47 results about "Machine-readable data" patented technology

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

The invention relates to the technical field of smart cities, and discloses a smart city multi-modal data acquisition and fusion method and system. The method comprises the following steps: acquiring smart city system data; performing data cleaning on the smart city system data according to a pre-trained naive Bayesian model, and extracting metadata of data items; performing data standardization according to the metadata of the data item to obtain standardized data information; performing data conversion on the standardized data information according to a pre-formed semantic mapping rule base to obtain machine readable data; merging and compressing the machine readable data based on principal component analysis to obtain dimension-reduced readable data; and performing data fusion on the dimension-reduced readable data according to a preset fusion strategy to obtain fused data information, and storing the fused data information. According to the method, various kinds of heterogeneous data are converted into information which can be understood by a machine, data fusion is effectively carried out, and the availability of the data is improved.

The present invention provides a method and system for automatically and seamlessly processing clinical trial source data into electronic data capture (EDC) systems. In one embodiment, a file structure is defined for an electronic binder system (eBinder); source data is uploaded to the eBinder; the source data is encrypted, Patient Identifiable Information in the source data is masked; the source data is converted into machine readable plain text in the JavaScript Object Notation (JSON) format using Natural Language Processing (NPL) technologies; the JSON data is converted into tabulated machine readable data in the HyperText Markup Language (HTML) format using NPL technologies; the HTML data is converted into machine understandable data using NPL technologies; the machine understandable data is populated into EDC datasets using NPL technologies; the source data and converted data are displayed side-by-side for source data verification; and a platform is provided for regulatory data verification or auditing.

A method comprises receiving at least one data record associated with a patient, the data record including one or more data items represented as image data. Then, the method comprises pre-processing the at least one data record to enhance legibility of at least one data item of the one or more data items of the at least one data record. Then, the method comprises, using at least a first machine learning model, converting at least a portion of the pre-processed at least one data record into at least one machine-readable data record. Then, the method comprises identifying a standardized format. Then, the method comprises converting the machine-readable data record to the standardized record format and using at least a second machine learning model and assigning one or more predetermined activity codes to the at least one machine-readable data record in the standardized record format.

The invention discloses an information fusion and reasoning method and system based on multi-agent collaborative networking search, and relates to the technical field of network information, and the method comprises the steps: a deep research strategy scheduling agent deconstructs an unstructured request through a task formalization mechanism, generates a high-dimensional instruction code, and dynamically optimizes an execution path. And the network intelligence index generation agent extracts and screens the trusted URL by using a rule engine and a semantic evaluator based on the code, and generates a structured link instruction stream. The structured analysis intelligent agent implements multi-level semantic distillation and conversion on an original document to obtain key value pair representations, and the key value pair representations are packaged into machine readable data. And generating a comprehensive intelligence abstract with consistent logic through the information fusion analysis agent. And the natural language generation engine converts the information into a professional report, and returns the professional report to the scheduling module through a feedback interface to realize cognitive closed-loop iterative optimization. The technical problem that in the prior art, it is difficult for geologists to obtain accurate, comprehensive and credible professional conclusions is solved.

Techniques are provided for processing rules expressed in a domain-specific language. A computing system may receive a rule including an entity identifier, a property of the entity, and one or more logical operators delimited by syntactic tokens of the language. The rule is parsed into an abstract syntax tree (AST) representation that structurally represents entities, properties, and conditional logic, and the AST is transformed into an executable function configured to evaluate input data objects defined according to a machine-readable data model. Execution of the function on input data produces a logical outcome along with intermediate results corresponding to evaluations of individual conditions and logical operators. The computing system generates an execution trace comprising the logical outcome and the intermediate results, the trace being stored in a structured log retrievable to provide evidence of how outcomes were determined.

There is provided a computer implemented method in which a deep learning model detects and interprets real time events from an input data stream, in which the detected and interpreted events are output in a structured, machine-readable representation of data that conforms to a machine-readable language.

Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. A first AI agent associated with a first entity obtains a machine-readable data structure defining one or more operative boundaries for a second AI agent associated with a second entity. The system generates a unique fixed reference value representing the machine-readable data structure by applying a first transformation operation set, and transmits the unique fixed reference value to a multi-agent storage to store the value. The system receives, via the multi-agent storage, a verification artifact from the second AI agent that indicates an observed value based on internal operational data of the second AI agent corresponding to the operative boundaries. The first AI agent determines a verification status of the verification artifact by comparing the unique fixed reference value with the observed value, and autonomously generates a verification record including a representation of the verification status.

There is provided a computer implemented method for automated analysis or use of data, comprising the steps of: (a) storing in a memory store a structured, machine-readable representation of data that conforms to a machine-readable language; in which the data includes personal health or medical data; (b) automatically processing the structured representation of the data to analyse the personal health or medical data; in which the method includes the steps of (c) the machine-readable language representing a question in a memory in the structured, machine-readable representation of data; and (d) automatically generating a response to the question, using the following steps: (i) matching the question with the structured, machine-readable representations of data previously stored in the memory store; (ii) fetching and executing one or more computation units, wherein the computation units represent computational capabilities relevant to answering the question; (iii) fetching and execution of one or more reasoning passages.

Systems, methods, and computer-readable storage media for scientific manuscript generation using large language models are disclosed. A system can include one or more processors configured to receive an input including at least one machine-readable data object divided by one or more input node identifiers, generate a multi-level resource mapping encoding associations between the input node identifiers and one or more output node identifiers, construct a set of model input parameters based on a selection of resources from the machine-readable data object, provide each set of model input parameters to a first generative model to generate an output data object, generate at least one metric for each output data object based on a comparison to a structural or coverage parameter, automatically generate modified model input parameters to regenerate the output, and generate a function that creates an annotation linking each output to at least one input node identifier.

A system and method in which road markings, such as center-lane, shoulder markings and vertically oriented sign markings, are surface coatings configured to transmit machine-readable data to electronic equipment on self-driving or driving-assisted vehicles. In an embodiment, painted markings create a type of matrix code or barcode that can be read by optical detection systems in a vehicle. The data is used to inform active safety systems about road conditions, obstacles, grades, speed limits, turns, HOV, EV, or autonomous lane options, as well as other data, to safely conduct autonomous and semi-autonomous vehicles. In some embodiments markings are applied with coatings that are readable by electronic components and cameras onboard a vehicle, and may not be visible to humans.

The present invention provides a method and system for automatically and seamlessly processing clinical trial source data into electronic data capture (EDC) systems. In one embodiment, a file structure is defined for an electronic binder system (eBinder); source data is uploaded to the eBinder; the source data is encrypted, Patient Identifiable Information in the source data is masked; the source data is converted into machine readable plain text in the JavaScript Object Notation (JSON) format using Natural Language Processing (NPL) technologies; the JSON data is converted into tabulated machine readable data in the HyperText Markup Language (HTML) format using NPL technologies; the HTML data is converted into machine understandable data using NPL technologies; the machine understandable data is populated into EDC datasets using NPL technologies; the source data and converted data are displayed side-by-side for source data verification; and a platform is provided for regulatory data verification or auditing.

A system includes at least one processor and at least one memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform operations. The operations include providing a secure link to an auditable, machine-readable dataset to a client device of a user, the auditable, machine-readable dataset comprising data. At least one policy constraint is provided to at least a portion of the data in the dataset. In response to detecting activation of the secure link at the client device, one or both of the user and the client device is authenticated based on the policy constraint. Streaming access is provided to the auditable, machine-readable dataset in real-time.

The proposed technical solution relates to methods for automated text processing and can be used in the generation of text corpora. The claimed invention solves the technical problem of creating a method and / or a computing device and / or a system and / or a machine-readable data carrier which overcome the disadvantages of the prior art and, in so doing, provide for the precise automated generation of a text corpus which can subsequently be used for pre-training, or training, or post-training classification models and / or clustering models.

