PATENT DOCUMENT

Publication Number: US-11250041-B2
Application Number: US-201816147444-A
Country: US
Kind Code: B2

Title: Expanding indexed terms for searching files

Abstract:
A device implementing a system for expanded search includes a processor configured to identify plural words, and generate, for each word of the plural words, a word vector based on a proximity of the word relative to other words of the plural words, the word vector comprising plural dimensions. The processor is further configured to create a compressed word vector structure comprising clusters of subsets of the plural dimensions across the word vectors, each cluster including similar values of the respective dimensions, convert the word vectors to points on at least one plane, and partition the at least one plane into nested groupings of the points based on a threshold number of points per nested grouping. The processor is further configured to create a tree look-up structure of the nested groupings, and provide the compressed word vector structure and the tree look-up structure to a client device.

Claims:
What is claimed is: 
     
       1. A device, comprising:
 at least one processor; and 
 a memory including instructions that, when executed by the at least one processor, cause the at least one processor to:
 identify plural words in a corpus of data; 
 generate, for each word of the plural words, a word vector based on a proximity of the word relative to other words of the plural words in the corpus of data, the word vector comprising plural dimensions; 
 create a compressed word vector structure comprising clusters of subsets of the plural dimensions across the word vectors, each cluster including similar values of the respective dimensions; 
 convert the word vectors to points on at least one plane; 
 partition the at least one plane into nested groupings of the points based on a threshold number of points per nested grouping; 
 create a tree look-up structure of the nested groupings; and 
 provide the compressed word vector structure and the tree look-up structure to a client device. 
 
 
     
     
       2. The device of  claim 1 , wherein the compressed word vector structure and the tree look-up structure are configured to be accessed by the client device, to increase a number of words for searching for a file stored on the client device. 
     
     
       3. The device of  claim 2 , wherein the file is an image file, and wherein identifying the words comprises selecting words from the corpus of data that relate to images. 
     
     
       4. The device of  claim 2 , wherein the file may be searched for by matching metadata of the file with a first word, and
 wherein the client device is configured to query the tree look-up structure with the first word, to determine at least one second word for searching for the file. 
 
     
     
       5. The device of  claim 4 , wherein the tree look-up structure, in conjunction with the compressed word vector structure, is configured to determine the at least one second word which is related to the first word. 
     
     
       6. The device of  claim 4 , wherein the client device is configured to query the tree look-up structure with the first word as part of a file indexing process. 
     
     
       7. The device of  claim 1 , wherein the compressed word vector structure and the tree look-up structure are stored locally on the client device. 
     
     
       8. The device of  claim 1 , wherein each of the subsets of the plural dimensions comprises the same number of clusters. 
     
     
       9. A computer program product comprising code stored in a tangible computer-readable storage medium, the code comprising:
 code to identify words from a corpus of data; 
 code to generate, for each word, a word vector based on a proximity of the word relative to other words in the corpus of data, the word vector comprising plural dimensions; 
 code to create a compressed word vector structure comprising clusters of subsets of the plural dimensions across the word vectors, each cluster including similar values of the respective dimensions; 
 code to convert the word vectors to points on at least one plane; 
 code to partition the at least one plane into nested groupings of the points based on a threshold number of points per nested grouping; 
 code to create a tree look-up structure of the nested groupings; and 
 code to provide the compressed word vector structure and the tree look-up structure to a device. 
 
     
     
       10. The computer program product of  claim 9 , wherein the compressed word vector structure and the tree look-up structure are configured to be accessed by the device, to increase a number of words for searching for a file stored on the device. 
     
     
       11. The computer program product of  claim 10 , wherein the file is an image file, and wherein identifying the words comprises selecting words that relate to images. 
     
     
       12. The computer program product of  claim 10 , wherein the file may be searched for by matching metadata of the file with a first word, and
 wherein the device is configured to query the tree look-up structure with the first word, to determine at least one second word for searching for the file. 
 
     
     
       13. The computer program product of  claim 12 , wherein the tree look-up structure, in conjunction with the compressed word vector structure, is configured to determine the at least one second word which is related to the first word. 
     
     
       14. The computer program product of  claim 12 , wherein the device is configured to query the tree look-up structure with the first word as part of a file indexing process. 
     
     
       15. The computer program product of  claim 9 , wherein the compressed word vector structure and the tree look-up structure are stored locally on the device. 
     
     
       16. A method comprising:
 obtaining a first word at a device, the device storing a tree look-up structure; 
 querying the tree look-up structure with the first word, 
 wherein the tree look-up structure comprises nested groupings of points which represent words and which are partitioned based on a threshold number of points per nested grouping, and 
 wherein each word is associated with a word vector that indicates a proximity of the word relative to other words, each word vector comprising plural dimensions, 
 identifying, based on the query to the tree look-up structure, at least one second word related to the first word; and 
 providing the at least one second word as a result to the query. 
 
     
     
       17. The method of  claim 16 , wherein a file may be searched for by matching metadata of the file with the first word. 
     
     
       18. The method of  claim 17 , further comprising:
 designating the at least one second word as a word for searching for the file. 
 
     
     
       19. The method of  claim 18 , wherein the obtaining, the querying, the identifying, the providing and the designating are performed as part of a file indexing process for the device. 
     
     
       20. The method of  claim 16 , wherein the device further stores a compressed word vector structure comprising clusters of subsets of the plural dimensions across the word vectors, each cluster including similar values of the respective dimensions. 
     
     
       21. The method of  claim 20 , wherein the device generates the tree look-up structure and the compressed word vector structure. 
     
