30 results about "Artificial intelligence software" patented technology

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

Techniques for displaying a graphical user interface of a software development system are described. An initial description of an initial structure of a software system is obtained, the initial description including an identification of interconnections amongst provider network resources that form the initial structure. A modified description of a modified structure of the software system is obtained, the modified structure based on at least a change to the initial structure of the software system described in the initial description. A difference between the initial description and the modified description is determined. An electronic device is caused to display a graphical user interface including a visual depiction of the initial structure of the software system and an indication of the difference, the visual depiction based at least in part on the initial description.

A system and method for improving generative artificial intelligence (AI) software application response is provided. The method includes: receiving a query directed to a generative AI software application; receiving a response to the query, the response generated by the generative AI software application; generating a first contextual value based on the received query; generating a second contextual value based on the received response; generating a verification score based on the first contextual value and the second contextual value; and initiating a mitigation action in response to detecting that the verification score is below a predetermined threshold

Various embodiments of the present disclosure include systems, methods, and / or apparatuses for executing an agentic-AI-based software modernization system. An example system may be configured to receive one or more portions of monolithic legacy code of a legacy software program product, the monolithic legacy code comprising a plurality of code segments, group the monolithic legacy code by functionally related code segments of the plurality of code segments to define a plurality of groups, generate an API for each identified group, generate modern microservice code for each identified group to define a modernized software program product, and validate the modernized software program product.

A method for secure collection of sensitive data at a computer device includes receiving a request for sensitive data. The request indicates a set of parameters and a dynamic encryption key. The dynamic encryption key is generated by an authorized data requester based on a login session identifier. The method includes creating a user interface for collecting the set of parameters by an artificial intelligence (AI) software of the computer device. The method includes providing the user interface for collecting the set of parameters. Responsive to collecting the set of parameters from the user, the method includes encrypting the set of parameters using the dynamic encryption key and an encryption algorithm associated with the authorized data requester. The method includes transmitting the encrypted set of parameters to the authorized data requester to be decrypted.

An artificial intelligence (AI) software product for providing scalable intelligent video surveillance for the artificial intelligence of things. The AI software product performs operations including receiving, at an edge node, a sequence of images of a target environment, where the AI software product resides in the edge node, and where the edge node is local to the target environment. The AI software product further observes one or more target objects in the sequence of images of the target environment in real time. The AI software product further detects at the edge node one or more anomalies in the target environment based on the observing of the one or more target objects in real time, where the one or more anomalies are specific to the target environment.

The application relates to the technical field of artificial intelligence assisted software engineering, and discloses a management method and system based on artificial intelligence software development, which comprises the following steps: collecting multidimensional data in development, testing and running stages, constructing a unified software development knowledge graph, establishing a cross-stage time sequence causal diagram based on the graph, extracting a defect correlation mode by combining a time sequence convolution network and a causal discovery algorithm, capturing an operation context in real time when a developer codes, submits or tests, matching a historical defect mode through two-stage retrieval, dynamically generating intelligent guidance information, finally recording a developer adoption behavior and a subsequent verification effect, and continuously iterating model parameters and guidance generation strategies through a double-channel feedback optimization mechanism. The application associates development, testing and running stage data in real time, intelligently pushes an interpretable early warning and correction guidance, forms a closed-loop optimization system, and significantly improves the quality and efficiency of software development.

The present application relates to the technical field of display devices, and discloses an artificial intelligence interaction method and apparatus, a display device, a storage medium, and a product. The method is applied to a display device. The method comprises: in response to a wake-up instruction, displaying a preset interaction interface, and acquiring video information in real time; inputting the video information and interaction information into preset artificial intelligence software so as to obtain output information of the artificial intelligence software; and displaying the output information by means of the interaction interface.

The invention relates to the technical field of artificial intelligence and software engineering, and discloses a small sample individual fairness testing method based on a domain adaptive flow model. Aiming at the problems of difficulty in obtaining target model training set data, low discrimination sample generation efficiency, insufficient sensitive attribute decoupling and the like in the existing black box fairness test, the invention provides the following technical scheme: firstly, constructing a gating residual domain adaptive flow model, and performing dynamic fusion on a frozen source domain projection matrix and a trainable low-rank adapter to obtain an adaptive flow model; domain adaptation of the flow model is realized under the condition of not depending on a target model training set; secondly, a two-stage anti-fact generation strategy is adopted, a seed sample set is constructed by utilizing a dynamic radius attenuation mechanism, and discriminated samples are efficiently expanded in combination with triple disturbance; finally, gradient orthogonal constraint is applied to the hidden space of the flow model, and decoupling control over the sensitive attributes and the semantic features is achieved. The black box fairness testing method can be used for black box fairness testing of classification models such as tables, texts and images, and has good practical significance and actual effect.

