# US 11,121,858 B2 · Blockchain analytics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,121,858 B2 - Document ID: US11121858B2 - Status: Granted - Kind: B2 - Application number: 15/995,062 - Filed: 2018-05-31 - Published: 2019-01-10 - Granted: 2021-09-14 - Earliest claimed: 2017-07-07 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Marc E. Mercuri; Zeyad Rajabi; Eric I. Maino - Web page: https://patentgrep.com/patent/US11121858B2 ## CPC - G06Q 20/308: {using the Internet of Things} - H04L 9/0637: {Modes of operation, e.g. cipher block chaining [CBC], electronic codebook [ECB] or Galois/counter mode [GCM]} - G06F 9/451: Execution arrangements for user interfaces - G06F 9/542: {Event management; Broadcasting; Multicasting; Notifications} - G06F 9/546: {Message passing systems or structures, e.g. queues} - G06F 11/3072: {where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting} - G06F 11/3476: {Data logging (G06F11/14, G06F11/2205 take precedence)} - G06F 16/212: {with details for data modelling support} - G06F 16/27: Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor - G06F 16/904: Browsing; Visualisation therefor (for navigating the web G06F16/954; browsing optimisation for the web G06F16/957) - G06F 21/602: {Providing cryptographic facilities or services} - G06F 21/645: {using a third party} - G06N 20/00: Machine learning - G06Q 20/02: involving a neutral party, e.g. certification authority, notary or trusted third party [TTP] - G06Q 20/06: Private payment circuits, e.g. involving electronic currency used among participants of a common payment scheme - G06Q 20/3827: {Use of message hashing} - G06Q 20/389: {Keeping log of transactions for guaranteeing non-repudiation of a transaction} - G06Q 20/405: {Establishing or using transaction specific rules} - H04L 9/0643: {Hash functions, e.g. MD5, SHA, HMAC or f9 MAC} - H04L 9/3236: {using cryptographic hash functions} - H04L 9/3239: {involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD} - H04L 9/3242: {involving keyed hash functions, e.g. message authentication codes [MACs], CBC-MAC or HMAC} - H04L 9/3247: {involving digital signatures} - H04L 9/3265: {using certificate chains, trees or paths; Hierarchical trust model} - H04L 63/101: {Access control lists [ACL]} - H04L 63/102: {Entity profiles} - H04L 63/123: {received data contents, e.g. message integrity} - G06N 3/08: Learning methods - G06Q 2220/00: Business processing using cryptography - H04L 9/50: {using hash chains, e.g. blockchains or hash trees} - H04L 2209/56: Financial cryptography, e.g. electronic payment or e-cash - H04W 4/70: Services for machine-to-machine communication [M2M] or machine type communication [MTC] - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract A blockchain analytics system facilitates determination of parameters of blockchain objects for analytics. Examples of parameters of the blockchain object may include an identity of a participant, a role of a participant, a type of the blockchain object and the like. The system may store parameters of blockchain objects in the data repository. The system may use the determined parameters to generate a machine learning blockchain analytics model. The system may generate visualizations, detect patterns and/or for detecting anomalies based on the machine learning blockchain analytics model. ## Claims ### Claim 1 A blockchain analytics system comprising: a hardware processor; and a non-transitory computer readable medium storing machine-readable instructions that when executed by the processor cause the processor to: receive a plurality of events from a blockchain; place the plurality of events on an event stack; determine an event in the plurality of events that matches specifications in a context schema; determine parameters of the determined event; store the determined parameters in a data repository; provide read-write access to the data repository to a blockchain agnostic service; determine a blockchain identity associated with a blockchain object; determine an off-chain identity associated with the blockchain identity; generate an integrated profile that combines the off-chain and blockchain identities; determine a further blockchain identity associated with a participant, wherein the further blockchain identity is based on public key and private key cryptography; determine a further off-chain identity associated with the further blockchain identity; and generate a profile for the participant that integrates an off-chain profile of the participant with information from the blockchain. ### Claim 2 (depends on 1) The system according to claim 1, wherein the machine-readable instructions are executed by the processor to: identify a write request to the data repository from the blockchain agnostic service, wherein the write request is based on the read-write access to the data repository; determine a change to the data repository; determine a message blockchain object including the change based on the context schema; and deploy the message blockchain object to the blockchain. ### Claim 3 (depends on 1) The system according to claim 1, wherein the machine-readable instructions to determine parameters of the determined event are executed by the processor to: determine the participant on the blockchain; determine a plurality of blockchain objects associated with the participant; and determine the profile associated with the participant. ### Claim 4 (depends on 1) The system according to claim 1, wherein the machine-readable instructions are executed by the processor determine a type of blockchain object: determine a plurality of blockchain objects associated with the type of blockchain object; and determine a profile