     
       22. The method of  claim 16 , wherein the first word corresponds to an input query entered by a user at the device. 
     
     
       23. The method of  claim 16 , wherein the first word is included as a first label in metadata associated with a file and the method further comprises:
 adding the at least one second word as a second label in the metadata associated with the file, wherein the file may be searched for using the metadata.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims the benefit of U.S. Provisional Patent Application Ser. No. 62/679,885, entitled “Expanding Indexed Terms For Searching Files,” filed on Jun. 3, 2018, which is hereby incorporated by reference in its entirety for all purposes. 
    
    
     TECHNICAL FIELD 
     The present description relates generally to file searching, including expanding the number of words that can be used to search for file(s) stored on a device. 
     BACKGROUND 
     Files stored on a device may be searched for by one or more word(s). A user may input a query (e.g., a word) at the device in association with a file-based search. The device may return one or more files that relate to the word. For example, a user may input the search query “beach” in association with an image-based search, and the device may return one or more image files that relate to the word “beach.” 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Certain features of the subject technology are set forth in the appended claims. However, for purpose of explanation, several embodiments of the subject technology are set forth in the following figures. 
         FIG. 1  illustrates an example network environment for expanding the number of words used to search for files in accordance with one or more implementations. 
         FIG. 2  illustrates an example device that may implement a system for expanding the number of words used to search for files in accordance with one or more implementations. 
         FIG. 3  illustrates an example of a compressed word vector structure in accordance with one or more implementations. 
         FIG. 4  illustrates an example of a partitioned vector space associated with a tree look-up structure in accordance with one or more implementations. 
         FIG. 5  illustrates an example of an additional word determination module for expanding the number of words used to search for files in accordance with one or more implementations. 
         FIG. 6  illustrates an example process for expanding the number of words used to search for files in accordance with one or more implementations. 
         FIG. 7  illustrates another example process for expanding the number of words used to search for files in accordance with one or more implementations. 
         FIG. 8  illustrates an example electronic system with which aspects of the subject technology may be implemented in accordance with one or more implementations. 
     
    
    
