Methods, systems, articles of manufacture, and apparatus to map client specifications with standardized characteristics

{
  "publication_number": "US11163805B2",
  "country": "US",
  "kind": "B2",
  "title": "Methods, systems, articles of manufacture, and apparatus to map client specifications with standardized characteristics",
  "abstract": "Methods, systems, articles of manufacture, and apparatus are disclosed to map client specifications to standardized characteristics. An example apparatus includes a cluster identifier to cluster client databases into client clusters based on a threshold quantity of overlapping universal product codes (UPCs) between respective ones of the client databases, a characteristic analyzer to identify custom characteristics from the respective ones of the client clusters, ones of the custom characteristics having dissimilar nomenclature, and a graph builder to cluster the ones of the custom characteristics based on a similarity metric, and normalize the ones of the custom characteristics as a proxy characteristic, the proxy characteristic having a common nomenclature to represent the ones of the custom characteristics, the characteristic analyzer to enable improved product marketing analysis by replacing dissimilar nomenclature with the proxy characteristic.",
  "claims": [
    "1. An apparatus to map client specifications with standardized characteristics, the apparatus comprising: a cluster identifier to cluster client databases into client clusters based on a threshold quantity of overlapping universal product codes (UPCs) between respective ones of the client databases; a characteristic analyzer to identify custom characteristics from the respective ones of the client clusters, ones of the custom characteristics having dissimilar nomenclature; and a graph builder to: cluster the ones of the custom characteristics based on a similarity metric; and normalize the ones of the custom characteristics as a proxy characteristic, the proxy characteristic having a common nomenclature to represent the ones of the custom characteristics, the characteristic analyzer to enable improved product marketing analysis by replacing dissimilar nomenclature with the proxy characteristic.",
    "2. The apparatus as defined in claim 1, wherein the graph builder is to: generate first nodes associated with UPCs having first ones of the custom characteristics; and generate second nodes associated with UPCs having second ones of the custom characteristics.",
    "3. The apparatus as defined in claim 2, wherein the first ones of the custom characteristics have a first nomenclature, and the second ones of the custom characteristics have a second nomenclature different than the first nomenclature.",
    "4. The apparatus as defined in claim 2, wherein the graph builder is to generate a micro-similarity score between pairs of (a) the first nodes and (b) the second nodes.",
    "5. The apparatus as defined in claim 4, wherein the graph builder is to generate the micro-similarity scores based on a threshold overlap of UPCs between the pairs of the first and second nodes.",
    "6. The apparatus as defined in claim 4, wherein the graph builder is to apply Maximum Weighted Bipartite Graph Matching (MWBGM) to the first nodes and the second nodes, respective first ones of the second nodes paired with respective ones of the first nodes based on a relative maximum micro-similarity score, and respective second ones of the second nodes unpaired with the respective ones of the first nodes based on not satisfying the relative maximum micro-similarity score.",
    "7. The apparatus as defined in claim 6, wherein the graph builder is to merge the second ones of the second nodes with respective ones of the first ones of the second nodes.",
    "8. A non-transitory computer readable medium including instructions, which when executed, cause at least one processor to, at least: cluster client databases into client clusters based on a threshold quantity of overlapping universal product codes (UPCs) between respective ones of the client databases; identify custom characteristics from the respective ones of the client clusters, ones of the custom characteristics having dissimilar nomenclature; cluster the ones of the custom characteristics based on a similarity metric; normalize the ones of the custom characteristics as a proxy characteristic, the proxy characteristic having a common nomenclature to represent the ones of the custom characteristics; and enable improved product marketing analysis by replacing dissimilar nomenclature with the proxy characteristic.",
    "9. The non-transitory computer readable medium as defined in claim 8, wherein the instructions, when executed, cause the at least one processor to: generate first nodes associated with UPCs having first ones of the custom characteristics; and generate second nodes associated with UPCs having second ones of the custom characteristics.",
    "10. The non-transitory computer readable medium as defined in claim 9, wherein the instructions, when executed, cause the at least one processor to identify first ones of the custom characteristics have a first nomenclature, and identify second ones of the custom characteristics have a second nomenclature different than the first nomenclature.",
    "11. The non-transitory computer readable medium as defined in claim 9, wherein the instructions, when executed, cause the at least one processor to generate a micro-similarity score between pairs of (a) the first nodes and (b) the second nodes.",
    "12. The non-transitory computer readable medium as defined in claim 11, wherein the instructions, when executed, cause the at least one processor to generate the micro-similarity scores based on a threshold overlap of UPCs between the pairs of the first and second nodes.",
    "13. The non-transitory computer readable medium as defined in claim 11, wherein the instructions, when executed, cause the at least one processor to apply Maximum Weighted Bipartite Graph Matching (MWBGM) to the first nodes and the second nodes, respective first ones of the second nodes paired with respective ones of the first nodes based on a relative maximum micro-similarity score, and respective second ones of the second nodes unpaired with the respective ones of the first nodes based on not satisfying the relative maximum micro-similarity score.",
    "14. The non-transitory computer readable medium as defined in claim 13, wherein the instructions, when executed, cause the at least one processor to merge the second ones of the second nodes with respective ones of the first ones of the second nodes.",
