Abstract: The present disclosure relates to a vehicle quality problem management system and a data processing method thereof. The system includes a management server including a server communication device that performs wireless communication with a mobile device, and a server processing device connected to the server communication device, and the server processing device receives a voice signal containing a current quality problem from the mobile device, converts the current quality problem in the voice signal to text using speech to text (STT), and registers the current quality problem converted into the text in a database (DB).

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations that include analyzing interactions by a user within a network and generating a user profile for the user. The operations by the processor may further include identifying an attempt by the user to share a post via the network and prompting the user with a personalized alert to evaluate the post, wherein the personalized alert is generated based on the interactions, the user profile, and the properties of the post.

Abstract: In a method for generating a normalized sequential model using a processor, a sequential energy-based model computed by a parameterized neural network is provided. The sequential energy-based model defines an unnormalized probability distribution over a target sequence for a context source. The normalized sequential model is generated by projecting the sequential energy-based model onto a target autoregressive model that approximates a normalized distribution associated with the sequential energy-based model.

Abstract: A system may be configured to detect custom acoustic events, where the system generates an acoustic event profile for the custom acoustic event based on a natural language description provided by a user and using an audio sample of the described acoustic event. For example, the user may describe the custom acoustic event as “dog bark.” The system may ask the user questions to refine the description (e.g., dog breed, dog gender, age, etc.). Using an audio sample of the refined description, the system may then determine that audio captured in the user's environment is a potential sample of the custom acoustic event. Such captured audio may be presented to the user for confirmation, and then may be used to detect future occurrences of the custom acoustic event in the user's environment.

Abstract: Implementations set forth herein relate to employing dynamic regulations for governing responsiveness of multiple automated assistant devices, and specifically the responsiveness an automated assistant to a given spoken utterance that has been acknowledged by two or more of the assistant devices. The dynamic regulations can be context-dependent and adapted over time in order that the automated assistant can accommodate assistant interaction preferences that may vary from user to user. For instance, a spoken utterance such as “stop,” may be intended to affect different assistant actions based on a context in which the user provided the spoken utterance. The context can refer to a location of the user relative to other rooms in a home, a time of day, a user providing the spoken utterance, an arrangement of the assistant devices within a home, and/or a state of each device in the home.

Abstract: A system for generating a contextualized text using a character-based convolutional neural network architecture (CNN) is disclosed. The system includes a memory, a character-based CNN engine having executable modules and a processor configured to execute the character-based CNN engine and the executable modules for generating contextualized text using a character-based convolutional neural network architecture (CNN).

Abstract: An embodiment for creating a dialog based on anticipated questions for database driven conversations is provided. The embodiment may include receiving content from a database. The embodiment may also include identifying one or more schemas, one or more entities, and relational data in the content. The embodiment may further include identifying a semantic type and a number of distinct entries for each entity. The embodiment may also include presenting choices for one or more query targets and one or more filtering conditions to a user. The embodiment may further include prompting the user for one or more annotations and one or more clarifying questions for each chosen query target and filtering condition. The embodiment may also include generating a plurality of modular phrases and combining the plurality of modular phrases into one or more sentences and paraphrases of the one or more sentences.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations using natural language input data. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using sentence embedding machine learning models that are trained in coordination with similarity-based machine learning models.

Abstract: Mechanisms for implementing a text encoder and text encoder operations are provided. A contrastive machine learning training operation trains an encoder of a machine learning computer model, to learn a sense and similarity preserving embedding that operates to encode input natural language text data to generate encoded natural language text data based on a sense attribute of one or more terms in the input natural language text data. The contrastive machine learning training operation learns to separate positive samples in training data from negative samples in the training data. The trained encoder processes a term specified in an input natural language text to generate an encoded natural language text based on the learned embedding and inputs, to a downstream computing system, the encoded natural language text to cause the downstream computing system to perform a computer natural language processing operation based on the embedding.

Abstract: Disclosed implementations include systems, methods, and apparatus that process multiple, disparate streams of data, determine correlations and relationships between the data and provide natural language responses that provide insights for events or activities that have occurred and foresights for events or activities that are forecasted to occur. The disclosed implementations include a model that understands data statistics and provides both insights and foresights that are backed with statistical support that can be presented to and understood by operators. Still further, the disclosed implementations are capable of operating at edge locations that may be frequently or permanently disconnected from conventional or cloud based systems.

