 
               
               
                   
                   
                 (PV1, PV2) 
               
               
                 15 
                 Some faces assigned to PV; some faces assigned to PU; user 
                 Assign all faces to PV; assign 
               
               
                   
                 confirmation to PV; user rejection face to PV 
                 rejection face to PU; user confirmed 
               
               
                   
                   
                 faces K remain 
               
               
                 16 
                 Some faces assigned to PV1; some faces assigned to PU; user 
                 Assign all faces to PV1; user 
               
               
                   
                 confirmation to PV1; user rejection face to PV2 
                 confirmed faces K remain 
               
               
                 17 
                 Some faces assigned to PV; some faces assigned to PU; user 
                 Assign all faces to PU 
               
               
                   
                 confirmation to PV; user rejection to PV; user rejection face 
               
               
                   
                 matches confirmation face 
               
               
                 18 
                 All faces assigned to PU; user confirmation for PV1 and PV2; 
                 Assign L0 to respective PV1 and 
               
               
                   
                 user rejecting face to PV1 
                 PV2; assign all other faces to PU; 
               
               
                   
                   
                 create candidate between (PU, PV1) 
               
               
                   
                   
                 and (PU, PV2); user confirmed faces 
               
               
                   
                   
                 K remain 
               
               
                 19 
                 Some faces assigned to PV1; some faces assigned to PU; user 
                 Assign all faces to PV1; single faces 
               
               
                   
                 confirmation to PV1; single face confirmation to PV1 and PV2 
                 confirmation remain; user confirmed 
               
               
                   
                   
                 faces K remain 
               
               
                 20 
                 Some faces assigned to PV; some faces assigned to PU; user 
                 Assigned all faces to PV; user 
               
               
                   
                 confirmation to PV; some unverified faces from the same media 
                 confirmed faces K remain; unverified 
               
               
                   
                 asset A 
                 faces from the same asset A are 
               
               
                   
                   
                 quarantined to PU; PU is not a 
               
               
                   
                   
                 candidate to PV as all its faces are 
               
               
                   
                   
                 quarantined 
               
               
                 21 
                 Some faces assigned to PV; some faces assigned to PU; user 
                 Assign all faces to PV; user confirmed 
               
               
                   
                 confirmation to PV; some faces from the same asset A 
                 faces K remain; unverified faces from 
               
               
                   
                   
                 the same asset A are quarantined to 
               
               
                   
                   
                 PU; PU is not a candidate to PV as all 
               
               
                   
                   
                 its faces are quarantined 
               
               
                 22 
                 All faces assigned to PU; user confirmation for PV1 and PV2; 
                 Assigned L0 to respective PV1 and 
               
               
                   
                 some unverified faces from the same asset A 
                 PV2; assign all other faces to PU; 
               
               
                   
                   
                 create candidate between (PU, PV1), 
               
               
                   
                   
                 (PU, PV2) and (PV1, PV2); user 
               
               
                   
                   
                 confirmed faces K remain; unverified 
               
               
                   
                   
                 faces from the same asset A are 
               
               
                   
                   
                 quarantined to PU; PU is a candidate 
               
               
                   
                   
                 as not all its faces are quarantined 
               
               
                 23 
                 Some faces assigned to PV; some faces assigned to PU; user 
                 Assign all faces to PV; user confirmed 
               
               
                   
                 confirmation to PV; some verified faces from the same asset A 
                 faces K remain; faces from the same 
               
               
                   
                   
                 asset A become quarantined to PU; 
               
               
                   
                   
                 PU is not a candidate to PV as all its 
               
               
                   
                   
                 faces are quarantined 
               
               
                 24 
                 All faces assigned to PU; no training data 
                 NOP 
               
               
                 25 
                 All faces assigned to PV; no training data 
                 NOP 
               
               
                 26 
                 All faces assigned to PU; user confirmation for PV1 and PV2; 
                 All faces assigned to PU; user 
               
               
                   
                 user rejection for PV1 and PV2; candidate between (PU, PV1); 
                 confirmation for PV1 and PV2; user 
               
               
                   
                 rejected candidates between (PU, PV2) and (PV1, PV2) 
                 rejection to PV1 and PV2; candidate 
               
