International Business Machines Corporation

Xianjun Zhu

Bradley C Herrin

Bo Zhang

Alexander Sobran

Jarett Stein

Hin Ting Chui

Aaron J Quirk

Rahul Ghosh

Ravi Krishnan Muthukrishnan

Morris Stanley Johnson, Jr

William R LaRiccia

Pu Yang

Liwei Wang

Dan Yang

Software for securely storing digital content using a distributed ledger. The software performs the following operations: (i) receiving identification information of an owner of a digital asset, metadata of the digital asset, a digital hash of the digital asset, and the digital asset; (ii) verifying authenticity of the owner based, at least in part, the received identification information; (iii) in response to verifying the authenticity of the owner, creating a watermarked version of the digital asset based on the identification information of the owner, the metadata of the digital asset, and the digital hash; and (iv) storing the digital asset and the watermarked version of the digital asset in a first distributed ledger.

Securely storing digital content using a distributed ledger

Software for producing and verifying computational determinations using a distributed ledger, by: (i) receiving an indication of a first artificial intelligence (AI) inferencing event, the first AI inferencing event including a first AI inferencing result produced by a first machine learning model based, at least in part, on a first input from a user; (ii) computing a hash of the first machine learning model using a cryptographic hash function; (iii) sending a record of the first AI inferencing event to a verification system, the record of the first AI inferencing event including the hash of the first machine learning model; and (iv) receiving a verification from the verification system indicating that the hash of the first machine learning model matches a hash of a second machine learning model and that the record of the first AI inferencing event has been stored in a first distributed ledger.

Producing and verifying computational determinations using a distributed ledger

In an approach to quarantining source code to prevent confidential information exposure, one or more computer processors store a pushed codebase associated with a user to an isolated quarantine area, wherein access to the quarantine area is restricted to the user through user interface code visibility enforcement and protocol code visibility enforcement. The one or more computer processors dynamically adjust a timeout period based on codebase complexity, user preferences, associated dependencies, codebase size, minimum execution temporal period for one or more scans, and system specifications. The one or more computer processors responsive to identifying one or more instances of confidential information, perform one or more remedial actions.

Dynamically adjusted timeout quarantined code scanning

In an approach, one or more computer processors create a dictionary for each source code commit in a set of historical source code commits associated with a software deployment; create a similarity model based on the created dictionary for each source code commit in the set of historical source code commits; generate a vector embedding for a source code commit pair based on a set of log differences between source code commit pairs utilizing the created similarity model; generate, responsive to a new source code commit, a new vector embedding based on a set of log differences between the new source code commit and a preceding source code commit utilizing the created similarity model; generate a defect likelihood utilizing the generated new vector embedding; determine, responsive to the generated defect likelihood exceeding a defect likelihood threshold, that the new source code commit contains defects.

Vector embedding of relational code sets

Software for producing and verifying computational determinations using a distributed ledger, by: (i) receiving a first input from a user; (ii) producing a first computational determination utilizing a first computational model, wherein the first computational determination includes a first computational output that is based, at least in part, on the first input; (iii) computing a hash of the first computational model; (iv) sending a record of the first computational determination to a verification system, wherein the record of the first computational determination includes the hash of the first computational model; (v) receiving a verification from the verification system indicating that the hash of the first computational model matches a hash of a second computational model and that the record of the first computational determination has been stored in a first distributed ledger; and (vi) in response to receiving the verification, providing the first computational output to the user.

An example operation may include one or more of transmitting a copy of the master ledger when a developer joins a network, receiving proposed changes to the master ledger, and updating the master ledger at the branch hash.

Blockchain technique for agile software development framework

An example operation may include one or more of seeking consensus among users of a source tree to approve execution of a blocked command to a source control system, creating a child ledger for a user issuing the blocked command and initiating a blockchain transaction to link the child ledger to a master ledger, merging the child ledger into the master ledger when consensus is reached, and executing the blocked command.

