103 results about "Retrieval algorithm" patented technology

The invention relates to the technical field of operation and maintenance automation, and provides an operation and maintenance field professional question and answer and diagnosis Agent design method and system, and the method comprises the steps: receiving and structurally analyzing an original query request of a user, carrying out the parameter validity check and safety verification, and extracting the query content and a session identifier; identifying task types through an intention classification algorithm based on the pre-training language model and performing content risk assessment; performing semantic extension on the query to generate an extended query word, performing similarity matching in the operation and maintenance knowledge base by using a hybrid retrieval algorithm, and fusing related knowledge fragments; inputting the enhanced query information and the task type into an inference engine for intelligent inference to obtain a diagnosis result; the reasoning result is stored in a historical memory library, and session context state information is updated; and performing formatting processing and security check on the reasoning result, packaging the result and context information, and outputting a standard response result. According to the method, the accuracy and the intelligent level of operation and maintenance professional question answering and diagnosis are improved.

The invention relates to the technical field of artificial intelligence, and discloses an enterprise-level large-model agent application system supporting multi-modal collaboration, and the system comprises a user interaction terminal, an agent engine server, a knowledge engine server, a plug-in integration center, and a distributed storage unit. The agent engine server is responsible for intention recognition and task arrangement of a multi-modal input signal, and dynamically loads a differential reasoning strategy based on an environment isolation mechanism. And the knowledge engine server constructs a cross-modal semantic anchor point, analyzes an unstructured document into a tetrad knowledge unit, and realizes accurate recall of images and texts by using a hybrid retrieval algorithm. And the plug-in integration center executes outbound replacement and inbound restoration of the sensitive data through the context-aware dynamic desensitization gateway. According to the method, through a multi-modal semantic association and closed-loop verification mechanism, the problems of low complex document retrieval precision and leakage of external calling data are solved, and the service processing capacity and safety of the system are improved.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention is suitable for the technical field of natural language processing, relates to a hierarchical memory and context awareness retrieval method of a large role model and a related product, and aims to solve the problems of limited model memory duration, insufficient retrieval correlation and insufficient personality consistency in a long dialogue. According to the invention, a short-term-middle-term-long-term three-level memory architecture is adopted, and a memory attenuation and migration mechanism is combined, so that dynamic metabolism of memory is realized; related memories are recalled accurately through a context semantics and role personality double-sensitive double-stage retrieval algorithm; relying on a personality-linked memory fusion and response generation strategy, the reply is ensured to fit personality setting; and a closed-loop adaptive learning mechanism of dialogue-memory-retrieval-generation-feedback is constructed, and the memory quality is continuously optimized. According to the method, the role large model can have the human-like continuous memory ability, the continuity, retrieval accuracy and personality consistency of long dialogues are remarkably improved, and the long-term personalized interaction requirements of scenes such as digital personality assistants and dialogue agents are met.

The invention relates to a PCB component type selection method and system based on a knowledge graph and intelligent retrieval. Relates to the technical field of PCB design, and the method integrates knowledge in multiple aspects of performance parameters, packaging forms, supplier information and the like of components by constructing a PCB component knowledge graph, and establishes semantic association among the knowledge. And performing intelligent retrieval in the knowledge graph by using an information retrieval algorithm based on design requirements and constraint conditions input by a user, and quickly and accurately screening out components meeting requirements. Meanwhile, the multi-mode large model is combined to carry out comprehensive evaluation on the components, factors such as cost and supply stability are considered, optimal component type selection suggestions are provided for users, the efficiency and accuracy of component type selection in PCB design are improved, the cost is reduced, and intelligent development in the field of PCB design of electronic products is promoted.

The invention relates to a rail transit large model retrieval enhancement generation method and system, an application and a medium, and the method comprises the steps: receiving a user query text, converting the user query text into a triad sequence through a large language model, and sorting triads based on a semantic relationship; the method comprises the following steps: obtaining embedding vectors of entities and relationships in a sequence through an embedding model, and retrieving candidate entities and relationship sets from a knowledge graph according to the embedding vectors; and calculating a triple sequence semantic distance between the sequenced sequence and the candidate sequences by adopting a triple sequence retrieval algorithm, and selecting the first K candidate sequences meeting the structural constraint. And finally, inputting the user query and the first K candidate sequences into the large language model to generate an answer, thereby avoiding knowledge fragmentation and structure mismatch, and realizing reliable and accurate response to complex multi-hop query in the urban rail transit field.

