diff --git a/utils/hashfunctions.py b/utils/hashfunctions.py
index 9f7bcca..6c41197 100644
--- a/utils/hashfunctions.py
+++ b/utils/hashfunctions.py
@@ -69,11 +69,12 @@ def augmentAggregatedHashes(url, agg_df: pd.DataFrame) -> pd.DataFrame:
     return aug_df
  
 def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publishers: list):
-   if untrusted_publishers is None:
-       untrusted_publishers = []
+    if untrusted_publishers is None:
+        untrusted_publishers = []
 
-   df = aug_df.copy()
-   def reputationtool(row, threat_tolerance):
+    df = aug_df.copy()
+
+    def reputationtool(row, threat_tolerance):
         if row["reputation_scannermatch"] == "N/A":
             return True
         try:
@@ -83,11 +84,15 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
             pass
         return False
 
-   mask_needsreview = (df["publisher_y"] == "Not Signed") & df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)
-   mask_approved = (df["publisher_y"] != "Not Signed") & (~df["publisher_y"].isin(untrusted_publishers))
+    mask_needsreview = (df["publisher_y"] == "Not Signed") & df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)
 
-   needsreview_df = df[mask_needsreview]
-   approved_df = df[mask_approved]
-   remaining_df = df[~(mask_needsreview | mask_approved)]
+    mask_approved = (
+        ((df["publisher_y"] != "Not Signed") & (~df["publisher_y"].isin(untrusted_publishers))) |
+        ((df["publisher_y"] == "Not Signed") & (~df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)))
+    )
 
-   return needsreview_df, approved_df, remaining_df
+    needsreview_df = df[mask_needsreview]
+    approved_df = df[mask_approved]
+    remaining_df = df[~(mask_needsreview | mask_approved)]
+
+    return needsreview_df, approved_df, remaining_df
diff --git a/utils/pathfunctions.py b/utils/pathfunctions.py
index 735c969..128da2a 100644
--- a/utils/pathfunctions.py
+++ b/utils/pathfunctions.py
@@ -41,18 +41,44 @@ def filepathInitialGroup(df: pd.DataFrame):
         return join_parts(prefix)
 
     # Step 6: Group directories by shared prefix using custom logic
+    """
+    Loop through each directory path
+    directories: list of all directory paths.
+    groups: will hold lists of grouped directories.
+    used: tracks which directories have already been grouped.
+    """
     directories = df["directory"].tolist()
     groups = []
     used = set()
 
+    #For Each directory, compare it with others
+    """
+    Skip if already grouped.
+    Start a new group with the current path.
+    parts_i is the list of folder names in the path (e.g., ["C:", "Users", "John", "Documents"]).
+    """
+    
     for i, path in enumerate(directories):
         if path in used:
             continue
         group = [path]
         parts_i = get_parts(path)
+
+    #Compare with all other directories: For each other directory, split it into parts and find the common prefix (shared folder structure).
+        """
+        Logic:
+        If the directory is deep (>3 parts) and shares at least 3 parts → group it.
+        If it's exactly 3 parts long and shares at least 2 → group it.
+        Or, if it shares all but one part and is deep → group it.
+        These rules are designed to:
+        Group directories that are closely related in structure.
+        Avoid grouping unrelated paths that just happen to start similarly.
+        """  
+
         for j in range(i + 1, len(directories)):
             parts_j = get_parts(directories[j])
             common = os.path.commonprefix([parts_i, parts_j])
+        #Apply grouping rules
             if (len(parts_i) > 3 and len(common) >= 3) or (len(parts_i) == 3 and len(common) >= 2):
                 group.append(directories[j])
                 used.add(directories[j])
@@ -81,7 +107,7 @@ def filepathInitialGroup(df: pd.DataFrame):
     path_ineligible = grouped_df[grouped_df["depth"] <= 2].drop(columns=["depth"])
 
     # Step 10: Move entries from eligible to ineligible if grouped_directory contains 'C:\Users' or 'c$\Users'
-    mask = path_eligible["grouped_directory"].str.contains(r"(?i)(?:\\Users|\\c\$\\Users)")
+    mask = path_eligible["grouped_directory"].str.contains(r"(?i)(?:\\Users|\\c\$\\Users|inetpub\\wwwroot|windows\\temp)", na=False)
     move_to_ineligible = path_eligible[mask]
     path_eligible = path_eligible[~mask]
     path_ineligible = pd.concat([path_ineligible, move_to_ineligible], ignore_index=True)