MLchartDataset catalogue

Patent · US12153580B2 · B2 · US

Dynamic-ledger-enabled edge-device query processing

(11) Publication number
US12153580B2
(21) Application number
18/180,023
(22) Filing date
2023-03-07
(30) Priority date
2021-05-11
(43) Publication date
2024-11-26
(45) Date of grant
2024-11-26
(51) IPC
G05D 1/00; G06F 16/182; G06F 16/2453; G06F 16/2455; G06F 16/2458; G06F 16/27; G06Q 10/0631; G06Q 10/0833; G06Q 10/087; G06Q 20/38; G06Q 30/0201; G06Q 30/0202; G06V 10/774; H04N 23/67
(52) CPC
  • G06F Electric digital data processing: 16/2455, 16/182, 16/24537, 16/24544, 16/24552, 16/2456, 16/2462, 16/2471, 16/27, 16/278
  • B25J Manipulators; chambers provided with manipulation devices: 11/00, 11/005, 11/008, 9/08, 9/1602, 9/1617, 9/1628, 9/1674, 9/1679, 9/1697
  • B33Y Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering: 30/00, 50/02
  • B63B Ships or other waterborne vessels; equipment for shipping: 25/004
  • B65D Containers for storage or transport of articles or materials, e.g. bags, barrels, bottles, boxes, cans, cartons, crates, drums, jars, tanks, hoppers, forwarding containers; accessories, closures, or fittings therefor; packaging elements; packages: 2588/12, 2590/0083, 88/12
  • G05B Control or regulating systems in general; functional elements of such systems; monitoring or testing arrangements for such systems or elements: 13/0265, 19/4099, 19/418, 19/41865, 19/4188, 2219/49023
  • G05D Systems for controlling or regulating non-electric variables: 1/0291, 1/69
  • G06N Computing arrangements based on specific computational models: 10/00, 20/00, 3/00, 3/04, 3/08, 3/12
  • G06Q Information and communication technology [ICT] specially adapted for administrative, commercial, financial, managerial or supervisory purposes; systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not otherwise provided for: 10/06, 10/06315, 10/08, 10/0833, 10/087, 10/10, 10/103, 20/389, 2220/00, 30/02, 30/0202, 30/0206, 30/0283, 30/06, 40/06
  • G06V Image or video recognition or understanding: 10/774
  • G16Y Information and communication technology specially adapted for the internet of things [IoT]: 30/00, 40/10
  • H04L Transmission of digital information, e.g. telegraphic communication: 2209/56, 67/02, 67/1097, 67/125, 67/34, 67/52, 67/60, 9/50
  • H04N Pictorial communication, e.g. television: 19/00, 23/675
(73) Assignee
Strong Force VCN Portfolio 2019 LLC
(72) Inventors
Charles Howard Cella; Andrew Cardno
(54) Title
Dynamic-ledger-enabled edge-device query processing
(57) Abstract

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

Full text
View on Google Patents

Claims (20)

  1. A method for processing a query for data stored in a distributed database, the method comprising: receiving, at an edge device, the query for the data stored in the distributed database from a query device; causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database; detecting, by the edge device, that summary data has been stored on the dynamic ledger; generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger, by: generating, using the summary data, a probability distribution model for data corresponding to the query, and generating, using the probability distribution model, the approximate response; and transmitting, to the query device, the approximate response.
  2. The method of claim 1 wherein the query is an edge query language (EDQL) query.
  3. The method of claim 1 wherein: the query specifies a shard algorithm; and the shard algorithm specifies a location of the data stored in the distributed database.
  4. The method of claim 1 wherein the dynamic ledger is a blockchain.
  5. The method of claim 1 wherein the causing the query to be stored on the dynamic ledger includes transmitting, by the edge device, the query to an aggregator.
  6. The method of claim 5 wherein the aggregator is a blockchain node.
  7. The method of claim 1 further comprising: receiving a second query for data stored in the distributed database; and generating an approximate response to the second query using the probability distribution model without causing the second query to be stored on the dynamic ledger.
  8. The method of claim 1 wherein: the probability distribution model is implemented using a neural network, and the generating the probability distribution model includes training the neural network.
  9. The method of claim 1 further comprising: generating a query plan in response to the receiving the query, wherein the query plan includes at least one of: transmitting of the query to other edge devices or transmitting of the query to an aggregator.
  10. The method of claim 1 further comprising executing the query against an edge storage connected to the edge device to obtain partial query results.
  11. The method of claim 10 wherein the approximate response to the query is further based on the partial query results.
  12. The method of claim 1 wherein the summary data includes at least one of statistical data or outlier data.
  13. The method of claim 1 wherein at least a portion of the data stored in the distributed database is sensor data.
  14. The method of claim 1 wherein the approximate response to the query is associated with a response that exceeds a statistical confidence threshold.
  15. An edge device system comprising: at least one processor that executes a set of computer-readable instructions, wherein, by executing the set of computer-readable instructions, the at least one processor collectively: receives a query for data stored in a distributed database from a query device; causes the query to be stored on a dynamic ledger maintained by the distributed database; detects, by an edge device, that summary data has been stored on the dynamic ledger; generates, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger, by: generating, using the summary data, a probability distribution model for data corresponding to the query, and generating, using the probability distribution model, the approximate response; and transmits, to the query device, the approximate response.
  16. The edge device system of claim 15 wherein the query is an edge query language (EDQL) query.
  17. The edge device system of claim 15 wherein the dynamic ledger is a blockchain.
  18. The edge device system of claim 15 wherein the summary data includes at least one of statistical data or outlier data.
  19. The edge device system of claim 15 wherein at least a portion of the data stored in the distributed database is sensor data.
  20. A method for processing a query for data stored in a distributed database, the method comprising: receiving, at an edge device, the query for the data stored in the distributed database from a query device; causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database; detecting, by the edge device, that summary data has been stored on the dynamic ledger, including detecting that a threshold percentage of edge devices have caused the summary data to be stored on the dynamic ledger; generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger; and transmitting, to the query device, the approximate response.

