A computer-implemented method and system defines a uniform decision-tree formation to store decision-making processes. Each node in a decision tree represents a factor decision. All nodes of a decision tree are interlinked in a hierarchical structure based on a decision-making process. Any decision tree of the present invention can serve as a sub-tree of another decision tree. Users can convert their decision-making processes into decision trees and make collaborative decisions through network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-based system and method of defining a uniform formation using a distributed decision-tree structure to convert and store people's decision-making processes, comprising of: a) defining all nodes of decision trees using an uniform formation; b) linking said nodes to form a decision tree; c) linking two nodes by storage addresses, wherein one is the parent node and the another one is the child node; d) mapping output values of a node to input values of its parent node; e) storing said plurality of nodes in a readable storage medium by computer devices; f) linking another decision tree to the current decision tree as a sub-tree; g) storing said sub-tree in either the same or a different data storage medium; h) performing the same decision processing steps in said each node. 2. The method of claim 1, wherein all nodes have the same components that include a set of factor functions, a set of action functions, a set of weight functions, a set of processing functions, a set of input counters, a set of decision functions, a selection function, a conclusion function, an output function, and a set of learning functions. 3. The method of claim 2, further comprising: a) a set of factor functions, F={F1, . . . Fi, . . . , Fn}, defining values and a range of decision factors, wherein n is a positive integer number; b) a set of action functions, A={A1, . . . Ai, . . . , An}, defining a list of actions; c) a set of factor inputs, X={x1, . . . , xj, . . . xm}, being collected from human inputs, child nodes, data sources, and/or software applications, where xjΣ{F1, . . . , Fi, . . . , Fn}, 1≦j≦m, and m is a positive integer number; d) a set of weight functions, W(X)={W1(x1), . . . Wj(xj), . . . , Wm(xm)}, assigning weight values to the corresponding factor inputs in the set of X; e) a set of decision functions, D(F)={D1(F1), . . . Di(Fi), . . . , Dn(Fn)}, determining each factor-decision-action relation or the Di(Fi)=Aj, where 1≦j≦n; f) a set of input counters, N={N1, . . . , Ni, . . . , Nn}, storing weighted input values of each corresponding factor Fi, where 1≦i≦n; g) a set of processing functions, P(X, W, F)={P1(X, W, F1), . . . , Pi(X, W, Fi), . . . , Pn(X, W, Fn)}, calculating each weighted input value N, of the factor Fi or Pi(X, W, Fi)=Ni based on collected factor inputs and assigned weight values, where 1≦i≦n; h) an output function R(A, N) producing a set of output actions {Ak, . . . , Aj, . . . , Ap} based on values in the set of N, where 1≦k, k≦j≦p and p≦n; i) a selection function a(t) collecting an action Ar being taken at time t, where Arε{Ak, . . . Aj, . . . , Ap} and k≦r≦p; j) a conclusion function c(t) collecting an action Aq that is considered to be a correct action at time t, where Aqε{A1, . . . Ai, . . . , An} and 1≦q≦n; k) a set of matrices, M={M1, . . . , Mi, . . . Mn}, storing decision historical data, wherein the Mi stores the last s pairs of taken and correct actions {[a(t1), c(t1)], . . . [a(tj), c(tj)], . . . , [a(ts), c(ts)}], wherein the s is a length of the matrix M t, is a time sequence, and Di(Fi)=a(tj); l) A set of learning functions, L(M)={L1(M1), . . . Li(Mi), . . . , Ln(Mn)}, adjusting the decision functions D(F), wherein the Li(Mi) can modify a decision function from the current Di(Fi)=Aj to a new decision function Di′(Fi)=Ak based on statistics of decision historical data stored in the matrix M, and 1≦i≦n. 4. The method of claim 3, wherein said a function can be, but not limited to, an executable program, data link, constant value, or database query and the value of a function can be a number, range, fuzzy value, percentage, multiple status, text, or statistics. 5. The method of claim 3, wherein a set of the function P(X, W, F) collects factor inputs from human, child nodes, data sources, and/or software applications, calculates input values with assigned weight functions, and determines which factor value is used in the decision process of the node. 6. The method of claim 1, wherein a set of action functions A={A1, . . . Ai, . . . , An} of a node being mapped to a set of factor functions F={F1, . . . , Fi, . . . , Fn} of its parent node or Ai→Fi. 7. The method of claim 3, wherein the decision outputs of every node is available for generating decision reports. 8. The method of claim 3, wherein an action output of the root node can trigger control actions or other decision processes. 9. The method of claim 3, wherein input counters and output actions of all nodes of a decision tree can used for generating a decision report. 10. The method of claim 5, wherein a user can specify input sources for each node. 11. The method of claim 5, wherein a user can set whether a node participates in the current decision process or not. 12. The method of claim 1, wherein a decision process of a decision tree can be performed on multiple computer devices including, but not limited to, a personal computers, computer server, tablets, smart phones, and cloud servers. 13. The method of claim 10, wherein a decision tree can be processed in multiple computer processors. 14. The method of claim 10, wherein any sub-tree of a decision tree can be processed in a computer process independently. 15. The method of claim 1, wherein the distributed decision trees can be stored in encrypted formation. 16. The method of claim 3, wherein a user can define functions for a node. 17. The method of claim 3, wherein a user can schedule to adjust decision functions using learning functions. 18. The method of claim 1, wherein users can share decision trees by a copying or linking method.