The application provides an intelligent grading and early warning system for intraoperative bleeding of a medical endoscope based on artificial intelligence and a working method thereof, and has the characteristics that the system comprises a medical endoscope catheter, a medical endoscope image processor, a medical lavage pump, a medical high-definition highlight liquid crystal display and end-side artificial intelligence software; the working method comprises real-time image recognition, bleeding point monitoring and early warning, automatic regulation of lavage and negative pressure suction; and the bleeding can be monitored in real time, intelligently and efficiently during surgery, and innovative medical technology support is provided for the surgery.The application has the advantages of convenient, intelligent and reliable surgical treatment effect.

An artificial intelligence (AI) software product for providing scalable intelligent video surveillance for the artificial intelligence of things. The AI software product performs operations including receiving, at an edge node, a sequence of images of a target environment, where the AI software product resides in the edge node, and where the edge node is local to the target environment. The AI software product further observes one or more target objects in the sequence of images of the target environment in real time. The AI software product further detects at the edge node one or more anomalies in the target environment based on the observing of the one or more target objects in real time, where the one or more anomalies are specific to the target environment.

A system, computer program product, and method comprising the steps of tracking a work or school performance and an interaction at work or participation in an educational program at an educational institution with at least one tracking module; learning a physical movement or interaction, with at least one self-learning and analytics module, derived from the work or school performance and interaction at work or participation in an educational program at an educational institution; identifying a behavioral anomaly pattern, with an AI software module, founded on the physical movement or interaction; associating a predetermined movement value as measure to the behavioral anomaly pattern; generating a digital work or school performance review by the AI software module; generating a digital resume, with a resume module, based at least on one of, the digital work or school performance review, a work or school history and a place of employment or educational institution.

A method for using artificial intelligence software to manage and optimize the energy consumption of a vertical farm and greenhouse, comprising a grid of solar panels supplying solar power, solar cells, and sensors throughout the vertical farm, which can heat or cool the greenhouse, with heating and cooling controlled according to plant species and growth stage, and comprising a composite cycle sensor and instrumentation, to which data from both the vertical farm and greenhouse is fed; an outdoor light measuring sensor that measures and estimates the conditions of solar power in the solar grid and transmits the data to the artificial intelligence software; and the artificial intelligence software determines how much light various plants in the hydroponic greenhouse require to maximize the yield from those plants.

The invention relates to the field of artificial intelligence, discloses an artificial intelligence software operation management platform detection method and system and a storage medium, and aims to solve the problems that in the prior art, the operation behavior monitoring granularity is rough, a cross-dimension linkage detection mechanism is lacked, a static rule is relied on and no closed-loop response capability exists. The method comprises the steps that user operation behavior data flow and platform operation state data are collected in real time; performing multi-modal feature coding on the heterogeneous data to generate an operation behavior semantic representation vector and a platform state dynamic representation vector; an operation-state joint representation vector is generated through cross-modal association analysis; and outputting an abnormal confidence score by using a pre-trained abnormal discrimination model based on the joint representation, and triggering a grading response mechanism. According to the scheme, accurate identification and automatic closed-loop disposal of the abnormal operation are realized, and the detection accuracy and the platform safety are remarkably improved.

An angioplasty system is provided. The system comprises an inflation device for angioplasty devices within a blood vessel presenting stenosis, and includes a contrast fluid reservoir with a pressure-adjusting device; a connector; a pressure sensor; an electronic-actuation valve; safety features; powering features; a digital interface; a microprocessor with a first software; a memory; a connection of the microprocessor to the Internet or an intranet; a second artificial intelligence software; a database; and a radiological arch to acquire and process images of the blood vessel or the area where the blood vessel is located. The microprocessor is connected to the radiological arch and to the database, in such a way that the inflation device may learn to operate autonomously and adjust itself automatically taking into account previous data or treatment models.

Aspects of the subject disclosure may include, for example, receiving, over a communication network, a query for a first artificial intelligence (AI) software application from a client computing device associated with a first user, determining a client identifier associated with the query, wherein the client identifier is associated with the first user, and determining a group of characteristics associated with user based on the client identifier. Further embodiments can include generating query metadata based on the group of characteristics, providing the query and the query metadata to the first AI software application, generating a query response utilizing the first AI software application based on the query and the query metadata, and providing, over the communication network, the query response to the client computing device. Other embodiments are disclosed.

