GB2573519A

GB2573519A - Verified resource supply system and method of operation thereof

Info

Publication number: GB2573519A
Application number: GB1807456.7A
Authority: GB
Inventors: Philip Gardner Stephen; Lykke Moller Gert; Erik Jensen Claus
Original assignee: Rowanalytics Ltd
Current assignee: Rowanalytics Ltd
Priority date: 2018-05-07
Filing date: 2018-05-07
Publication date: 2019-11-13
Also published as: GB201807456D0

Abstract

A method of verifying resources using a blockchain includes ledger arrangement hosted by a data processing arrangement into which entries representative of resources are recorded in the blockchain. The verified resource supply system includes voting arrangement for a plurality of members to input votes in respect of whether or not the entries of the blockchain are valid. The votes are derived from a multidimensional array system model accessing a database arrangement. Data recorded in database arrangement describes the suppliers of the resources of the entries of the blockchain and the expected properties of the resources represented by the entries. The ledger arrangement controls in operation whether or not a given entry of the blockchain is to be retained in the blockchain depending upon the votes. Resources include feedstock for use in feeding animals, food supplements for animals and drugs for animals.

Description

VERIFIED RESOURCE SUPPLY SYSTEM AND METHOD OF OPERATION THEREOF

TECHNICAL FIELD

The present disclosure relates generally to verified resource supply systems, namely to verified resource supply systems that employ blockchain ledger arrangements to verify a quality of feed materials used in agricultural industry; the invention relates to operation of the verified resource supply systems wherein the verified resource supply systems employ a new configuration of essential features. Moreover, the present disclosure also relates to method of (for) operating aforesaid verified resource supply systems. Furthermore, the present disclosure also relates to computer program products comprising a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computerreadable instructions being executable by a computerized device comprising processing hardware to execute aforementioned methods.

BACKGROUND

In recent years, there has been a rapid increase in the demand of livestock in agricultural setting to obtain labour and commodities. Typically, livestock includes animals such as cows, goats, horses, chicken, pigs and so forth that are raised for meat, eggs, milk and other functional use. Additionally, the process of breeding, maintenance and raising the livestock is termed as animal husbandry. Notably, a large number of rural populations depend on the process of animal husbandry. Consequently, the process of animal husbandry plays an important role in rural economy.

However, the process of animal husbandry is greatly affected due to the negligence in the health of the livestock. Specifically, the quality of the feedstock play a vital role in maintaining the health of the livestock. Typically, the feedstock is raw material such as an agricultural foodstuff, specifically used to feed the livestock. Therefore, an unverified quality of feedstock may result in serious health issues related to the livestock. Furthermore, such health issues pertaining to the livestock may also result in increased maintenance cost, a loss in income and difficulty in operations. Moreover, a health issue pertaining to one of the member of a heard may possess a threat for the entire herd and may further affect the human population by adulterating food chain.

Conventionally, various statutory standards pertaining to feedstock have been put into practice. However, mostly, the standards are simply ignored by the suppliers or distributors of the feedstock. Additionally, several suppliers or distributors of the feedstock merely label the sub-standard feedstock to conform to the statutory. However, such conformity pertaining to the feedstock does not exist in practice. Furthermore, there is no system to verify persistently, the standard of the feedstock supplied by the supplier or the distributor of the feedstock. Moreover, the standard of the feedstock assured by the supplier is not supervised continuously leading to downgraded quality of the delivered feedstock.

It will be appreciated that inadequate monitoring on animal feed resulted in a costly crisis in the United Kingdom in relation to BSE, wherein huge economic loss to farmers occurred, and many healthy animals had to be slaughtered to try to reduce a risk of cross-contamination of pathogens from farm animals to human beings. In will be appreciated that Bovine spongiform encephalopathy (BSE), commonly known as mad cow disease”, is a transmissible spongiform encephalopathy and fatal neurodegenerative disease in cattle that may be passed to humans who have eaten infected flesh. BSE causes a spongiform degeneration of the brain and spinal cord. BSE has a long incubation period, of 2.5 to 5 years, usually affecting adult cattle at a peak age onset of four to five years. BSE is caused by a misfolded protein, namely a prion. In the United Kingdom, more than 180,000 cattle were infected and 4.4 million slaughtered during the eradication program. In France, the country worst affected overall, over 300,000 cases were identified, although most were not recorded at the time.

Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with the conventional resource supply systems

SUMMARY

The present disclosure seeks to provide a verified resource supply system that supplies resources that have been verified using a voting arrangement.

The present disclosure also seeks to provide a method of using a verified resource supply system that supplies resources that have been verified using a voting arrangement

According to a first aspect, an embodiment of the present disclosure provides a verified resource supply system that is useable in operation to verify the resources using a voting arrangement, wherein the verified resource supply system employs a data processing arrangement that provides a platform through which members interact with the verified resource supply system, characterized in that (i) the verified resource supply system includes a ledger arrangement hosted by the data processing arrangement into which entries representative of resources are recorded in a blockchain, wherein the entries include characterizing data that are submitted to describe technical properties of the resources to be verified;

(ii) the verified resource supply system includes the voting arrangement for a plurality of members of the verified resource supply system to input votes in respect of whether or not the entries of the blockchain are valid, wherein the votes are derived from data recorded in an array system model of the data processing arrangement describing one or more suppliers of the resources of the entries of the blockchain and information describing expected properties of the resources represented by the entries; and (ill) the ledger arrangement controls in operation whether or not a given entry of the blockchain is to be retained in the blockchain depending upon the votes, and also whether the resources when verified have been allocated for use or are available to be allocated for use.

Optionally, the array system model is a multi-dimensional array system model. By ''multi-dimensional is meant 3-dimensional or greater, more optionally, 4-dimensional or greater, yet more optionally 5-dimensional or greater, yet more optionally 10-dimensional or greater, yet more optionally 20-dimensional or greater, and even yet more optionally 50-dimensional or greater (if a size of data set of sensor signals generated by the sensor arrangement allows). Embodiments of the invention also optionally employ a multi-dimension combinatorial grouping of an order 3 or greater, more optionally of an order 4 or greater, yet more optionally of an order 5 or greater, yet more optionally of an order 10 or greater, yet more optionally of an order 20 or greater, and even yet more optionally of an order 50 or greater (if a size of data set of sensor signals generated by the sensor arrangement allows).

The verified resource supply system with the blockchain technology enables validation of the transaction and quality associated with a resource.

Typically, the verified resource supply system ensures that the safety standards of the resources are maintained. Beneficially, the verified resource supply system employs blockchain technology, which assures that no damage (such as, tamper or manipulation) has been done to the data recorded associated with the resource.

Optionally, the verified resource supply system includes a membership control arrangement for the members, wherein the members are operable to vote after the members have been approved by a peer voting arrangement of the system.

Optionally, the resources include at least one of: feedstock for use in feeding animals, food supplements for animals, drugs for animals.

The multi-dimensional array system model describes a model spanned by state variables on finite domains and/or intervals, wherein the state variables include one or more suppliers of the resources of the entries and information describing expected properties of the resources represented by the entries, wherein the multi-dimensional inference engine is operable to:

(i) generate and store, in the database arrangement of the data processing arrangement, an addressable solution space defining all valid states or combinations satisfying a conjunction of substantially all system constraints on all variables; and (ii) generate and store, one or more object functions or values associated with the addressable solution space to make the values addressable from an environment including the state variables.

Optionally, that the multi-dimensional inference engine is further operable to use the addressable solution space to process one or more inputs provided to the verified resource supply system when in operation, and to generate corresponding outputs from the database arrangement.

Optionally, the voting arrangement prevents contaminated resources from being presented and allocated via use of the blockchain.

Optionally, that the contaminated resources include at least one of: mould contamination, radioactive contamination, chemical residue contamination, heavy metal poisoning, recycled animal-derived pathogens.

In a second aspect, an embodiment of the present disclosure provides a method of (for) operating a verified resource supply system that supplies resources that have been verified using a voting arrangement, wherein the verified resource supply system employs a data processing arrangement that provides a platform through which members interact with the verified resource supply system, characterized in that the method includes:

(i) arranging for the verified resource supply system to include a ledger arrangement hosted by the data processing arrangement into which entries representative of resources are recorded in a blockchain, wherein the entries include characterizing data that are submitted to describe technical properties of the resources to be verified;

(II) using the voting arrangement of the verified resource supply system, wherein the voting arrangement includes for a plurality of members of the verified resource supply system, to input votes in respect of whether or not the entries of the blockchain are valid, and wherein the votes are derived from an array system model describing one or more suppliers of the resources of the entries of the blockchain and information describing expected properties of the resources represented by the entries; and (iii) using the ledger arrangement to control in operation whether or not a given entry of the blockchain is to be retained in the blockchain depending upon the votes, and also whether the resources when verified have been allocated for use or are available to be allocated for use.

Optionally, the method includes arranging for the verified resource supply system to include a membership control arrangement for the members, wherein the members are operable to vote after the members have been approved by a peer voting arrangement of the system.

Optionally, the method includes arranging for the array system model to be interrogated via a multi-dimensional inference engine.

Optionally, the method includes generating a multi-dimensional describing a model spanned by state variables on finite domains and/or intervals, wherein the state variables include one or more suppliers of the resources of the entries and information describing expected properties of the resources represented by the entries, wherein the method include arranging for the multi-dimensional search engine to:

(i) generate and store, in a multi-dimensional arrangement of the data processing arrangement, an addressable solution space defining all valid states or combinations satisfying a conjunction of substantially all system constraints on all variables; and (ii) generate and store, one or more object functions or values associated with the addressable solution space to make the values addressable from an environment including the state variables.

Optionally, the method further includes arranging for the multi-dimensional inference engine to use the addressable solution space to process one or more inputs provided to the verified resource supply system when in operation, and to generate corresponding outputs from the multi-dimensional data arrangement.

Optionally, the method includes arranging the voting arrangement to prevent contaminated resources from being presented and allocated via use of the blockchain.

Optionally, the contaminated resources include at least one of: mould contamination, radioactive contamination, chemical residue contamination, heavy metal poisoning, recycled animal-derived pathogens.

In a third aspect, embodiments of the present disclosure provide a computer program product comprising a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer readable instructions being executable by a computerized device comprising processing hardware to execute the method of operating a verified resource supply system that supplies resources that have been verified using a voting arrangement.

Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments construed in conjunction with the appended claims that follow.

It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

FIG. 1 is a block diagram of the verified resource supply system, in accordance with an embodiment of the present disclosure; and

FIG. 2 is an illustration of steps of a method of (for) operating a verified resource supply system, in accordance with an embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

DESCRIPTION OF EMBODIMENTS

In overview, embodiments of the present disclosure are concerned with a resource supply system that is useable in operation to verify a resource, and a method of using a resource supply system.

Referring to FIG. 1, there is shown a block diagram of the verified resource supply system 100, in accordance with an embodiment of the present disclosure. As shown, the verified resource supply system 100 employs a data processing arrangement 102 that provides a platform 104 through which members interact with the verified resource supply system 100. The verified resource supply system 100 includes a ledger arrangement 106 hosted by the data processing arrangement 102 into which entries representative of resources are recorded in a blockchain. The verified resource supply system 100 includes a voting arrangement 108 for a plurality of members of the verified resource supply system 100 to input votes in respect of whether or not the entries of the blockchain are valid.

It will be appreciated that the aforesaid verified resource supply system 100 is not limited to verifying a resource supply system for only a single use case, and can be employed to verify a plurality of resources. In such a case, separate resource supply systems are implemented for each of the plurality of resources.

Throughout the present disclosure, the term 'resource' as used herein relates to a source or supply from which a benefit is produced. Notably, the resource is a substance that is required by a living organism for normal growth, maintenance and reproduction.

Optionally, the resources include at least one of: feedstock for use in feeding animals, food supplements for animals, drugs for animals. In an embodiment, a resource may be a food substance such as a feedstock for feeding an animal. In such a case, the food substance is consumed to provide a nutritional support for the animal. Typically, the food substance is of plant or animal origin and contains essential nutrients such as carbohydrates, fats, proteins, vitamins and minerals. Furthermore, the food substance is ingested by the animal and assimilated by the organs of the animal to provide energy and nourishment. The energy and nourishment extracted from the food substance is utilized by the animal to sustain life and simulate growth.

In another embodiment, a resource may be a food supplement. In such a case, the food supplement may be used adjunct to the feedstock for feeding an animal. Notably, the food supplement is a concentrated source of nutrients such as carbohydrates, fats, proteins, vitamins and minerals. Specifically, the food supplement is a substance with a nutritional or physiological effect. Additionally, the food supplement is used to supplement the normal diet. Furthermore, the food supplement is used to ameliorate nutritional deficiencies or maintain an adequate intake of certain nutrients.

In yet another embodiment, a resource may be a drug for an animal. In such a case, the drug may be used to cause a biological, physiological or psychological change in the body of the animal. Typically, the drug is a chemical substance of known structure. Furthermore, the drug is a substance distinct to a nutrient which is ingested by the animal. Notably, the drug is also termed as a medication or a medicine. Furthermore, the drug is used to prevent or cure any irregularity such as a disease pertaining to the health of the animal. Consequently, the drug is administered into the animal to promote well-being of the animal. Specifically, the drug may be extracted from medicinal plants or synthesized organically. Moreover, the drug may be used for a limited duration or on a regular basis for chronic disease.

The conveyance of the resource from a supplier to a consumer is known as resource supply system. Notably, the transfer or conveyance of resource from the supplier to the consumer is aided by a transaction between the supplier and the consumer. Typically, the transaction between the supplier and the consumer is an exchange of a monetary value associated with the resource for a given quantity of the resource and a given quality of the resource. Furthermore, the transactions associated with the resource supply system is verified using the voting arrangement, as discussed later herein.

The verified resource supply system 100 employs a data processing arrangement 102 for the operation of the verified resource supply system 100. Throughout the present disclosure, the term data processing arrangement as used herein relates to programmable and/or nonprogrammable components configured to execute one or more software application for storing, processing and/or sharing data and/or set of instructions. Optionally, the data processing arrangement 102 can include, for example, a component included within an electronic communications network. Additionally, the data processing arrangement 102 include one or more data processing facilities for storing, processing and/or sharing data and/or set of instructions. Furthermore, the data processing arrangement 102 includes hardware, software, firmware or a combination of these, suitable for storing and processing various information and services accessed by the one or more member using the one or more platform. Optionally, the data processing arrangement 102 include functional components, for example, a display, a processor, a memory, a network adapter and so forth. Optionally, a platform 104 is rendered on the display of the data processing arrangement 102. Furthermore, the platform enables a member to interact with the data processing arrangement 102 and with other components of the verified resource supply system 100.

The data processing arrangement 102 is operable to store, process and/or share data and/or set of instructions, stored in a database arrangement 110, to be accessed by the members of the verified resource supply system 100 using a network. Throughout the present disclosure, the term network relates to an arrangement of interconnected programmable and/or nonprogrammable components that are configured to facilitate data communication between systems employed by the data processing arrangement and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations. Additionally, the network includes wired or wireless communication that can be carried out via any number of known protocols, including, but not limited to, Internet Protocol (IP), Wireless Access Protocol (WAP), Frame Relay, or Asynchronous Transfer Mode (ATM).

Optionally, the term 'member' as used herein relates to a person having knowledge pertaining to at least one of: the resources, statutory standards associated with the resource, computing ledger. More optionally, the member is any entity including a person (i.e., human being) or a virtual personal assistant (an autonomous program or a bot) using the verified resource supply system 100 described herein.

The member is operable to access the data processing arrangement 102 using the platform 104. Furthermore, the platform 104 enables the member to access the data associated with the transaction and/or the resources, stored in the database arrangement 110 or the data processing arrangement 102 by employing the network. Throughout the present disclosure, the term 'platform' relates to a structured set of user interface elements rendered on a display screen. Optionally, the platform 104 rendered on the display screen of the data processing arrangement 102 is generated by any collection or set of instructions executable by an associated digital system. Additionally, the platform 104 is operable to interact with the members to convey graphical and/or textual information and receive input from the members. Specifically, the platform 104 used herein is a graphical user interface (GUI). Furthermore, the platform elements refer to visual objects that have a size and a position in the platform 104. A platform element may be visible, though there may be times when a platform element is hidden. A platform control is considered to be a platform element. Text blocks, labels, text boxes, list boxes, lines, and images windows, dialog boxes, frames, panels, menus, buttons, icons, etc. are examples of platform elements. In addition to size and position, a platform element may have other properties, such as a margin, spacing, or the like.

The verified resource supply system 100 includes a ledger arrangement 106 hosted by the data processing arrangement 102 into which entries representative of resources are recorded in a blockchain, wherein the entries include characterizing data that are submitted to describe technical properties of the resources to be verified. Throughout the present disclosure, the term ledger arrangement relates to a plurality of systems employed for the recording and reproducing entries associated with at least one of: the transactions and the resources. Furthermore, the ledger arrangement 106 may be configured to store and maintain entries associated with the transactions of the resources. Furthermore, the ledger arrangement 106 may also be capable of keeping information associated with previous as well as related transactions associated with the resources. For example, the ledger arrangement 106 may be implemented as a distributed ledger arrangement. Such distributed ledger arrangement may exist as plurality of copies thereof on various nodes in the system that may be arranged at different geographical locations. Furthermore, the plurality of copies of the distributed ledger arrangement may be able to communicate with each other, such as to converge on a single consensus (such as the absolute state of the system) without a centralized authority to supervise the communication. Additionally, a ledger arrangement 106 may be implemented using a public and nonpermissioned ledger. However, it will be appreciated that the resource ledger may be implemented in any other implementation, such as a private and permissioned ledger.

Optionally, the entries are made in chronological order to form a record and a data associated with the record. Furthermore, the record and the data associated with the record are accumulated into separate accounts, wherein the separate accounts relate to a given data, to form a ledger record. Notably, each of the plurality of entries defines one or more corresponding transactions relating to one or more corresponding resources. For example, the information about the one or more resources may be explicitly recorded in the corresponding records, implicitly recorded in the corresponding records and/or may be included in a smart contract associated with each of the transactions.

The entries made into the ledger arrangement 106 are recorded in a blockchain. The term blockchain as used herein relates to a digitized and decentralized public ledger of all transaction associated with the resource. Typically, a 'current transaction'associated with a resource is recorded in form of a block. Furthermore, on the completion of the current transaction associated with the block, the block is stored as a 'completed transaction' in the blockchain as a permanent database. Similarly, the subsequent transactions (represented as, blocks) pertaining to the resource is added to the blockchain in chronological order. Moreover, each of the plurality of blocks in a blockchain comprises a hash function associated with the previous block in the blockchain. Notably, the blockchain allows the participants of the verified resource supply system 100 to keep a track of all the transactions associated with the resource without a centralized governing body. In an embodiment, the blockchain employs the distributed ledger technology for tracking various transactions associated with the resource. Furthermore, the blockchain creates an indelible record that cannot be changed and/or deleted. Consequently, the entries stored in the blockchain cannot be tampered and further the authenticity of the entry can be verified by the plurality of members of the verified resource supply system 100 upon interrogation.

The blockchain is beneficially set up in a server arrangement hosted within the Internet®. Throughout the present disclosure, the term 'server arrangement’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server arrangement includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks. Furthermore, it will be appreciated that the server arrangement may be both single hardware server and/or plurality of hardware servers operating in a parallel or distributed architecture. In an example, the server arrangement may include components such as memory, a processor, a network adapter and the like, to store, process and/or share information with other computing components, such as user device/user equipment. Optionally, the server arrangement is implemented as a computer program that provides various services (such as database service) to other devices, modules or apparatus. Furthermore, the blockchain is arranged using the server arrangement. Additionally, the data of the blockchain may be stored at the server arrangement for access.

Optionally, the server arrangement may be accessed using the Internet®. The term internet relates to any collection of networks using standard protocols. For example, the term includes a collection of interconnected (public and/or private) networks that are linked together by a set of standard protocols (such as TCP/IP, HTTP, and FTP) to form a global, distributed network. While this term is intended to refer to what is now commonly known as the Internet®, it is also intended to encompass variations that may be made in the future, including changes and additions to existing standard protocols or integration with other media (e.g., television, radio, etc). The term is also intended to encompass non-public networks such as private (e.g., corporate) Intranets. As used herein, the terms World Wide Web or web refer generally to both (i) a distributed collection of interlinked, user-viewable hypertext documents (commonly referred to as Web documents or Web pages) that are accessible via the Internet ®, and (ii) the client and server software components which provide user access to such documents using standardized Internet ® protocols. Notably, the plurality of members of the verified resource supply system is operable to access the data associated with the blockchain, stored at the server arrangement using the internet.