     DETAILED DESCRIPTION 
     The detailed description set forth below is intended as a description of various configurations of the subject technology and is not intended to represent the only configurations in which the subject technology can be practiced. The appended drawings are incorporated herein and constitute a part of the detailed description. The detailed description includes specific details for the purpose of providing a thorough understanding of the subject technology. However, the subject technology is not limited to the specific details set forth herein and can be practiced using one or more other implementations. In one or more implementations, structures and components are shown in block diagram form in order to avoid obscuring the concepts of the subject technology. 
     A device of a user may store multiple files (e.g., images, videos, documents and/or other types of files associated with the user), and the files may be searched for on the device using one or more associated words. For example, an image file may be designated or labeled (e.g., via metadata) with a respective word(s) which can be used to search a device file system for the image file. Thus, the user can query the device with the word, and the device may provide the corresponding image file as a result to the query. 
     However, the number of words designated to search for a file may be limited. For example, an image file depicting a beach scene may be searched for by the word “beach,” based on a prior designation associating the word “beach” with the file. However, the file may not be searched for by other words such as “sand,” “ocean” and/or “shore,” since there is no designated association between the other words and the file. 
     The subject system provides for expanding the number of words used to search for file(s) stored on a device. In doing so, the subject system generates data structures (e.g., a compressed word vector structure and a tree look-up structure) for local storage on the device, where the data structures facilitate in determining additional words used to search for the respective files. 
     In one or more implementations, the subject system provides for identifying multiple words (e.g., from a corpus of data) and generating, for each word, a word vector based on a proximity of the word relative to other words of the multiple words in the corpus of data, the word vector having multiple dimensions. A compressed word vector structure is created with clusters of subsets of the multiple dimensions across the word vectors, where each cluster includes similar values of the respective dimensions. The word vectors are converted to points on at least one plane, and the at least one plane is partitioned into nested groupings of the points based on a threshold number of points per nested grouping. A tree look-up structure is created from the nested groupings, and the compressed word vector structure and the tree look-up structure are provided to a device. 
     The device may receive and store the compressed word vector structure and the tree look-up structure, and use these structures to expand the number of words used to search for files stored on the device. For example, as part of a file indexing process, the device may query the tree look-up structure with a word already designated to search for a file (e.g., “beach” for a beach image). In another example, a user of the device may input the query (e.g., a spoken query, a text-based query, or the like). In response, the tree look-up structure, in conjunction with the compressed word vector structure, may provide additional words that are related to the word from the query, and the device may designate the additional words as words to use in searching for the file. 
     Thus, the subject system provides for expanding the number of words used to search for files. Once the data structures are received and stored on the device, the expanded search capability may be available without network access. Further, user queries for files are not sent to a server, thereby maintaining user privacy. Moreover, by virtue of using a compressed data structure, storage space on the device may be preserved, and by virtue of using a tree look-up structure, the amount of processing time and resources associated with the indexing may be reduced. 
       FIG. 1  illustrates an example network environment for expanding the number of words used to search for files in accordance with one or more implementations. Not all of the depicted components may be used in all implementations, however, and one or more implementations may include additional or different components than those shown in the figure. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth herein. Additional components, different components, or fewer components may be provided. 
     The network environment  100  includes electronic devices  102 ,  103  and  104  (hereinafter  102 - 104 ), a network  106  and a server  108 . The network  106  may communicatively (directly or indirectly) couple, for example, any two or more of the electronic devices  102 - 104  and/or the server  108 . In one or more implementations, the network  106  may be an interconnected network of devices that may include, or may be communicatively coupled to, the Internet. For explanatory purposes, the network environment  100  is illustrated in  FIG. 1  as including electronic devices  102 - 104  and a single server  108 ; however, the network environment  100  may include any number of electronic devices and any number of servers. 
     One or more of the electronic devices  102 - 104  may be, for example, a portable computing device such as a laptop computer, a smartphone, a smart speaker, a peripheral device (e.g., a digital camera, headphones), a tablet device, a wearable device such as a smartwatch, a band, and the like, or any other appropriate device that includes, for example, one or more wireless interfaces, such as WLAN radios, cellular radios, Bluetooth radios, Zigbee radios, near field communication (NFC) radios, and/or other wireless radios. In  FIG. 1 , by way of example, the electronic device  102  is depicted as a smartphone, the electronic device  103  is depicted as a smart speaker, and the electronic device  104  is depicted as a laptop computer. Each of the electronic devices  102 - 104  may be, and/or may include all or part of the device discussed below with respect to  FIG. 2 , and/or the electronic system discussed below with respect to  FIG. 8 . 
     The server  108  may be, and/or may include all or part of the device discussed below with respect to  FIG. 2 , and/or the electronic system discussed below with respect to  FIG. 8 . The server  108  may include one or more servers, such as a cloud of servers. For explanatory purposes, a single server  108  is shown and discussed with respect to various operations, such generating a compressed word vector structure and a tree look-up structure, and providing these structures to the electronic devices  102 - 104 . However, these and other operations discussed herein may be performed by one or more servers, e.g., at one or more different data centers, and each different operation may be performed by the same or different servers. 
       FIG. 2  illustrates an example device that may implement a system for expanding the number of words used to search for files in accordance with one or more implementations. For example, the device  200  of  FIG. 2  can correspond to any of the electronic devices  102 - 104 , or to the server  108  of  FIG. 1 . Not all of the depicted components may be used in all implementations, however, and one or more implementations may include additional or different components than those shown in the figure. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth herein. Additional components, different components, or fewer components may be provided. 
     The device  200  may include a processor  202 , a memory  204 , and a communication interface  206 . The processor  202  may include suitable logic, circuitry, and/or code that enable processing data and/or controlling operations of the device  200 . In this regard, the processor  202  may be enabled to provide control signals to various other components of the device  200 . The processor  202  may also control transfers of data between various portions of the device  200 . Additionally, the processor  202  may enable implementation of an operating system or otherwise execute code to manage operations of the device  200 . In the subject system, the processor  202  may implement software architecture for expanding the number of words used to search for files as discussed further below with respect to  FIGS. 3-5 . 
