Patent Publication Number: US-2021182774-A1

Title: Intelligent planogram producing system and method thereof

Description:
This application claims the benefit of Taiwan application Serial No. 108145260, filed Dec. 11, 2019, the disclosure of which is incorporated by reference herein in its entirety. 
     TECHNICAL FIELD 
     The disclosure relates in general to an intelligent planogram producing system and method thereof capable of increasing the recognition rate. 
     BACKGROUND 
     Planogram is a diagram indicates the placement of objects in conventional stores or warehouses. The planning of planogram plays an important role in the fields of retailing and warehousing. For the retailing field, a well-planned planogram could increase sales and make the most of the space. For the warehousing field, a well-planned planogram could increase the access rate and make the most of the space. 
     Conventionally, the planogram is planned by people or is produced according to the statistic analysis based on the historical data such as sales and the disposing location of products. In response to the rise of unmanned stores and unmanned warehouses, the recognition of objects on the shelf does not merely depend on human eyes. If the machine has a poor recognition rate in recognizing the objects on the shelf, access error or replenishment error may easily occur. Therefore, it has become a prominent task for the industries to provide a planogram with high recognition rate of objects. 
     SUMMARY 
     The present disclosure relates to an intelligent planogram producing system and a method thereof capable of increasing the recognition rate of objects. 
     According to one embodiment of the present disclosure, an intelligent planogram producing method is provided. The intelligent planogram producing method includes the following steps: obtaining a relevance between each of a plurality of objects and producing a relevance array; re-weighting the relevance array according to the displacing limitation of each object and producing at least one complete graph; obtaining a representing route of the at least one complete graph; outputting a planogram of the disposing location of each object on a shelf according to the representing route. In the at least one complete graph, each vertex represents an object, every two vertexes are connected by an edge whose value represents a re-weighted relevance, and the representing route, being the route with minimum summation of the value of each edge, passes through each edge only once. 
     According to another embodiment of the present disclosure, an intelligent planogram producing system is provided. The intelligent planogram producing system includes a relevance array producing unit, a complete graph creating unit, a route analysis unit and an output unit. The relevance array producing unit is configured to obtain a relevance between each of a plurality of objects to produce a relevance array. The complete graph creating unit is configured to convert the relevance array and re-weight the relevance array according to the displacing limitation of each object to obtain at least one complete graph, wherein in the at least one complete graph, each vertex represents an object, and every two vertexes are connected by an edge whose value represents a re-weighted relevance. The route analysis unit is configured to obtain a representing route of the at least one complete graph, wherein the representing route, being the route with minimum summation of the value of each edge, passes through each edge only once. The output unit is configured to output a planogram of the disposing location of each object on a shelf according to each at least one representing route. 
     The above and other aspects of the disclosure will become better understood with regards to the following detailed description of the preferred but non-limiting embodiment (s). The following description is made with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of an intelligent planogram producing system according to an embodiment. 
         FIG. 2  is a flowchart of an intelligent planogram producing method according to an embodiment. 
         FIG. 3A  is a schematic diagram of to-be-placed objects according to an embodiment. 
         FIG. 3B  is a schematic diagram of a complete graph obtained according to the relevance array of Table 1. 
         FIG. 3C  is a schematic diagram of a representing route obtained according to  FIG. 3B . 
         FIG. 3D  is a planogram according to  FIG. 3C . 
         FIG. 4A-4C  are schematic diagrams of a complete graph obtained according to the original relevance array, a complete graph obtained according to the re-weighted relevance array, and a representing route according to another embodiment. 
         FIG. 5A  is a schematic diagram of to-be-placed objects according to an alternate embodiment. 
         FIG. 5B  is at least one complete graph produced by grouping and re-weighting the to-be-placed objects of  FIG. 5A . 
         FIG. 5C  is a schematic diagram of a representing route obtained according to  FIG. 5B . 
     
    
    
     In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. It will be apparent, however, that one or more embodiments may be practiced without these specific details. In other instances, well-known structures and devices are schematically shown in order to simplify the drawing. 
     DETAILED DESCRIPTION 
     The present disclosure increases the recognition rate of objects by using suitable relevance analysis method. Detailed descriptions are disclosed in several embodiments below. However, the contents disclosed in the embodiments below are not for limiting the scope of protection of the present disclosure. 
     Referring to  FIG. 1 , a schematic diagram of an intelligent planogram producing system  10  according to an embodiment is shown. The intelligent planogram producing system  10  includes a relevance array producing unit  100 , a complete graph creating unit  200 , a route analysis unit  300  and an output unit  400 . The relevance array producing unit  100  includes a receiver  1100  and a relevance array producer  1200 . The complete graph creating unit  200  includes a re-weighter  2100 , a graph creator  2200  and a group calculator  2300 . The route analysis unit  300  includes an analyzer  3100  and a screener  3200 . The relevance array producing unit  100 , the complete graph creating unit  200 , the route analysis unit  300 , the receiver  1100 , the relevance array producer  1200 , the re-weighter  2100 , the graph creator  2200 , the group calculator  2300 , the analyzer  3100  and the screener  3200  could be realized by such as a circuit, a chip, a circuit board, a or multiple programming codes, or a storage device storing multiple programming codes. The output unit  400  could be realized by such as a wireless network transmission device, a wired network transmission device, a memory card access device, a connection port, a keyboard, a screen, or a combination thereof. The operations of the above elements are disclosed below with a flowchart. 
     Referring to  FIG. 2 , a flowchart of an intelligent planogram producing method according to an embodiment is shown. In step S 100 , relevance between each of a plurality of to-be-placed objects is obtained by the relevance array producing unit  100  from the receiver  1100 , and a relevance array is outputted by the relevance array producer  1200 . Referring to  FIG. 3A , a schematic diagram of to-be-placed objects according to an embodiment is shown. As indicated in  FIG. 3A , the features of to-be-placed objects P 1 ˜P 5  could be obtained from the image information, the weight information, and the appearance information such as length, height or width of the objects. In the present embodiment, the image information is taken for example, but the present disclosure is not limited thereto. After the receiver  1100  receives the image information of the to-be-placed objects P 1 ˜P 5 , the relevance array producer  1200  calculates a relevance between each of a plurality of the to-be-placed objects P 1 ˜P 5  and produces a relevance array as indicated in Table 1. 
     