Description

The present disclosure relates to information technology methods and systems for management of value chain network entities, including supply chain and demand management entities. The present disclosure also relates to the field of enterprise management platforms, more particularly involving an edge-distributed database and query language for storing and retrieving value chain data.

Historically, many of the various categories of goods purchased and used by household consumers, by businesses and by other customers were been supplied mainly through a relatively linear fashion, in which manufacturers and other suppliers of finished goods, components, and other items handed off items to shipping companies, freight forwarders and the like, who delivered them to warehouses for temporary storage, to retailers, where customers purchased them, or directly to customer locations. Manufacturers and retailers undertook various sales and marketing activities to encourage and meet demand by customers, including designing products, positioning them on shelves and in advertising, setting prices, and the like.

Orders for products were fulfilled by manufacturers through a supply chain, such as depicted in FIG. 1, where suppliers 122 in various supply environments 160, operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order.

Citations (69)

  • US20140222610A1
  • US6829654B1
  • US20020073086A1
  • US20070240061A1
  • US20080009968A1
  • US20110238468A1
  • US20140365258A1
  • US20130211870A1
  • US9582603B1
  • US20130268520A1
  • WO2014015492A1
  • KR20140054897A
  • US20160203407A1
  • US20150088807A1
  • US20150312125A1
  • CN104036007A
  • US20160236867A1
  • US20200273278A1
  • US20170032281A1
  • US20170057081A1
  • US20180136633A1
  • US20200012966A1
  • US20180157688A1
  • US20200111092A1
  • US20180322157A1
  • US20200050612A1
  • WO2019070644A2
  • US20210178575A1
  • US20190138662A1
  • US20210084105A1
  • US20190317935A1
  • US20190266278A1
  • US20190278527A1
  • US20190042878A1
  • US20200294129A1
  • US20200294128A1
  • US20190347358A1
  • WO2018172593A2
  • US20200294142A1
  • US20200019935A1
  • US20200097582A1
  • US20190050854A1
  • US20200104730A1
  • US20200118131A1
  • US20200153627A1
  • US20200193449A1
  • US20200160288A1
  • US20200184559A1
  • US20200184560A1
  • US20200184565A1
  • US20200210966A1
  • WO2020142499A1
  • US10938634B1
  • US11438221B1
  • US20200272664A1
  • US10986173B1
  • US10642847B1
  • US20200372104A1
  • US20210118166A1
  • US20210133669A1
  • US20210138656A1
  • US20210166188A1
  • US20210201236A1
  • US20210342339A1
  • US20210383523A1
  • US11762442B1
  • US20220044117A1
  • EP4180953A1
  • US20220198782A1
Record as JSON 
{
  "publication_number": "US12153580B2",
  "country": "US",
  "kind": "B2",
  "title": "Dynamic-ledger-enabled edge-device query processing",
  "abstract": "A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.",
  "claims": [
    "1. A method for processing a query for data stored in a distributed database, the method comprising: receiving, at an edge device, the query for the data stored in the distributed database from a query device; causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database; detecting, by the edge device, that summary data has been stored on the dynamic ledger; generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger, by: generating, using the summary data, a probability distribution model for data corresponding to the query, and generating, using the probability distribution model, the approximate response; and transmitting, to the query device, the approximate response.",
    "2. The method of claim 1 wherein the query is an edge query language (EDQL) query.",
    "3. The method of claim 1 wherein: the query specifies a shard algorithm; and the shard algorithm specifies a location of the data stored in the distributed database.",
    "4. The method of claim 1 wherein the dynamic ledger is a blockchain.",
    "5. The method of claim 1 wherein the causing the query to be stored on the dynamic ledger includes transmitting, by the edge device, the query to an aggregator.",