This invention discloses a large-model-driven multi-dimensional intelligent detection method and apparatus for software defects, belonging to the field of artificial intelligence software testing technology. It includes generating abstract syntax trees, control flow graphs, and data flow graphs from source code through static analysis, and matching and filtering relevant historical defect knowledge. By combining multi-dimensional code representation with historical knowledge, a multi-level prompt input pre-trained large language model is constructed to generate an initial draft report containing the location, type, and context of potential defects. A strategy consisting of dynamic symbolic execution path verification, historical defect pattern matching verification, and code change impact scope verification is employed to verify the authenticity and prioritize the draft report, outputting the final report. This method can deeply understand code semantics and combine historical experience, improving the accuracy of defect detection and effectively reducing false positives through multi-source verification.

The invention provides a file marking method, a marking information identification method, a file identification method and device, electronic equipment and a readable storage medium, and is applied to the technical field of data processing. The to-be-marked file is generated through artificial intelligence software; the to-be-marked file is composed of original digital content, and the original digital content corresponds to the type of the to-be-marked file; obtaining first mark information; the first mark information comprises a software identifier of the artificial intelligence software; converting the first mark information into target mark information matched with the format of the original digital content; embedding the target mark information into the original digital content to obtain target digital content; according to the embodiment, the target digital content is used for forming the marked file, the target software information is embedded into the original digital content forming the to-be-marked file, the target digital content used for forming the marked file is obtained, and the file marking safety is improved.

An agentic-AI-based software modernization system may be configured to receive one or more portions of monolithic legacy code of a legacy software program product, the monolithic legacy code comprising a plurality of code segments, group the monolithic legacy code by functionally related code segments of the plurality of code segments to define a plurality of groups, generate an API for each identified group, generate modern microservice code for each identified group to define a modernized software program product, and validate the modernized software program product.

This invention discloses a post-processing method, apparatus, medium, and device for AI-generated text. The application acquires the AI-generated text to be processed and inputs it into a pre-trained text classifier model. This model, trained on a pre-defined academic writing dataset and a social media text dataset, can accurately identify the portions of the AI-generated text that are generated by artificial intelligence software (the first text). Next, a pre-defined integral gradient method and Shapley value analysis method are used to perform fine-grained detection on the first text, accurately identifying each AI-generated text unit. Finally, these AI-generated text units are input into a pre-defined Agent system. The Agent system uses a generative language model to refine these units, outputting more natural and accurate text results. This application effectively solves the problems of insufficient naturalness and accuracy in existing AI-generated text.

Methods and systems are disclosed for obtaining and using multi-modal data and its derivatives within an intelligent injection device platform. A device may establish wireless communication connections with a plurality of intelligent injection devices deployed across a healthcare network. A device may continuously collecting real-time geolocation coordinates from each intelligent injection device using an integrated geolocation detection system. A device may receive administration data from the intelligent injection devices. A device may transmit the geolocation data and administration data to a centralized AI-powered software system using a real-time connectivity module. A device may analyze, by an artificial intelligence module, patterns in medication administration across different geographic locations within the healthcare network. A device may correlate geolocation data with treatment schedules and patient outcomes to identify location-specific trends. A device may generate location-based intelligence reports for healthcare network optimization.

A home vision monitoring system is disclosed herein. The home vision monitoring system includes a vision target or chart; a light source; at least one fluidic lens disposed between an eye of a patient and the vision target or chart; a fluid control system operatively coupled to the at least one fluidic lens; a Shack-Hartmann sensor assembly operatively coupled to the fluid control system; and a data processing device operatively coupled to the fluid control system and the Shack-Hartmann sensor assembly, the data processing device being configured to control an operation of the fluid control system based upon one or more output signals from the Shack-Hartmann sensor assembly to automatically correct the refractive errors of the eye of the patient so that a focus target on the vision target or chart is in focus for the patient; and the data processing device is configured to execute bot-assisted artificial intelligence software.

This invention relates to the field of medical artificial intelligence software, and discloses an AI-assisted diagnosis and treatment system for refractive errors based on an internet hospital. The system comprises five modules: a patient information management module, an intelligent diagnostic assistance module, a remote monitoring module, a disease progression risk warning and prognosis prediction module, and an online consultation and eye health education module. The image segmentation model uses wavelet transform operators to decompose and extract multi-scale frequency domain features from the original 3D-OCT volumetric data of the eye. These multi-scale frequency domain features are then merged and concatenated with the original 3D-OCT volumetric data of the eye in the channel dimension to generate a multi-channel feature cube. This multi-channel feature cube is input into the Unet algorithm network unit, and after encoder, decoder, and hierarchical skip connection operations, a multi-channel probability map is generated. Finally, the multi-channel probability map is converted into a binary lesion segmentation mask for output. The intelligent image analysis module of this invention can improve the accuracy and efficiency of image screening for early identification of pathological myopia.