               
                   
                   
                 between (PU, PV1); rejected 
               
               
                   
                   
                 candidates between (PU, PV1) and 
               
               
                   
                   
                 (PV1, PV2) 
               
               
                 27 
                 All faces assigned to PU; user confirmation for PV1 and PV2; 
                 All faces assigned to PU; user 
               
               
                   
                 user rejection for PV1 and PV2; candidate between (PU, PV1); 
                 confimiation to PV1 and PV2; user 
               
               
                   
                 rejected candidates between (PU, PV2) and (PV1, PV2) 
                 rejections to PV1, PV2; candidate 
               
               
                   
                   
                 between (PU, PV1); rejected 
               
               
                   
                   
                 candidates between (Pu, PV1) and 
               
               
                   
                   
                 (PV1, PV2) 
               
               
                 28 
                 All faces to PU; graph confirmation to PV 
                 Assign all faces to PV; graph 
               
               
                   
                   
                 confirmed faces G remain; PP: Graph 
               
               
                   
                   
                 confirmed face G is unchanged 
               
               
                 29 
                 All faces assigned to PU; two graph confirmed faces 
                 Action-preprocessor only; only one 
               
               
                   
                   
                 graph confirmed face remains; only 
               
               
                   
                   
                 one graph confirmed person remains; 
               
               
                   
                   
                 it should be the one with the lowest 
               
               
                   
                   
                 GUID; all graph confirmed faces 
               
               
                   
                   
                 associated with the deleted person 
               
               
                   
                   
                 become unassigned 
               
               
                 30 
                 All faces assigned to PU; Two graph confirmed faces 
                 Action pre-processor only; only the 
               
               
                   
                   
                 user confirmed face remains; the 
               
               
                   
                   
                 graph confirmed person is deleted; 
               
               
                   
                   
                 the graph confirmed face is also 
               
               
                   
                   
                 deleted; all graph confirmed faces 
               
               
                   
                   
                 become unassigned 
               
               
                 31 
                 All faces assigned to PU; two graph confirmed faces 
                 Action pre-processor only; only two 
               
               
                   
                   
                 user confirmed faces remain; the 
               
               
                   
                   
                 graph confirmed person is deleted; 
               
               
                   
                   
                 the graph confirmed face is also 
               
               
                   
                   
                 deleted; all graph confirmed faces 
               
               
                   
                   
                 become unassigned 
               
               
                 32 
                 Some faces assigned to PU; some faces assigned to PV1; user 
                 Assign all faces in confirmed L0 to 
               
               
                   
                 confirmation to PV1 only; mixed verified and unverified L0 
                 PV1; ignore other faces assigned to 
               
               
                   
                 pending candidates 
                 PU; user confirmed faces K remain 
               
               
                   
               
            
           
         
       
     