The entry associated with the resource includes characterizing data that are submitted to describe technical properties of the resources to be verified. The term 'characterizing data’ relates to a set of information or a set of record associated with the resource. Notably, the characterizing data describes technical properties of the resource to be verified. Furthermore, the technical properties of the resource may comprise at least one of: a physical property associated with the resource, a mechanical property associated with the resource, and a chemical property associated with the resource.

Optionally, the physical property associated with the resource refers to any measurable property of the resource. Furthermore, the physical properties of the resource relates to a physical state of the resource. Additionally, the changes in the physical properties of the resource can be used to describe a transformation or an evolution between momentary states. Moreover, the transformations or the evolutions of the physical properties of the resource can be observed throughout the momentary states. Additionally, the physical properties of the resource may comprise a value associated with at least one of: a shape, a size, a weight, a surface area, a colour, a volume, a density and an appearance associated with the resource.

Optionally, the mechanical property associated with the resource describes an intensive quality of the resource. Furthermore, the mechanical property associated with the resource refers to a property, wherein the changes exhibited by the resource upon the application of an external force. In other words, the mechanical property of the resource affects a mechanical strength of the resource. Furthermore, the mechanical properties of the resource may comprise a value associated with at least one of: a hardness, a strength, a resistance, a compressibility, an elasticity and a plasticity.

Optionally, the chemical property associated with the resource refers to a characteristic or a behaviour of the resource that may be observed when the resource undergoes a chemical change. In other words, the chemical properties associated with the resource may be observed either during the chemical reaction or after the chemical reaction. Typically, the arrangement of the atoms within the resource is affected due to the chemical properties of the resource. Furthermore, the chemical property associated with the resource comprises a value associated with at least one of: a toxicity, an oxidation, a flammability, an enthalpy of formation, and a chemical stability.

Optionally, the resource includes feedstock, food supplements and drugs for animals, wherein the technical properties of the resource may include quality and quantity of the resource, moisture content in the resource, type of the resource, impurities present in the feed and so forth.

The verified resource supply system 100 includes a voting arrangement 108 for a plurality of members of the verified resource supply system 100. Additionally, the plurality of members of the verified resource supply system 100 may input votes in respect of whether or not the entries of the blockchain are valid. Throughout the present disclosure, the term 'voting arrangement' refers to programmable and/or non-programmable components configured to execute one or more software application for processing and/or voting for a set of data and/or a set of transactions. The voting arrangement 108 can include, for example, a component included within an electronic communication network. Additionally, the voting arrangement 108 enables the one or more members associated with the system to process and/or vote for a set of data and/or set of instructions. Furthermore, the voting arrangement 108 may employ a hardware arrangement, a software arrangement, a firmware arrangement or a combination of these, to enable the one or more members associated with the system to cast their vote.

The voting arrangement 108 is operable to derive the votes from the data recorded in a database arrangement of the data processing arrangement 102 describing one or more suppliers of the resource of the entries of the blockchain and information describing expected properties of the resources represented by the entries. Typically, the data associated with at least one of: the transactions and the resources is stored in the form of entries in the data processing arrangement 102. Furthermore, the members are operable to cast a vote, for example, by suitably processing the entries of the data associated with the transactions and the resources, wherein the data associated with the transaction may be accessed by the one or more member using the one or more platform 104. Specifically, the platform 104 enables the member to make a choice, for example, a consent or a dissent to validate the expected properties of the resources recorded in each of the plurality of entries. In an example, the voting arrangement 108 may enable the member of the verified resource supply system 100 to cast a vote regarding a quality associated with the feedstock. In such a case, the quality of the resource may be evaluated on the basis of a moisture content of the resource, a nutrient content of the resource, a colour of the resource, and so forth. In another example, the voting arrangement 108 may enable the member of the verified resource supply system 100 to cast a vote to describe one or more supplier of the resource. In such a case, the one or more supplier may be evaluated on the basis of a quality of the resource supplied, a price of the resource, a delivery time of the resource, and so forth.

Optionally, the entries include characterizing data that are submitted to describe technical properties of the resources to be verified. Consequently, the plurality of members are operable to assess the characterizing data related to the resources with respect to data recorded in the database arrangement 110 to ensure the validity of a given entry. Specifically, the plurality of members are operable to assess the characterizing data and determine the given supplier and properties of the given resource. Consequently, the plurality of members are operable to derive data recorded in the database related to the given supplier and expected properties of the given resource. Therefore, if the characterizing data is determined to be valid with respect to the data recorded in the database, the plurality of members are operable to determine the given entry as valid.

Optionally, the database arrangement 110 is arranged to be interrogated via a multi-dimensional search engine. Throughout the present disclosure, the term 'database arrangement' relates to an organized body of digital information regardless of the manner in which the data or the organized body thereof is represented. Optionally, the database arrangement 110 may be hardware, software, firmware and/or any combination thereof. For example, the organized body of related data may be in the form of a table, a map, a grid, a packet, a datagram, a file, a document, a list or in any other form. The database arrangement 110 includes any data storage software and systems, such as, for example, a relational database like IBM DB2 and Oracle 9. Optionally, the database arrangement 110 may be used interchangeably herein as database management system, as is common in the art. Furthermore, the database management system refers to the software program for creating and managing one or more databases. Optionally, the database arrangement 110 may be operable to supports relational operations, regardless of whether it enforces strict adherence to the relational model, as understood by those of ordinary skill in the art. Additionally, the database arrangement 110 is populated by data elements. Furthermore, the data elements may be include data records, bits of data, cells, are used interchangeably herein and all intended to mean information stored in cells of a database arrangement 110.

In an embodiment, the database arrangement 110 is arranged in an array system model to be interrogated via a multi-dimensional inference engine. Specifically, the multi-dimensional inference engine is operable to search through the array system model 110 and extract data recorded therein. It will be appreciated that the votes are derived from data, recorded in an array system model 110, describing one or more suppliers of the resources of the entries of the blockchain and information describing expected properties of the resources represented by the entries. Therefore, data recorded in the array system model 110 may be requested by the plurality of members to derive votes therefrom. Specifically, a member, from amongst the plurality of members, provides an input to the verified resource supply system (specifically, to the array system model or the multi-dimensional inference engine) relating to the data required thereby. Consequently, the multidimensional inference engine is operable to generate output, corresponding to the input provided, from the database arrangementarray system model 110.

Optionally, multi-dimensional inference engine operates on a model spanned by state variables on finite domains and/or intervals. The state variables include one or more suppliers of the resources of the entries and information describing expected properties of the resources represented by the entries. Specifically, the one or more suppliers, as a state variable, may include data related to the supplier such as names of the one or more suppliers, identification data of the one or more suppliers, types of resources supplied by the one or more suppliers and so forth. Furthermore, information describing expected properties of the resources represented by the entries, as a state variable may include information such as quality and quantity of the resources, impurities present in the resources and so forth. It will be appreciated that each of the state variables may comprise a finite domain and/or interval. In an example, the information describing expected properties of the resources, as a state variable comprising information related to the quality of feed may comprise a finite domain of 1 to 100 and have finite intervals such as 10.

Optionally, the multi-dimensional inference engine is operable to generate and store, in the array system model 110 of the data processing arrangement 102, an addressable solution space defining all valid states or combinations satisfying a conjunction of substantially all system constraints on all variables. In other words, the addressable solution space include valid Cartesian subspaces of states or combinations satisfy a conjunction of all the constraints of the verified resource supply system for all interconnected state variables such as the one or more suppliers of the resources and the information describing expected properties of the resources. Specifically, each combination of a given state variable (for example, such as a given supplier of the resources), with the remaining state variables is defined in the addressable solution space. Optionally, the multi-dimensional inference engine is operable to generate and store, one or more object functions or values associated with the addressable solution space to make the values addressable from an environment including the state variables. In general, each valid combination in a solution space computed in embodiments of the present disclosure may have one or more associated attributes or object functions, for example a price. In a special case of an embodiment of the present disclosure, all combinations may be valid, namely without any constraints on the system model being employed when computing results. An object function of a given subset of state variables, wherein the object function derives characteristics of a given subset of state variables, and is linked to a complete solution space by deducing constraints imposed by the object function on each state variable connected to the given subset of state variables.

Optionally, the multi-dimensional inference engine is further operable to use the addressable solution space to process one or more inputs provided to the verified resource supply system when in operation, and to generate corresponding outputs from the database arrangement 110. The addressable solution space is a compact and complete representation of all valid combinations and associated object functions of constraint problems on finite domains or intervals. The addressable solution space of valid states or combinations is beneficially represented geometrically in terms of nested data arrays, and is simulated very efficiently in operation by simple operations on these arrays using CPUs (central processing units), GPUs (graphics processing units) or hardware devices designed for this specific use. Furthermore, the one or more inputs provided to the verified resource supply system (specifically to, the database arrangement and/or the multi-dimensional inference engine) relate to a given data in the array system model and the multi-dimensional inference engine is operable to use the addressable solution space to generate required data as the output from the database arrangement. Furthermore, various rules may be applied to the addressable solution space to exclude certain regions of the addressable solution space. Beneficially, the multi-dimensional inference engine has to index through a reduced addressable solution space to provide the output (all deduced implications) corresponding to the provided input.

