13 results about "Snippet" patented technology

A method includes obtaining descriptive information for a first machine learning project, identifying, based on the descriptive information, a plurality of past machine learning projects which are similar to the first machine learning project, retrieving digital documents that describe the bias evaluation pipelines that were used to evaluate the plurality of past machine learning projects, detecting a common bias evaluation pipeline step among at least a subset of the digital documents, extracting, from the subset, a snippet of machine-executable code that corresponds to the common bias evaluation pipeline step, modifying the snippet of machine-executable code with use case data that is specific to the first machine learning project to generate modified machine-executable code, and generating a proposed bias evaluation pipeline for evaluating the first machine learning project, wherein the proposed bias evaluation pipeline includes the modified machine-executable code.

A computer-implemented system is disclosed which enhances legal research by utilizing a first user interface (UI) to receive search criteria as text prompts from users, which are then processed by an application program interface (API) across an electronic connection. The system includes an instruction base for expanding the prompts into snippets for a machine learning network, which defines the legal database searches. The process further refines the prompts through a transformation module, breaking down and tagging the text for compatibility with the machine learning network. Coupled with non-transitory computer memory, the system efficiently outputs results in response to the applied search criteria. A second interactive UI displays these results and allows for user interaction to delve into detailed information, optimizing the legal information retrieval process. Also disclosed is a method for processing legal research queries enhanced by a machine learning network.

Systems and methods for presenting an interface for additional content suggestion can include obtaining data descriptive of the displayed content and determining additional content associated with the displayed content. An interface can then be provided that displays data associated with the displayed content and the additional content. The interface can include a first viewing window for displaying a portion of the displayed content and a second viewing window for displaying a snippet associated with the additional content.

The invention relates to a method for acoustic interaction between a vehicle and a user, wherein the vehicle, in response to an action (1) performed by the user, outputs a sound response (2) via acoustic output means, wherein the sound response (2) is artificially generated from the musical patterns inherent in a piece of music (3), which are identified by an analysis of the piece of music (3) using artificial intelligence. The method according to the invention is characterized in that - the piece of music (3) is analyzed by a processing cascade (4), comprising at least one interacting digital signal processor algorithm and a suitable pre-trained machine learning model, which divides the piece of music (3) into music snippets (5) corresponding to individual temporal sections of the piece of music (3), and assigns metadata (6) to the music snippets (5), describing music-characterizing properties of the respective music snippet (5); - the music snippets (5) are stored in a snippet database (7); - for various actions (1) that can be performed by the user, at least one sound response blueprint (8) is provided, containing a metadata specification; - the sound response blueprints (8) are applied to the music snippets (5) stored in the snippet database (7), whereby for each sound response (2) at least one music scheme is generated from at least one music snippet (5) depending on the metadata specification in order to generate the sound response (2) for the respective action (1); and - a corresponding sound response (2) is emitted when performing a respective action (1).

An intelligent document analysis platform transforms unstructured documents into structured, searchable data by aligning them with a domain specific taxonomy. The system may segment each document into snippets, store vector embeddings and use a structure map to target key sections. For every datapoint defined in the taxonomy, the platform may automatically retrieve semantically similar snippets, construct prompt to a language model and extracts candidate values with supporting citations. A normalization phase may resolve conflicts and enforce categorical answer formats, while confidence scores may guide iterative refinement and fallback strategies. Users may receive normalized datapoints with highlighted citations via an interactive interface, and the platform can logs feedback to refine future extractions.

A core data services (CDS) explain agent receives a CDS explain request to generate an explanation with respect to a CDS data model. A CDS explain process is orchestrated for the CDS data model using a CDS explain handler of the CDS explain agent. A validator of the CDS explain agent and advanced business application programming (ABAP) dictionary metadata is used to validate if a CDS entity exists and if the CDS entity can be explained. Using a prompt assembler of the CDS explain agent, a large language model (LLM) prompt is assembled by combining multiple prompt snippets that satisfy the explanation with respect to a CDS data model. Using a CDS explain request processor of the CDS explain agent, the LLM prompt is transmitted to an LLM. Using a post-processor of the CDS explain agent, relevant information of a response from the LLM is extracted.

In one embodiment, a method receives, by a computing device, a text change in a text entry which includes one or more text units. The method may access, using a data store, a record associated with a user identifier which includes one or more top N similar hash values associated with the user identifier. The method may determine one or more hash values by applying a hash function over the one or more text units of the text change. The method may compare each of the one or more hash values to the one or more top N similar hash values. In response to determining a match between at least one of the one or more hash values and the one or more top N similar hash values, the method may determine a phrase suggestion using the text change to visually present on the computing device.

The invention relates to a method for acoustic interaction between a vehicle and a user, wherein the vehicle, in response to an action (1) performed by the user, outputs a sound response (2) via acoustic output means, wherein the sound response (2) is artificially generated from the musical patterns inherent in a piece of music (3), which are identified by an analysis of the piece of music (3) using artificial intelligence. The method according to the invention is characterized in that - the piece of music (3) is analyzed by a processing cascade (4), comprising at least one interacting digital signal processor algorithm and a suitable pre-trained machine learning model, which divides the piece of music (3) into music snippets (5) corresponding to individual temporal sections of the piece of music (3), and assigns metadata (6) to the music snippets (5), describing music-characterizing properties of the respective music snippet (5); - the music snippets (5) are stored in a snippet database (7); - for various actions (1) that can be performed by the user, at least one sound response blueprint (8) is provided, containing a metadata specification; - the sound response blueprints (8) are applied to the music snippets (5) stored in the snippet database (7), whereby for each sound response (2) at least one music scheme is generated from at least one music snippet (5) depending on the metadata specification in order to generate the sound response (2) for the respective action (1); and - a corresponding sound response (2) is emitted when performing a respective action (1).

An intelligent document analysis platform transforms unstructured documents into structured, searchable data by aligning them with a domain specific taxonomy. The system may segment each document into snippets, store vector embeddings and use a structure map to target key sections. For every datapoint defined in the taxonomy, the platform may automatically retrieve semantically similar snippets, construct prompt to a language model and extracts candidate values with supporting citations. A normalization phase may resolve conflicts and enforce categorical answer formats, while confidence scores may guide iterative refinement and fallback strategies. Users may receive normalized datapoints with highlighted citations via an interactive interface, and the platform can logs feedback to refine future extractions.

Mechanisms are provided for automatically generating source code to perform an intended code functionality. The mechanisms create a pairwise data source as a pairwise retrieval pool, where each data sample includes a pairing of a textual description and a corresponding relevant source code snippet. The mechanisms comprise an encoder that encodes an input natural language text description of an intended code functionality, to thereby generate an input encoding. The mechanisms search the pairwise retrieval pool for one or more candidate data samples based on the input encoding and encodings of the textual descriptions of the data samples. The mechanisms select a candidate data sample and generate the output source code based on the selected candidate data sample and the input natural language text description.

Implementations improve object classification / detection by leveraging visual attributes. An image depicting instance(s) of object class(es) is obtained with a textual snippet that includes: noun(s) identifying target object class(es); and adjective(s) describing target visual attribute(s). The textual snippet may be encoded as text embedding(s) that represent target object class(es) and visual attribute(s) in a shared embedding space. The image may be processed using an image encoder to generate image encoder output tokens (IEOTs) that are used to generate object visual embedding(s) in the shared embedding space. The text embedding(s) and the object visual embeddings may be used to classify the IEOTs as depicting an instance of the target object class(es) having target visual attribute(s). The IEOTs may also be processed using a localization head to predict annotation(s) for the digital image.