The invention discloses a verifiable retrieval method and device, equipment, a medium and a product, and relates to the technical field of block chains. Obtaining a query request; performing data positioning from a retrieval algorithm tree based on the query request to obtain at least one target leaf node and a data index set corresponding to the target leaf node; generating a target node proof and a path proof based on the retrieval algorithm tree, the target leaf node and the data index set; and verifying the target node proof and the path proof based on the retrieval algorithm tree. By the adoption of the technical scheme, under-chain retrieval and verification are carried out through the retrieval algorithm tree, on-chain and off-chain cooperation of query verification is achieved, it is ensured that the certification size is small, verification calculation complexity is lowered, on-chain storage overhead is reduced, and the problems that at present, request query certification is large in size and low in verification efficiency are solved.

The invention provides an intelligent retrieval and question-answering system for knowledge base context awareness and traceability enhancement, and the system obtains an analysis result, an optical character recognition result and a semantic analysis result through the analysis of PDF physical layout, Word / Excel paragraphs, titles, tables and style attributes thereof, and the optical character recognition. Structured knowledge blocks, positions and levels of path images and vector representation are generated and stored in a vector database, and related results of user query requests are extracted by executing a mixed retrieval algorithm with keyword retrieval and vector semantic retrieval. Performing intelligent reordering by considering semantic similarity, keyword matching, knowledge block type weight, source knowledge base weight and page position weight to obtain a candidate knowledge block list, constructing cue words according to an intelligent reordering result, and calling an external large language model to generate answers; source labels in answers are managed to be associated with metadata of corresponding numbers in a candidate knowledge block list, and the problem that text blocks and traceability are not accurate is solved.

The invention relates to the technical field of artificial intelligence, and provides a domain relation extraction method and system based on a large language model. The method comprises the following steps: identifying entity information in a field text set, and obtaining a labeled entity set with type labels; on the basis of entity pairs in the labeled entity set and predefined relation types, a question set is constructed by utilizing a judgment question generation algorithm, and a domain relation judgment question set is obtained; reconstructing the structured domain data into training data through a dialogue format conversion algorithm, and training a large language model based on the training data by adopting a QLoRA quantization fine tuning algorithm to obtain a domain fine tuning model; a double-layer retrieval algorithm is applied to retrieve and obtain related information from the knowledge graph, the domain relation judgment question set and the related information are combined and input into the domain fine tuning model for reasoning, and a domain relation triple is obtained. According to the method, high-precision and interpretable domain relation extraction is realized, and the accuracy and robustness of the model in the vertical domain are improved.

The invention provides a policy intelligent question-answering method and system based on a knowledge graph and a storage medium. The method comprises the following steps: performing intention recognition and key information extraction on user query content to obtain a query intention and a key entity corresponding to the user query content; determining a plurality of seed entities in a pre-constructed knowledge graph according to the key entities; based on a multi-hop retrieval algorithm, according to the seed entities, determining an importance score of each entity in the knowledge graph; according to the importance score, determining a target entity in the knowledge graph and a target relationship directly associated with the target entity; and generating a query result corresponding to the user query content according to the target entity, the target relationship and the user query content by applying a pre-trained large language model, and outputting the query result. The method is used for achieving the effect of efficiently and accurately outputting the query result.

This invention relates to a semantically aware cross-modal encrypted retrieval method, belonging to the fields of information retrieval and data encryption. By introducing deep learning, it enhances the semantic feature mining capability for multimodal data, replacing the traditional keyword retrieval mode with semantic feature retrieval. A low-overhead multimodal data encrypted retrieval method is introduced, designing a parallel retrieval tree structure based on the block-based approach to achieve low-overhead, privacy-preserving, and fast semantic similarity retrieval. The rationality of the scheme is analyzed from aspects such as precision, search time, and storage overhead. This invention obtains multimodal features by extracting features from query requests; it uses a similarity-hiding encrypted retrieval algorithm to generate encrypted query trapdoors for multimodal query features; and it enables secure multimodal data retrieval in the medical IoT, combining the powerful edge computing capabilities and fast response of traditional scenarios, overcoming the problems of insufficient end-user computing resources and key leakage.