The proposed technical solution relates to methods for automated text processing and can be used in the generation of text corpora. The claimed invention solves the technical problem of creating a method and / or a computing device and / or a system and / or a machine-readable data carrier which overcome the disadvantages of the prior art and, in so doing, provide for the precise automated generation of a text corpus which can subsequently be used for pre-training, or training, or post-training classification models and / or clustering models.

An apparatus for managing inventory is described. The apparatus includes a plurality of labels each having a unique pre-printed indicia. A printer prints machine-readable data corresponding to product information on one or more of the labels, such that the machine-readable data is printed on a same side of the label as the unique pre-printed indicia. An imaging device reads the machine-readable data and the unique pre-printed indicia on the label. A computing device includes software that associates the machine-readable data with the unique pre-printed indicia to create an association that is stored in a memory.

A cover for a drainage pipe comprising a machine-readable data pattern provided on the cover, the machine-readable data pattern being linked to a resource displaying building information. A building information system comprising a cover for a drainage pipe located within a perimeter of a building; a machine-readable data pattern on the cover, the machine-readable data pattern being linked to a resource displaying building information; and a database for storing the resource, the database providing the resource in response to the machine-readable data pattern being scanned.

A system to classify image of a document using neural network architecture is provided. The system includes a storage device storing the image derived from the document having text information. The system includes a document importer operable to perform optical character recognition to convert image data in the image to machine readable data. The system includes a neural network that perform semantic enrichment and positional context for the terms of interest present in the image. The neural network is configured to take as input the machine-readable data and the image and combine both the machine-readable data and the image to classify the image of the document based on the positional context of the terms of interest.

The description relates to computer-assisted generation of long-form text by creating a schema that includes a declarative and machine-readable data format. Based on the schema, processes iteratively generate suggested code to populate a specification that provides the narrative framework for the long-form text. The specification includes structure and substance for inclusion in the long-form text. The interactive nature of the specification development allows a user to progressively update and confirm automatically generated suggestions. In this manner, the specification is updated according to approved code selected from the iteratively generated code. Additional processes serialize the specification to generate multiple unit specifications. A large language model (LLM) is used to generate the long-form text based on the unit specifications.

A computer-implemented method for providing personalized feeding recommendations based on a trained model of a user's recording habits related to a dietary and an activity of a subject, obtaining visual image including handwritten data related to the dietary and the activity of the subject by using a camera of a computing device; employing a trained OCR model to scan and extract textual information from the obtained visual image, thereby converting the data in the visual image into a machine-readable data.

The invention discloses a method for determining a maximum open time (31) of a reactive material (21) during a joining process (22), which maximum open time is valid for current conditions. The method comprises providing first machine-readable data (28) which characterize batch-accurate material properties provided by the manufacturer (27). Furthermore, second digital data characterizing current parameters of the joining process (22), such as the ambient temperature, are received. The maximum open time (31) valid for the current conditions is determined by means of a mathematical-physical model, into which the first and second data are input. This enables more flexible and more robust process control compared to static worst case parameters.

The description relates to computer-assisted generation of long-form text by creating a schema that includes a declarative and machine-readable data format. Based on the schema, processes iteratively generate suggested code to populate a specification that provides the narrative framework for the long-form text. The specification includes structure and substance for inclusion in the long-form text. The interactive nature of the specification development allows a user to progressively update and confirm automatically generated suggestions. In this manner, the specification is updated according to approved code selected from the iteratively generated code. Additional processes serialize the specification to generate multiple unit specifications. A large language model (LLM) is used to generate the long-form text based on the unit specifications.

The proposed technical solution relates to methods for automated text processing and can be used in the generation of text corpora. What is proposed is a computing device for the automated processing of natural language text. The claimed invention solves the technical problem of creating a method and / or a computing device and / or a system and / or a machine-readable data carrier which overcome the disadvantages of the prior art and, in so doing, provide for the precise automated generation of a text corpus which can subsequently be used for pre-training, or training, or post-training classification models and / or clustering models.

