System and method for authenticity service

A system for determining an authenticity of an asset includes one or more hardware processors. The system also includes a non-transitory memory, the non-transitory memory storing instructions that, when executed by the one or hardware processors, causes the one or more hardware processors to perform actions. The actions include receiving an asset, wherein the asset includes a digital asset or a digital representation of a physical asset. The actions also include receiving an input related to the asset to assist in determining the authenticity of the asset. The actions further include utilizing an augmented intelligence module to analyze the asset and to determine the authenticity of the asset based on the analysis of the asset and the received input.

BACKGROUND

Authenticity services typically are performed in person and may take time and effort. In the digital age, people and businesses are increasingly performing more transactions online and expect quicker service. In addition, a tremendous amount of data is available online (e.g., through the Internet of Things (IoT), cloud storage, and big data) that is underutilized or not effectively utilized. At the same time, an increase in tokenization and generation of non-fungible tokens (NFT) provide additional assets of potential value that may be subject to authenticity services. Therefore there is a need for authenticity services that can take advantage of all of the potential data and provide analysis in an efficient manner online.

SUMMARY

In certain embodiments, a system for determining an authenticity of an asset is provided. The system includes one or more hardware processors. The system also includes a non-transitory memory, the non-transitory memory storing instructions that, when executed by the one or hardware processors, causes the one or more hardware processors to perform actions. The actions include receiving an asset, wherein the asset includes a digital asset or a digital representation of a physical asset. The actions also include receiving an input related to the asset to assist in determining the authenticity of the asset. The actions further include utilizing an augmented intelligence module to analyze the asset and to determine the authenticity of the asset based on the analysis of the asset and the received input.

In certain embodiments, a non-transitory computer-readable medium includes processor-executable code that when executed by a processor, causes the processor to perform actions. The actions include receiving an asset, wherein the asset includes a digital asset or a digital representation of a physical asset. The actions also include receiving an input related to the asset to assist in determining the authenticity of the asset. The actions further include utilizing an augmented intelligence module to analyze the asset and to determine the authenticity of the asset based on the analysis of the asset and the received input.

In certain embodiments, a computer-implemented method for determining an authenticity of an asset is provided. The method includes receiving, at a processor, an asset, wherein the asset includes a digital asset or a digital representation of a physical asset. The actions also include receiving, at a processor, an input related to the asset to assist in determining the authenticity of the asset. The actions further include utilizing, via the processor, an augmented intelligence module to analyze the asset and to determine the authenticity of the asset based on the analysis of the asset and the received input.

Various refinements of the features noted above may exist in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination.

The brief summary presented above is intended only to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.

DETAILED DESCRIPTION

The present disclosure relates generally to systems and methods for authenticity service.

As used herein, the term “computing system” refers to an electronic computing device such as, but not limited to, a single computer, virtual machine, virtual container, host, server, laptop, and/or mobile device, or to a plurality of electronic computing devices working together to perform the function described as being performed on or by the computing system. As used herein, the term “medium” refers to one or more non-transitory, computer-readable physical media that together store the contents described as being stored thereon. Embodiments may include non-volatile secondary storage, read-only memory (ROM), and/or random-access memory (RAM). As used herein, the term “application” refers to one or more computing modules, programs, processes, workloads, threads and/or a set of computing instructions executed by a computing system. Example embodiments of an application include software modules, software objects, software instances and/or other types of executable code.

The present embodiments provide systems and methods for providing an authenticity service by utilizing augmented intelligence and machine learning in determining an authenticity of an asset. For example, these embodiments enable the authenticity for an asset (e.g., non-fungible asset such as a non-fungible token (NFT) representing a tangible or non-tangible item or a fungible asset) to be determined as a service and an analysis of the authenticity to be provided. Various inputs (e.g., providing additional information that may assist in determining authenticity) may be provided to an authenticity system along with the asset (if in digital form) or a representation of the asset (e.g., digital representation of a physical item). The authenticity system may utilize augmented intelligence to analyze the asset and the provided inputs to further help the authenticity system search for additional information related to the authenticity of the asset and to determine the authenticity of the item. The authenticity system may then provide an assessment of the authenticity of the asset and/or a score indicating a level of confidence in the authenticity assessment of the asset.