Blockchain technique for immutable source control

An example operation may include one or more of transmitting a copy of the master ledger, wherein the master ledger comprises a code and history, receiving one or more changes to the copy of the master ledger, updating the master ledger when a required smart contract code standard is met, and initiating a push of the updated master ledger to one or more developer branches to enforce synchronization across the one or more developer branches.

A method, system, and computer product for prompting a patient to take a medicine including collecting medicine prescription information associated with a patient, collecting medicine usage data associated with the patient, comparing the medicine usage data with the medicine prescription information, determining whether a medicine is taken by the patient as prescribed in the prescription information based on a comparison result of the medicine usage data and the prescription information, and generating one or more control signals to interrupt operations of one or more devices within an environment of the patient or environments of the patient's family member or caregivers, in response to determining that the medicine is not taken as prescribed.

Method to determine, remind and validate medication usage

An example operation may include one or more of intercepting a command from a user to modify a source tree in a source control system, creating a child ledger associated with a master ledger when the intercepted command is destructive, seeking consensus among users of the source tree to approve execution of the intercepted command, merging the child ledger into the master ledger with a transaction describing the intercepted command, a commit tree history, and status of the consensus, and a tree hash, and merging execution results of the intercepted command into a history of the source tree.

Software for immutably storing computational determinations using distributed ledgers. The software performs the following operations: (i) receiving an indication that a first computational model is ready to be deployed; (ii) storing a copy of the first computational model in a first distributed ledger; (iii) computing a hash of the first computational model using a cryptographic hash function; (iv) receiving an indication that a second computational model has been used to produce a first computational determination; (vi) receiving a hash of the second computational model; and (vii) in response to determining that the hash of the second computational model matches the hash of the first computational model, storing a record of the first computational determination in a second distributed ledger, wherein the record of the first computational determination identifies the second computational model as being the first computational model and includes the hash of the first computational model.

Immutably storing computational determinations using distributed ledgers

A computer-implemented method, system and computer program product for identifying implicit dependencies between code artifacts. Co-defect association rules between code artifacts are generated, where such co-defect association rules include a prediction of how likely there will be a defect in a code artifact when there is a defect in an associated code artifact. After detecting a defect in a first code artifact, the co-defect association rules are reviewed to identify any code artifacts associated with the first code artifact. If there is a code artifact associated with the first code artifact, and if the probability of the associated code artifact being defected when the first code artifact is defected exceeds a threshold value, then a recommendation is made to the user to review not only the first code artifact that was defected but also its associated code artifact for a potential defect.

Identifying implicit dependencies between code artifacts

A method, system and computer program product for detecting potential failures in completing a continuous delivery (CD) pipeline using machine learning. A CD pipeline is defined to include stages, where each stage includes a binary event(s). A model is created by applying an Apriori algorithm and a sequential pattern mining algorithm to a set of previous patterns of sequences of binary events to calculate confidence scores for completing a set of binary events in a particular order. After identifying an ongoing CD sequence (ordered set of binary events) for a software application, the model is used to predict a likelihood of the ongoing CD sequence for the software application completing the CD pipeline by generating confidence score(s) for the ongoing CD sequence. A notification is issued regarding a potential failure in completing the CD pipeline for the software application if a confidence score is below a threshold value.

Providing insight of continuous delivery pipeline using machine learning

Software for preventing fraud in digital content licensing and distribution using a distributed ledger technology. The software performs the following operations: (i) receiving a request for a license of a digital asset, wherein a record of the digital asset is stored in a first distributed ledger; (ii) verifying a consensus for the request of the license of the digital asset; and (iii) responsive to verifying the consensus for the request of the license of the digital asset: storing a transaction settlement record in a second distributed ledger, creating a sharded copy of the digital asset including a plurality of shards of the digital asset, and storing at least one shard of the sharded copy of the digital asset in the second distributed ledger with sharding instructions for reconstructing the digital asset from the sharded copy.

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Cary, NC, United States of America

Xianjun Zhu