The proposed technical solution relates to methods for automated text processing and can be used in the generation of text corpora. The claimed invention solves the technical problem of creating a method and / or a computing device and / or a system and / or a machine-readable data carrier which overcome the disadvantages of the prior art and thus provide for the precise automated generation of a text corpus which can subsequently be used for pre-training, or training, or post-training classification models and / or clustering models.

The self-propelled construction machine I according to the invention has a DGNSS rover unit 14 for receiving satellite signals from a global navigation satellite system and correction signals from a reference station 15 to be set up in the vicinity of the self-propelled construction machine. The DGNSS rover unit 14 comprises a processing and evaluation unit 18, which is configured such that, based on the satellite signals and the correction signals from the reference station, construction machine position data describing the position of a construction machine reference point R on the construction machine is determined. The processing and evaluation unit 18 is further configured such that, for a reference station position data record stored in a reference station position data memory 23, at least one machine-readable data record, in particular a QR code, is generated, which is displayed on a display 8A.The QR code can be scanned with a smartphone 26 equipped with a map service and navigation function, making it easy to locate the installation point for a reference station in the field. Alternatively, the data can also be read using an NFC receiver (NFC reader) based on RFID technology.

A self-propelled construction machine I according to the invention has a DGNSS mobile station unit 14 for receiving satellite signals of a global navigation satellite system and correction signals of a reference station 15 arranged in the vicinity of the self-propelled construction machine. The DGNSS mobile station unit 14 comprises a calculation and evaluation unit 18 configured to determine construction machine position data describing a position of a construction machine reference point R on the construction machine based on the satellite signal and the correction signal of the reference station. The calculation and evaluation unit 18 is further configured to generate at least one machine-readable dataset, in particular a QR code, for a reference station location dataset stored in the reference station location data memory device 23, the dataset being displayed on a display 8A. The QR code can be scanned through a smart phone 26 provided with a map service with a navigation function, so that a setting point for setting the reference station can be easily found on a construction site. In an alternative embodiment, data may also be scanned using an RFID technology-based NFC receiving unit (NFC reader).

The proposed technical solution relates to methods for automated text processing and can be used in the generation of text corpora. The claimed invention solves the technical problem of creating a method and / or a computing device and / or a system and / or a machine-readable data carrier which overcome the disadvantages of the prior art and, in so doing, provide for the precise automated generation of a text corpus which can subsequently be used for pre-training, or training, or post-training classification models and / or clustering models.

The present application relates to the technical field of smart city, and discloses a smart city multi-modal data acquisition and fusion method and system.The present application comprises: acquiring smart city system data;performing data cleaning on the smart city system data according to a pre-trained naive Bayes model, and extracting metadata of data items;performing data standardization according to the metadata of the data items to obtain standardized data information;performing data conversion on the standardized data information according to a pre-formed semantic mapping rule library to obtain machine-readable data;merging and compressing the machine-readable data based on principal component analysis to obtain reduced dimension readable data;performing data fusion on the reduced dimension readable data according to a pre-set fusion strategy to obtain fusion data information, and storing the fusion data information.This method converts various heterogeneous data into machine-understandable information, effectively performs data fusion, and improves the usability of data.

Described herein relates to a system and method that supports the creation and execution of collaborative intelligent agents to process machine-readable data into dynamic and / or related outputs. As such, the collaborative intelligent agent system and / or methods thereof may include a compiler to generate an executable expression tree, an executor to manage intelligent agent execution using the expression tree, and / or a processor to manage intelligent agent collaboration during execution. Additionally, using machine-readable inputs, the collaborative intelligent agent system and / or methods thereof may automatically execute the collaborative intelligent agents to dynamically induce outputs, without requiring user interaction. As such, the collaborative intelligent agent system and / or methods thereof may also be configured to realize the second pillar of the Semantic Web and / or may improve upon HTML or machine-readable code only, providing a mechanism to automatically produce dynamic outputs based on input data.