    "6. The method of claim 5 wherein the aggregator is a blockchain node.",
    "7. The method of claim 1 further comprising: receiving a second query for data stored in the distributed database; and generating an approximate response to the second query using the probability distribution model without causing the second query to be stored on the dynamic ledger.",
    "8. The method of claim 1 wherein: the probability distribution model is implemented using a neural network, and the generating the probability distribution model includes training the neural network.",
    "9. The method of claim 1 further comprising: generating a query plan in response to the receiving the query, wherein the query plan includes at least one of: transmitting of the query to other edge devices or transmitting of the query to an aggregator.",
    "10. The method of claim 1 further comprising executing the query against an edge storage connected to the edge device to obtain partial query results.",
    "11. The method of claim 10 wherein the approximate response to the query is further based on the partial query results.",
    "12. The method of claim 1 wherein the summary data includes at least one of statistical data or outlier data.",
    "13. The method of claim 1 wherein at least a portion of the data stored in the distributed database is sensor data.",
    "14. The method of claim 1 wherein the approximate response to the query is associated with a response that exceeds a statistical confidence threshold.",
    "15. An edge device system comprising: at least one processor that executes a set of computer-readable instructions, wherein, by executing the set of computer-readable instructions, the at least one processor collectively: receives a query for data stored in a distributed database from a query device; causes the query to be stored on a dynamic ledger maintained by the distributed database; detects, by an edge device, that summary data has been stored on the dynamic ledger; generates, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger, by: generating, using the summary data, a probability distribution model for data corresponding to the query, and generating, using the probability distribution model, the approximate response; and transmits, to the query device, the approximate response.",
    "16. The edge device system of claim 15 wherein the query is an edge query language (EDQL) query.",
    "17. The edge device system of claim 15 wherein the dynamic ledger is a blockchain.",
    "18. The edge device system of claim 15 wherein the summary data includes at least one of statistical data or outlier data.",
    "19. The edge device system of claim 15 wherein at least a portion of the data stored in the distributed database is sensor data.",
    "20. A method for processing a query for data stored in a distributed database, the method comprising: receiving, at an edge device, the query for the data stored in the distributed database from a query device; causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database; detecting, by the edge device, that summary data has been stored on the dynamic ledger, including detecting that a threshold percentage of edge devices have caused the summary data to be stored on the dynamic ledger; generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger; and transmitting, to the query device, the approximate response."
  ],
  "description_excerpt": "The present disclosure relates to information technology methods and systems for management of value chain network entities, including supply chain and demand management entities. The present disclosure also relates to the field of enterprise management platforms, more particularly involving an edge-distributed database and query language for storing and retrieving value chain data.\n\nHistorically, many of the various categories of goods purchased and used by household consumers, by businesses and by other customers were been supplied mainly through a relatively linear fashion, in which manufacturers and other suppliers of finished goods, components, and other items handed off items to shipping companies, freight forwarders and the like, who delivered them to warehouses for temporary storage, to retailers, where customers purchased them, or directly to customer locations. Manufacturers and retailers undertook various sales and marketing activities to encourage and meet demand by customers, including designing products, positioning them on shelves and in advertising, setting prices, and the like.\n\nOrders for products were fulfilled by manufacturers through a supply chain, such as depicted in FIG. 1, where suppliers 122 in various supply environments 160, operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order.",