       
         
           
               
               
               
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 P1 
                 P2 
                 P3 
                 P4 
                 P5 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 P1 
                 1.0 
                 0.1 
                 0.6 
                 0.5 
                 0.2 
               
               
                   
                 P2 
                 0.1 
                 1.0 
                 0.1 
                 0.7 
                 0.3 
               
               
                   
                 P3 
                 0.6 
                 0.1 
                 1.0 
                 0.2 
                 0.6 
               
               
                   
                 P4 
                 0.5 
                 0.7 
                 0.2 
                 1.0 
                 0.2 
               
               
                   
                 P5 
                 0.2 
                 0.3 
                 0.6 
                 0.2 
                 1.0 
               
               
                   
                   
               
            
           
         
       
     
     In step S 200 , the relevance array is re-weighted by the complete graph creating unit  200  according to the displacing limitation of each object to produce at least one complete graph. Referring to  FIG. 3B , a schematic diagram of a complete graph obtained according to the relevance array of Table 1 is shown. In an embodiment as indicated in  FIG. 3B , the to-be-placed objects P 1 ˜P 5  are represented by vertexes of the complete graph, every two vertexes are connected by an edge, which represents a re-weighted relevance between the two vertexes. In the present embodiment, since the displacing limitation has not yet been applied to the to-be-placed objects, the graph creator  2200  of the complete graph creating unit  200  could directly create a complete graph as indicated in  FIG. 3B  according to the relevance array produced by the relevance array producer  1200 . 
     In another embodiment as indicated in  FIG. 4A  and  FIG. 4B , a complete graph obtained according to the original relevance array and a complete graph obtained according to the re-weighted relevance array are respectively shown. In the present embodiment, given that the number of to-be-placed objects P 1 ˜P 7  is 7, the available places on the shelf is 5, and the to-be-placed objects P 1 ˜P 4  must be placed together, which means the displacing limitation of each object is adjacency and recommendation. That is, with the objects P 1 ˜P 4  taking 4 of the 5 places, there is an available place left unoccupied, and one of the objects P 5 ˜P 7  could be recommended to take this place. Based on the displacing limitation of each object disclosed above, the re-weighter  2100  of the complete graph creating unit  200  provides a corresponding weight. For example, if the objects P 1 ˜P 4  must be adjacent, then the re-weighter  2100  provides a weight, such as 0.5. When the weight is multiplied by the original relevance value, the weighted value of each edge of the objects P 1 ˜P 4  on the complete graph is less than the original relevance. Besides, the original relevance value could be deducted by the weight, and the weight could be any value as long as the weighted value of the edge whose vertexes are subjected to the displacing limitation of adjacency is less than the original relevance, and the present disclosure is not limited thereto. If the displacing limitation of each objects P 5 ˜P 7  is recommendation, then the re-weighter  2100  provides another weight, such as 1. When another weight is added to the original relevance value, the weighted value of each edge connecting one of the objects P 5 ˜P 7  and other vertex on the complete graph is greater than the original relevance. Or, the another weight could be set to be greater than 1, and the original relevance value is multiplied by the another weight, and the weight could be any value as long as the weighted value of the edge whose vertexes are subjected to the displacing limitation of recommendation is greater than the original relevance. Through the re-weighting operation of the re-weighter  2100 , the graph creator  2200  could produce a complete graph as indicated in  FIG. 4B . 
     In the embodiment as indicated in  FIGS. 4A and 4B , the relevance array is firstly converted to a complete graph ( FIG. 4A ), and then the value of each edge is re-weighted to produce a re-weighted complete graph ( FIG. 4B ). According to the present disclosure, instead of producing a complete graph as indicated in  FIG. 4A  and then producing a re-weighted complete graph as indicated in  FIG. 4B , the relevance array of Table 1 could be directly re-weighted to produce a re-weighted complete graph. 