This application relates to the field of program repair processing technology, and in particular to an automatic program repair method and system based on a multi-level tree structure of a large language model. The method includes: using an information retrieval algorithm to retrieve a set of similar code examples corresponding to the program code to be repaired from a pre-defined code library; determining the model input content based on the set of similar code examples and the program code to be repaired; constructing a thought forest based on the model input content; performing error cause analysis based on the thought forest to obtain a set of high-confidence error causes; constructing a repair forest based on the set of high-confidence error causes; formulating repair strategies based on the repair forest to obtain a set of repair strategies; and performing autoregressive decoding operations based on the program code to be repaired, the set of similar code examples, and the set of repair strategies to obtain the patch code corresponding to the program code to be repaired. This application facilitates improvements in the accuracy and stability of the program repair process.

This invention discloses a method and device for auxiliary diagnosis of gas turbine faults, belonging to the field of industrial equipment fault diagnosis technology. Utilizing intelligent retrieval algorithms and efficient database support, it can quickly match relevant cases from a massive database of historical cases and provide diagnostic suggestions when a gas turbine experiences a warning or anomaly, shortening the fault diagnosis time. By characterizing historical fault cases of the gas turbine and combining them with current operating background parameters for multi-dimensional, dynamically weighted similarity matching, it can more accurately identify historical cases most similar to the current abnormal operating conditions, thereby improving the accuracy of fault diagnosis. In the specific application scenario of gas turbine power plants, it demonstrates unique technical contributions and significant practical value, which is of great significance for ensuring the safe and stable operation of gas turbines, improving power generation efficiency, and reducing operation and maintenance costs.

The invention discloses a continuous learning algorithm based on multi-task learning, which is used for solving the problems of real-time target detection and continuous learning of an unmanned system in a complex dynamic environment. The method comprises the steps that S1, an intelligent unmanned vehicle carries a high-precision sensor to collect multi-dimensional environment data, and importance samples are screened through a maximum gradient retrieval algorithm; s2, performing preliminary training on the pre-training model by using the screening data to enable the pre-training model to have basic target detection and recognition capability; s3, building a sea area image data enhancement continuous learning framework, and enhancing the image feature learning ability of the model under different weather conditions through an image compression reconstruction model, a cross attention module and an alternate training mode; s4, developing a stability and plasticity balancing strategy based on multi-task learning, relieving disastrous forgetting by solving a dual-objective optimization problem, and introducing a novel objective selection strategy to enhance the core set selection efficiency; s5, adding a regularization technology based on an influence function, and optimizing the performance of the model in the current environment; s6, in combination with a fine tuning technology, a general data pre-training model is firstly used, then the model is fine-tuned by using environment specific data, and the detection precision in a specific environment is improved; and S7, carrying out online learning and model evaluation optimization, continuously collecting new data to update the model, establishing a real-time evaluation and feedback mechanism, and continuously optimizing a target detection algorithm. According to the invention, powerful real-time target detection and continuous learning capabilities are provided for the unmanned system in a complex and changeable actual scene.

This application proposes a social text enhancement method based on multimodal retrieval and keyword extraction, comprising: S1, extracting keywords from sample sentences of different categories using a category keyword extraction algorithm; S2, training a sentence generation model using an RNN model combined with a self-attention mechanism, and controlling the sentence generation direction of the generation model according to the keywords of the corresponding categories of the training samples; S3, inputting the original sentence into the generation model to generate a first generated sentence with text enhancement; S4, based on a multimodal retrieval algorithm, determining whether the first generated sentence contains keywords from the keyword file; if so, finding the keyword to be replaced in the first generated sentence, and retrieving synonyms of the keyword to be replaced for replacement, thereby generating multiple second generated sentences with data enhancement. This application has the effect of controlling the generation direction and number of generated sentences of the generation model.