With this in mind, FIG. 1 is a schematic diagram of an embodiment of a system 10 for determining an authenticity of an asset (e.g., online). The asset may be a fungible item or non-fungible asset. The asset may be tangible or non-tangible. As utilized in the system 10, the asset is a digital asset or a digital representation of a physical asset. In certain embodiments, the asset may be an NFT in a variety of digital forms (e.g., art, music, video, etc.).

The system 10 may include an authenticity service system 12 (e.g., a physical computing system and/or a cloud-computing system) configured to receive inputs (e.g., user inputs, professional appraiser inputs, etc.) related to an asset to assist in determining an authenticity of the asset. The inputs may be authenticity evidence itself (e.g., origin data, watermarks, key information, etc.). The authenticity service system 12 may utilize augmented intelligence (e.g., augmented intelligence module or engine) to search for authenticity evidence based on the received inputs. The authenticity service system 12 (e.g., the augmented intelligence module) may also analyze the asset for a variety of purposes. For example, the asset may be analyzed for the type of asset (e.g., physical piece of art, digital art, video, original digital document, etc.) to assist in determining specific features of the asset to analyze for authenticity evidence or to determine specific external sources (e.g., title company, social media platform, etc.) to access to find authenticity evidence. The type of asset may also be utilized for the selection of a specific augmented intelligence model (which is specific to the type of asset). In another example, the asset may be analyzed for the presence of sources of authenticity evidence (e.g., watermark, origin data, etc.). The asset may also include a computer chip, a quick response code, or other source that stores or provides access to a variety of authenticity evidence (e.g., hardware that created asset, chain of custody of asset, date of creation of asset, location data, etc.). The authenticity service system 12 may also provide an indication of the authenticity of the asset to a user. The indication of authenticity may be a declaration that an asset is authentic, likely authentic, likely not authentic, not authentic, or some other statement. The indication of authenticity may also include a score indicating a likelihood of authenticity. In the case of an NFT, the authenticity service system 12 may determine if the NFT is in alignment with the physical world (i.e., a physical asset). The authenticity service system 12 is configured to quickly provide an authenticity assessment for an asset solely through an online environment.

To this end, the authenticity service system 12 may include a memory 14 and a processor or processing circuitry 16. The memory 14 may include volatile memory, such as random-access memory (RAM), and/or non-volatile memory, such as read-only memory (ROM), optical drives, hard disc drives, solid-state drives, or any other non-transitory computer-readable medium that includes instructions executable by the processor 16. The processor 16 may include one or more application specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), one or more general purpose processors, or any combination thereof, configured to execute the instructions stored in the memory 14, such as to determine the authenticity of an asset. The memory 14 also stores the augmented intelligence module (and one or more models) for determining the authenticity of the asset.

The authenticity service system 12 may be communicatively coupled to one or more searchable databases or external data sources 18 (e.g., a physical storage and/or a cloud-based storage) via any suitable communication network or networks 20, including a mobile communication network, a Wi-Fi network, local area network (LAN), wide area network (WAN), and/or the Internet. The one or more databases 18 (e.g., financial database, insurance database, social platform databases, location databases, etc.) are configured to store a variety of data. The databases 18 may include authenticity evidence for the asset. For example, the data may include financial information (e.g., previous financial transactions related to the asset, purchase of the asset from an art gallery). The data may include insurance information (e.g., past or current insurance information for the asset) from one or more insurance providers, a third party, or a group associated that provide authenticity evidence. Also, the data may include information from various social media platforms that may provide information related to the asset (including origination of the asset on a particular platform). Even further, location information (e.g., current or past locations of the asset) may be provided in a location database. Any type of database 18 may be searched for authenticity evidence.

