Patent ID: 11915180
Assignee: BENCHMARK SOLUTIONS, LLC
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. A computer-implemented method for determining a risk score reflecting the risk that a police officer may be involved in an adverse event in the future, comprising:
preprocessing, with a preprocessing module, collected, historical data about a plurality of characteristics and events relating to a plurality of police officers to create a first-level residual feature ε=representing the number of arrests that the officer has more or less than the average, holding all other variables constant, as follows:

Y=β0+unit+geo+time+ε, where:
Y=the number of arrests that have occurred in a certain time period for the officer
β0=the average number of arrests for the officer's department, 𝕍
   unit
  
  =
  
   
    
     
      ∑
       
     
     
      
       unit
       i
      
      =
      0
     
     u
    
    ⁢
    
     β
     
      unit
      i
     
    
    *
    
     Unit
     i
    
   
   =
   
    A
    ⁢
       
    vector
    ⁢
        
    of
    ⁢
       
    coefficients
    ⁢
       
    that
    ⁢
       
    represent
    ⁢
        
    units
    ⁢
      
    the
    ⁢
       
    plurality
    ⁢
       
    of
    ⁢
       
    police
    ⁢
       
    officers
    ⁢
       
    work
    ⁢
       
    in
    ⁢
       
    and
    ⁢
       
    whether
    ⁢
       
    the
    ⁢
       
    individual
    ⁢
       
    officer
    ⁢
       
    works
    ⁢
       
    in
    ⁢
       
    that
    ⁢
       
    unit
    ⁢
       
    
     (
     
      
       the
       ⁢
          
       coefficient
       ⁢
          
       is
       ⁢
          
       the
       ⁢
          
       difference
       ⁢
          
       between
       ⁢
          
       that
       ⁢
          
       
        unit
        '
       
       ⁢
       s
       ⁢
          
        
       number
       ⁢
          
       of
       ⁢
          
       arrests
       ⁢
          
       and
       ⁢
          
       the
       ⁢
          
       average
      
      ,
      
       holding
       ⁢
          
       all
       ⁢
          
       else
       ⁢
          
       constant
      
     
     )
    
   
  
 

 
  
   𝕍
   
    g
    ⁢
    e
    ⁢
    o
   
  
  =
  
   
    
     
      ∑
       
     
     
      
       g
       ⁢
       e
       ⁢
       
        o
        i
       
      
      =
      0
     
     g
    
    ⁢
    
     β
     
      g
      ⁢
      e
      ⁢
      
       o
       i
      
     
    
    *
    G
    ⁢
    e
    ⁢
    
     o
     i
    
   
   =
   
    A
    ⁢
       
    vector
    ⁢
       
    of
    ⁢
       
    coefficients
    ⁢
       
    that
    ⁢
       
    represent
    ⁢
       
    the
    ⁢
       
    geographies
    ⁢
       
    the
    ⁢
       
    plurality
    ⁢
       
    of
    ⁢
       
    police
    ⁢
       
    officers
    ⁢
       
    are
    ⁢
       
    deployed
    ⁢
       
    to
    ⁢
       
    and
    ⁢
       
    whether
    ⁢
       
    the
    ⁢
       
    individual
    ⁢
       
    police
    ⁢
       
    officer
    ⁢
       
    works
    ⁢
       
    in
    ⁢
       
    that
    ⁢
       
    individual
    ⁢
       
    
     geography
     ⁡
     (
     
      
       the
       ⁢
          
       coefficient
       ⁢
          
       is
       ⁢
          
       the
       ⁢
          
       difference
       ⁢
          
       between
       ⁢
          
       that
       ⁢
          
       
        geography
        '
       
       ⁢
       s
       ⁢
          
        
       number
       ⁢
          
       of
       ⁢
          
       arrests
       ⁢
          
       and
       ⁢
          
       the
       ⁢
          
       average
      
      ,
      
       holding
       ⁢
          
       all
       ⁢
          
       else
       ⁢
          
       constant
      
     
     )
    
   
  
 

 
  
   
    𝕍
    
     t
     ⁢
     i
     ⁢
     m
     ⁢
     e
    
   
   =
   
    
     
      
       ∑
        
      
      
       
        t
        ⁢
        i
        ⁢
        m
        ⁢
        
         e
         i
        
       
       =
       0
      
      t
     
     ⁢
     
      β
      
       time
       i
      
     
     *
     T
     ⁢
     i
     ⁢
     m
     ⁢
     
      e
      i
     
    
    =
    
     A
     ⁢
        
     vector
     ⁢
        
     of
     ⁢
        
     coefficients
     ⁢
        
     that
     ⁢
        
     represent
     ⁢
        
     the
     ⁢
        
     time
    
   
  
  ⁢
  ‐
  ⁢
  
   periods
   ⁢
      
   during
   ⁢
      
   the
   ⁢
      
   day
   ⁢
      
   when
   ⁢
      
   an
   ⁢
      
   the
   ⁢
      
   individual
   ⁢
      
   police
   ⁢
      
   officer
   ⁢
      
   is
   ⁢
      
   deployed
   ⁢
      
   and
   ⁢
      
   whether
   ⁢
      
   the
   ⁢
     
   individual
   ⁢
      
   officer
   ⁢
      
   works
   ⁢
      
   in
   ⁢
      
   that
   ⁢
      
   time
   ⁢
      
   
    period
    ⁡
    (
    
     
      the
      ⁢
         
      coefficient
      ⁢
         
      is
      ⁢
         
      the
      ⁢
         
      difference
      ⁢
         
      between
      ⁢
         
      that
      ⁢
         
      time
     
     ⁢
     ‐
     ⁢
     
      
       period
       '
      
      ⁢
      s
      ⁢
         
      number
      ⁢
         
      of
      ⁢
         
      arrests
      ⁢
         
      and
      ⁢
         
      the
      ⁢
         
      average
     
     ,
     
      holding
      ⁢
         
      all
      ⁢
         
      else
      ⁢
         
      constant
     
    
    )
   
  
 

ε=the officer's residual=The number of arrests that the individual officer has more or less than the average, holding all other variables constant;
constructing and storing, with a machine learning module, a plurality of models that predict the risk of an adverse event and generate a risk score, each of the models including the first-level residual feature ε;
running each of the models, with the machine learning module, using the collected, historical data and for identifying which one of the plurality of models would have best predicted an adverse event in the past and saving the identified model for use by the system to predict an adverse incident in the future; and,
running the identified model, with a model application module, using data about a plurality of characteristics and events collected about the officer and generating with the model application module a risk score for the officer and other information relating to the risk score, the risk score indicating a level of risk that a police officer may be involved in an adverse event in the future;
outputting the risk score and the other information relating to the risk score from the model application module to a display and displaying, on the display, the risk score and information relating to the officer's risk score.