The invention belongs to the technical field of natural language processing and information retrieval, and relates to an intelligent question answering method based on structured semantic indexing and double-layer memory enhancement. The method comprises six steps of query preprocessing, Agent-based multi-tool dynamic routing, structured and semantic enhanced index construction, self-adaptive context assembly, double-layer memory management, and data closed loop and self-evolution, a differential segmentation strategy is adopted for texts, codes and multi-modal data, query optimization, intelligent tool routing and a mixed retrieval algorithm are combined, and the multi-modal data are subjected to self-adaptive context assembly. The technical defects of semantic rupture, low retrieval accuracy, no long-term memory and lack of self-optimization of an existing RAG system are overcome. According to the method, the code retrieval accuracy and the context utilization rate are improved, personalized long-term service and automatic operation and maintenance are achieved, and the method is suitable for scenes such as technology research and development, code library maintenance and intelligent question and answer.

The invention discloses a cryptographic intelligent question and answer method based on maximum marginal correlation retrieval, and relates to the technical field of natural language process.The method comprises the steps that firstly, a cryptographic field document is subjected to analysis and semantic partitioning, and the integrity of mathematical formulas and algorithm logic is ensured by dynamically calculating text similarity; and information splitting caused by traditional fixed partitioning is avoided. And then, obtaining knowledge fragments which are related to the question and have various contents from the vector database by utilizing a maximum marginal correlation retrieval algorithm, and providing multi-angle reliable contexts for answer generation. And finally, through field adaptive prompt engineering and dynamic parameter adjustment, guiding the large language model to play a role of a cryptography expert based on the retrieved context, and generating an accurate personalized answer meeting the user demand.

The invention relates to the technical field of smart home, in particular to a quick intelligent voice assistant response method based on an approximate nearest neighbor retrieval algorithm. The method comprises the following steps that historical voice instruction information of a user is collected to construct a cloud database, voice feature extraction is carried out on data in the cloud database, and the voice feature extraction is stored in a cloud vector database; carrying out clustering layering on the voice feature vectors in the cloud vector database, and introducing a multi-stage balance degree adjustment strategy to carry out balance degree adjustment; the clustering center is managed by using an IVF-HNSW framework; migrating data in the voice feature extraction model, extracting a user voice instruction feature vector and historical voice instruction information of the user as auxiliary information, and transmitting the auxiliary information to the cloud; and querying the user voice instruction feature vector through the IVF-HNSW index structure to obtain a key value of a matched voice feature, and returning the instruction to the local intelligent voice terminal. According to the method, efficient index construction and approximate nearest neighbor retrieval can be realized.

The invention provides a retrieval enhancement generation method based on theme enhancement and a related product. The method comprises the following steps: acquiring a preset knowledge dictionary, and identifying at least one target named entity in a query statement in response to the received query statement input by a user and the business depth and semantic weight of the query statement; determining a named entity theme set corresponding to the query statement according to each target named entity and a knowledge dictionary; determining a target retrieval algorithm corresponding to the named entity theme set according to the business depth and the semantic weight; executing data retrieval operation based on the named entity theme set and a target retrieval algorithm to obtain at least one piece of corpus data matched with the query statement; and inputting the query statement and the at least one piece of corpus data into a preset response result generation model, and generating a response result for the query statement.

The application provides a retrieval enhancement generation method and device, and relates to the technical field of artificial intelligence. The retrieval enhancement generation method comprises the following steps: constructing a word element dictionary tree according to a pre-constructed knowledge graph; inputting a to-be-reasoned question and the word element dictionary tree into a pre-constructed relation type generation model to obtain a relation type set; performing entity recognition on the to-be-reasoned question, and mapping the recognized entity to an entity node of the knowledge graph to obtain a subject entity set; taking the subject entity set as a starting point, searching in the knowledge graph under the constraint of the relation type set by using a k-BET reasoning subgraph retrieval algorithm to obtain a target reasoning subgraph; converting a triple in the target reasoning subgraph into a preset format, and inputting the triple after the format conversion and the to-be-reasoned question into a pre-constructed reasoning result generation model to obtain a generation result, so that the retrieval enhancement generation with complete structure, faithful semantics, and consideration of efficiency and resource consumption is realized.