     Example Process 
       FIG. 21  is a flow diagram of an example process  2100  of face detection data in a distributed computing environment, according to an embodiment Process  2100  can be implemented by architectures  2200  and  2300 , as described in reference to  FIGS. 22 and 23 , respectively. 
     Process  2100  can begin by detecting faces in media assets ( 2101 ). For example, a local face detection process is applied to media assets stored in a local media asset library. One or more faces may be detected in each media asset. Any suitable face detection technology can be used, including but not limited to face detection technology that uses appearance-based methods and that rely on statistical analysis and machine learning to find relevant characteristics of face and non-face images. The learned characteristics can be in the form of distribution models or discriminant functions that are used for face detection. 
     Process  2100  continues by generating faceprints for detected faces ( 2102 ). For example, faceprints can be derived from an image can be used as a random variable that that is characterized for faces and non-faces using Bayesian classification, or maximum likelihood, to classify a candidate image location as face or non-face. In an embodiment, a faceprint is a feature vector defined in n-space that uniquely identifies a face image but does not associate a name with the face. 
     Process  2100  continues by clustering faceprints ( 2103 ). Any suitable clustering technique can be used to cluster faceprints, including but not limited to k-means, HCA, and DBSCAN. The result of the clustering is one or more clusters of faceprints. For example, a cluster can include 10 faceprints associated with 10 or less different media assets. When a cluster is generated it may be unverified meaning that the faces associated with the cluster are not associated with a name. Some of the faces may have been incorrectly clustered. For example, faces of two different people may have been placed in the same cluster. 
     Process  2100  continues by obtaining key media assets based on the clusters and other data ( 2104 ). The key media assets can be selected based on any suitable criteria. In an embodiment, the key media assets are selected based on the centroids of clusters. For example, the media asset in a cluster that has a faceprint that is nearest to the centroid of the cluster can be selected as the key media asset. Additionally, the key media asset can be based on other data, such as specific attributes of the media asset, including but not limited to: whether the face in the media asset is smiling, whether the face is a frontal view or rotated, whether the face is in the background or foreground, the quality of the lighting, location, noise or blurriness, type of media asset (e.g., photo, video frame) and any other suitable criteria. 
     Process  2100  continues by determining conflicts between faceprints in clusters ( 2105 ). For example, some clusters may be associated with two key media assets or face crop images. 
     Process  2100  continues by obtaining user actions to resolve conflicts ( 2106 ). For example, various GUIs are presented to the user that display media assets associated with a cluster or a single media asset and request that the user take certain user actions with respect to the media asset(s). For example, various affordances are provided by the GUI that allow the user to add names to faces in a cluster, merge face clusters, confirm or reject names assigned to faces and confirm or reject suggested names for faces. 
     Process  2100  continues by generating face crops from verified media assets ( 2107 ). For example, after a user verifies a key media asset or other verified media asset (e.g., a single face image), a face crop image is generated from the verified key media asset or other verified media asset. In an embodiment, the face crop image is a thumbnail image. 
     Process  2100  continues by syncing face crop images and associated metadata to other devices in the distributed computing environment ( 2108 ). For example, the face crop images and metadata associated with the user action that verified or rejected the image data is sent to server computers for syncing with other client devices in a distributed computing environment. In an embodiment, the face crop images and metadata are encrypted prior to sending to the server computers. During syncing, the face detection data is transferred to the user&#39;s other client devices. The face crop image is treated like any other media asset on each client device and is included in the local clustering on client device  103  and processed as described above. The face crop will be included in a cluster and every faceprint in the same cluster wilt be associated with the person associated with the face crop image and associated metadata (e.g., name, contact ID, “favorites”, “likes”). This results in the cluster of media assets being verified. Any conflicts that arise on the client device as a result of syncing can be result through user actions, as described in reference to  FIGS. 7-20 . For example, if clustering was performed while a device is offline, two face crop images may be present in a single cluster. The user will be presented with a GUI to resolve the conflict by selecting one of the two face crop images to be the truth face. 
     Example Server Architecture 
       FIG. 22  is a block diagram of example server architecture  2200  for implementing the server-side features and processes described in reference to  FIGS. 1-21 , according to an embodiment. Other architectures are possible, including architectures with more or fewer components. In some implementations, architecture  2200  includes one or more processor(s)  2202  (e.g., dual-core Intel® Xeon® processors), one or more network interface(s)  2206 , one or more storage device(s)  2204  (e.g., hard disk, optical disk, flash memory) and one or more computer-readable medium(s)  2208  (e.g., hard disk, optical disk, flash memory, etc.). These components can exchange communications and data over one or more communication channel(s)  2210  (e.g., buses), which can utilize various hardware and software for facilitating the transfer of data and control signals between components. 