associated with the type of blockchain object. ### Claim 5 (depends on 1) The system according to claim 1, wherein the blockchain object includes a cryptlet that is executable in a secure enclave. ### Claim 6 (depends on 1) The system according to claim 1, wherein an event of the plurality of events includes a message addressed to the blockchain object that is designated a first blockchain object, and wherein the message includes a second blockchain object addressed to the first blockchain object. ### Claim 7 (depends on 1) The system according to claim 1, wherein the machine-readable instructions when executed by the processor further cause the processor to: generate a hash of the blockchain object; store the blockchain object and the hash in an off-chain storage; and verify, based on the hash, whether the blockchain object stored on the blockchain is same as the blockchain object stored in the off-chain storage. ### Claim 8 (depends on 1) The system according to claim 1, wherein the machine-readable instructions when executed by the processor further cause the processor to: generate a hash of the blockchain object; store the blockchain object and the hash in an off-chain storage; and store the hash on the blockchain without storing the blockchain object on the blockchain. ### Claim 9 A blockchain analytics system comprising: a hardware processor; and a non-transitory computer readable medium storing machine-readable instructions that when executed by the processor cause the processor to: retrieve blockchain objects from a blockchain; determine parameters of blockchain objects retrieved from the blockchain; store determined parameters in a data repository; generate a machine learning blockchain analytics model based on the determined parameters; determine a blockchain identity associated with a blockchain object; determine an off-chain identity associated with the blockchain identity; generate an integrated profile that combines the off-chain and blockchain identities; determine a further blockchain identity associated with a participant, wherein the further blockchain identity is based on public key and private key cryptography; determine a further off-chain identity associated with the further blockchain identity; and generate a profile for the participant that integrates an off-chain profile of the participant with information from the blockchain. ### Claim 10 (depends on 9) The system according to claim 9, wherein the machine-readable instructions are executed by the processor to: generate a visualization based on determined parameters. ### Claim 11 (depends on 9) The system according to claim 9, wherein the machine-readable instructions are executed by the processor to: determine an anomaly in a block update; deploy an event to an event stack; and trigger, based on the event, a change in a state of the blockchain object. ### Claim 12 (depends on 9) The system according to claim 9, wherein the machine-readable instructions are executed by the processor to: identify a pattern in a blockchain update, based on the machine learning blockchain analytics model; generate a message blockchain object based on the identified pattern; and deploy the message blockchain object to the blockchain. ### Claim 13 (depends on 9) The system according to claim 9, wherein the machine-readable instructions are executed by the processor to: identify a pattern in a block update, based on the blockchain analytics model; and alert the participant. ### Claim 14 (depends on 9) The system according to claim 9, wherein the machine-readable instructions to determine parameters of blockchain objects retrieved from the blockchain are executed by the processor to: identify parameters of blockchain objects based on a context schema. ### Claim 15 A computer-implemented method for blockchain analytics, the method comprising: receiving a context schema for a blockchain object, wherein the context schema comprises: a state map describing states of the blockchain object; and a persona list of acceptable personas that can interact with the blockchain object in different states; receiving a plurality of blockchain objects from a blockchain; identifying a parameter in the plurality of blockchain objects based on the context schema; determining a profile based on the identified parameter; determining a blockchain identity associated with a blockchain object; determining an off-chain identity associated with the blockchain identity; generating an integrated profile that combines the off-chain and blockchain identities; determining a further blockchain identity associated with a participant, wherein the further blockchain identity is based on public key and private key cryptography; determining a further off-chain identity associated with the further blockchain identity; and generating a further profile for the participant that integrates an off-chain profile of the participant with information from the blockchain. ### Claim 16 (depends on 15) The computer-implemented method according to claim 15, wherein the parameter is an identity of the participant. ### Claim 17 (depends on 15) The computer-implemented method according to claim 15, wherein the parameter is a type of the blockchain object. ### Claim 18 (depends on 15) The computer-implemented method according to claim 15, wherein the parameter is a state of the blockchain object. ### Claim 19 (depends on 15) The computer-implemented method according to claim 15, wherein the parameter is a persona from the acceptable personas that is authorized to interact with the blockchain object. ### Claim 20 (depends on 15) The computer-implemented method according to claim 15, wherein the parameter is a blockchain id.