The system 10 also includes a user device 22 (e.g., computing device), which may be implemented as or on one or more suitable electronic computing devices, such as a laptop, personal computer, mobile device, smartphone, tablet, wearable device, and so on. The user device 22 may be utilized by a user requesting or performing the authenticity service.

FIG. 2 is a schematic diagram of an embodiment of an arrangement for providing authenticity services. As depicted, a clearinghouse entity 24 provides an authenticity service 26 (e.g., utilizing the authenticity service system 12 described in FIG. 1). The clearinghouse entity 24 may be a bank and/or insurance provider. An individual member 28 of the clearinghouse entity 24 may request the authenticity service 26 to be performed on an asset. Alternatively, a business member 30 of the clearinghouse entity 24 may request the authenticity service 26 to be performed on an asset of a customer 32 of the business member 30 or its own asset. In certain embodiments, the clearinghouse entity 24 may provide access to the clearinghouse entity's authenticity service 26 (e.g., in the form of an authenticity service kit) to the business member 30 to enable the member 30 to determine an authenticity of an asset. The authenticity service 26 may be utilized to prove the value of the asset with a higher confidence level for insurance purposes, resale of the asset, or general liquidity uses (e.g., loan/lien). For example, a person going to college may possess one or more NFTS as assets. The person (the customer 32) may approach the business member 30 or the clearinghouse entity 24 (if an individual member) for a student loan and NFTs may be subject to the authenticity service 26 to see if the NFTs could potentially provide collateral for the student loan.

FIG. 3 illustrates an augmented intelligence (AI) module or engine 34 for determining an authenticity of an asset. The AI module 34 may be part of the computing system described in FIG. 8 or the authenticity service system 12 described in FIG. 1. The AI module 34 may utilize machine learning capabilities coupled with natural language processing and automated insights in determining an authenticity of an asset. As previously mentioned, the AI module 34 may be utilized to search for authenticity evidence based on received inputs. The AI module 34 may also analyze the asset for a variety of purposes. For example, the asset may be analyzed for the type of asset (e.g., physical piece of art, digital art, video, original digital document, etc.) to assist in determining specific features of the asset to analyze for authenticity evidence or to determine specific external sources to access to find authenticity evidence. The type of asset may also be utilized for the selection of a specific augmented intelligence model (which is specific to the type of asset). In another example, the asset may be analyzed for the presence of sources of authenticity evidence. These sources may be computer chips, quick response codes, or other sources that store or provide access to a variety of authenticity evidence (e.g., hardware that created asset, chain of custody of asset, date of creation of asset, location data, etc.). The AI module 34 may also generate an indication of the authenticity of the asset for a user. The indication of authenticity may be a declaration that an asset is authentic, likely authentic, likely not authentic, not authentic, or some other statement. The indication of authenticity may also include a score indicating a likelihood of authenticity.

The AI module 34 may utilize one or more machine learning algorithm models 36. In certain embodiments, a single machine learning algorithm model 36 may be trained for and utilized for determining an authenticity of all kinds of assets. In certain embodiments, the AI module 34 may include multiple machine learning algorithm models 36 each trained for and utilized for determining an authenticity of a different kind or type of asset (e.g., digital art, physical art, video, etc.). The AI module 34 may utilize the inputs received to direct the respective models 36 in their search for authenticity evidence and determining the authenticity of an asset. Each machine learning algorithm model 36 is able to make correlations or derive information from the data sources related to the authenticity of the asset (with assistance from received inputs) that otherwise would not be readily apparent. With each determination of the authenticity of an asset, the models 36 are updated to improve their function (and improve the function of the computing system) in determining the authenticity of assets.