     The term “computer-readable medium” refers to any storage medium that stores and provides instructions to processor(s)  2202  for execution, including without limitation, non-volatile media (e.g., optical or magnetic disks, ROM) and volatile media (e.g., memory, RAM). Computer-readable medium(s)  2208  can further include computer program instructions for implementing operating system  2212  (e.g., Mac OS® server, Windows® NT server), network communication stack  2214 , media asset application  2216  and distributed syncing, as described in reference to  FIGS. 1-21 . Computer program instructions can be based on any suitable computer language (e.g., C++, Java). 
     Operating system  2212  can be multi-user, multiprocessing, multitasking, multithreading, real time, etc. Operating system  2212  performs basic tasks, including but not limited to: recognizing input from and providing output to devices  2202 ,  2204 ,  2206  and  2208 ; keeping track and managing files and directories on computer-readable medium(s)  2208  (e.g., memory or a storage device); controlling peripheral devices; and managing traffic on the one or more communication channel(s)  2210 . Network communications stack  2213  includes various components for establishing and maintaining network connections (e.g., software for implementing communication protocols, such as TCP/IP, HTTP, etc.). 
     Architecture  2200  can be included in any computer device, including one or more server computers in a local or distributed network each having one or more processing cores. Architecture  2200  can be implemented in a parallel processing or peer-to-peer infrastructure or on a single device with one or more processors. Software can include multiple software components or can be a single body of code. 
     Example Mobile Device Architecture 
       FIG. 23  illustrates an example device architecture  2300  of a mobile device implementing client-side features and operations described in reference to  FIGS. 1-21 . Architecture  2300  can include memory interface  2302 , one or more data processors, image processors and/or processors  2304  and peripherals interface  2306 . Memory interface  2302 , one or more processors  2304  and/or peripherals interface  2306  can be separate components or can be integrated in one or more integrated circuits. The various components in architecture  2300  can be coupled by one or more communication buses or signal lines. 
     Sensors, devices and subsystems can be coupled to peripherals interface  2306  to facilitate multiple functionalities. For example, one or more motion sensors  2310 , light sensor  2312  and proximity sensor  2314  can be coupled to peripherals interface  2306  to facilitate motion sensing (e.g., acceleration, rotation rates), lighting and proximity functions of the mobile device. Location processor  2315  can be connected to peripherals interface  2306  to provide geopositioning. In some implementations, location processor  2315  can be a GNSS receiver, such as the Global Positioning System (GPS). Electronic magnetometer  2316  (e.g., an integrated circuit chip) can also be connected to peripherals interface  2306  to provide data that can be used to determine the direction of magnetic North. Electronic magnetometer  2316  can provide data to an electronic compass application. Motion sensors)  2310  can include one or more accelerometers and/or gyros configured to determine change of speed and direction of movement of the mobile device. Barometer  2317  can be configured to measure atmospheric pressure around the mobile device. 
     Camera subsystem  2320  and an optical sensor  2322 , e.g., a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor, can be utilized to facilitate camera functions, such as capturing photographs and recording video clips. 
     Communication functions can be facilitated through one or more wireless communication subsystems  2324 , which can include radio frequency (RF) receivers and transmitters (or transceivers) and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of the communication subsystem  2324  can depend on the communication network(s) over which a mobile device is intended to operate. For example, architecture  2300  can include communication subsystems  2324  designed to operate over a GSM network, a GPRS network, an EDGE network, a Wi-Fi™ or WiMax™ network and a Bluetooth™ network. In particular, the wireless communication subsystems  2324  can include hosting protocols, such that the mobile device can be configured as a base station for other wireless devices. 
     Audio subsystem  2326  can be coupled to a speaker  2328  and a microphone  2330  to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording and telephony functions. Audio subsystem  2326  can be configured to receive voice commands from the user. 
     I/O subsystem  2340  can include touch surface controller  2342  and/or other input controller(s)  2344 . Touch surface controller  2342  can be coupled to a touch surface  2346  or pad. Touch surface  2346  and touch surface controller  2342  can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch surface  2346 . Touch surface  2346  can include, for example, a touch screen. I/O subsystem  2340  can include a haptic engine or device for providing haptic feedback (e.g., vibration) in response to commands from a processor. 
     Other input controller(s)  2344  can be coupled to other input/control devices  2348 , such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port and/or a pointer device such as a stylus. The one or more buttons (not shown) can include an up/down button for volume control of speaker  2328  and/or microphone  2330 . Touch surface  2346  or other controllers  2344  (e.g., a button) can include, or be coupled to, fingerprint identification circuitry for use with a fingerprint authentication application to authenticate a user based on their fingerprint(s). 
     In one implementation, a pressing of the button for a first duration may disengage a lock of the touch surface  2346 ; and a pressing of the button for a second duration that is longer than the first duration may turn power to the mobile device on or off. The user may be able to customize a functionality of one or more of the buttons. The touch surface  2346  can, for example, also be used to implement virtual or soft buttons and/or a virtual touch keyboard. 
     In some implementations, the mobile device can present recorded audio and/or video files, such as MP3, AAC and MPEG files. In some implementations, the mobile device can include the functionality of an MP3 player. Other input/output and control devices can also be used. 