Abstract: Techniques are described herein for producing machine-generated findings given a set of user experience test results. In some embodiments, the system generates the findings using an artificial intelligence and machine learning engine. The findings may highlight areas that are predicted to provide the most insight into optimizing a product's design. A finding may be generated based on all or a subset of the test result elements, including qualitative and/or quantitative data contained therein. A finding may summarize a subset of the UX test results that are interrelated. A finding may link a summary to one or more references extracted from the set of test results to show support for the machine-generated insights in the underlying raw test data. Machine-generated findings reports may provide near instantaneous guidance for optimizing product designs while removing extraneous information from a vast quantity of raw test result data.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to determine mutex entropy for malware classification. An example apparatus includes interface circuitry to access a mutex associated with a software application, the mutex to include a mutex identifier string, normalizer circuitry to normalize the mutex identifier string, character probability circuitry to determine character probabilities of characters within the normalized mutex identifier string, the character probabilities based on a historical mutex character distribution, entropy calculator circuitry to calculate an entropy value for the mutex based on the character probabilities, classifier circuitry to classify the mutex as clean or malicious based on the entropy value, and protector circuitry to mitigate malicious attacks based on the classification.

Abstract: The in-home care of seniors is augmented using Internet of Things (IOT) technologies. In-home sensors monitor a senior and their caregiver. Physical conditions and psychological conditions may be monitored. In some implementations, a machine learning system has a classifier trained to detect a specified condition, such as depression. The system may perform various transformations of raw sensor data into a format indicative of a particular condition. In one implementation, a psychological or medical condition has symptoms in which each symptom has one or more measurable events. Mappings between symptoms, events, sensor data, and sensor transformation functions may be supported.

Abstract: A document processing system is configured to identify, for each accessed electronic document in a first set of multiple electronic documents, a set of identified multi-word phrases determined to be in ordered text information in the accessed electronic document, each multi-word phrase of the set of identified multi-word phrases including adjacent words in the ordered text information; and determine, for each accessed electronic document in the first set of multiple electronic documents, a selected document type from the first set of document types based at least on an analysis of the set of identified multi-word phrases with respect to multi-word-phrase characteristics identified by a first definition and associated with each document type in a first set of document types associated with a first document-set type.

Abstract: Systems, methods and non-transitory computer readable media for attributing generated audio contents to training examples are provided. A first audio content generated using a generative model may be received. The generative model may be a result of training a machine learning model using training examples. Each training example may be associated with a respective audio content. Properties of the first audio content may be determined. For each training example of the training examples, the respective audio content may be analyzed to determine properties of the respective audio content. The properties of the first audio content and the properties of the audio contents associated with the training examples may be used to attribute the first audio content to a subgroup of the training examples. A respective data-record associated with a source associated with the training examples of the subgroup may be updated based on the attribution.

Abstract: Systems and methods are provided for generating a content item banner for display based on a session type and a content item type. A user profile is accessed at a computing device, and a session type associated with consumption of at least a content item of a plurality of content items is determined based on the user profile. A content item type associated with the plurality of content items is determined, and a content item banner for a subsequent content item of the plurality of content items is identified based on the session type and the content item type. The content item banner is generated for display.

Abstract: Systems for providing high-fidelity data management for cross domain analytics may include multiple components. An access management function component may control access to data stored in a data store of a business domain by a user account associated with a search engine domain. A data management function component may select based on at least one of one or more data access privileges for the user account associated with the search engine domain or one or more privacy policies, a data view of multiple data views for viewing the data, and one or more data filters for application to the data. An external API manager component may store in the data store of the business domain cross correlation information that correlates a plurality of machine learning model identifiers of machine learning models of the search engine domain with one or more corresponding business APIs of the business domain.