The ledger arrangement 106 controls in operation whether or not a given entry of the blockchain is to be retained in the blockchain depending upon the votes, and also whether the resources when verified have been allocated for use or are available to be allocated for use. Typically, the plurality of members of the verified resource supply system 100 are operable to verify the entries made in the ledger arrangement 106. In an embodiment, an entry 'X' made in the ledger arrangement 106 associated with at least one of: a transaction and a resource, stored in the database arrangement 110, may be interrogated to check for inconsistencies. In such case, an analysis of past entries associated with 'X' as well as current entry associated with 'X' is performed by the one or more members. Furthermore, the voting arrangement 108 is employed by the one or members of the verified resource supply system 100 to validate the consistency of the entry. In an example, a majority of members of the verified resource supply system 100 may vote in favour of the entry 'X'. In such a case, the entry 'X' is validated and added to the blockchain. In another example, a majority of members of the verified resource supply system 100 may vote against the entry 'X'. In such case, the entry 'X' is invalidated and removed from the blockchain. Moreover, the entry 'X' may be deleted from the database arrangement 110. Furthermore, the resources when verified are allocated for use or are available to be allocated for use. Specifically, the quality of the verified resources is ensured and can be made allocated or made available for allocation.

Optionally, the voting arrangement 108 prevents contaminated resources from being presented and allocated via use of the blockchain. Typically, each of the blocks in a blockchain represents a record of a transaction associated with a resource. Furthermore, each of the blocks in a blockchain is connected to each of the previous blocks and each of the following blocks in a blockchain. The term 'contaminated resources' as used herein relates to a fallacious block or an invalid state associated with a block representing a transaction associated with the resource. Notably, the contaminated resource may be an outcome of at least one of: a misreporting by a supplier, a misreporting by a consumer, and a change made into an existing block. However, the voting arrangement 108 is utilized by the verified resource supply system 100 to prevent the addition of such contaminated resources into the blockchain.

In an embodiment, the voting arrangement 108 evaluates the votes casted by each of the members of the verified resource supply system 100. Furthermore, the number of votes approving an entry 'X' associated with the resource is evaluated. Additionally, for the presentation and allocation of the entry 'X', the number of votes approving the entry 'X' associated with the resource should be at least half of the total votes casted. In other words, at least 50% of the members of the verified resource supply system 100 must approve the entry 'X' in order to present and allocate the entry 'X' in the blockchain. Consequently, an entry that is not approved by at least half of the members of the verified resource supply system 100 may be disregarded. Such, disregarded entry may be the contaminated entry associated with the resource (namely, contaminated resource).

Optionally, the contaminated resources include at least one of: mould contamination, radioactive contamination, chemical residue contamination, heavy metal poisoning, recycled animal-derived pathogens. Typically, any hindrance or deviation from the desired quality of resource is operable to result in a contaminated resource.

Optionally, the mould contamination refers to adulteration of the resource due to the presence of a mould. Typically, the mould is a fungus that grows in form of multi cellular filaments. Furthermore, the mould consists of diverse number of fungal species. Moreover, the growth of such mould on the resources leads to discoloration, fuzzy appearance, and biodegradation of the resource. In an example, a feedstock resource may be contaminated due to the presence of an aspergillus mould.

Optionally, the radioactive contamination relates to unintended or undesirable presence of radioactive substances on the surface of the resource or within the resource. Furthermore, the presence of such radioactive substances may emit ionizing radiations such as alpha particles, beta particles, gamma rays and/or neutrons. Such ionizing radiations may cause harmful and hazardous effects on the quality of the feedstock. Typically, the level of hazard caused to the resource due to the ionizing radiation may depend on at least one of: a concentration of the radioactive substance, an energy emitted by the radioactive substance, a type of radiation emitted by the radioactive substance, and a proximity of the resource with the radioactive substance. Such radioactive contamination may be a result of a nuclear explosion, improper disposal of nuclear waste, or a leakage of nuclear fuel. In an example, a radioactive contamination may be derived from the Sellafield, Fukushima, wherein a nuclear explosion lead to the radioactive contamination of the resources.

Optionally, the chemical residue contamination of the resource refers to the addition of a hazardous chemical substance to the resource. Generally, the chemical residue contamination of the resource is a result of at least one of: an agrochemical used (such as a pesticide or a veterinary drug), a contamination from environmental sources (such as, a water contamination, a soil contamination, or an air contamination), a cross-contamination (such as, during food processing), a migration of chemical compounds from food packaging materials, an unapproved food additive or adulterant used, or contamination by natural toxins.

Optionally, the heavy metal poisoning of the resource refers to accumulation of one or more heavy metal, in toxic amounts, in the resource. Typically, the level of heavy metal poisoning is determined by the quantity of heavy metal accumulated in the resource. Notably, a small amount of some heavy metals such as zinc, manganese, copper, iron, and chromium are vital for the maintenance of the animal (or livestock). However, presence of such heavy metals in quantity greater than the required quantity in the resource may cause severe damage to the animal (or livestock). Additionally, the presence of some heavy metals such as lead, mercury, arsenic and cadmium, even in fractions, may cause severe health issues. Furthermore, the heavy metal poisoning may occur due to at least one of: an industrial exposure, mixing of pollutants through air or water, improperly coated resource containers, and mixing of inadequate quantity of heavy metal during manufacturing.

Optionally, the recycled animal derived pathogens refers to the addition of germs (namely, pathogens) in the resource due to the intake or addition of affected animal. Furthermore, the pathogens may be a virus, a bacteria, a protozoa, a prion, or any other micro-organism. In an example, the consumption of grains affected by spongiform encephalopathy by an animal (namely, cow) may lead to a disease spongiform encephalopathy leading to a neuro-degenerative disease in the cattle.

Optionally, the verified resource supply system 100 includes a membership control arrangement for the members, wherein the members are operable to vote after the members have been approved by a peer voting arrangement 108 of the system. Typically, the membership control arrangement keeps a check on the members of the verified resource supply system 100. Furthermore, the membership control arrangement is operable to allow the entry of a new member. However, the membership control arrangement validates the new member. Furthermore, upon the validation of the new member, the membership control arrangement grants voting access to the new member. Typically, the membership control arrangement validates the new member upon evaluating the votes by the peer member of the verified resource supply system 100. In an example, the new member may be validated upon receiving a majority (at least a 50%) votes in favour of the new member.

Referring to FIG. 2, there is shown an illustration of steps of a method 200 of (for) operating a verified resource supply system (such as the verified resource supply system 100 of FIG. 1), in accordance with an embodiment of the present disclosure. The verified resource supply system supplies resources that have been verified using a voting arrangement. The verified resource supply system employs a data processing arrangement that provides a platform through which members interact with the verified resource supply system. At a step 202, the verified resource supply system is arranged to include a ledger arrangement hosted by the data processing arrangement into which entries representative of resources are recorded in a blockchain. The entries include characterizing data that are submitted to describe technical properties of the resources to be verified. At a step 204, the voting arrangement of the verified resource supply system is used. The voting arrangement includes for a plurality of members of the verified resource supply system, to input votes in respect of whether or not the entries of the blockchain are valid. The votes are derived from data recorded in a database arrangement of the data processing arrangement describing one or more suppliers of the resources of the entries of the blockchain and information describing expected properties of the resources represented by the entries. At a step 206, the ledger arrangement is used to control in operation whether or not a given entry of the blockchain is to be retained in the blockchain depending upon the votes, and also whether the resources when verified have been allocated for use or are available to be allocated for use.

The steps 202 to 206 are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein. Optionally, the method includes arranging for the verified resource supply system to include a membership control arrangement for the members, wherein the members are operable to vote after the members have been approved by a peer voting arrangement of the system. In an example, the resources include at least one of: feedstock for use in feeding animals, food supplements for animals, drugs for animals. In another example, the method includes arranging for the array system model to be interrogated via a multi-dimensional inference engine.

Optionally, the method includes generating a multi-dimensional array sytem model spanned by state variables on finite domains and/or intervals, wherein the state variables include one or more suppliers of the resources of the entries and information describing expected properties of the resources represented by the entries, wherein the method include arranging for the multi-dimensional inference engine to:

(i) generate and store, in a database arrangement of the data processing arrangement, an addressable solution space defining all valid states or combinations satisfying a conjunction of substantially all system constraints on all variables; and (ii) generate and store, one or more object functions or values associated with the addressable solution space to make the values addressable from an environment including the state variables.

Optionally, the method further includes arranging for the multi-dimensional inference engine to use the addressable solution space to process one or more inputs provided to the verified resource supply system when in operation, and to generate corresponding outputs from the array system model In an example, the method includes arranging the voting arrangement to prevent contaminated resources from being presented and allocated via use of the blockchain. In another example, the contaminated resources include at least one of: mould contamination, radioactive contamination, chemical residue contamination, heavy metal poisoning, recycled animal-derived pathogens.

Furthermore, there is disclosed a computer program product comprising a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a computerized device comprising processing hardware to execute the method of operating a verified resource supply system that supplies resources that have been verified using a voting arrangement. Optionally, the computer-readable storage medium comprises one of a floppy disk, a hard disk, a high capacity read only memory in the form of an optically read compact disk or CD-ROM, a DVD, a tape, a read only memory (ROM), and a random access memory (RAM).

The verified resource supply system and the method of using the verified resource supply system as described in the present disclosure enables validation or verification of the quality of the resource. Notably, a verified quality associated with the resource enables the maintenance of positive health on the animal (or livestock). Furthermore, the overall all positive health of the livestock enhances the functional output as well as the produce of the livestock. Subsequently, the greater functional output and produce provided by the livestock complements the rural economy. Beneficially, the verified resource supply system prevents any illegal or unverified transactions associated with the resource. Typically, the verified resource supply system is implemented using a blockchain. Therefore, the blockchain is operable to verify the data recorded in the blocks, associated with the transactions of the resource. Consequently, this system enables the supplier and the consumer of the resource to interrogate a given transaction in case of a disagreement. Furthermore, the verified resource supply system employs a voting arrangement to validate a new transaction. Notably, the new transaction is added to the blockchain only, upon the validation of the block (or transaction) by the voting arrangement. Consequently, any intuition or manipulation of data stored in the blocks of the blockchain, associated with the transactions, is improbable. Beneficially, the verified resource supply system also recommend regarding the quality of a given resource, offered by a supplier. Furthermore, the system may also provide information indicating the most favorable manner and quantity of using a resource in a given industry of field.

Modifications to embodiments of the invention described in the foregoing are possible without departing from the scope of the invention as defined by the accompanying claims. Expressions such as including, comprising, incorporating, consisting of, have, is used to describe and claim the present invention are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. Numerals included within parentheses in the accompanying claims are intended to assist understanding of the claims and should not be construed in any way to limit subject matter claimed by these claims.