     Memory interface  2302  can be coupled to memory  2350 . Memory  2350  can include high-speed random access memory and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices and/or flash memory (e.g., NAND, NOR). Memory  2350  can store operating system  2352 , such as iOS, Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. Operating system  2352  may include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, operating system  2352  can include a kernel (e.g., UNIX kernel). 
     Memory  2350  may also store communication instructions  2354  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers, such as, for example, instructions for implementing a software stack for wired or wireless communications with other devices. Memory  2350  may include graphical user interface instructions  2356  to facilitate graphic user interface processing; sensor processing instructions  2358  to facilitate sensor-related processing and functions; phone instructions  2360  to facilitate phone-related processes and functions; electronic messaging instructions  2362  to facilitate electronic-messaging related processes and functions; web browsing instructions  2364  to facilitate web browsing-related processes and functions; media processing instructions  2366  to facilitate media processing-related processes and functions; GNSS/Location instructions  2368  to facilitate generic GNSS and location-related processes and instructions; and camera instructions  2370  to facilitate camera-related processes and functions. Memory  2350  further includes client face syncing instructions  2372  for performing the client-side features and processes described in reference to  FIGS. 1-21 . The memory  2350  may also store other software instructions (not shown), such as security instructions, web video instructions to facilitate web video-related processes and functions and/or web shopping instructions to facilitate web shopping-related processes and functions. In some implementations, the media processing instructions  2366  are divided into audio processing instructions and video processing instructions to facilitate audio processing-related processes and functions and video processing-related processes and functions, respectively. 
     Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. Memory  2350  can include additional instructions or fewer instructions. Furthermore, various functions of the mobile device may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits. 
     One or more features or steps of the disclosed embodiments may be implemented using an Application Programming Interface (API). An API may define on or more parameters that are passed between a calling application and other software code an operating system, library routine, function) that provides a service, that provides data, or that performs an operation or a computation. The API may be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a call convention defined in an API specification document. A parameter may be a constant, a key, a data structure, an object, an object class, a variable, a data type, a pointer, an array, a list, or another call. API calls and parameters may be implemented in any programming language. The programming language may define the vocabulary and calling convention that a programmer will employ to access functions supporting the API. In some implementations, an API call may report to an application the capabilities of a device running the application, such as input capability, output capability, processing capability, power capability, communications capability, etc. 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub combination or variation of a sub combination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous. 
     As described above, some aspects of the subject matter of this specification include gathering and use of data available from various sources to improve services a mobile device can provide to a user. The present disclosure contemplates that in some instances, this gathered data may identify a particular location or an address based on device usage. Such personal information data can include location-based data, addresses, subscriber account identifiers, or other identifying information. 
     The present disclosure further contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally, recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. For example, personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection should occur only after receiving the informed consent of the users. Additionally, such entities would take any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. 
     In the case of advertisement delivery services, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services. 
     Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users by inferring preferences based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device associated with a user, other non-personal information available to the content delivery services, or publically available information.

Metadata:
Filing Date: 20190715
Publication Date: 20210504
Grant Date: 20210504
Priority Date: 20170602
Inventors: THOMSON, GAVIN B.
CIRCLAEYS, ERIC
BOMBACH, PAUL M.
KINNIS, TONY F.
SUBRAMANIAM, BASKARAN
Assignee: APPLE INC
CPC Classifications: [{"code": "H04L67/1095", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T11/60", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F18/23", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F18/23", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04L67/1095", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06V40/161", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06V40/161", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06V20/30", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V20/30", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/172", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06V40/172", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2210/22", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T11/60", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06K9/00677", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2210/22", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06K9/6218", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04L67/1095", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06K9/00228", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06K9/00288", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 64458369