     The memory  204  may include suitable logic, circuitry, and/or code that enable storage of various types of information such as received data, generated data, code, and/or configuration information. The memory  204  may include, for example, random access memory (RAM), read-only memory (ROM), flash, and/or magnetic storage. 
     In one or more implementations, in a case where the device  200  corresponds to one of the electronic devices  102 - 104 , the memory  204  may store files such as images, videos, documents and the like. The memory  204  may also store a compressed word vector structure and a tree look-up structure, which may be queried to determine words used to search for the files stored on the device. 
     In one or more implementations, in a case where the device  200  corresponds to the server  108 , the memory  204  may store logic for creating the compressed word vector structure and tree look-up structure described herein. 
     The communication interface  206  may include suitable logic, circuitry, and/or code that enables wired or wireless communication, such as between any of the electronic devices  102 - 104  and the server  108  over the network  106 . The communication interface  206  may include, for example, one or more of a Bluetooth communication interface, a cellular interface, an NFC interface, a Zigbee communication interface, a WLAN communication interface, a USB communication interface, or generally any communication interface. 
     In one or more implementations, one or more of the processor  202 , the memory  204 , the communication interface  206 , and/or one or more portions thereof, may be implemented in software (e.g., subroutines and code), may be implemented in hardware (e.g., an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable devices) and/or a combination of both. 
       FIG. 3  illustrates an example of a compressed word vector structure  300  in accordance with one or more implementations. While  FIG. 3  is described with reference to the server  108  and the electronic device  102 , any of the electronic devices  103 - 104  may apply instead. The compressed word vector structure  300  can be generated by one or more software modules running on the processor  202  of the server  108  and/or any other device (e.g., any of the electronic devices  102 - 104 ). In another example, the compressed word vector structure  300  can be generated by one or more software modules implemented by custom hardware (e.g., one or more coprocessors). 
     To expand the number of words used to search for files for the electronic device  102 , the server  108  may determine one or more words that are related to a given word (e.g., a word already designated to search for a file). In this regard, the server  108  may obtain a vocabulary of words from a corpus of data (e.g., a collection of documents from which relationships between words may be determined). For example, the corpus of data may provide a vocabulary of 100,000 words. 
     The server  108  may convert each word from the corpus of data into a respective word vector, where each word vector represents a “meaning” of a word based on its context or surrounding words from within the corpus of data. In addition, each word vector may be multi-dimensional. For example, the server  108  may consider 300 different aspects (e.g., contextual aspects) for each word with respect to the corpus of data, resulting in a word vector of 300 dimensions per word. By comparing the dimensional values between words, the server  108  may determine which words are proximate to each other in the corpus of data. As a result, words determined to be proximate to each other in the corpus of data may be related to each other. For example, related words may have the same or similar meaning (e.g., “ocean” and “sea”). Alternatively or in addition, related words may not have the same meaning but be contextually similar (e.g., “mother” and “baby”). 
     In one or more implementations, the server  108  may prune the vocabulary of words initially obtained from the corpus of data by removing words corresponding to functional words. For example, words appearing within a predefined list of functional words (e.g., “on,” “to,” “the,” “a” and the like) may be removed from the vocabulary initially obtained from the corpus of data. 
     As noted above, the compressed word vector structure  300  may store a representation of all words within a vocabulary (e.g., after pruning) together with their respective word vectors. Without compression, the storage of a 100,000 word vocabulary with a 300-dimensional vector per word may require a significant amount of memory, particularly for storage on the electronic devices  102 - 104 . 
     In one or more implementations, the server  108  may create the compressed word vector structure  300  by the dividing the multiple dimensions into predefined subsets of dimensions. As shown in the example of  FIG. 3 , and using the example of 300 dimensions, the compressed word vector structure  300  includes the dimension subset A, the dimension subset B, . . . , and the dimension subset M (hereinafter the “dimension subsets A-M”), where M=60. Thus, each of the dimension subsets A-M is associated with 5 respective dimensions of the 300 dimensions per word vector. 
     In addition, the server  108  may divide each of the dimension subsets A-M into a predefined number of clusters. Each cluster may specify a value (e.g., or value range) for each of its respective dimensions (e.g., 5 dimensions). In turn, the sever  108  may assign each word to a cluster based on the respective 5 dimension values of that word corresponding to those values (e.g., or value ranges) specified for the cluster. 
     As shown in the example of  FIG. 3 , the compressed word vector structure  300  includes N clusters per dimension subset. For example, dimension subset A has dimension values clusters A1-AN, dimension subset B has dimension values clusters B1-BN, . . . , and dimension subset M has dimension values cluster M1-MN. Using an example of N=256 (e.g., 256 clusters), each dimension values cluster specifies a respective value (e.g., or value range) for the 5 dimensions in the corresponding dimension subset. With the example vocabulary of 100,000 words, each word of the 100,000 words is assigned to a respective cluster for each of the dimension subsets A-M, based on the dimension values of the word with respect to the dimension subset. 
     For example, the word “beach” may be assigned to: cluster A20 of dimension subset A (e.g., based on the 5 dimensional values of the word vector for “beach” corresponding with those specified for cluster A20), cluster B135 of dimension subset B, . . . , and cluster M13 of dimension subset M. The server  108  may store a set of cluster identifiers in association with each word (e.g., A20, B135, . . . , M13 for the word “beach”), and store all sets of cluster identifiers in a cluster identifier data structure  302 . Thus, the cluster identifier data structure  302  may be used to obtain the respective word vector associated with a given word, and/or to obtain the respective word associated with a given word vector. In one or more implementations, the cluster identifier data structure  302  may correspond to a set of vectors that identifies the respective clusters for each word. 
     The compressed word vector structure  300  may reduce the storage space required to store words and their associated word vectors. Using the example of 256 clusters (e.g., N=256), it is possible to identify a specific cluster (e.g., one of dimension values cluster A1-AN) in a dimension subset (e.g., dimension subset A) using 1 byte. As such, the set of identifiers for a single word may be represented by M bytes, each of which identifies a respective cluster for the dimension subsets A-M. Using the above-described example of a 100,000 word vocabulary with 300 dimensions per word, the compressed word vector structure  300  with 60 dimension subsets (e.g., M=60) and 256 dimension values clusters (e.g., N=256) may require approximately 6 MB of storage space. On the other hand, a technique without such compression may require 120 MB of storage space, for the same 100,000 word vocabulary with 300 dimensions per word. 