FIG. 4 illustrates a flow diagram of a method 38 for determining an authenticity of an asset (e.g., online). One or more steps of the method 38 may be carried out by one or more components of the computing system illustrated in FIG. 8 or the authenticity service system in FIG. 1. One or more steps of the method 38 may be performed simultaneously or in a different order from the order depicted in FIG. 4. The method 38 includes receiving an asset and/or request to determine an authenticity of the asset (block 40). The asset is a digital asset or a digital representation of a physical asset. The asset may be a fungible item or non-fungible asset. The assets may be tangible or non-tangible. In certain embodiments, the asset may be an NFT in a variety of digital forms (e.g., art, music, video, etc.).

The method 38 also includes requesting input related to the authenticity (block 42). The method 38 further includes receiving the input from a user or another source (block 44). The input itself may be authenticity evidence. In other embodiments, the input may provide assistance in searching for authenticity evidence. Examples of input may be where or how the asset was created, the type of asset, past and/or current location of the asset, chain of custody of asset, videos, pictures, title, etc. Other examples of inputs may be keys (e.g., private keys) generated from hardware of devices utilized to generate the media (e.g., camera, computing device, smart phone, social platform, etc.). In certain embodiments, the inputs may be in the form of NFTs. In certain embodiments, the inputs may be professional appraiser inputs that provide authenticity evidence from certified professionals.

The method 38 includes analyzing the asset (block 46). For example, the asset may be analyzed for the type of asset (e.g., physical piece of art, digital art, video, original digital document, etc.) to assist in determining specific features of the asset to analyze for authenticity evidence or to determine specific external sources to access to find authenticity evidence. The type of asset may also be utilized for the selection of a specific augmented intelligence model (which is specific to the type of asset). If the type of asset is known (e.g., a video), it may be analyzed for features related to its authenticity (e.g., code to determine if it was spliced).

In another example, the asset may be analyzed for the presence of sources of authenticity evidence. These sources may be computer chips, quick response codes, or other sources that store or provide access to a variety of metadata that may include authenticity evidence (e.g., hardware that created the asset, chain of custody of the asset, date of creation of the asset, location data, etc.). These sources may put the asset on a block chain to increase the likelihood of the asset being authentic.

The method 38 also includes accessing certain external sources or databases to search for authenticity evidence based on the received inputs and/or analysis of the asset (block 48). For example, a digital picture of a physical piece of art in a particular location may include information related to a location of the physical piece of art (e.g., in a specific house or building) which may lead to looking for authenticity evidence (e.g., inventory of house or building, title of house or building, etc.) related to the physical piece of art in certain sources. In another example, an NFT of a video clip may lead to searching for authenticity evidence (e.g., postings including the video clip) in social media platforms.

The method 38 further includes determining an authenticity of the asset based on the authenticity evidence gathered (block 50). The more authenticity evidence gathered (or provided via the inputs) increases the confidence in the authenticity determination. The method 38 still further includes providing an indication (e.g., to a user) of the authenticity of the asset (block 52). The indication of authenticity may be a declaration that an asset is authentic, likely authentic, likely not authentic, not authentic, or some other statement. The indication of authenticity may also include a score indicating a likelihood of authenticity. For example, for a physical piece of art (provided via a digital picture) to the authenticity service, if no information can be derived from the digital picture or any additional input information provided that leads to more authenticity evidence, then it is less likely to receive an indication that the item is authentic. However, if additional information was provided or authenticity evidence discovered (e.g., location of physical piece of art, name of person in possession of physical piece of art, documentation from professional appraiser, etc.), the physical piece of art is more likely to receive an indication that it is authentic.

