Dynamic provisioning of data exchanges based on detected relationships within processed image data

Abstract

The disclosed exemplary embodiments include computer-implemented systems, apparatuses, devices, and processes that, among other things, dynamically provision exchanges of data based on detected relationships within processed image data. For example, a network-connected apparatus may receive, from a device, image data that identifies a plurality of individuals associated with an exchange of data. Based on an analysis of the image data, the apparatus may determine a value of a first characteristic associated with each of the individuals and generate relationship data characterizing a relationship between the individuals. The apparatus may also determine candidate values of parameters that characterize the data exchange based on portions of the first characteristic values and the relationship data, transmit the candidate parameter values to the device. An application program executed by the device may cause the device to present at least a portion of the candidate parameter values within a digital interface.

1 - 20 . (canceled) 
     
     
         21 . An apparatus, comprising:
 a communications unit;   a memory storing instructions; and   at least one processor coupled to the communications unit and to the memory, the at least one processor being configured to execute the instructions to:
 receive, via the communications unit, image data that identifies a plurality of individuals from a device; 
 based on an application of a trained machine learning process to elements of the image data, determine a structure of a familial relationship between at least two of the individuals, and generate candidate parameter values of an exchange of data based on the determined structure of the familial relationship; and 
 transmit the candidate parameter values to the device via the communications unit, the candidate parameter values representing discrete elements of a policy associated with the data exchange, and the device being configured to present at least a portion of the candidate parameter values within a digital interface. 
   
     
     
         22 . The apparatus of  claim 21 , wherein the at least one processor is configured to execute the instructions to:
 determine a value of a first characteristic for each of the individuals based on the application of the trained machine learning process to the elements of the image data, the first characteristic comprising at least one of a physical or a demographical parameter of the individuals;   determine the structure of the familial relationship based on the first characteristic values associated with the at least two of the individuals; and   generate the candidate parameter values based on portions of the first characteristic values and the determined structure of the familial relationship.   
     
     
         23 . The apparatus of  claim 22 , wherein the at least one processor is configured to execute the instructions to:
 apply the trained machine learning process to input data, the input data comprising (i) the first characteristic values associated with the at least two of the individuals and (ii) one or more of the elements of image data that identify the at least two of the individuals;   based on the application of the trained machine learning process to the input data, determine a value of a second characteristic associated with the familial relationship, the second characteristic value being consistent with the first characteristic values, and the second characteristic value indicating the determined structure of the familial relationship; and   generate the candidate parameter values based on portions of the first and second characteristic values.   
     
     
         24 . The apparatus of  claim 22 , wherein the at least one processor is configured to execute the instructions to:
 recognize a face of each of the individuals within the image data based on an application of a trained facial recognition process to the elements of the image data; and   determine at least one first spatial position associated with the each of the recognized faces within the image data.   
     
     
         25 . The apparatus of  claim 24 , wherein: the at least one processor is configured to execute the instructions to:
 decompose the image data into a plurality of image data elements based on the first spatial positions, each of the image data elements being associated with a corresponding one of the recognized faces; and   determine the first characteristic value for each of the individuals based on an analysis of the image data elements, the first characteristic value for each of the individuals comprising an age, a gender, a height, or a weight of a corresponding one the individuals.   
     
     
         26 . The apparatus of  claim 24 , wherein the at least one processor is configured to execute the instructions to:
 identify one or more facial features within each of the recognized faces based on the application of the facial recognition process to the elements of the image data;   determine second spatial positions associated with the one or more facial features within each of the recognized faces;   generate input data comprising one or more one or more of elements of the image data and at least one of the first spatial positions or the second spatial positions; and   based on an application of the trained machine learning process to the input data, determine the value of the first characteristic for each of the individuals.   
     
     
         27 . The apparatus of  claim 22 , wherein the at least one processor is further configured to execute the instructions to:
 recognize a physical object within the image data based on an application of a trained object recognition process to one or more of the elements of the image data; and   generate the candidate parameter values based on the first characteristic values associated with the at least two of the individuals, the determined structure of the familial relationship, and an object type associated with the recognized physical object.   
     
     
         28 . The apparatus of  claim 27 , wherein the at least one processor is further configured to execute the instructions to determine the object type associated with the recognized physical object based on the application of the trained object recognition process to the one or more elements of the image data. 
     
     
         29 . The apparatus of  claim 21 , wherein the device is configured to execute an application program, and the executed application program causing the device to:
 present at least the portion of the candidate parameter values within the digital interface;   perform operations that capture the image data via a digital camera or receive the image data from a third-party device; and   transmit the image data to the apparatus.   
     
     
         30 . The apparatus of  claim 21 , wherein:
 the image data identifies the plurality of individuals during a first temporal interval; and   the at least one processor is further configured to execute the instructions to:
 generate elements of training data associated with second temporal interval disposed prior to the first temporal interval, the generated elements of training data comprising additional elements of image data identifying the plurality of individuals during the second temporal interval and outcome data comprising characteristics of each of the plurality of individuals; and 
 perform operations that train the machine learning process based on an application of the machine learning process to the generated elements of training data; and 
 apply the trained machine learning process to the elements of the image data that identify the plurality of individuals during the first temporal interval. 
   