  "cpc": [
    "G06F 16/2455",
    "B25J 11/00",
    "B25J 11/005",
    "B25J 11/008",
    "B25J 9/08",
    "B25J 9/1602",
    "B25J 9/1617",
    "B25J 9/1628",
    "B25J 9/1674",
    "B25J 9/1679",
    "B25J 9/1697",
    "B33Y 30/00",
    "B33Y 50/02",
    "B63B 25/004",
    "B65D 2588/12",
    "B65D 2590/0083",
    "B65D 88/12",
    "G05B 13/0265",
    "G05B 19/4099",
    "G05B 19/418",
    "G05B 19/41865",
    "G05B 19/4188",
    "G05B 2219/49023",
    "G05D 1/0291",
    "G05D 1/69",
    "G06F 16/182",
    "G06F 16/24537",
    "G06F 16/24544",
    "G06F 16/24552",
    "G06F 16/2456",
    "G06F 16/2462",
    "G06F 16/2471",
    "G06F 16/27",
    "G06F 16/278",
    "G06N 10/00",
    "G06N 20/00",
    "G06N 3/00",
    "G06N 3/04",
    "G06N 3/08",
    "G06N 3/12",
    "G06Q 10/06",
    "G06Q 10/06315",
    "G06Q 10/08",
    "G06Q 10/0833",
    "G06Q 10/087",
    "G06Q 10/10",
    "G06Q 10/103",
    "G06Q 20/389",
    "G06Q 2220/00",
    "G06Q 30/02",
    "G06Q 30/0202",
    "G06Q 30/0206",
    "G06Q 30/0283",
    "G06Q 30/06",
    "G06Q 40/06",
    "G06V 10/774",
    "G16Y 30/00",
    "G16Y 40/10",
    "H04L 2209/56",
    "H04L 67/02",
    "H04L 67/1097",
    "H04L 67/125",
    "H04L 67/34",
    "H04L 67/52",
    "H04L 67/60",
    "H04L 9/50",
    "H04N 19/00",
    "H04N 23/675"
  ],
  "ipc": [
    "G05D 1/00",
    "G06F 16/182",
    "G06F 16/2453",
    "G06F 16/2455",
    "G06F 16/2458",
    "G06F 16/27",
    "G06Q 10/0631",
    "G06Q 10/0833",
    "G06Q 10/087",
    "G06Q 20/38",
    "G06Q 30/0201",
    "G06Q 30/0202",
    "G06V 10/774",
    "H04N 23/67"
  ],
  "assignees": [
    "Strong Force VCN Portfolio 2019 LLC"
  ],
  "inventors": [
    "Charles Howard Cella",
    "Andrew Cardno"
  ],
  "filing_date": "2023-03-07",
  "publication_date": "2024-11-26",
  "grant_date": "2024-11-26",
  "priority_date": "2021-05-11",
  "application_number": "US-202318180023-A",
  "family_id": "83977512",
  "cited_by_count": 2,
  "citations": [
    "US20140222610A1",
    "US6829654B1",
    "US20020073086A1",
    "US20070240061A1",
    "US20080009968A1",
    "US20110238468A1",
    "US20140365258A1",
    "US20130211870A1",
    "US9582603B1",
    "US20130268520A1",
    "WO2014015492A1",
    "KR20140054897A",
    "US20160203407A1",
    "US20150088807A1",
    "US20150312125A1",
    "CN104036007A",
    "US20160236867A1",
    "US20200273278A1",
    "US20170032281A1",
    "US20170057081A1",
    "US20180136633A1",
    "US20200012966A1",
    "US20180157688A1",
    "US20200111092A1",
    "US20180322157A1",
    "US20200050612A1",
    "WO2019070644A2",
    "US20210178575A1",
    "US20190138662A1",
    "US20210084105A1",
    "US20190317935A1",
    "US20190266278A1",
    "US20190278527A1",
    "US20190042878A1",
    "US20200294129A1",
    "US20200294128A1",
    "US20190347358A1",
    "WO2018172593A2",
    "US20200294142A1",
    "US20200019935A1",
    "US20200097582A1",
    "US20190050854A1",
    "US20200104730A1",
    "US20200118131A1",
    "US20200153627A1",
    "US20200193449A1",
    "US20200160288A1",
    "US20200184559A1",
    "US20200184560A1",
    "US20200184565A1",
    "US20200210966A1",
    "WO2020142499A1",
    "US10938634B1",
    "US11438221B1",
    "US20200272664A1",
    "US10986173B1",
    "US10642847B1",
    "US20200372104A1",
    "US20210118166A1",
    "US20210133669A1",
    "US20210138656A1",
    "US20210166188A1",
    "US20210201236A1",
    "US20210342339A1",
    "US20210383523A1",
    "US11762442B1",
    "US20220044117A1",
    "EP4180953A1",
    "US20220198782A1"
  ]
}

Record 301 of 8,000 in Patents full text (MLC-0201). Request the full dataset.