Abstract: A method provided for cross-lingual transfer trains a pre-trained multi-lingual language model based on a gold labeled training set in a source language to obtain a trained model. The method assigns each sample in an unlabeled target language set to a silver label according to a model prediction by the trained model to obtain set of silver labels, and performs uncertainty-aware label selection based on the silver label assigned to each sample according to the model prediction and the trained model to obtain selected silver labels. The method performs iterative training on the selected labels by applying the selected silver labels in the target language set as training labels and re-training the trained model with the gold labels and the selected silver labels to obtain an iterative model, and performs task-specific result prediction in target languages based on the iterative model to generate a final predicted result in target languages.

Abstract: In various embodiments, a bootstrapping training subsystem performs sampling operation(s) on a training database that includes subjective scores to generate resampled dataset. For each resampled dataset, the bootstrapping training subsystem performs machine learning operation(s) to generate a different bootstrap perceptual quality model. The bootstrapping training subsystem then uses the bootstrap perceptual quality models to quantify the accuracy of a perceptual quality score generated by a baseline perceptual quality model for a portion of encoded video content. Advantageously, relative to prior art solutions in which the accuracy of a perceptual quality score is unknown, the bootstrap perceptual quality models enable developers and software applications to draw more valid conclusions and/or more reliably optimize encoding operations based on the perceptual quality score.

Abstract: Systems and methods for multilingual keyword generation for low-resource language passages are disclosed. For example, high-resource language passages describing items in a catalog or other such passages may include numerous keywords for classifying and searching for the item while a low-resource language description of the item may not include the same rich keyword environment due to lower usage and traffic in the low-resource language. The systems and methods herein provide for leveraging the high-resource language through a multilingual natural language processing algorithm to identify similar passages across language barriers and identify keywords in the high-resource language for input into a decoder to generate keywords in the low-resource language.

Abstract: Methods are provided, such as a method of interacting with a large language model (LLM), including the step of a processing system using a structured, machine-readable representation of data that conforms to a machine-readable language, such as a universal language, to provide new context data for the LLM, in order to improve the output, such as continuation text output, generated by the LLM in response to a prompt; and such as a method of interacting with a LLM, including the step of providing continuation data generated by the LLM to a processing system that uses a structured, machine-readable representation of data that conforms to a machine-readable language, such as a universal language, in which the processing system is configured to analyse the continuation output generated by the LLM in response to a prompt to enable an improved version of that continuation output to be provided to a user. Related computer systems are provided.

Abstract: The present disclosure relates to a system for infusing knowledge graphs and language models (LM) for natural language sentence pair tasks, the system include a processor operatively coupled to an inference engine, the inference engine configured to receive a sentence indicative of a premise and a sentence indicative of a hypothesis, extract LM embeddings for the corresponding sentence, generate a common-sense knowledge graph for corresponding sentence and nodes are derived from the common-sense knowledge graph, assign node importance scores for each of the derived nodes, compute node parameters for the derived nodes, apply an aggregation function to generate pooled values, concatenate the LM embeddings, graph embeddings and pooled values to generate concatenated data and classify the concatenation data to indicate a relationship between the natural language inference pair.

Abstract: The disclosure deals with a system and method for improved general task-oriented virtual assistants (VAs). The presently disclosed framework incorporates discovery of knowledge from online sources to accomplish tasks (open world), user-specific knowledge for personalization, and domain-specific knowledge for context adaptation to recommend and assist the users over procedural tasks such as cooking and Do-it-Yourself (DIY) tasks. The approach also focuses on content curation for fault-tolerant execution to ensure the end goal is reached despite common failures.

Abstract: A system, method, and computer program are provided for text sanitization. The system builds a corpus of document vectors (including tokenizing each document, creating a vector representation based on the tokens, and building a corpus of vector representations), obtains a new document for text sanitization, tokenizes the new document, creates a new document vector based on the tokens in the new document, and accesses the corpus of document vectors. The system filters each of the tokens in the new document against a privacy threshold. The system performs a k-anonymity sanitization process such that the new document vector becomes indistinguishable from at least k other document vectors in the corpus of document vectors. The system replaces or redacts the tokens in the document flagged as unsafe. The system updates the corpus of document vectors to include the new document vector in its form prior to the filtering and k-anonymity sanitization steps.