ANNEX

GLOSSARY

Constraint resolution means establishing substantially all valid combinations of variables satisfying substantially all constraints of a given system. Optionally, all valid combinations of the variables satisfying all the constraints of the given system are established, namely computed, wherein, in an optional case, valid Cartesian sub-spaces of states or combinations satisfy a conjunction of all system constraints for all interconnected variables. The valid Cartesian sub-spaces may comprise Cartesian planes. A point in such Cartesian planes can be represented as tuples (or a list) of 'n' real numbers, wherein 'n' can be dimensions associated with the Cartesian plane. It will be appreciated that when the Cartesian sub-spaces are associated with Cartesian planes, the variables and the constraints corresponding to the variables can comprise more than 3 values associated with abscissa (x-axis), ordinate (yaxis) and applicate (z-axis) of the Cartesian co-ordinate system.

The term optimizing means applying a heuristic selection of combinations within a set of valid combinations.

The term a system spanned by variables on finite domains and/or intervals indicates that each variable of a given system consists of a finite set of elements or state values (for example, logical truth values) or a finite set of intervals.

The term an addressable solution space indicates that substantially all valid combinations are explicitly represented.

The term a Cartesian sub-space is a compact representation of one or more valid combinations, wherein all combinations are derivable/calculable as a Cartesian product of elements or state values for each variable. It will be appreciated that when the Cartesian sub-space comprises Cartesian planes, the Cartesian product of elements or state values of each variable may be associated with products of more than the 3 values corresponding to the Cartesian coordinates x, y and z.

The term system constraints refers to relations (namely propositional functions) for variables defined for a given system.

The term interconnecting variables indicates variables present in at least two relations.

The term Hnk variable means a variable generated by a method according to the present disclosure and added to a given relationship with a unique index, wherein the unique index identifies one corresponding Cartesian subspace.

The term interconnected valid Cartesian sub-spaces means valid Cartesian sub-spaces with at least one common variable associated therewith.

The term external variables means variables that are to be used by or being accessible from an environment during a runtime simulation. The term external variable is used herein interchangeably as external state variable.

The term internal variables or interim variables means variables that are not to be used by, or are not to be accessible from an outer environment during a runtime simulation.

The term duster means an accumulation of states, or a list of state vectors associated with known attributes. The state variables are subsets of domain of static array system model and/or external variables.

Moreover, the embodiments are capable of performing real-time processing. Furthermore, real-time” means in practice while a user of the system waits for results of computations that are delivered in a time scale of tens of seconds, or within several minutes, even when large-scale constraint problems are being computed and resolved by the system. It will be appreciated that, in practice, personalized (context-sensitive) recommendations from hyper-dimensional data feeds may be provided in real time on a wearable, mobile or loT (Internet of Things”) device. The aforementioned hyper-dimensional data feeds provide hyper-dimensional data that are stored in an array system model, wherein the array system model may represent constraints as well as other types of knowledge associated with each valid combination of the array system model, namely one or more object functions, all of which must interface with an environment provided to a given in a simple interactive way, via a user interface.

The term tractable-time means in practice a time, of the polynomial order (i.e. n², n³, n⁴ and so on), required by a computing arrangement for computation of a large-scale constraint problem.

In overview, embodiments of the present disclosure employ in operation a multi-dimensional system model (namely, an array system model), for performing data processing using a computing arrangement of a control apparatus. The terms multi-dimensional system model and array system model are hereinafter used interchangeably in the description. Moreover, the computing arrangement is capable of performing real-time processing. Notably, real-time means that results of computations are delivered in a time scale of tens of seconds, or within several minutes, even when largescale constraint problems are being computed and resolved by the computing arrangement. It is thereby feasible, in practice, to provide personalized (context-sensitive) recommendations from hyper-dimensional data feeds in real time on a wearable, mobile or loT (Internet of Things) device. The aforementioned hyper-dimensional data feeds provide hyper-dimensional data that are stored in the array system model, wherein the array system model may represent constraints as well as other types of knowledge associated with each valid combination of the array system model, namely one or more object functions, all of which must interface with an environment provided to a given in a simple interactive way, via a user interface.

Furthermore, the control apparatus includes a user interface for interacting with a user of control apparatus for controlling operation of the control apparatus, a data processing arrangement that is operable to receive the one or more data inputs and to output the one or more control outputs and/or one or more analysis output and/or one or more recommendation outputs, and the computing arrangement that supports automatic modelling, analysis and real-time inference processing on multi-dimensional system models, can be implemented by way of a wide range of computational hardware. Such computational hardware includes, signal processing and embedded controllers to mobile devices (for example, smart watches, smartphones and tablets), standard computers (for example, personal computers or laptop computer), graphics processing units (GPUs), distributed computers with parallel processing capabilities, and so forth. The data processing using such computing hardware of the control apparatus, is capable of enabling a wide range of innovative decision support tools, such as clinical decision support systems, to be realized in practice.

Optionally, the multi-dimensional system models are constraint problems expressed in terms of truth tables with MN combinations, wherein each combination assumes either a truth-value true (valid) or a truth-value false (invalid). In such a case, the multi-dimensional system models assume that N variables are involved, wherein each variable has M elements. In general, each valid combination in a solution space computed in embodiments of the present disclosure may have one or more associated attributes or object functions, for example a price. In a special case of an embodiment of the present disclosure, all combinations may be valid, namely without any constraints on the system model being employed when computing results.

It will be appreciated that computing MN combinations using contemporary known computation methods will result in a combinatorial explosion in a contemporary computing device, that would result in an unacceptably long computation time for providing results to users via a user interface. Therefore, it is not currently a trivial task to solve large constraint problems with a multitude of variables. Nevertheless, embodiments of the present disclosure make it possible to unify seemingly contradictory requirements for completeness (all combinations must be accessible to ensure logical consistency) with compactness of representation and real time inference processing even with complex combinatorial applications on relatively low power computational devices (for example, as aforementioned).

Furthermore, it will be appreciated that the control apparatus is practical and useful, and optionally, compact and portable. As mentioned previously, the control apparatus includes data processing arrangements that are operable to execute software products that are able to provide solutions to medical problems and other types of technical control problems, without resulting in a combinatorial explosion that results when multi-dimensional tasks are being addressed. As it will be understood from the following description, embodiments of the present disclosure employ an advantageous form of data representation, referred to as the array system model or the multidimensional system model. While the array system model is an optimal tool for complex constraint problems described in the foregoing, it will be appreciated that embodiments of the present disclosure are not limited to addressing medical related problems; for example, embodiments of the present disclosure can be used in safety critical power stations (for example, nuclear power plant, arrays of wind turbines, arrays of ocean wave energy converters and so forth), for supervising oil well equipment, for chemical plant, for airborne radar systems, for railway network management, for automatic driverless vehicle systems, and similar.

Thus, embodiments of the present disclosure concern a method of generating the array system model useful for interrogating and/or configuring and/or optimizing and/or verifying a logical system spanned by variables on finite domains and/or intervals, wherein the method comprises: (i) generating and storing, in a memory or a storage medium of the computing arrangement, an addressable solution space for a set of external variables, wherein the addressable solution space is expressed in terms of all valid Cartesian subspaces of states or combinations for the set of external variables with interconnected valid Cartesian sub-spaces being addressable as valid combinations of indices of link variables and/or core link variables; and (ii) arranging for the solution space to satisfy a conjunction of all, or substantially all, relations of the set of external variables, in order to establish a system model in which all, or substantially all, valid solutions are stored as nested relations.

In embodiments of the present disclosure, there are encountered raw data feeds (one or more inputs provided to the system) that are complex and multidimensional; such raw data feeds are, for example, derived, at least in part, from sensor arrangements. However, there arises a need to transform such complex and multi-dimensional raw data feeds into transformed to useful actionable insight, wherein real-time inferencing is required to be performed and personalized, and wherein there is generated context-specific recommendations or advice. In practice, for embodiments of the present disclosure, there are distinct advantages to being able to compute across such raw data on data collection hardware itself that generates the raw data in operation (for example, a smartwatch, a mobile phone or a remote sensing platform), as such a manner of operation negates requirements for large data transfers that are a potential target of data interception; moreover, such large data transfers potentially consume expensive resources in terms of both network bandwidth and power on small, battery powered mobile devices.

Embodiments of the present disclosure are operable to employ, for their variables and constraints, a semantically normalized knowledge graph (namely, 'knowledge graph’}. Moreover, such knowledge graphs are beneficially used in the embodiments to represent all available information from a variety of public and other data sources containing information associated with variables, relationships and constraints operating on a given complex system.

Such knowledge graphs are optionally based on a master multi-relational ontology, which includes a plurality of individual assertions, wherein an individual assertion comprises a first concept, a second concept, and a relationship between the first concept and the second concept, wherein at least one concept in a first assertion of the plurality of individual assertions is a concept in at least a second assertion of the plurality of assertions.

For each pair of related concepts, there is beneficially a broad set of descriptive relationships connecting the related concepts, for example expressed in a logical and/or probabilistic as well as linguistic manner. As each concept within each pair is potentially paired (and thus related by multiple descriptive relationships) with other concepts within a given ontology, a complex set of logical connections is formed. A corresponding superset of these connections provides a comprehensive knowledge graph describing what is known directly and indirectly about an entirety of concepts within a single domain. The knowledge graph is also optionally used to represent knowledge and relationships between and among multiple domains and derived from multiple original sources.

In another beneficial embodiment of the present disclosure, a semantic distance or relatedness of concepts in a specific context is calculated. Such probabilistic semantic distance metrics are susceptible to being represented as relationships between two concepts in the semantically normalized knowledge graph and used to determine a degree of connectedness of concepts above, below or between selected thresholds in a context of a specific domain or corpus.

In these aforementioned embodiments of the present disclosure, the specification of a given subset of the knowledge graph to be derived for the array system model optionally includes a selection of two or more concepts or types of concepts from a plurality of assertions of a master multi-relational ontology, applying one or more queries to two or more concepts or concept types to yield a subset of individual assertions from the plurality of assertions, wherein the queries identify one or more individual assertions from the plurality of individual assertions of the master multi-relational ontology. Specifically, the master multi-relational ontology connects the two or more concepts directly or indirectly. In a context of complex domains such as healthcare examples described in the foregoing, such derived knowledge graphs potentially contain millions of concepts, each of which has multiple properties (namely, variables) with multiple potential values, and each of which may have up to tens of thousands of direct or derived logical constraints.