FIGS. 5-7 are illustrations of different examples for displaying an authenticity assessment on a graphical user interface or screen. FIGS. 5-7 illustrate graphical user interfaces 54, 56, and 58, respectively. As shown in graphical user interfaces 54, 56, and 58, a declaration or statement 60 on the assessment of the authenticity of the asset may be provided. The declaration 60 may state an asset appears to be authentic (as shown in FIG. 5), an asset does not appear to be authentic (as shown in FIG. 6), or an authenticity cannot be determined for the asset (as shown in FIG. 7). The declaration 60 may include within it qualifying language indicating a likelihood of the assessment (e.g., a high likelihood of authenticity, a low likelihood of authenticity, an intermediate likelihood of authenticity). As shown in screens 54 and 56, a score 62 indicating a likelihood of authenticity or a confidence level in the authenticity is provided. As depicted, the score 62 may range from 1 to 10 (with 1 being not authentic and 10 being absolutely authentic). In certain embodiments, the range of the score 62 may be different (e.g., 1 to 100). In certain embodiments, the format of the score 62 may be different. For example, the score 62 may be letter based (e.g., with A being absolutely authentic and F being absolutely not authentic). In certain embodiments, a statement 64 (as shown in FIG. 6) may be provided to provide a reason for the declaration 60 and/or the score 62. In certain embodiments, when an authenticity cannot be determined for an asset, a statement 66 (as shown in FIG. 7) may be provided giving the reasons why an assessment cannot be made and items or actions that can be taken to enable an assessment of the authenticity to proceed. The more authenticity evidence provided or discovered will result in a higher likelihood of an asset being deemed authentic. In certain embodiments, the score 62 provided may not relate to a likelihood of authenticity or a confidence level in the authenticity but instead relate to a likelihood of or a confidence in the assessment (e.g., in declaration 60) being correct.

FIG. 8 depicts an example computing system, according to implementations of the present disclosure. The system 1000 may be used for any of the operations described with respect to the various implementations discussed herein. For example, the system 1000 may be included, at least in part, in one or more of user device(s) and/or other computing device(s) or system(s) described herein. The system 1000 may include one or more processors 1010, a memory 1020, one or more storage devices 1030, and one or more input/output (I/O) devices 550 controllable via one or more I/O interfaces 1040. The various components 1010, 1020, 1030, 1040, or 1050 may be interconnected via at least one system bus 1060, which may enable the transfer of data between the various modules and components of the system 1000.

The processor(s) 1010 may be configured to process instructions for execution within the system 1000. The processor(s) 1010 may include single-threaded processor(s), multi-threaded processor(s), or both. The processor(s) 1010 may be configured to process instructions stored in the memory 1020 or on the storage device(s) 1030. For example, the processor(s) 1010 may execute instructions for the various software module(s) described herein. The processor(s) 1010 may include hardware-based processor(s) each including one or more cores. The processor(s) 1010 may include general purpose processor(s), special purpose processor(s), or both. The processor may include machine learning circuitry for operating functions of machine learning, including building, training, and/or generating predictions using a machine learning model.

The memory 1020 may store information within the system 1000. In some implementations, the memory 1020 includes one or more computer-readable media. The memory 1020 may include any number of volatile memory units, any number of non-volatile memory units, or both volatile and non-volatile memory units. The memory 1020 may include read-only memory, random access memory, or both. In some examples, the memory 1020 may be employed as active or physical memory by one or more executing software modules.

The storage device(s) 1030 may be configured to provide (e.g., persistent) mass storage for the system 1000. In some implementations, the storage device(s) 1030 may include one or more computer-readable media. For example, the storage device(s) 1030 may include a floppy disk device, a hard disk device, an optical disk device, or a tape device. The storage device(s) 1030 may include read-only memory, random access memory, or both. The storage device(s) 1030 may include one or more of an internal hard drive, an external hard drive, or a removable drive.