     In one or more implementations, the compressed word vector structure  300  (including its corresponding components dimension subsets A-M, dimension values clusters A1-AN, B1-BN, . . . , M1-MN) may be stored in memory  204  of the server  108 . The server  108  may provide the compressed word vector structure  300 , including the cluster identifier data structure  302 , together with a tree look-up structure (discussed below with respect to  FIG. 4 ) to any of the electronic device  102 - 104 . Additional features and functions of the compressed word vector structure  300  according to various aspects of the subject technology are further described in the present disclosure. 
       FIG. 4  illustrates an example of a partitioned vector space  400  associated with a tree look-up structure in accordance with one or more implementations. While  FIG. 4  is described with reference to the server  108  and the electronic device  102 , any of the electronic devices  103 - 104  may apply instead. The tree look-up structure (e.g., element  504 , discussed below with reference to  FIG. 5 ) associated with the partitioned vector space  400  can be generated by one or more software modules running on the processor  202  of the server  108  and/or any other device (e.g., any of the electronic devices  102 - 104 ). In another example, the tree look-up structure  504  can be generated by one or more software modules implemented by custom hardware (e.g., one or more coprocessors). 
     The tree look-up structure  504  may facilitate in the searching and/or the indexing of files stored on the device. As described above, words may be determined to be related to each other by comparing the respective dimensional values of word vectors corresponding to the words. For example, one technique for determining if words are related to each other may be to individually calculate the distance of each word to all other words in a vocabulary based on dimensional values. However, with a large vocabulary (e.g., 100,000 words), this technique may be slow and computationally expensive. 
     In one or more implementations, the partitioned vector space  400  may be used in conjunction with a k-nearest neighbors algorithm (k-NN). In this regard, the partitioned vector space  400  may represent k dimensions. For a given point (e.g., point  406 ) in the partitioned vector space  400 , values for the k dimensions may be determined (e.g., extracted) based on the position of that point in the partitioned vector space  400 . In addition, nearest neighbors for the point can be determined based on their respective positions relative to the point in the partitioned vector space  400 . 
     The server  108  may initially convert the above-described word vectors, each corresponding to a respective word in the vocabulary, to points in the partitioned vector space  400 . In the example of  FIG. 4 , the partitioned vector space  400  is divided into five planes (e.g., plane  402  is one of the five planes). In its conversion, the server  108  positions the points within the partitioned vector space  400  (e.g., corresponding to k dimensions) based on the dimensional values of the word vectors corresponding to the points. 
     After converting all of the word vectors to points, the server  108  performs partitioning of the vector space, for example, based on the distribution of the points. The partitioning may be performed using a light-weight clustering technique for an approximate and even distribution of the points. The partitioning may be performed in a recursive manner, so as to create nested groupings of the points. The server  108  may stop partitioning when the number of points per nested grouping reaches a threshold number of points (e.g., 60 points per nested grouping). 
     Thus, in the example of  FIG. 4 , the plane  402  is one of five planes within the partitioned vector space  400 . The plane is partitioned into nested groupings of points based on the threshold number of points per nested grouping. For example, the grouping  404  represents one nested grouping of points which meets the threshold number of points (e.g., the number of points in the grouping  404  is equal to or less than the threshold number of points per nested grouping). 
     The server  108  may create a tree look-up structure (e.g., the tree look-up structure  504  of  FIG. 5 ) based on the partitioned vector space  400 . For example, the tree look-up structure  504  may consider each plane of the partitioned vector space  400  as a respective binary tree, and traverse the trees based on the dimensional values for a particular word vector. For example, the tree look-up structure  504  may initially select one of the five trees (e.g., respectively corresponding to the planes of the partitioned vector space  400 ) based on a subset of dimension values of the word vector. The tree look-up structure  504  may traverse the selected tree using the remaining dimension values of the word vector, and continue such traversal until a terminal nested grouping (e.g., the grouping  404 ) is reached. 
     The server  108  may then calculate distances (e.g., using a cosine distance function) of the word vector (e.g., represented by point  406 ) relative to the other word vectors in the nested grouping (e.g., the other points in the nested grouping  404 ), in order to determine a predefined number word vectors corresponding to nearest neighbor words. For example, if the nested grouping  404  were to include 60 points, the server  108  may calculate distances of the point  406  relative to the 59 remaining points, and return a predefined number of points (e.g., 10 points) with the smallest distance values. 
     To determine the corresponding words for each of the 10 points, the server  108  may first determine a word vector for each point by extracting the k dimensions for the point based on the position of the point in the partitioned vector space  400 . The server  108  may then access (e.g., query) the compressed word vector structure  300  (including the cluster identifier data structure  302 ) based on the word vector, in order to obtain the words corresponding to each point. These words (e.g., 10 words) may correspond to words that are related to the word represented by point  406 . 
     In one or more implementations, the compressed word vector structure  300  (including its corresponding components dimension subsets A-N, dimension values clusters A1-AN, B1-BN, . . . , M1-MN) may be stored in memory  204  of the server  108 , and the server  108  may provide the compressed word vector structure  300  to any of the electronic device  102 - 104 . Additional features and functions of the compressed word vector structure  300  according to various aspects of the subject technology are further described in the present disclosure. 
       FIG. 5  illustrates an example of an additional word determination module  500  for expanding the number of words used to search for files in accordance with one or more implementations. For example, the additional word determination module  500  can be implemented by one or more software modules running on the processor  202  of the electronic device  102 . While  FIG. 5  is described with reference to the electronic device  102 , any of the electronic devices  103 - 104  may apply instead. The additional word determination module  500  can be implemented by one or more software modules implemented by custom hardware (e.g., one or more coprocessors). Not all of the depicted components may be used in all implementations, however, and one or more implementations may include additional or different components than those shown in the figure. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth herein. Additional components, different components, or fewer components may be provided. 
     The additional word determination module  500  receives a word  502  as input and provides related word(s)  506  as output. In addition, additional word determination module  500  includes the compressed word vector structure  300  described above with respect to  FIG. 3 , and the tree look-up structure  504  corresponding to the partitioned vector space  400  described above with respect to  FIG. 4 . 
     As noted above, the server  108  may generate the compressed word vector structure  300  and the tree look-up structure  504 . In addition, the server  108  may send the compressed word vector structure  300  (including the cluster identifier data structure  302 ) and the tree look-up structure  504  to the electronic device  102 , for example, via the network  106 . Alternatively, the electronic device  102  may be configured to generate the compressed word vector structure  300  and the tree look-up structure  504  (e.g., locally). The electronic device  102  may store each of the compressed word vector structure  300  and the tree look-up structure  504  (e.g., as received from the server  108 , or as locally generated) in the memory  204  of the device. 