In describing embodiments of the present disclosure, a term logical system is used to mean a complete system, alternatively a sub-system that is a part of a larger system. When used to refer to a sub-system, variables associated with other sub-systems are treated as being external variables.

In embodiments of the present disclosure, for example implemented as a control apparatus employing data processing hardware, all invalid states or combinations violating constraints of a given system are excluded from relations that are employed in operation of the multi-dimensional system model. Such exclusion of invalid states or combinations is beneficially performed when the system model is generated by a method pursuant to the present disclosure; in other words, in embodiments of the present disclosure, the invalid states or combinations are excluded from computations whenever identified to enable more rapid computation of useful results to be achieved. In practice, a state of contradiction or inconsistency is present in a system if just one relation of the system has no valid combination or state. Conversely, the system is regarded as being consistent if at least one state or combination of states is valid; namely, one state or a combination of states satisfies all system constraints. At an instance, when generating a given system model, just one relation of a system is found to have no valid combination or state, then that whole system is in a state of contradiction or inconsistency and is excluded for achieving enhanced computational efficiency.

Optionally, the method includes operating the multi-dimensional system model to have a plurality of system model states, and to change state from a given preceding system model state in among the system model states to a subsequent system model state among the system model states, depending upon a computed solution to the given preceding system model state and operative input data applied to the multi-dimensional system model.

Furthermore, a process of colligating relations (that is, combining relations to arrive at a more complex sub-system or system) is elucidated in detail. It will be appreciated that on each level of a process of colligation, inconsistencies or contradictions are identified in embodiments of the present disclosure, and will, thus, result in exclusion of the colligated sub-system or system. Thus, when a generating process has been completed in embodiments of the present disclosure, the system will be consistent, as manifested by all relations having at least one valid Cartesian sub-space.

In the present disclosure, the term system is used to refer to an entire system of variables or, alternatively, to a part of the entire system of variables, for example as aforementioned. With reference to a specific application (for example healthcare), the system provides a representation of a complete set of available domain knowledge upon which real-time reasoning or inferencing can be performed using embodiments of the present disclosure to provide useful, actionable controls, insights and recommendations using decision support tools incorporating an array system model (for example, for selecting a best available therapy for a specific patient at a point of care; for example, for selecting a best available selection of replacement component parts to be used when repairing an item of machinery). There is thereby provided an interaction between the array system model and an environment that is carried out by a state vector representing states of all variables involved, including physical measurements as well as decision parameters. Thus, in example embodiments of the present disclosure, variables involved can include sensor signals acquired using physical sensors, and decision parameters can be outputs that are used to control operating states of various apparatus, for example in a hospital, in an industrial plant, in a vehicle, in an energy power plant, and so forth.

In embodiments of the present disclosure, the given system is completely defined in that every combination under the system is either valid or invalid with respect to each of the system constraints relevant to use of the multidimensional system model and preferably with respect to absolutely each of the system constraints. Thus, the term 'system', used about the entire system of variables, indicates that the entire system is completely defined with respect to all system constraints relevant to the use of the system model, and optionally with respect to absolutely all system constraints. When the system of variables is not completely defined in above sense of this term, then only that part of the system which is in fact completely defined is covered by the term system pursuant to the present disclosure. The term substantially indicates a system in which process of colligation has not been completed, and where a runtime environment must be adapted to perform certain tests for consistency; for example, substantially all refers to at least 90%, more optionally at least 95%, and most optionally at least 99%.

As aforementioned, the system constraints are optionally determined by conjugating one or more relations, wherein each relation represents valid Cartesian sub-spaces of states or combinations on a given subset of variables. The conjugation of the one or more relations comprises calculating Cartesian sub-spaces satisfying the combined constraints of the one or more relations. If no relations have common variables, no further action is required to conjugate the relations in embodiments of the present disclosure.

According to an important optional feature of the invention, all relations with at least one common variable are colligated. The colligation comprises conjugating the constraints of two or more relations that are connected by having common variables therebetween to establish one or more Cartesian sub-spaces satisfying combined constraints of the two or more relations. Furthermore, the colligation of two or more relations will normally be performed by joining the two or more relations up to a predetermined limit. Such joining comprises an operation of replacing a set of relations with a single relation satisfying combined constraints of the set of relations.

The set of relations is not limited to two relations, but can in general be any finite number of relations. In an example embodiment of the present disclosure, a case where three or more relations are joined is typically decomposed into a number of pairwise joins; this pairwise joining optionally comprises a predetermined strategy or this pairwise joining is optionally in a random order. Moreover, joining of relations will typically reduce the number of relations, and the result will be one or more relations with common link variables. Moreover, the linking of the relations consists of adding link variables and adding one or more calculated relations representing constraints on the link variables.

In an embodiment of the present disclosure, any relation with non-connecting variables as well as connecting variables is extended by adding a unique link variable with a unique index identifying each valid Cartesian sub-space on either the non-connecting variable or the connecting variables. In such situations, it is often advantageous to split a given relation into two relations, wherein one relation pertains to the non-connecting variables and the link variable, and the other relation pertains to the connecting variables and the link variable.

In relation to embodiments of the present disclosure, a term completeness of deduction indicates that all logical consequences are required to be deduced for one or more variables. Moreover, embodiments of the present disclosure, the completeness of deduction relates to all logical consequences on all variables, but as indicated above, the embodiments of the present disclosure are not limited to computing all logical consequences.

When the colligation process is completed, relations for isolated variables are optionally split into a plurality of smaller interconnected relations with the isolated variables are expanded to form (namely tuples). It is to be understood that such a representation is potentially more compact than compressed Cartesian arguments, and will make it possible to associate object functions to each single combination of the defining variables.

When the array system model is to be used for optimization or learning, one or more object functions, for example pricing functions, are optionally incorporated into the array system model. An object function of a given subset of variables, wherein the object function derives characteristics of a given subset of variables, and is linked to a complete solution space by deducing constraints imposed by the object function on each link variable connected to the given subset of variables. After the array system model has been generated by the method pursuant to the present disclosure, object functions can provide information between a set of variables and a set of object function values, for example cost, price, risk or weight.

As an example in healthcare, given a patient's set of co-morbidities and coprescriptions, it is potentially contemporarily not possible to select a drug for a particular disease from any of available options that does not present some significant risk of interactions or side-effects arising. In such a case, it is necessary to choose a best available drug, which reduces, for example minimizes, a likelihood and/or severity of any of these potential interactions or side-effects. Such a reduction, for example minimization, can be achieved by accepting a partially incomplete deduction (with, for example, a single invalid variable), and then using object functions as described below to evaluate and optimize the likely outcomes, such as potential patient benefit, treatment cost and side-effect risk.

If a set of object function values does not have a natural order, in contrast, for example with numbers, an arbitrary order can be assigned to the set of object function values.

Characteristics of the object function are susceptible to being determined; moreover, constraints on the link variables deduced on each combination of the given variables can be determined, wherein the result is represented as a relation on the object function, the given variables, and the link variables.

These characteristics are optionally values of the object function given by functional mapping of a set of independent variables or a set of constrained variables. The mapping can also be a general relation yielding one or more object function values for each combination of the variables.

Embodiments of the present disclosure provide a method of interrogating and/or configuring and/or optimizing and/or verifying and/or controlling a system spanned by variables on finite domains, wherein the method comprises:

(i) providing an array system model in which substantially all valid solutions in the system are stored as nested arrays representing valid Cartesian subspaces on all external variables, with all interconnected valid Cartesian subspaces being addressable as valid combinations of indices of link variables; and (ii) deducing any sub-space, corresponding to an input statement and/or query, of states or combinations spanned by one or more variables of the system represented by the nested arrays by deriving the consequences of a statement and/or an query by applying the constraints defined by the statement and/or query to the system model.

In respect of embodiments of the present disclosure, deducing refers to deriving or determining logical inferences or conclusions, for example all inferences or conclusions, from a given set of premises, namely all the system constraints.

In respect of embodiments of the disclosure, the term query refers to a question for which the array system model is operable to provide answers, for example, a question regarding a particular combination of sensor signal values, but not limited thereto, subject to defined conditions. An exemplary question concerns one or more valid combinations of a given set of variables satisfying the system constraints and, optionally, also satisfying an external statement. An external statement may be a number of asserted and/or measured states and/or constraints from the environment. Moreover, a deduction of any subspace of states or combinations is performed on a given subset of one or more variables either without or colligated with asserted and/or measured states and/or constraints from the environment.

An interaction between the system represented by the array system model and the environment is suitably performed by means of a state vector (SV) representing all valid states or values of each variable.

Thus, an input state vector (SV1) is employed to represent the asserted and/or measured states from the environment, whereas an output state vector (SV2) is used to represent one or more deduced consequences on each variable of the entire system when the constraints of SV1 are colligated with all system constraints in the array system model.

Optionally, the multidimensional system model includes static constraints, clusters of accumulated states, and dynamic rules which represents valid transitions between valid states.

In a preferred embodiment of the present disclosure, each invalid variable may be either discarded from the environment (SV1) or may be deduced as a consequence (SV2). Furthermore, optionally, variables defined as output variables are allowed to change a state without causing a contradiction. Moreover, deduction may be optionally performed by consulting one or more relations and/or one or more object functions at a time by colligating a given subset of variables in a relation with given subsets of states in a state vector and then there is deduced therefrom possible states of each variable.

In embodiments of the present disclosure, clustering and dynamic properties are employed in operation of the array system model. Such clusters represent a list of state vectors associated with known attributes. States of the cluster are determined from external variables (EV) and/or internal state variables that span the array system model. Relationships between the states of the clusters and state variables are defined by a cluster relation. For example, a given cluster relation has three state variables: a state of the cluster, and variables VI and V2. In operation of embodiments of the present disclosure, there may be a logical OR between rows in a relation table (namely, as in a disjunctive form). Alternatively, the cluster relation is a relation between the states of the clusters and state variables, wherein states of clusters are input and state variables are output. The cluster relations reduce a hyperdimensional space, having millions of parameters, to a corresponding multidimensional array system model. When the states of the external variables are known, processing of the cluster relation in run time may be described as including steps as follows:

(i) comparing external measurements with the states of cluster in the cluster relation and identify corresponding matching rows;

(ii) deducing values of the output state variables VI and V2; and (ill) deducing the constraints on all other state variables by a state propagation in the array system model.