    "15. A method to map client specifications with standardized characteristics, the method comprising: clustering, by executing an instruction with at least one processor, client databases into client clusters based on a threshold quantity of overlapping universal product codes (UPCs) between respective ones of the client databases; identifying, by executing an instruction with the at least one processor, custom characteristics from the respective ones of the client clusters, ones of the custom characteristics having dissimilar nomenclature; clustering, by executing an instruction with the at least one processor, the ones of the custom characteristics based on a similarity metric; normalizing, by executing an instruction with the at least one processor, the ones of the custom characteristics as a proxy characteristic, the proxy characteristic having a common nomenclature to represent the ones of the custom characteristics; and enabling, by executing an instruction with the at least one processor, improved product marketing analysis by replacing dissimilar nomenclature with the proxy characteristic.",
    "16. The method as defined in claim 15, further including: generating first nodes associated with UPCs having first ones of the custom characteristics; and generating second nodes associated with UPCs having second ones of the custom characteristics.",
    "17. The method as defined in claim 16, wherein the first ones of the custom characteristics have a first nomenclature, and the second ones of the custom characteristics have a second nomenclature different than the first nomenclature.",
    "18. The method as defined in claim 16, further including generating a micro-similarity score between pairs of (a) the first nodes and (b) the second nodes.",
    "19. The method as defined in claim 18, further including generating the micro-similarity scores based on a threshold overlap of UPCs between the pairs of the first and second nodes.",
    "20. The method as defined in claim 18, further including applying Maximum Weighted Bipartite Graph Matching (MWBGM) to the first nodes and the second nodes, respective first ones of the second nodes paired with respective ones of the first nodes based on a relative maximum micro-similarity score, and respective second ones of the second nodes unpaired with the respective ones of the first nodes based on not satisfying the relative maximum micro-similarity score.",
    "21. An apparatus comprising: at least one memory; instructions in the apparatus; and processor circuitry to execute the instructions to: cluster client databases into client clusters based on a threshold quantity of overlapping universal product codes (UPCs) between respective ones of the client databases; identify custom characteristics from the respective ones of the client clusters, ones of the custom characteristics having dissimilar nomenclature; cluster the ones of the custom characteristics based on a similarity metric; normalize the ones of the custom characteristics as a proxy characteristic, the proxy characteristic having a common nomenclature to represent the ones of the custom characteristics; and enable improved product marketing analysis by replacing dissimilar nomenclature with the proxy characteristic.",
    "22. The apparatus as defined in claim 21, wherein the processor circuitry is to: generate first nodes associated with UPCs having first ones of the custom characteristics; and generate second nodes associated with UPCs having second ones of the custom characteristics.",
    "23. The apparatus as defined in claim 22, wherein the processor circuitry is to identify first ones of the custom characteristics have a first nomenclature, and identify second ones of the custom characteristics have a second nomenclature different than the first nomenclature.",
    "24. The apparatus as defined in claim 22, wherein the processor circuitry is to generate a micro-similarity score between pairs of (a) the first nodes and (b) the second nodes.",
    "25. The apparatus as defined in claim 24, wherein the processor circuitry is to generate the micro-similarity scores based on a threshold overlap of UPCs between the pairs of the first and second nodes.",
    "26. The apparatus as defined in claim 24, wherein the processor circuitry is to apply Maximum Weighted Bipartite Graph Matching (MWBGM) to the first nodes and the second nodes, respective first ones of the second nodes paired with respective ones of the first nodes based on a relative maximum micro-similarity score, and respective second ones of the second nodes unpaired with the respective ones of the first nodes based on not satisfying the relative maximum micro-similarity score.",
    "27. The apparatus as defined in claim 26, wherein the processor circuitry is to merge the second ones of the second nodes with respective ones of the first ones of the second nodes."
  ],
  "description_excerpt": "This disclosure relates generally to characteristics normalization and, more particularly, to methods, systems, articles of manufacture, and apparatus to map client specifications with standardized characteristics.\n\nIn recent years, cloud-based platforms have been combining data, analytics, and role-based applications to deliver actionable insights to manufacturers and retailers. Onboarding a client to have the proper architecture to function within the cloud-based platforms typically requires mapping custom client specifications with previously entered characteristics.\n\nFIG. 1 illustrates an example custom specifications mapping system.\n\nFIG. 2 is a block diagram of an example characteristic analyzer.\n\nFIG. 3 is a diagram representative of an example characterization breakdown tree.\n\nFIGS. 4A, 4B, and 4C are diagrams representative of an example graph matching process that implements an iterative process to create concept clusters.\n\nFIG. 5-8 are flowcharts representative of example methods that may be executed by the example characteristic analyzer of FIGS. 1 and/or 2 to map custom client specifications with standardized characteristics.\n\nFIG. 9 is a block diagram of an example processing platform structured to execute machine readable instructions to implement the methods of FIGS. 5-8 and/or the example characteristic analyzer of FIGS. 1 and/or 2.\n\nThe figures are not to scale. In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts.",
  "cpc": [
    "G06F 16/285",
    "G06F 16/24578",
    "G06F 16/258",
    "G06F 16/9024",
    "G06Q 30/0201"
  ],
  "ipc": [
    "G06F 16/2457",
    "G06F 16/25",
    "G06F 16/28",
    "G06F 16/901",
    "G06Q 30/02"
  ],
  "assignees": [
    "Nielsen Co US LLC"
  ],
  "inventors": [
    "Cesar Arocho",
    "Jonathan Sullivan",
    "Michael D. Morgan",
    "Andrew Stannard",
    "Kali Bogovich",
    "Calvin James Bissett",
    "Logan THOMAS"
  ],
  "filing_date": "2019-11-25",
  "publication_date": "2021-11-02",
  "grant_date": "2021-11-02",
  "priority_date": "2019-11-25",
  "application_number": "US-201916694623-A",
  "family_id": "75973976",
  "cited_by_count": 19,
  "citations": [
    "US6654731B1",
    "US20120284269A1",
    "US20080208671A1",
    "US8620919B2",
    "US20110213784A1",
    "KR20110110683A",
    "US20120303412A1",
    "US8583648B1",
    "US20180293294A1",
    "US20190057430A1"
  ]
}