The application discloses a non-supervised adaptive large model routing method based on verifiable execution feedback and belongs to the field of artificial intelligence software engineering. The method comprises the following steps: for each reasoning task, the current online reputation value of each proxy model in the heterogeneous large model pool is obtained, the utility function of each proxy model is calculated, the first proxy model with the highest utility is selected, and the initial output is obtained; the initial output is verified to obtain a task score, and a comprehensive confidence score is calculated; it is judged whether the comprehensive confidence score is higher than a preset threshold; if yes, the initial output is output as the routing result; if no, the last b target proxy models in the utility ranking are called, the outputs of the first proxy model and the target proxy models are clustered for equivalence, the routing result is selected and output, and the online reputation values of the participating models are updated respectively. The scheme does not need to rely on labeled data, can reduce the API calling cost, and can guarantee the accuracy of the generated result.

The embodiment of the invention relates to the technical field of software systems, in particular to a software deployment method, a product, equipment and a storage medium, and aims to improve the deployment efficiency of artificial intelligence software. The software deployment method comprises the following steps: starting installation of an operation system in an operation window corresponding to a system installation platform; in response to starting of operation system installation, multiple pieces of artificial intelligence software to be installed are determined from a preset configuration file; and deploying the multiple pieces of artificial intelligence software into target equipment through a preset software installation script.

The application discloses a general artificial intelligence knowledge infrastructure and a multi-modal knowledge processing method, and belongs to the field of artificial intelligence software systems. In view of the problems of existing multi-modal data semantic fragmentation, fragile knowledge storage format and static and rigid knowledge graph, the application provides a full-stack solution through a four-layer software architecture: a virtual unified knowledge bus (V-UKB) realizes automatic discovery and millisecond to microsecond time synchronization of multi-modal devices; a multi-modal knowledge record format (MKRF) realizes atomic-level fusion of perception data and semantic knowledge, and forms self-description, cross-platform and traceable knowledge units; and a Qian Kun knowledge core realizes neural-symbol hybrid reasoning and dynamic evolution of knowledge. All technical solutions of the application are realized based on general computing devices and open source technologies, do not require special hardware support, and can be widely applied to scenes such as intelligent libraries, long-term archives, cross-modal intelligent retrieval, distributed knowledge services and the like.

This invention discloses a low-code artificial intelligence software engineering system for biometric recognition, belonging to the field of artificial intelligence technology. It includes a standardized biometric access module, a low-code visual orchestration module, a lightweight AI model adaptation module, an automated process deployment module, a performance monitoring and iterative optimization module, and a permission security control module. By standardizing the processing of multiple types of biometric data and combining it with low-code visual orchestration to construct the recognition process, it performs lightweight customization and hyperparameter tuning of pre-trained models, achieving the fusion compilation of engineering scripts and models and cross-platform automated deployment. Simultaneously, it monitors system performance in real time and completes self-supervised iterative optimization of the model, supplemented by end-to-end permission security control. This invention lowers the development threshold of biometric recognition AI systems, improves the system's cross-scenario adaptability and deployment efficiency, realizes full lifecycle engineering management, ensures biometric data security, and can be quickly adapted to multiple application scenarios such as access control and attendance.

A matrix code based on pan-artificial intelligence, comprising three layers of structure from an outside to an inside, namely, an outer open code area, a core layer code area, and a matrix core layer code area; the outer open code area is generated by AI software according to specific requirements; the core layer code area has a firewall layer and advanced encryption functions, and the core layer code area manages and controls the outer open code area; the matrix core layer code area serves as the most core intervention and correction method of AI, acting as a deep encryption core information zone. Through the use of multi-layer structures and optical media composite materials, the information storage capacity is significantly improved to meet the needs of the big data era; the dual encryption mechanism of the core layer and matrix core layer effectively prevents information leakage and tampering, improves security.

A method of providing goal orientated guidance that includes interaction with a mentor. The method can be used with person to person interaction, software applications (APP), Artificial Intelligence software (AI) or a combination of all three. A participant desires to improve an area of the participant's life and initiates contact with the service provider. There is an intake of the participant's data. Included in the intake of data are at least three evaluation surveys.