Abstract: Systems, media, and methods for automated response to social queries comprising: monitoring queries from users, each query submitted to a vendor via an interactive chat feature of an external electronic communication platform, monitoring human responses to the queries, monitoring subsequent communications conducted via the electronic communication platform until each query is resolved; applying a first machine learning algorithm to the monitored communications to identify a query susceptible to response automation; applying a second machine learning algorithm to the query susceptible to response automation to identify one or more responses likely to resolve the query; and either i) notifying a human to respond to the query susceptible to response automation with the one or more responses likely to resolve the query, or ii) instantiating an autonomous software agent configured to respond to the query susceptible to response automation with the one or more responses likely to resolve the query.

Abstract: In an embodiment of a messaging system, a method for presenting a commercial message to a user is provided. A target language in which the user is comfortable communicating may be determined based on at least one communication received by the user or at least one communication provided by the user. The commercial message may be presented to the user in the target language.

Abstract: Provided is a limited utterance determination unit that determines whether or not a user utterance is a limited utterance among one or more pre-registered limited utterances, a risk determination unit that, in the case where the result of the determination is that the user utterance is not any one of the one or more pre-registered limited utterances, determines a risk of a process corresponding to the user utterance, and a process execution unit that, in the case where the risk of the process corresponding to the user utterance is not a low risk, outputs a guidance that prompts a user to make a limited utterance included in the pre-registered limited utterances are provided. In the case where the risk is not the low risk, the process execution unit outputs an alert including explanation information regarding the risk.

Abstract: Solutions for custom display post processing (DPP) in speech recognition (SR) use a customized multi-stage DPP pipeline that transforms a stream of SR tokens from lexical form to display form. A first transformation stage of the DPP pipeline receives the stream of tokens, in turn, by an upstream filter, a base model stage, and a downstream filter, and transforms a first aspect of the stream of tokens (e.g., disfluency, inverse text normalization (ITN), capitalization, etc.) from lexical form into display form. The upstream filter and/or the downstream filter alter the stream of tokens to change the default behavior of the DPP pipeline into custom behavior. Additional transformation stages of the DPP pipeline perform further transforms, allowing for outputting final text in a display format that is customized for a specific user. This permits each user to efficiently leverage a common baseline DPP pipeline to produce a custom output.

Abstract: Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term is associated with a respective part of speech, and a frequency of occurrence of each term in content metadata is determined. A relevance of each term is then determined based on its respective part of speech and frequency. The natural language search query is then interpreted based on the importance or relevance of each term.

Abstract: A processor is adapted to automatically generate and validate rules for monitoring suspicious activity by: For a first period of time, collecting a first group of transactions, automatically identifying and storing key indicators from the transactions, and automatically storing which of the transactions are pre-identified as fraudulent. Based on the key indicators and the pre-identified fraudulent transactions, training a learning algorithm and, with the learning algorithm, generating a decision tree of logical predicates including the key indicators. Based on the decision tree, generating a plurality of rules, each of which incorporates only one logical predicate from each layer of the decision tree. For a second period of time: collecting a second group of transactions, and generating a quality metric for each rule, by automatically testing the rules against the second group of transactions, and identifying a subset of rules for which the quality metric exceeds a threshold.

Abstract: Provided is an electronic device including a communication module, a memory, and at least one processor operatively connected to the communication module and the memory, and the memory stores instructions configured to, when executed, enable the at least one processor to: receive a first sentence via the communication module; identify a first newly coined word included in the first sentence; obtain a second sentence by identifying a first alternative word corresponding to the first newly coined word, and replacing the first newly coined word in the first sentence with the first alternative word; obtain a third sentence by correcting a grammatical error in the second sentence based on the first alternative word occurring the grammatical error when replacing the first newly coined word; and transmit the third sentence to an external device via the communication module. Other embodiments can be made.

Abstract: Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically assessing consumer sentiment with regard to an attribute descriptor associated with a commercial entity and/or a commercial item. A computer-executed method includes programmatically accessing, via a network device, text of a consumer review and in indication of an attribute descriptor included in the text of the consumer review. The method also includes executing a consumer review processing engine to programmatically analyze the text of the consumer review to generate a sentiment score associated with the attribute descriptor in the context of the consumer review. The method further includes storing, on a non-transitory computer-readable storage device, an indication of the attribute descriptor and the generated sentiment score in association with the commercial entity and/or the commercial item.