Completeness without colligation can be ensured as the given cluster may be only a part of one relation and therefore considered as an isolated variable in the multi-dimensional and complete array system model. Exemplary applications of clustering include, precision medicine, state-event processing and many other exceptionally complex applications.

A consultation of a relation is beneficially performed by colligating, for example joining, the relation and states of variables present in the relation. The consultation provides a result that can be a projection (namely, a union of all elements) on each variable of the colligated relation, or the result can be the colligated relation. The colligation is optionally a joining, however, it will be appreciated that the consultation of each relation is not limited thereto. In an example embodiment of the present disclosure, two or more variables are colligated in parallel; projections on two or more variables are similarly performed in parallel. However, it will be appreciated that embodiments of the present disclosure are not limited to such parallel implementation, and the embodiments are optionally susceptible to being implemented sequentially.

In an embodiment of the present disclosure, completeness of deduction is obtained by consulting connected relations, until no further consequences can be deduced on any link variable. Such an operation is termed state propagation. Moreover, such a state propagation comprises consulting two or more relations in parallel, namely concurrently.

The parallel execution of the state propagation may be implemented on one or more GPUs (Graphics Processing Units) or hardware designed for such parallel execution. The interaction between the array system model and the environment by the state vector may be carried out by simple operations that are suitable for a hardware implementation on devices such as embedded control systems, Internet of Things (loT) sensors or Field Programmable Gate Arrays (FPGAs).

An important feature of configurations and/or optimizations employed in embodiments of the present disclosure is that states of contradiction can be identified, namely when no valid states or values are deduced when consulting, namely investigating or checking, at least one relation. Such identification of contradictions and an elimination of a need to perform computations in connection with the contradictions, enable methods of the present disclosure to reduce computational resources required for performing complex hyper-dimensional computations.

The array system model (referred to as ASM in the following) is a compact and complete representation of all valid combinations and associated object functions of constraint problems on finite domains or intervals. The ASM is used to represent a person, an apparatus, a facility, a factory or similar system. A solution space of valid states or combinations is beneficially represented geometrically in terms of nested data arrays, and the ASM is simulated very efficiently in operation by simple operations on these arrays using CPUs (central processing units), GPUs (graphics processing units) or hardware devices designed for this specific use.

Major data flows required for performing ASM modelling include input data, for example a user-defined specification of system constraints in terms of a set of rules or relations pertaining to a given set of variables. Thus, the ASM modelling is implemented in a six-step procedure, wherein the six-step procedure includes STEP 1 to STEP 6 as follows:

STEP 1: Compile variables and relations

Each user-defined variable and each relation is compiled into the internal array representation. At this stage STEP 1, the relations are considered as independent items.

STEP 2: Colligate relations, verification of system

The solution space of the entire system is determined by colligating interconnected relations (constraint elimination). The system is simultaneously tested for logical consistency and redundancy. Embodiments of the present disclosure relate inter alia to a more efficient colligation strategy.

STEP 3: Minimize and link complete solution space

The complete solution space can be, for example, minimized and restructured in order to meet requirements in a runtime environment. Examples include: minimizing memory footprint to enable operation on a wearable device; splitting the array system model into multiple instances for parallel processing hardware; adding object functions on combinations of selected variables; and adding dynamic constraints in terms of relations as well as states to enable real-time response to signals from loT or wearable sensors.

Step 4: Link object functions

Optionally, the relations may be extended with further attributes, when the valid combinations satisfying the system constraints are associated with values or object functions to be optimized or used for specific applications, such as, for example, a price or soft constraints such as side-effect risk and severity with further values than just true or false.

Step 5: Cluster states and cluster relations

Optionally, clustering is performed to reduce the hyper-dimensional space, potentially with millions of parameters, to the multi-dimensional ASM for performing decision support. Examples include: millions of genomic phenotypic and clinical variables that are condensed/reduced to a few hundred variables, which is utilized by decision support system. Clustering is based on cluster states (i.e. states of clusters) and cluster relations.

Step 6: State-event relations

Optionally, state-event relations utilize external events to describe the change from one state to another. Clustering is based upon internal state variables representing the conditions for change of state.

At this stage, the process of ASM modelling is finished. The entire solution space is now susceptible to being addressed by coordinate indexing and other simple operations on the nested arrays.

Each item of the state vector SV represents the state (namely the valid values) of an associated variable. For example, in respect of the input state vector SV1, one or more variables are bounded due to external measurements or assertions. Moreover, the input state vector SV2 represents the resulting constraints on all variables. The properties of the ASM are summarized as follows: (I) a run-time execution on the ASM is performed with completeness of deduction in real-time, namely with predictable use of processing time and memory. The ASM technology is therefore suitable for use in embedded decision support or for use in control systems on small computer devices: and (ii) the ASM representation is compact and complete. Embedded applications of embodiments of the present disclosure are required to fulfill all requirements for compactness, completeness and real-time capability with limited computing resources, even on large system models.

SIMPLE COLLIGATION STRATEGY (ADB)

Optionally, a generation of the ASM technology (to be abbreviated to ADB in the following) is based on a simple colligation strategy by pairwise joins of relations and then linking isolated variables whenever possible, the relations are operable to share variables. Moreover, the colligation graph is an illustration of a structure of interconnected relations, wherein nodes represent relations and arcs represent common variables of two of the relations.

A first colligation step is to compile each relation, namely to determine valid combinations of each relation. It will be appreciated that all invalid combinations are eliminated from each of the relations. Moreover, the valid combinations are expressed in terms of Cartesian sub-spaces; however, it will be appreciated that other coordinate spatial reference frames may be optionally employed for implementing embodiments of the present disclosure.

A second colligation step is to colligate the relations to determine the solution space of the conjunction of all relations. It is now possible to perform inference processing by performing simple array operations. The state vector is the important link between the compiled (colligated) array system model and the environment. The output state vector is deduced by consulting the complete solution space. The state of each variable is deduced by computing the union of elements from the two valid Cartesian sub-spaces. In general, the colligation process is carried out by pairwise joins of the relations, and after each join isolated variables are separated (assuming at least two isolated variables) into new relations connected by common link variables representing the valid Cartesian sub-spaces. The state vector is deduced by consulting one relation at a time, until no further constraints are added to each variable (state propagation).

Thus, the state propagation on a tree structure of interconnected relations (by the valid states of the common link variables) ensures completeness of a given deduction; in other words, all constraints on all variables are deduced in embodiments of the present disclosure. It will be appreciated that completeness of deduction is not possible by state propagation on the array representation of user-defined relations. Thus, there arises a need to colligate all interconnected relations in advance, even on such a very simple cyclic structure with only a single variable connecting each relation pair.

The simple colligation strategy (namely ADB) technology described in the following is susceptible to being summarized as follows:

1. A given process of joining relations with common variables and linking isolated relations on isolated variables is potentially impossible to implement in practice on large sets of relations due to a possible blow-up in size of a corresponding joined result (namely, is computationally impossible to achieve in practice using contemporary computing hardware). Such requirement for huge computational resources is an insurmountable and constant issue arising on account of a complexity of constraints in a range of practical technical fields of use of intelligent data processing systems in fields such as healthcare and life sciences.

2. If the process of joining relations can be completed, a binary output that is thereby achieved does satisfy requirement for completeness, but does not satisfy other requirements for embedded solutions, namely:

a. A representation thereby derived will not be as compact as possible, and potentially must be reduced in size, for example minimized in size, to meet specific hardware requirements for achieving size and real-time capability.

b. A complete solution space will not necessarily be accessible by parallel processing hardware using simple instructions, for example using GPUs.

c. The complete solution space must be addressable in order to include object functions. A compressed data format (namely, nested Cartesian arguments) may not be a suitable representation for variables defining an object function; relations for these variables are beneficially in an expanded form representing all valid tuples rather than Cartesian arguments.

d. Relations without any constraints (tautologies) are potentially also a part of static constraints of a system model interacting with dynamic constraints from an environment.

PARALLEL COLLIGATION

Initially, relations are joined pairwise using an approach as described in published patent documents WO 09948031A1 and WO 2001022278A1.

Moreover, isolated variables (namely, variables only present in their corresponding single relations) are separated and linked into new relations. A trivial case of parallel colligation is to join all relations into a single relation (wherein such an approach is suitable for smaller problems) or into a tree structure of interconnected relations with isolated variables (wherein such an approach is suitable for larger problems), and the colligation is thus thereby completed. In respect of aforementioned larger problems, it is not potentially feasible to use known joining methods due to a size of the joined result arising from such joining methods. It is thereby beneficial to introduce a parallel colligation of smaller parts of the system, wherein:

(I) a parallel join of a relation pair is performed (ii) a parallel colligation of variable groups is performed

PARALLEL JOIN OF RELATION PAIRS

In an internal array representation in compressed form relations are represented by 5 and 3 Cartesian arguments. Such small relations are susceptible to being joined in different ways. However, in respect of large relations, such an approach would cause a combinatorial explosion of possible argument intersections, which would be very expensive in terms of central processing unit (CPU) resources and data memory to compute in a practical example. Thus, pursuant to embodiments of the present disclosure, it is therefore beneficial to use a much more efficient methodology for colligating a smallest possible subsystem spanned by just a single variable step of a join algorithm as a result of expanding the local intersections of each variable to the matching indices of arguments in the joined relation. This indexing procedure is highly efficient and does not benefit from being implemented by employing parallel data processing. Thus, it will be appreciated from the foregoing that the number of arguments in the joined result, and data memory requirements, for computing and storing results, can be predicted from said indexing procedure. The local results of each colligated variable are now expanded to the attributes of the joined relation using the associated indices.

COLLIGATION STRATEGY FOR VARIABLE GROUPS

Initially, relations are joined and compressed pairwise using an approach as described in published patent documents WO 09948031A1 and WO 2001022278A1 (namely, as per Step 1 in the foregoing). Isolated variables (only present in a single relation) are separated and linked into new relations. A trivial case is to join all relations into a single relation (suitable for small problems) or the tree structure of connected relations with isolated variables (suitable for larger problems), and the colligation is thus thereby completed. On large problems, it is not potentially feasible to join all relations due to the size of the joined result. It is thereby beneficial to introduce the colligation of relations on selected variable groups.

