# US 12,112,146 B2 · Graph outcome determination in domain-specific execution environment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,112,146 B2 - Document ID: US12112146B2 - Status: Granted - Kind: B2 - Application number: 18/357,011 - Filed: 2023-07-21 - Published: 2023-11-30 - Granted: 2024-10-08 - Earliest claimed: 2019-09-06 - Assignees: Digital Asset Capital, Inc. - Applicants: Digital Asset Capital, Inc. - Inventors: Edward Hunter - Web page: https://patentgrep.com/patent/US12112146B2 ## CPC - G06F 8/33: Intelligent editors - G06F 21/64: Protecting data integrity, e.g. using checksums, certificates or signatures - G06F 8/65: Updates (security arrangements therefor G06F21/57) - G06F 9/44505: {Configuring for program initiating, e.g. using registry, configuration files} - G06F 9/5038: {considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration (scheduling strategies G06F9/4881 and subgroups)} - G06F 9/547: {Remote procedure calls [RPC]; Web services} - G06F 16/1858: {Parallel file systems, i.e. file systems supporting multiple processors} - G06F 16/23: Updating - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06F 16/904: Browsing; Visualisation therefor (for navigating the web G06F16/954; browsing optimisation for the web G06F16/957) - G06F 16/951: Indexing; Web crawling techniques - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/048: Activation functions - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/086: using evolutionary algorithms, e.g. genetic algorithms or genetic programming - G06N 3/092: Reinforcement learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - H04L 9/3239: {involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD} - H04L 9/3263: {involving certificates, e.g. public key certificate [PKC] or attribute certificate [AC]; Public key infrastructure [PKI] arrangements (network architectures or network communication protocols for supporting authentication of entities using certificates in a packet data network H04L63/0823)} - H04L 63/123: {received data contents, e.g. message integrity} - H04L 67/133: Protocols for remote procedure calls [RPC] - G06N 3/044: Recurrent networks, e.g. Hopfield networks - H04L 9/50: {using hash chains, e.g. blockchains or hash trees} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory. ## Claims ### Claim 1 A computer-implemented method, comprising: obtaining, with a computer system, a portfolio of computable contracts; obtaining, with the computer system, a plurality of scenarios; applying, with the computer system, the scenarios to the computable contracts in the portfolio, wherein the computable contracts are expressed in a formal language and applying the scenarios comprises determining changes in state of the computable contracts specified by the formal language in response to the scenarios; and determining, with the computer system, a distribution of outcomes in contract states resulting from applying the plurality of scenarios to the computable contracts in the portfolio; and saving, with the computer system, the distribution of outcomes in memory. ### Claim 2 (depends on 1) The method of claim 1, wherein: applying the scenarios comprises performing a Monte Carlo analysis. ### Claim 3 (depends on 1) The method of claim 1, wherein: the formal language specifies rights, obligations, and prohibitions. ### Claim 4 (depends on 1) The method of claim 1, wherein: the portfolio of computable contracts are obtained by codifying a portfolio of contracts in natural language into the formal language with natural-language processing. ### Claim 5 (depends on 1) The method of claim 1, wherein: the formal language is a contract definition language. ### Claim 6 (depends on 1) The method of claim 1, comprising: determining population statistics of the distribution of outcomes. ### Claim 7 (depends on 1) The method of claim 1, comprising: obtaining likelihoods of the scenarios; and weighting the outcomes based on corresponding obtained likelihoods; and determining, based on the weighting, likelihood-weighted population statistics of the distribution of outcomes. ### Claim 8 (depends on 1) The method of claim 1, comprising: steps for simulating outcomes of symbolic artificial intelligence models, wherein the computable contracts include the symbolic artificial intelligence models. ### Claim 9 (depends on 1) The method of claim 1, comprising determining an aggregate outcome corresponding to a plurality of computable contracts having a given entity as a counterparty. ### Claim 10 (depends on 1) The method of claim 1, comprising: determining an aggregate outcome corresponding to a plurality of computable contracts having a plurality of entities as respective counterparties. ### Claim 11 A tangible, non-transitory, machine-readable medium storing instructions that when executed effectuate operations comprising: obtaining, with a computer system, a portfolio of computable contracts; obtaining, with the computer system, a plurality of scenarios; applying, with the computer system, the scenarios to the computable contracts in the portfolio, wherein the computable contracts are expressed in a formal language and applying the scenarios comprises determining changes in state of the computable contracts specified by the formal language in response to the scenarios; and determining, with the computer system, a distribution of outcomes in contract states resulting from applying the plurality of scenarios to the computable contracts in the portfolio; and saving, with the computer system, the distribution of outcomes in memory. ### Claim 12 (depends on 11) The medium of claim 11, wherein: applying the scenarios comprises performing a Monte Carlo analysis. ### Claim 13 (depends on 11) The medium of claim 11, wherein: the formal language specifies rights, obligations, and prohibitions. ### Claim 14 (depends on 11) The medium of claim 11, wherein: the portfolio of computable contracts are obtained by codifying a portfolio of contracts in natural language into the formal language with natural-language processing. ### Claim 15 (depends on 11) The medium of claim 11, wherein: the formal language is a contract definition language. ### Claim 16 (depends on 11) The medium of claim 11, the operations comprising: determining population statistics of the distribution of outcomes. ### Claim 17 (depends on 11) The medium of claim 11, the operations comprising: obtaining likelihoods of the scenarios; and weighting the outcomes based on corresponding obtained likelihoods; and determining, based on the weighting, likelihood-weighted population statistics of the distribution of outcomes. ### Claim 18 (depends on 11) The medium of claim 11, the operations comprising: steps for simulating outcomes of symbolic artificial intelligence models, wherein the computable contracts include the symbolic artificial intelligence models. ### Claim 19 (depends on 11) The medium of claim 11, the operations comprising: determining an aggregate outcome corresponding to a plurality of computable contracts having a given entity as a counterparty. ### Claim 20 (depends on 11) The medium of claim 11, the operations comprising: determining an aggregate outcome corresponding to a plurality of computable contracts having a plurality of entities as respective counterparties.