     Referring to  FIG. 5A , a schematic diagram of to-be-placed objects according to an alternate embodiment is shown. Among the to-be-placed objects P 11 ˜P 20 , the to-be-placed objects P 11 ˜P 14 , P 15 ˜P 17  and P 18 ˜P 20  respectively are of the same brand. The objects P 18  and P 19  are of the same object. Since one layer of the shelf could serve only 5 objects and objects of the same brand need to be placed together, the displacing limitation of each object is adjacency and repetition. Due to the restriction of available places on one layer of the shelf, the group calculator  2300  of the complete graph creating unit  200 , first of all, divides the to-be-placed objects P 11 ˜P 20  into different groups. For example, the group calculator  2300  performs the multi-label graph cut grouping algorithm to divide the objects P 11 ˜P 14  into two groups. Furthermore, since objects of the same brand need to be placed together, the group calculator  2300  performs grouping in the manner that the relevance between the two groups is minimized but the relevance within the same group is maximized. For example, the objects P 11  and P 13  are grouped as one group, and the objects P 12  and P 14  are grouped as another group. Then, the re-weighter  2100  performs re-weighting according to the displacing limitation of each object to produce two complete graphs. For the objects subjected to the displacing limitation of adjacency, the re-weighting operation is already disclosed above and therefore is not repeated here. For the objects subjected to the displacing limitation of repetition, the re-weighter  2100  provides a weight, which causes the weighted value of the edge whose vertexes are subjected to the displacing limitation of repetition to be equivalent to 0. Then, the graph creator  2200  of the complete graph creating unit  200  produces two complete graphs, wherein the vertexes of one complete graph include P 11 , P 13 , and P 15 ˜P 20 , the vertexes of the other complete graph include P 12 , P 14  and P 15 ˜P 20  as indicated in  FIG. 5B .  FIG. 5B  is at least one complete graph produced by grouping and re-weighting the to-be-placed objects of  FIG. 5A . To make the complete graph simple and easy to read, only the edge length of the objects P 18  and P 19  are marked. Since the displacing limitation of each object is repetition, the re-weighted value of edge length is equivalent to 0, and the values of remaining edge lengths, which are re-weighted in the same way as the above embodiment, are not repeated here. 
     Then, the method proceeds to step S 300 , a representing route of each of the at least one complete graph is obtained by the route analysis unit  300  through analysis. Referring to  FIG. 3C . Through analysis, the route analysis unit  300  could obtain a route, which passes through each edge only once and has the minimum summation of the value of each edge. This route, marked by bold lines, is referred as the representing route. In step S 400 , a planogram of the disposing location of each of the objects P 1 ˜P 5  on the shelf as indicated in  FIG. 3D  is outputted by the output unit  400  according to the representing route.  FIG. 3D  is a planogram according to  FIG. 3C . As indicated in  FIG. 3D , the objects are disposed form left to right in the order of P 2 , P 1 , P 5 , P 4  and P 3 , and the order of the objects corresponds to the order of the vertexes on the representing route of  FIG. 3C . Also, the left-to-right order could be reversed to a right-to-left order, by which the objects are disposed in the order of P 3 , P 4 , P 5 , P 1  and P 2  as long as the relevance between adjacent objects is the minimum. Thus, the recognition rate of objects could be increased, and recognition error could be reduced. 
     According to another embodiment, in step S 300 , since the number of placeable objects is 5, the analyzer  3100  of the route analysis unit  300  could analyze the complete graph to obtain multiple routes, which pass through any 5 vertexes but pass through the edges of the 5 vertexes only once and further summarize these routes as a route list. Then, the screener  3200  of the route analysis unit  300  screens the route list to obtain a route with a minimum summation of the value of each edge as the representing route as indicated in  FIG. 4C . As indicated in  FIG. 4C , the representing route includes 5 vertexes in the order of P 3 , P 2 , P 1 , P 4  and P 5 , and the reverse order would also do. Thus, the route analysis unit  300  of the present disclosure recommends the object P 5  rather than the object P 6  or the object P 7 . In step S 400 , a planogram of the disposing location of each object on the shelf is outputted by the output unit  400  according to the representing route. 
     According to an alternate embodiment, in step S 300 , since one layer of the shelf could serve only 5 objects, the analyzer  3100  of the route analysis unit  300  could analyze the two complete graphs to obtain multiple routes, which pass through any 5 vertexes but pass through the edges of the 5 vertexes only once and further summarize these routes as a route list. Then, the screener  3200  of the route analysis unit  300  screens the route list to obtain a route with a minimum summation of the value of each edge as the representing route as indicated in  FIG. 5C . Lastly, in step S 400 , a planogram of the disposing location of each object on the shelf is outputted by the output unit  400  according to the representing route. The operation in this regard is similar to that in the above embodiment, and the details are not repeated here. 
     According to the intelligent planogram producing system and method disclosed in above embodiments, a planogram with higher recognition rate is obtained according to the relevance array, the re-weighting of the complete graph and the analysis of the representing route. Thus, the unmanned store or the unmanned warehouse could increase the accuracy of object disposition and make the most of the space. 
     It will be apparent to those skilled in the art that various modifications and variations could be made to the disclosed embodiments. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims and their equivalents.