     The electronic device  102  may access the compressed word vector structure  300  (including the cluster identifier data structure  302 ) and the tree look-up structure  504 , for example, to expand the number of words used to search for files (e.g., images, videos, documents and/or other types of files associated with the user) stored on the electronic device  102 . For example, the electronic device may perform a file indexing process based on a defined schedule (e.g., every night, and/or upon detection of the electronic device  102  being plugged in). As part of the file indexing process, the electronic device  102  may access metadata for each file to determine one or more words that are already designated for searching for the file (e.g., “beach” for a beach image). The electronic device may query the tree look-up structure  504  with the one or more words. In response to the query/queries, the tree look-up structure  504 , in conjunction with the compressed word vector structure  300  (and the cluster identifier data structure  302 ), may provide additional words (e.g., “sand,” “ocean,” “shore”) that are related to the one or more words of the query/queries. The electronic device  102  may designate the additional words as words that can also be used to search for the respective files. In another example, a user may query the electronic device  102 , for example, via a spoken query, a text-based query, or the like. The word(s) from the query may be used to query the tree look-up structure  504 , which in conjunction with the compressed word vector structure  300  (and the cluster identifier data structure  302 ), may provide additional words that are related to the word(s) of the user query. 
     Thus, prior to receiving the compressed word vector structure  300  and the tree look-up structure  504 , a user of the electronic device  102  may not receive a response to an image-based search query for the word “shore.” For example, an image file depicting a beach scene may only be associated with the word “beach,” based on a prior designation associating the word “beach” with the file. However, after receiving the compressed word vector structure  300  and the tree look-up structure  504 , the electronic device  102  may index its stored files, and create new designations for words and the files. Then, the user at the electronic device  102  may receive the image file depicting the beach, as well as any additional files (e.g., based on the file indexing), in response to the query “shore.” 
     Thus, the subject system provides for expanding the number of words used to search for files stored on the electronic device  102 . Once the compressed word vector structure  300  (including the cluster identifier data structure  302 ) and the tree look-up structure  504  have been received by the electronic device  102 , the expanded search capability may be available without network access. Further, queries for files input by the user at the electronic device  102  are not sent to the server  108 , thereby maintaining user privacy. Moreover, by virtue of using a compressed data structure (e.g., the compressed word vector structure  300 ), storage space on the electronic device  102  may be preserved. 
     In one or more implementations, the additional word determination module  500 , the compressed word vector structure  300  and the tree look-up structure  504  are implemented via software instructions, stored in the memory  204 , which when executed by the processor  202 , cause the processor  202  to perform particular function(s). 
     In one or more implementations, one or more of the additional word determination module  500 , the compressed word vector structure  300  and the tree look-up structure  504  may be implemented in software (e.g., subroutines and code) and/or hardware (e.g., an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable devices) and/or a combination of both. In one or more implementations, some or all of the depicted components may share hardware and/or circuitry, and/or one or more of the depicted components may utilize dedicated hardware and/or circuitry. Additional features and functions of these modules according to various aspects of the subject technology are further described in the present disclosure. 
       FIG. 6  illustrates an example process for expanding the number of words used to search for files in accordance with one or more implementations. For explanatory purposes, the process  600  is primarily described herein with reference to the server  108  and the electronic device  102  of  FIG. 1 . However, the process  600  is not limited to the server  108  and the electronic device  102  and for example, can apply to the server  108  and any of the electronic devices  103 - 104 . Moreover, one or more blocks (or operations) of the process  600  may be performed by one or more other components of the electronic device  102 , the server  108  and/or by other suitable devices. Further for explanatory purposes, the blocks of the process  600  are described herein as occurring in serial, or linearly. However, multiple blocks of the process  600  may occur in parallel. In addition, the blocks of the process  600  need not be performed in the order shown and/or one or more blocks of the process  600  need not be performed and/or can be replaced by other operations. 
     The server  108  identifies multiple words, for example, from a corpus of data ( 602 ). For each word of the multiple words, the server  108  generates a word vector based on a proximity of the word relative to other words of the multiple words in the corpus of data, the word vector having multiple dimensions ( 604 ). 
     The server  108  creates a compressed word vector structure  300  including clusters of subsets of the multiple dimensions across the word vectors, each cluster including similar values of the respective dimensions ( 606 ). Each of the subsets of the multiple dimensions may include the same number of clusters (e.g., 256 clusters per dimension subset). 
     The server  108  converts the word vectors to points on at least one plane ( 608 ). The server  108  partitions the at least one plane into nested groupings of the points based on a threshold number of points per nested grouping ( 610 ). The server  108  creates a tree look-up structure  504  of the nested groupings ( 612 ). 
     The server  108  provides the compressed word vector structure  300  and the tree look-up structure  504  to the electronic device  102  ( 614 ). The compressed word vector structure  300  and the tree look-up structure  504  may be stored locally on the electronic device  102 . 
     The compressed word vector structure  300  and the tree look-up structure  504  may be configured to be accessed by the electronic device  102 , to increase a number of words that can be used to search for a file stored on the electronic device  102 . The file may be an image file, and identifying the words may include selecting words that relate to images (e.g., removing functional words). 
     The file may be searched for by matching metadata of the file with a first word (e.g., within a file system, based on prior file indexing), and the electronic device  102  may be configured to query the tree look-up structure  504  with the first word, to determine at least one second word to designate for searching for the file (e.g., as part of a file indexing process). The tree look-up structure  504 , in conjunction with the compressed word vector structure  300  (and cluster identifier data structure  302 ), may be configured to determine the at least one second word related to the first word. 
       FIG. 7  illustrates another example process for expanding the number of words used to search for files in accordance with one or more implementations. For explanatory purposes, the process  700  is primarily described herein with reference to the electronic device  102  and the server  108  of  FIG. 1 . However, the process  700  is not limited to the electronic device  102  and the server  108  and for example, can apply to any of the electronic devices  103 - 104  and the server  108 . Moreover, one or more blocks (or operations) of the process  700  may be performed by one or more other components of the server  108 , the electronic device  102  and/or by other suitable devices. Further for explanatory purposes, the blocks of the process  700  are described herein as occurring in serial, or linearly. However, multiple blocks of the process  700  may occur in parallel. In addition, the blocks of the process  700  need not be performed in the order shown and/or one or more blocks of the process  700  need not be performed and/or can be replaced by other operations. 