The application discloses a kind of non-relational database cipher data retrieval methods, comprising: step one, obtaining the plaintext retrieval request that data user submits to database system, extract the plaintext keyword in the plaintext retrieval request;Step two, according to the plaintext keyword according to the proposed cipher data retrieval algorithm based on key characteristic value, generates cipher retrieval characteristic value by feedback reinforcement training learning, and executes cipher data retrieval in database to obtain cipher data;Its step three, the obtained cipher data is decrypted operation to restore plaintext data document.The application has the advantages that the encrypted data in non-relational database can be retrieved, it is convenient for user to find encrypted data in database, and can support non-relational database field-level encrypted data retrieval.

The application discloses an interactive self-service analysis and retrieval system based on a large model, which comprises a data source management module, a retrieval library construction module, an algorithm development module, a task scheduling management module and a service publishing module; the data source management module pre-processes initial information data to obtain structured information data; the retrieval library construction module constructs a retrieval library according to the structured information data; the algorithm development module extracts data features in the structured information data and recommends an adaptive retrieval algorithm according to the data features; the task scheduling management module performs retrieval analysis in the retrieval library according to the adaptive retrieval algorithm and an input retrieval instruction; and the service publishing module is used for converting the retrieval analysis result into an API service that can be called. The application aims to solve the pain points of the existing data analysis technology, such as fragmented functions, high operation threshold and lack of special text processing modules. The application provides an efficient solution for data modeling, text mining and service deployment.

This application provides a method, apparatus, device, and storage medium for self-correction of a medical visual language model based on a dynamic experience base (DEKB), relating to the field of medical visual language processing technology. The method includes: when the initial diagnostic result of the medical visual language model for a current clinical case does not match the fact label, constructing the current case as a structured experience unit and storing it in a dynamic experience knowledge base; upon receiving a query, retrieving historical experience cases related to the new query from the dynamic experience knowledge base; using the historical experience cases as contextual prompts to guide the medical visual language model in chain-like thinking, generating a corrected reasoning result. By constructing an endogenous dynamic experience knowledge base, designing a deep attribution analysis mechanism, and employing a dual-threshold retrieval algorithm, this application enables DEKB to transform the model's historical errors into structured knowledge that can be used for future reference, significantly improving the robustness and generalization ability of medical model image diagnosis reasoning.

The invention provides a log anomaly detection method and system based on deep learning, and relates to the technical field of log analysis and operation and maintenance monitoring, and the method comprises the steps of log access and preprocessing, connectable testing, data screening and analysis debugging; carrying out log mode recognition and anomaly detection, adopting a dynamic algorithm aggregation mode, eliminating noise and grading anomalies; aggregating alarms and faults, generating alarms, and analyzing cross-service line faults; intelligent analysis: constructing a knowledge base, generating an intelligent report and optimizing the intelligent report; and fault overview and disposal, operation and maintenance data display, and full-life-cycle operation support. According to the invention, by fusing the dynamic similarity aggregation and the mode stability screening algorithm, the log mode recognition accuracy is improved, the false report and the missing report are reduced, and the anomaly detection efficiency is improved; and meanwhile, an exclusive knowledge base and a business adaptation retrieval algorithm are constructed, intelligent fault analysis is realized in combination with a large language model, the troubleshooting and repairing period is shortened, a closed-loop optimization mechanism is formed, and the operation and maintenance efficiency is improved.

The invention discloses a profile customization order preprocessing method, and belongs to the field of the manufacturing industry and the field of the information technology. According to the method, the functions of customer demand analysis, profile database management, production pretreatment process planning, order tracking feedback and the like are integrated. When a client sends an order demand to an enterprise, the enterprise carries out production and processing according to an order, drives production through the order, reasonably allocates processing units in a preprocessing system, manages the delivery time and requirements of the order, optimizes order processing, provides real-time monitoring and feedback, and avoids order retention, production delay and inventory overstock. And the analyzed customer demands are converted into specific production parameters, matched profile types and specifications are quickly found in a database by adopting an efficient retrieval algorithm, and the optimal production preprocessing technological process and parameters are automatically planned by utilizing an optimization algorithm. Through order state tracking, an enterprise can know the state of an order at any time, including receiving, processing, production, shipping and delivery conditions of the order, so that problems can be found in advance and actions can be taken. And meanwhile, real-time monitoring is provided, raw material and finished product inventory is better managed, and smooth operation of a supply chain is ensured.