One or both of the memory 1020 or the storage device(s) 1030 may include one or more computer-readable storage media (CRSM). The CRSM may include one or more of an electronic storage medium, a magnetic storage medium, an optical storage medium, a magneto-optical storage medium, a quantum storage medium, a mechanical computer storage medium, and so forth. The CRSM may provide storage of computer-readable instructions describing data structures, processes, applications, programs, other modules, or other data for the operation of the system 1000. In some implementations, the CRSM may include a data store that provides storage of computer-readable instructions or other information in a non-transitory format. The CRSM may be incorporated into the system 1000 or may be external with respect to the system 1000. The CRSM may include read-only memory, random access memory, or both. One or more CRSM suitable for tangibly embodying computer program instructions and data may include any type of non-volatile memory, including but not limited to: semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. In some examples, the processor(s) 1010 and the memory 1020 may be supplemented by, or incorporated into, one or more application-specific integrated circuits (ASICs).

The system 1000 may include one or more I/O devices 1050. The I/O device(s) 1050 may include one or more input devices such as a keyboard, a mouse, a pen, a game controller, a touch input device, an audio input device (e.g., a microphone), a gestural input device, a haptic input device, an image or video capture device (e.g., a camera), or other devices. In some examples, the I/O device(s) 1050 may also include one or more output devices such as a display, LED(s), an audio output device (e.g., a speaker), a printer, a haptic output device, and so forth. The I/O device(s) 1050 may be physically incorporated in one or more computing devices of the system 1000, or may be external with respect to one or more computing devices of the system 1000.

The system 1000 may include one or more I/O interfaces 1040 to enable components or modules of the system 1000 to control, interface with, or otherwise communicate with the I/O device(s) 1050. The I/O interface(s) 1040 may enable information to be transferred in or out of the system 1000, or between components of the system 1000, through serial communication, parallel communication, or other types of communication. For example, the I/O interface(s) 1040 may comply with a version of the RS-232 standard for serial ports, or with a version of the IEEE 1284 standard for parallel ports. As another example, the I/O interface(s) 1040 may be configured to provide a connection over Universal Serial Bus (USB) or Ethernet. In some examples, the I/O interface(s) 1040 may be configured to provide a serial connection that is compliant with a version of the IEEE 1394 standard.

The I/O interface(s) 1040 may also include one or more network interfaces that enable communications between computing devices in the system 1000, or between the system 1000 and other network-connected computing systems. The network interface(s) may include one or more network interface controllers (NICs) or other types of transceiver devices configured to send and receive communications over one or more communication networks using any network protocol.

Computing devices of the system 1000 may communicate with one another, or with other computing devices, using one or more communication networks. Such communication networks may include public networks such as the internet, private networks such as an institutional or personal intranet, or any combination of private and public networks. The communication networks may include any type of wired or wireless network, including but not limited to local area networks (LANs), wide area networks (WANs), wireless WANs (WWANs), wireless LANs (WLANs), mobile communications networks (e.g., 3G, 4G, Edge, etc.), and so forth. In some implementations, the communications between computing devices may be encrypted or otherwise secured. For example, communications may employ one or more public or private cryptographic keys, ciphers, digital certificates, or other credentials supported by a security protocol, such as any version of the Secure Sockets Layer (SSL) or the Transport Layer Security (TLS) protocol.

The system 1000 may include any number of computing devices 138 of any type. The computing device(s) may include, but are not limited to: a personal computer, a smartphone, a tablet computer, a wearable computer, an implanted computer, a mobile gaming device, an electronic book reader, an automotive computer, a desktop computer, a laptop computer, a notebook computer, a game console, a home entertainment device, a network computer, a server computer, a mainframe computer, a distributed computing device (e.g., a cloud computing device), a microcomputer, a system on a chip (SoC), a system in a package (SiP), and so forth. Although examples herein may describe computing device(s) as physical device(s), implementations are not so limited. In some examples, a computing device may include one or more of a virtual computing environment, a hypervisor, an emulation, or a virtual machine executing on one or more physical computing devices. In some examples, two or more computing devices may include a cluster, cloud, farm, or other grouping of multiple devices that coordinate operations to provide load balancing, failover support, parallel processing capabilities, shared storage resources, shared networking capabilities, or other aspects.