     The electronic device  102  obtains a first word (e.g., as part of a file indexing process), the electronic device  102  storing a tree look-up structure  504  and a compressed word vector structure  300  ( 702 ). The electronic device  102  queries the tree look-up structure  504  with the first word ( 704 ). 
     The tree look-up structure  504  includes nested groupings of points which represent words (e.g., identified by the server  108  from a corpus of data) and which are partitioned based on a threshold number of points per nested grouping. Each word is associated with a word vector that indicates a proximity of the word relative to other words in the corpus of data, each word vector including multiple dimensions. The compressed word vector structure  300  includes clusters of subsets of the multiple dimensions across the word vectors, each cluster including similar values of the respective dimensions. Each of the subsets of the multiple dimensions may include the same number of clusters (e.g., 256 clusters per dimension subset). 
     The compressed word vector structure  300  and the tree look-up structure  504  may be configured to be accessed by the electronic device  102 , to increase a number of words by which a file stored on the electronic device  102  may be searched for (e.g., as part of the file indexing process). The filed stored on the electronic device  102  may be searched for by matching metadata of the file with the first word. Thus, the electronic device  102  identifies, based on querying the tree look-up structure  504 , at least one second word related to the first word ( 706 ), and provides the at least one second word as a result to the query ( 708 ). 
     The electronic device  102  may designate the at least one second word to also search for the file, thereby expanding the number of words for searching for the file. For example, the file may be an image file, and the server  108  may have identified the words by selecting words that relate to images (e.g., by removing functional words). 
     As described above, one aspect of the present technology is the gathering and use of data available from various sources to improve file searching. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, twitter ID&#39;s, home addresses, data or records relating to a user&#39;s health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other identifying or personal information. 
     The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to provide for expanded search capabilities for device files. Accordingly, use of such personal information data enables users to calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used to provide insights into a user&#39;s general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals. 
     The present disclosure 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. Such policies should be easily accessible by users, and should be updated as the collection and/or use of data changes. 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/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking 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 addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. Hence different privacy practices should be maintained for different personal data types in each country. 
     Despite the foregoing, 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, 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 or anytime thereafter. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app. 
     Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user&#39;s privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data a city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods. 
     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 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, or publicly available information. 
       FIG. 8  illustrates an electronic system  800  with which one or more implementations of the subject technology may be implemented. The electronic system  800  can be, and/or can be a part of, one or more of the electronic devices  102 - 104 , and/or one or the server  108  shown in  FIG. 1 . The electronic system  800  may include various types of computer readable media and interfaces for various other types of computer readable media. The electronic system  800  includes a bus  808 , one or more processing unit(s)  812 , a system memory  804  (and/or buffer), a ROM  810 , a permanent storage device  802 , an input device interface  814 , an output device interface  806 , and one or more network interfaces  816 , or subsets and variations thereof. 
     The bus  808  collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the electronic system  800 . In one or more implementations, the bus  808  communicatively connects the one or more processing unit(s)  812  with the ROM  810 , the system memory  804 , and the permanent storage device  802 . From these various memory units, the one or more processing unit(s)  812  retrieves instructions to execute and data to process in order to execute the processes of the subject disclosure. The one or more processing unit(s)  812  can be a single processor or a multi-core processor in different implementations. 
     The ROM  810  stores static data and instructions that are needed by the one or more processing unit(s)  812  and other modules of the electronic system  800 . The permanent storage device  802 , on the other hand, may be a read-and-write memory device. The permanent storage device  802  may be a non-volatile memory unit that stores instructions and data even when the electronic system  800  is off. In one or more implementations, a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) may be used as the permanent storage device  802 . 
     In one or more implementations, a removable storage device (such as a floppy disk, flash drive, and its corresponding disk drive) may be used as the permanent storage device  802 . Like the permanent storage device  802 , the system memory  804  may be a read-and-write memory device. However, unlike the permanent storage device  802 , the system memory  804  may be a volatile read-and-write memory, such as random access memory. The system memory  804  may store any of the instructions and data that one or more processing unit(s)  812  may need at runtime. In one or more implementations, the processes of the subject disclosure are stored in the system memory  804 , the permanent storage device  802 , and/or the ROM  810 . From these various memory units, the one or more processing unit(s)  812  retrieves instructions to execute and data to process in order to execute the processes of one or more implementations. 
     The bus  808  also connects to the input and output device interfaces  814  and  806 . The input device interface  814  enables a user to communicate information and select commands to the electronic system  800 . Input devices that may be used with the input device interface  814  may include, for example, alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output device interface  806  may enable, for example, the display of files generated by electronic system  800 . Output devices that may be used with the output device interface  806  may include, for example, printers and display devices, such as a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a flexible display, a flat panel display, a solid state display, a projector, or any other device for outputting information. One or more implementations may include devices that function as both input and output devices, such as a touchscreen. In these implementations, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. 
     Finally, as shown in  FIG. 8 , the bus  808  also couples the electronic system  800  to one or more networks and/or to one or more network nodes, such as the server  108  shown in  FIG. 1 , through the one or more network interface(s)  816 . In this manner, the electronic system  800  can be a part of a network of computers (such as a LAN, a wide area network (“WAN”), or an Intranet, or a network of networks, such as the Internet. Any or all components of the electronic system  800  can be used in conjunction with the subject disclosure. 