The invention discloses a heterogeneous equipment control instruction adaptive generation method based on a protocol semantic mapping graph, which comprises the following steps: constructing a three-layer core architecture, constructing a protocol semantic mapping graph on a semantic layer, receiving a user control intention through an intention layer, and generating a heterogeneous equipment control instruction based on the protocol semantic mapping graph. Analyzing the user control intention into a standard operation of a semantic layer, obtaining a protocol type and version information of target equipment, screening out an optimal protocol implementation path through graph matching and a retrieval algorithm of the semantic layer, and generating a pipeline through a parameterized template engine of a protocol layer in combination with equipment configuration information and an execution instruction in a version library. According to the method, the development complexity of multi-protocol equipment access is greatly reduced, and the method is suitable for heterogeneous equipment integration scenes in industrial automation, intelligent buildings, smart parks and smart homes.

A retrieval device and a retrieval method. The retrieval device (10) comprises a first processor (110) and a second processor (120), wherein the first processor (110) is a general central processor, used to perform table lookup operation in the retrieval process; the second processor (120) is a neural network processor, used to perform matrix / vector operation in the retrieval process. In the retrieval method, the steps of the retrieval algorithm (retrieval process) are reasonably allocated to different processors for execution, so as to exert the advantages of different kinds of processors and improve the retrieval efficiency.

The application provides a multi-dimensional enhanced RAG retrieval optimization method, device and medium, and belongs to the technical field of data management. The RAG retrieval optimization method comprises the following steps: a mixed guidance mechanism and a conflict resolution algorithm are designed based on a prompt word template and an inter-dimensional relationship, so that a large model automatically divides enterprise knowledge data into multi-level and multi-granularity feature dimensions, and the feature dimensions are fused or expanded; semantic attributes of original corpus on the feature dimensions are mined, and a whole embedding method and a block embedding method are used to generate and store feature embedding vectors in combination with dimension weights; feature dimensions are extracted from a user question obtained from a large language model, and the dimension granularity is dynamically scaled according to retrieval requirements, and a query vector is obtained by enhancing the feature dimensions; and according to an embedding vector retrieval algorithm, the most matched feature embedding vector is found in combination with a semantic weight. The application can solve the problem that the existing technology depends on manual dimension division.

This invention belongs to the field of computer information processing and proposes a document retrieval optimization method based on entity association. It performs secondary optimization while retaining the original retrieval algorithm, which not only improves the accuracy of the retrieval but also avoids the accuracy and efficiency problems caused by algorithm modifications. By performing entity recognition on both the document set and the user's search content, and calculating the association relationships between entities and documents, and entities and search content respectively, and comprehensively applying these association coefficients, it can uncover deep semantic relationships between search content and documents, and accurately locate documents that match the search intent. By calculating entity association degrees and re-ranking them according to these degrees, the accuracy of the search results is improved, resulting in a higher degree of alignment with user needs and allowing users to more easily obtain the information they require.

The invention provides a fatigue detection system. The fatigue detection system comprises an image sensor, a sound sensor, a memory, a database and a processor, wherein the image sensor is used for acquiring a face image of a driver; the sound sensor is used for acquiring sound data of the driver; the processor obtains a fatigue detection result of driving by using the fatigue detection model, extracts fatigue features from the face image according to the fatigue detection result, and matches the fatigue features with the music features by using a cross-modal retrieval algorithm to obtain a music file corresponding to the matched music features. And identifying the voice data by using a voice detection algorithm to obtain a voice identification result, judging the mental state of driving according to the fatigue detection result and the voice identification result, and determining a music file to be played according to the mental state of driving. The fatigue detection system provided by the invention can accurately judge the mental state of the driving driver.