Abstract: The present disclosure describes a system to use conversation data of patients to detect dangerous mental or physical conditions, such as suicidal thoughts, physical abuse, recent falls, and viral infection. A machine learning system may be trained to identify a dangerous mental or physical condition from conversations based on examples of patients evaluated to have a specific mental or physical condition. Conversations of patients may be monitored, natural language understanding (NLU) processing performed, and a machine learning system used to detect dangerous mental or physical conditions.

Abstract: A question answering system is provided that can be used to supply workers at a jobsite with relevant answers or information for situations occurring at the jobsite. The question answering system can include a knowledge base with different types of content that are related to situations that may occur at the jobsite. When a user submits a query to the question answering system, the system can then determine whether an appropriate response to the query is contained in the knowledge base. If the knowledge base does not have an appropriate response, then the system submits the query to a third party information source such as a generative artificial intelligence tool for an answer. The system then analyzes the response from the third party information source and selects either the response from the third party information source or the response from the knowledge base to provide to the user.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: In a first aspect, a system for creating fact-based content is presented. The system includes an application service provider operating on a network. The application service provider is configured to receive a user prompt and generate a web query for content based on the user prompt. The system includes a fact-based language model in communication with the application service provider. The fact-based language model is configured to receive the web query from the application service provider and retrieve, from a electronic library, relevant fact-based content based on the web query. The electronic library includes proprietary data. The fact-based language model is configured to provide the relevant fact-based content to the application service provider. The application service provider communicates content to a user based on the user prompt. The content includes at least a portion of the relevant fact-based content from the electronic library.

Abstract: Systems and methods are disclosed for using natural language processing to generate sound-alikes and look-alikes for potentially relevant terms that could be mis-transcribed in a transcript. The disclosed framework combines the advantages of several measures aimed at tackling a wide range of transcription errors including but not limited to word boundary errors, phonetic confusion of words, spelling mistakes, grammatical errors, character drops, etc. In some examples, morphological similarity and phonetic similarity are used to address the word boundary errors and phonetic confusion of words. In some examples, a spell checker, word formation, and look-alike sound-alike generator are used to address errors such as phonetic confusion, spelling mistakes and grammatical errors. The generated sound-alikes can be ranked, which enables a flexible application of the generated phrases.

Abstract: Entities and temporal information associated with the entities can be extracted from the documents using natural language processing. A graph structure can be created representing the document's temporal semantics, nodes of the graph structure including the entities and edges of the graph structure representing temporal relationships between the nodes. The graph structure can be linked with the document. Multiple documents can be received and a knowledgebase can be created including multiple graph structures representing the multiple documents according to the multiple documents' temporal semantics. An input document for query can be received and transformed into a graph structure for query, the graph structure for query representing the input document's temporal semantics. The knowledgebase can be searched for a matching document having a graph structure similar to the graph structure for query based on a similarity threshold. The matching document can be output.

Abstract: Described herein are systems and methods of generative machine-learning guided by modal classification. An apparatus may receive a characterization datum and output an inquiry as a function of the characterization datum. An apparatus may receive an inquiry response and may output a plurality of draft literature modes as a function of the characterization datum and the inquiry response. An apparatus may receive template feedback and may output a literature mode as a function of the template feedback and the plurality of draft literature modes.

Abstract: At least some embodiments are directed to an entity classification system receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: A method and device with natural language processing is disclosed. The method includes performing a word embedding of an input sentence, encoding a result of the word embedding, using an encoder of a natural language processing model, to generate a context embedding vector, decoding the context embedding vector, using a decoder of the natural language processing model, to generate an output sentence corresponding to the input sentence, generating a score indicating a relationship between the context embedding vector and each of a plurality of knowledge embedding vectors, determining a first loss based on the output sentence, determining a second loss based on the generated score, and performing training of the natural language processing model, including training the natural language processing model based on the determined first loss, and training the natural language processing model based on the determined second loss.