In an embodiment, the number of Cartesian arguments in the relations is very large, and it is not possible to join the relations. A corresponding workflow for colligation in respect of groups of variables shared by same given relations is:

Step 1: Determine distinct variable groups shared by two or more relations: all variables shared by same relations are grouped. An aim in the step 1 is to find distinct groups (namely, with no overlap), and therefore there is performed a merging of the small group into the larger one.

Step 2: Split relations on each variable group: all relations share the variable group. A copy of the relations on these variables and the associated link variables is made.

Step 3 and 4: Join and link relations on each variable group: joining the relations on the variable group yields a relation with the following variables.

Next, the variable group is isolated and a new link variable indexing each Cartesian argument is thereafter added. There is thereby generated a result that is a relation.

Step 5: Substitute variable groups in original relations with the associated link variables. The relation defines the relationship between variables.

Step 6: Colligate relations on link variables. The original relations are now defined on the link variables of isolated relations. These results are also colligated by join and, if possible, to isolate variables. Assuming that it is required to join to provide a single relation, there is thereby provided a relation.

MINIMIZE AND LINK COMPLETE SOLUTION SPACE

Furthermore, there is thereby now completed the colligation process yielding the addressable complete solution space. All invalid combinations are eliminated (with a state of contradiction as a special case). A final task is to prepare the model for embedded applications, namely to seek to minimize a size of the binary file (to achieve compactness) and to optimize a run-time performance in respect of specific hardware, whether with or without parallel processing capabilities, for example multi-core GPUs are susceptible of providing parallel processing functionality. Each individual relation is potentially split into more relations in two different ways, depending upon a size of an output to be generated and upon whether or not there is use made of parallel processing hardware.

Option 1: Split core relation into pairs and split model for parallel processing A given relation is extended with a link variable (LINK) indexing the Cartesian arguments (in a compressed form) or tuples (in an expanded form) of the given relation with variables VARI, VAR2, ... VARn. The given relation is then split into n derived relations on (VARI, LINK), (VAR2, LINK), (VARn, LINK), respectively; n is an integer of value 2 or greater (namely, a plurality). Such a method will always be used on a core relation of a complete array system model, whenever the model is to be split and distributed for parallel processing. In a runtime environment, it is thereby feasible to ensure completeness of deduction by a simple state propagation of a state vector. Option 2: Split relation into tree structure of interconnected relations: the original relation is split into a tree structure of relations (represented in bold boxes). There is employed a method as follows:

Step 1: Find smallest derived relation on N variables in the Step 1, the smallest number of Cartesian arguments (or, alternatively, tuples in expanded form) is on the variables.

Step 2, 3: Add new link variable and isolate relation in the Steps 2 and 3. The relation on variables is extended with a link variable and then isolated (namely stored) for the binary output file.

Step 4: Update relation R: remove VARI, VAR2 and substitute with link variable in the method. The aforementioned Steps 1 to 4 of the method are executed recursively to yield a list of relations and finally a relation which is not split (namely, representing a root of the aforementioned tree).

BUILDING DECISION SUPPORT SYSTEMS

The construction of the Array System Model and Decision Support Application is a multi-stage process involving the following steps:

Step 1 - Mining of Source Data and Semantic Normalization:

Step 2 - Compilation & Validation of Array System Model

Step3 - Accessing Array System Model on mobile/wearable device via Runtime API using the User's Input State Vector.

The array system model is converted by the Array System Model compiler into a verified and normalized structure, that can be represented in a 428 KB file, which is an amount of memory the model consumes when loaded into an Array Runtime API on a given user's mobile device, for example a smart phone or a smart watch. A significant proportion of this memory (namely, over 60% thereof) is simply used for storing names of drugs being considered in the computation, as well as diseases and foods; such data is potentially further optimized, if necessary, so that the Array System Model requires even less computing resources in operation. The Array System Model provides an analytical and predictive substrate to power a personalized decision support app (namely, application software) on the given user's mobile device. This substrate enables the Runtime API running directly on the user's mobile device (smart watch, phone, or tablet) to use the Array System Model to perform logical inferences on the data and deduce all the consequences of a given parameter for a selected data set. Embodiments of the present disclosure are operable to provide a decision support system for performing aforementioned analyses within a predictable and very short time; for example, a proprietary Google Nexus 7 tablet computer running an Android software platform is capable of implementing analyses within five to ten milliseconds. Such computational performance is provided with a constant and low memory footprint (namely, around 430kBytes in practice), and is guaranteed to find all the potential adverse consequences given by constraints imposed by a given user's input state vector.

Optionally, the computing arrangement includes at least one of: a computing device and a distributed arrangement including a plurality of computing devices.

Optionally, the sub-models are distributed over a plurality of computing devices that are mutually coupled together in operation via a data communication network.

Optionally, the method includes generating and storing, in a data memory or data storage medium of the computing arrangement, an addressable solution space defining all valid transitions between all valid states.

Optionally, the method includes computing the state of the entire system model in real-time by consulting one or more sub-systems and/or relations at a time by deducing possible states of each variable and propagating one or more bound link variables to connected one or more relations until no further constraints can be added to the state vectors.

Optionally, the control apparatus is configured to be employable for controlling one or more of:

(i) industrial production facilities;

(ii) agricultural production facilities;

(iii) healthcare providing facilities;

(iv) drug discovery systems;

(v) smart metering arrangements;

(vi) autonomous and self-drive vehicle driving arrangements;

(vii) in intelligent drones for surveillance use;

(viii) in airborne radar systems; and (ix) in intelligent apparatus for assisting medical surgery and/or treatment.

Claims

1. A verified resource supply system that supplies resources that have been verified using a voting arrangement, wherein the verified resource supply system employs a data processing arrangement that provides a platform through which members interact with the verified resource supply system, characterized in that (i) the verified resource supply system includes a ledger arrangement hosted by the data processing arrangement into which entries representative of resources are recorded in a blockchain, wherein the entries include characterizing data that are submitted to describe technical properties of the resources to be verified;

(ii) the verified resource supply system includes the voting arrangement for a plurality of members of the verified resource supply system to input votes in respect of whether or not the entries of the blockchain are valid, wherein the votes are derived from data recorded in an array system model of the data processing arrangement accessing a database arrangement describing one or more suppliers of the resources of the entries of the blockchain and information describing expected properties of the resources represented by the entries; and (iii) the ledger arrangement controls in operation whether or not a given entry of the blockchain is to be retained in the blockchain depending upon the votes, and also whether the resources when verified have been allocated for use or are available to be allocated for use.

2. A verified resource supply system of claim 1, characterized in that the verified resource supply system includes a membership control arrangement for the members, wherein the members are operable to vote after the members have been approved by a peer voting arrangement of the system.

3. A verified resource supply system of claim 1 or 2, characterized in that the resources include at least one of: feedstock for use in feeding animals, food supplements for animals, drugs for animals.

4. A verified resource supply system of claim 1, 2 or 3, characterized in that the database arrangement is arranged to be interrogated via a multidimensional search engine.

5. A verified resource supply system of claim 4, characterized in that the multi-dimensional search engine describes a model spanned by state variables on finite domains and/or intervals, wherein the state variables include one or more suppliers of the resources of the entries and information describing expected properties of the resources represented by the entries, wherein the multi-dimensional search engine is operable to:

6. A verified resource supply system of claim 5, characterized in that the multi-dimensional search engine is further operable to use the addressable solution space to process one or more inputs provided to the verified resource supply system when in operation, and to generate corresponding outputs from the database arrangement.

7. A verified resource supply system of any one of the preceding claims, characterized in that the voting arrangement prevents contaminated resources from being presented and allocated via use of the blockchain.

8. A verified resource supply system of claim 8, characterized in that the contaminated resources include at least one of: mould contamination, radioactive contamination, chemical residue contamination, heavy metal poisoning, recycled animal-derived pathogens.

9. A method of (for) operating a verified resource supply system that supplies resources that have been verified using a voting arrangement, wherein the verified resource supply system employs a data processing arrangement that provides a platform through which members interact with the verified resource supply system, characterized in that the method includes:

(II) using the voting arrangement of the verified resource supply system, wherein the voting arrangement includes for a plurality of members of the verified resource supply system, to input votes in respect of whether or not the entries of the blockchain are valid, and wherein the votes are derived from an array system model accessing a database arrangement describing one or more suppliers of the resources of the entries of the blockchain and information describing expected properties of the resources represented by the entries; and (iii) using the ledger arrangement to control in operation whether or not a given entry of the blockchain is to be retained in the blockchain depending upon the votes, and also whether the resources when verified have been allocated for use or are available to be allocated for use.

10. A method of claim 9, characterized in that the method includes arranging for the verified resource supply system to include a membership control arrangement for the members, wherein the members are operable to vote after the members have been approved by a peer voting arrangement of the system.

11. A method of claim 9 or 10, characterized in that the resources include at least one of: feedstock for use in feeding animals, food supplements for animals, drugs for animals.

12. A method of claim 9, 10 or 11, characterized in that the method includes arranging for the database arrangement to be interrogated via a multidimensional search engine.

13. A method of claim 12, characterized in that the method includes generating a multi-dimensional describing a model spanned by state variables on finite domains and/or intervals, wherein the state variables include one or more suppliers of the resources of the entries and information describing expected properties of the resources represented by the entries, wherein the method include arranging for the multi-dimensional search engine to:

14. A method of claim 13, characterized in that the method further includes arranging for the multi-dimensional search engine to use the addressable solution space to process one or more inputs provided to the verified resource supply system when in operation, and to generate corresponding outputs from the database arrangement.

15. A method of any one of the claims 9 to 14, characterized in that the method includes arranging the voting arrangement to prevent contaminated resources from being presented and allocated via use of the blockchain.

16. A method of claim 15, characterized in that the contaminated resources include at least one of: mould contamination, radioactive contamination, chemical residue contamination, heavy metal poisoning, recycled animalderived pathogens.

17. A computer program product comprising a non-transitory computerreadable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a computerized device comprising processing hardware to execute the method of any one of claims 9 to 16.