     
     
         31 . A computer-implemented method, comprising:
 receiving, using at least one processor, image data that identifies a plurality of individuals from a device;   based on an application of a trained machine learning process to elements of the image data, determining, using the at least one processor, a structure of a familial relationship between at least two of the individuals, and generating, using the at least one processor, candidate parameter values of an exchange of data based on the determined structure of the familial relationship; and   transmitting the candidate parameter values to the device using the at least one processor, the candidate parameter values representing discrete elements of a policy associated with the data exchange, and the device being configured to present at least a portion of the candidate parameter values within a digital interface.   
     
     
         32 . The computer-implemented method of  claim 31 , wherein:
 the computer-implemented method further comprises determining, using the at least one processor, a value of a first characteristic for each of the individuals based on the application of the trained machine learning process to the elements of the image data, the first characteristic comprising at least one of a physical or a demographical parameter of the individuals;   determining the structure of the familial relationship comprises determining the structure of the familial relationship based on the first characteristic values associated with the at least two of the individuals; and   generating the candidate parameter values comprises determining the candidate parameter values based on portions of the first characteristic values and the determined structure of the familial relationship.   
     
     
         33 . The computer-implemented method of  claim 32 , further comprising:
 recognizing, using the at least one processor, a face of each of the individuals within the image data based on an application of a trained facial recognition process to the elements of the image data; and   determining, using the at least one processor, at least one first spatial position associated with the each of the recognized faces within the image data.   
     
     
         34 . The computer-implemented method of  claim 33 , wherein:
 the computer-implemented method further comprises decomposing, using the at least one processor, the image data into a plurality of image data elements based on the first spatial positions, each of the image data elements being associated with a corresponding one of the recognized faces; and   determining the first characteristic values comprises determining the first characteristic value for each of the individuals based on an analysis of the image data elements, the first characteristic value for each of the individuals comprising an age, a gender, a height, or a weight of a corresponding one of the individuals.   
     
     
         35 . The computer-implemented method of  claim 33 , wherein:
 the computer-implemented method further comprises:
 identifying, using the at least one processor, one or more facial features within each of the recognized faces based on the application of the trained facial recognition process to the elements of the image data; 
 determining, using the at least one processor, second spatial positions associated with the one or more facial features within each of the recognized faces; 
 generating, using the at least one processor, input data comprising one or more one or more of elements of the image data and at least one of the first spatial positions or the second spatial positions; and 
   determining the first characteristic values comprises determining the value of the first characteristic for each of the individuals based on an application of the trained machine learning process to the input data.   
     
     
         36 . The computer-implemented method of  claim 32 , wherein:
 the computer-implemented method further comprises, based on an application of a trained object recognition process to one or more of the elements of the image data, recognizing, using the at least one processor, a physical object within the image data and determining, using the at least one processor, an object type associated with the recognized physical object; and   generating the candidate parameter values comprises generating the candidate parameter values based on the first characteristic values associated with the at least two of the individuals, the determined structure of the familial relationship, and the object type.   
     
     
         37 . The computer-implemented method of  claim 31 , wherein the device is further configured to execute an application program, and the executed application program causing the device to:
 present at least the portion of the candidate parameter values within the digital interface; and   perform operations that capture the image data via a digital camera or receive the image data from a third-party device.   
     
     
         38 . The computer-implemented method of  claim 31 , wherein:
 the image data identifies the plurality of individuals during a first temporal interval; and   the computer-implemented method further comprises:
 generating, using the at least one processor, elements of training data associated with second temporal interval disposed prior to the first temporal interval, the elements of training data comprising additional elements of image data identifying corresponding plurality of individuals during the second temporal interval and outcome data comprising characteristics of each of the plurality of individuals; and 
 performing operations, using the at least one processor, that train the machine learning process based on an application of the machine learning process to the generated elements of training data; and 
 using the at least one processor, applying the trained machine learning process to the elements of the image data that identify the plurality of individuals during the first temporal interval. 
   
     
     
         39 . A device, comprising:
 a display unit;   a communications unit;   a memory storing instructions; and   at least one processor coupled to the display unit, to the communications unit, and to the memory, the at least one processor being configured to execute the instructions to:
 transmit, via the communications unit, image data that identifies a plurality of individuals to a computing system, the computing system being configured to perform operations that, based on an application of a trained machine learning process to elements of the image data, determine a structure of a familial relationship between at least two of the individuals, and generate candidate parameter values of an exchange of data associated with the determined structure of the familial relationship; 
 receive, via the communications unit, data from the computing system that includes the candidate parameter values of the data exchange, the candidate parameter values representing discrete elements of a policy associated with the data exchange; and 
 perform operations that display, using the display unit, the candidate parameter values within a corresponding portion of a digital interface. 
   
     
     
         40 . The device of  claim 39 , further comprising a digital camera coupled to the at least one processor, the at least one processor being further configured to execute the instructions to receive at least a portion of the image data from the digital camera.