     Implementations within the scope of the present disclosure can be partially or entirely realized using a tangible computer-readable storage medium (or multiple tangible computer-readable storage media of one or more types) encoding one or more instructions. The tangible computer-readable storage medium also can be non-transitory in nature. 
     The computer-readable storage medium can be any storage medium that can be read, written, or otherwise accessed by a general purpose or special purpose computing device, including any processing electronics and/or processing circuitry capable of executing instructions. For example, without limitation, the computer-readable medium can include any volatile semiconductor memory, such as RAM, DRAM, SRAM, T-RAM, Z-RAM, and TTRAM. The computer-readable medium also can include any non-volatile semiconductor memory, such as ROM, PROM, EPROM, EEPROM, NVRAM, flash, nvSRAM, FeRAM, FeTRAM, MRAM, PRAM, CBRAM, SONOS, RRAM, NRAM, racetrack memory, FJG, and Millipede memory. 
     Further, the computer-readable storage medium can include any non-semiconductor memory, such as optical disk storage, magnetic disk storage, magnetic tape, other magnetic storage devices, or any other medium capable of storing one or more instructions. In one or more implementations, the tangible computer-readable storage medium can be directly coupled to a computing device, while in other implementations, the tangible computer-readable storage medium can be indirectly coupled to a computing device, e.g., via one or more wired connections, one or more wireless connections, or any combination thereof. 
     Instructions can be directly executable or can be used to develop executable instructions. For example, instructions can be realized as executable or non-executable machine code or as instructions in a high-level language that can be compiled to produce executable or non-executable machine code. Further, instructions also can be realized as or can include data. Computer-executable instructions also can be organized in any format, including routines, subroutines, programs, data structures, objects, modules, applications, applets, functions, etc. As recognized by those of skill in the art, details including, but not limited to, the number, structure, sequence, and organization of instructions can vary significantly without varying the underlying logic, function, processing, and output. 
     While the above discussion primarily refers to microprocessor or multi-core processors that execute software, one or more implementations are performed by one or more integrated circuits, such as ASICs or FPGAs. In one or more implementations, such integrated circuits execute instructions that are stored on the circuit itself. 
     Those of skill in the art would appreciate that the various illustrative blocks, modules, elements, components, methods, and algorithms described herein may be implemented as electronic hardware, computer software, or combinations of both. To illustrate this interchangeability of hardware and software, various illustrative blocks, modules, elements, components, methods, and algorithms have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. Various components and blocks may be arranged differently (e.g., arranged in a different order, or partitioned in a different way) all without departing from the scope of the subject technology. 
     It is understood that any specific order or hierarchy of blocks in the processes disclosed is an illustration of example approaches. Based upon design preferences, it is understood that the specific order or hierarchy of blocks in the processes may be rearranged, or that all illustrated blocks be performed. Any of the blocks may be performed simultaneously. In one or more implementations, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, 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. 
     As used in this specification and any claims of this application, the terms “base station”, “receiver”, “computer”, “server”, “processor”, and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. For the purposes of the specification, the terms “display” or “displaying” means displaying on an electronic device. 
     As used herein, the phrase “at least one of” preceding a series of items, with the term “and” or “or” to separate any of the items, modifies the list as a whole, rather than each member of the list (i.e., each item). The phrase “at least one of” does not require selection of at least one of each item listed; rather, the phrase allows a meaning that includes at least one of any one of the items, and/or at least one of any combination of the items, and/or at least one of each of the items. By way of example, the phrases “at least one of A, B, and C” or “at least one of A, B, or C” each refer to only A, only B, or only C; any combination of A, B, and C; and/or at least one of each of A, B, and C. 
     The predicate words “configured to”, “operable to”, and “programmed to” do not imply any particular tangible or intangible modification of a subject, but, rather, are intended to be used interchangeably. In one or more implementations, a processor configured to monitor and control an operation or a component may also mean the processor being programmed to monitor and control the operation or the processor being operable to monitor and control the operation. Likewise, a processor configured to execute code can be construed as a processor programmed to execute code or operable to execute code. 
     Phrases such as an aspect, the aspect, another aspect, some aspects, one or more aspects, an implementation, the implementation, another implementation, some implementations, one or more implementations, an embodiment, the embodiment, another embodiment, some implementations, one or more implementations, a configuration, the configuration, another configuration, some configurations, one or more configurations, the subject technology, the disclosure, the present disclosure, other variations thereof and alike are for convenience and do not imply that a disclosure relating to such phrase(s) is essential to the subject technology or that such disclosure applies to all configurations of the subject technology. A disclosure relating to such phrase(s) may apply to all configurations, or one or more configurations. A disclosure relating to such phrase(s) may provide one or more examples. A phrase such as an aspect or some aspects may refer to one or more aspects and vice versa, and this applies similarly to other foregoing phrases. 
     The word “exemplary” is used herein to mean “serving as an example, instance, or illustration”. Any embodiment described herein as “exemplary” or as an “example” is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, to the extent that the term “include”, “have”, or the like is used in the description or the claims, such term is intended to be inclusive in a manner similar to the term “comprise” as “comprise” is interpreted when employed as a transitional word in a claim. 
     All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. § 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for”. 
     The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but are to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more”. Unless specifically stated otherwise, the term “some” refers to one or more. Pronouns in the masculine (e.g., his) include the feminine and neuter gender (e.g., her and its) and vice versa. Headings and subheadings, if any, are used for convenience only and do not limit the subject disclosure.

Metadata:
Filing Date: 20180928
Publication Date: 20220215
Grant Date: 20220215
Priority Date: 20180603
Inventors: RANGARAJAN SRIDHAR, VIVEK KUMAR
XU, Xingwen
JAGADEESH, Vignesh
Assignee: APPLE INC
CPC Classifications: [{"code": "G06F16/3338", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/3347", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/322", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F16/35", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/13", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/3347", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F16/322", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/322", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/35", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/3338", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/3347", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F16/13", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 69055138