Abstract: A machine learning system passively monitors sensor data from the living space of a patient. The sensor data may include audio data. Audio features are generated. A trained machine learning model is used to detect a change in condition. In some implementations, the machine learning model is trained in a learning phase based on training data that includes questionnaires completed by caregivers and identified audio features.

Abstract: A non-transitory machine-readable medium having machine-executable instructions includes a translator, the machine-readable instructions for the translator being executable by a processor core to perform operations. The operations include translating records in model-based system engineering (MBSE) data for an MBSE platform to identify nodes and links for graph data of a graph database. The operations also include populating the graph data with the nodes and links from the translated nodes. A query to the graph data of the graph database reveals a subset of nodes of the nodes and links between the nodes of the subset of nodes and other nodes of the nodes of the graph data.

Abstract: A system is described for rendering normalized medical service provider data records by transforming received input records from a variety of sources into standardized medical service provider data records. The system digests a resource containing multiple data points to render a mapping between individually identifiable parts of the data points and normalized data types handled by data type-specific parsers. A matching operation is performed, on parsed data points, wherein individual ones of the set of parsed data points are matched to an identified entry on an authoritative listing. During the matching an algorithmic match is attempted by assigning a match score based upon a type of transformation on a parsed data point value, of a data point instance of the set of parsed data points, to render a match between the parsed data point value and an entry within an authoritative data listing.

Abstract: Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, a system suitable for providing a machine learning analysis can be different from a remote computer system on which training data for a machine learning model is located. A machine learning task can be defined that includes an identifier for at least one data source on the remote computer system. Data for the at least one data source is received from the remote computer system. At least a portion of the data is processed using a machine learning algorithm to provide a trained model, which can be stored for later use. Data on the remote computing system can be unstructured or structured. Particularly in the case of structured data, a remote computer system can make updated data available to the machine learning task.

Abstract: Generally discussed herein are devices, systems, and methods for generating a phrase that is confusing to a language classifier. A method can include determining, by the LC, a first classification score (CS) of a prompt indicating whether the prompt is a first class or a second class, predicting, based on the prompt and by a pre-trained language model (PLM), likely next words and a corresponding probability for each of the likely next words, determining, by the LC, a second CS for each of the likely next words, determining, by an adversarial classifier, respective scores for each of the likely next words, the respective scores determined based on the first CS of the prompt, the second CS of the likely next words, and the probabilities of the likely next words, and selecting, by an adversarial classifier, a next word of the likely next words based on the respective scores.

Abstract: In order to facilitate a search and identification of documents, an information retrieval system is provided for performing a search on a corpus of data objects. The information retrieval system comprises a device and a database. The database is configured to store at least one syntactic search index data structure and at least one semantic search index data structure. The syntactic search index data structure is configured to index and store in the database a plurality of terms from the corpus of data objects along with syntactic annotations indicating syntactic information. The at least one semantic search index data structure is configured to index and store in the database the plurality of terms from the corpus of data objects along with semantic annotations indicating semantic information. The device comprises an input unit, a processing unit, and an output unit. The input unit is configured to receive a syntactic query and a semantic query.

Abstract: A method is provided. The method includes obtaining sensor data indicative of a set of objects detected within an environment. The method also includes generating a state graph based on the sensor data. The state graph includes a set of object nodes and a set of property nodes. The method further includes obtaining user input data generated based on a natural language input. The method further includes updating the state graph based on the user input data to generate an enhanced state graph. The enhanced state graph includes additional nodes generated based on the user input data. The method further includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The method further includes operating the set of mechanical systems to achieve a set of objectives based on the set of instructions.

Abstract: The invention relates to a recommendation method for an internal service entity service entrance of a network platform. The method comprises the following steps of: S1, sending service keywords of each service entity as a word source of tokenizer to the tokenizer of a network platform; S2, sending a core corpus corresponding to each network content in the network platform to a tokenizer, wherein the core corpus is the core content of the network content; S3, carrying out word segmentation on that core corpus by the tokenizer, and matching with the service keywords after the word segmentation is completed; S4, if the participle of the core corpus is matched with the service keywords, setting a service entrance of the service entity corresponding to the matched service keywords in the network content corresponding to the core corpus.

Abstract: A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.