Abstract: Certain aspects of the present disclosure provide techniques for vectorization of transactions including: receiving electronic transaction information of one or more transactions of a user; for each transaction of the one or more transactions: segmenting the electronic transaction information of the transaction into one or more transaction words; generating a second transaction description related to the transaction; and identifying a category of the transaction; generating, based on the corresponding identified categories of the one or more transactions, a set of transaction history data of the user; providing the set of transaction history data of the user as an input to a machine learned model trained to output a set of word embedding vectors; determining, based on an output of the machine learned model comprising a set of word embedding vectors, a set of similar merchants; and providing the set of similar merchants for display to the user.

Abstract: Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Abstract: Systems and methods that may be used to automatically correct, complete and or suggest words or terms to the user of an electronic service (e.g., accounting service) while the user is entering a search keyword and or filling out a form field. The automatic correction, completion and or suggestion of words or terms are based natural language processing of historical data from a plurality of users of the electronic service.

Abstract: Systems and methods that may be used to provide guidance and or tag suggestions to a user of an electronic accounting system and or service that overcome the shortcomings associated with user-defined tags.

Abstract: The present disclosure provides a composite machine learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine learning model is updated based on the descriptive string and the label. The machine learning model is then trained against the updated set of training data.

Abstract: A method that predicts business income from user transaction data. A multinomial classifier is trained, using a vector of features from data related to a historical transaction and a label associated with the historical transaction, to generate a probability that the historical transaction belongs to a specific classification with respect to income. Data related to a new transaction is split into a set of unigrams. A new vector of features is generated from the data related to the new transaction. The new vector includes a set of values that correspond and are assigned to the set of unigrams. A classification with respect to income is determined for the new transaction by applying the multinomial classifier to the new vector. The new transaction is labeled with the classification. One or more fields of a form that is maintained by an online service is populated using the classification.

Abstract: A method includes obtaining data related to a plurality of historical transactions, where each historical transaction is associated with a label based on a click stream created by the first user, generating a vector of features from the data related to each historical transaction, training, using the vectors and labels, a multinomial classifier to generate a probability that a specific transaction belongs to a specific classification with respect to income, obtaining data related to a new transaction from a financial stream for a second financial account of a second user of the financial service, generating a new vector of features from the data related to the new transaction, determining a classification with respect to income for the new transaction, and presenting the classification to the second user for review in a view of a graphical user interface.

Abstract: The present disclosure provides a composite machine-learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine-learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine-learning model is updated based on the descriptive string and the label. The machine-learning model is then trained against the updated set of training data.

Abstract: A fabrication method of an organic electroluminescent device includes: providing a substrate configured to an anode of the device; fabricating a blue pixel emission layer on one side of the substrate with a universal mask plate; and fabricating a red pixel emission layer and a green emission layer successively on one side of the blue pixel emission layer which backs toward the substrate. The blue pixel emission layer includes an effective emission area and a non-effective emission area. The red pixel emission layer and the green pixel emission layer both are the same layer and arranged on the non-effective emission area. The present disclosure can reduce equipment expenditure in fabricating the emission layers and the complexities of technology.

Abstract: A method for category search. The method includes determining semantic relationships between terms in a corpus; obtaining, from an expense category hierarchy and for an expense category, a collection of keywords; and expanding the collection with a related keyword according to a semantic relationship of the relationships between the related keyword and a preexisting keyword in the collection. The method further includes extracting a segment from a description of a first historical transaction by a user of the financial product; and adding the extracted segment as an additional keyword to the collection when, for the extracted segment, the minimum of a first and a second confidence-interval bound calculated for a first transaction score and a first user score, respectively, satisfy a first threshold; and returning the name of the expense category in response to a user submitting a first query that comprises at least one the keyword in the collection.

Abstract: Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Abstract: Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Abstract: An OLED and a display device has OLED are provided. The OLED includes an anode; an organic emission layer positioned on the anode, the organic emission layer includes a multilayer functional layer; a diffusion layer is positioned between at least two adjacent functional layers of the multilayer functional layer, the diffusion layer dissociates metal ions, the metal ions is diffusing to the functional layer adjacent to the diffusion layer for filling traps of the functional layer which is adjacent to the diffusion layer. The OLED of this invention could fill traps of the organic molecular materials, and decreases joule heating effect which generated by getting rid of the carriers bonded by traps. Therefore, it achieves to enhance life time and luminous performance of OLED.

Abstract: An OLED and a display device has OLED are provided. The OLED includes an anode; an organic emission layer positioned on the anode, the organic emission layer includes a multilayer functional layer; a diffusion layer is positioned between at least two adjacent functional layers of the multilayer functional layer, the diffusion layer dissociates metal ions, the metal ions is diffusing to the functional layer adjacent to the diffusion layer for filling traps of the functional layer which is adjacent to the diffusion layer. The OLED of this invention could fill traps of the organic molecular materials, and decreases joule heating effect which generated by getting rid of the carriers bonded by traps. Therefore, it achieves to enhance life time and luminous performance of OLED.

Abstract: The invention provides a transparent OLED display panel and manufacturing method thereof, the OLED display panel comprising: a transparent substrate and a plurality of sub-pixels disposed on the substrate arranged in an array; each sub-pixel comprising: an anode disposed on the substrate, an organic light-emitting layer disposed on the anode, and a transparent cathode disposed on the organic light-emitting layer; the anode comprising: a first transparent electrode, a reflective electrode stacked on the first transparent electrode, a second transparent electrode stacked on the reflective electrode, the reflective electrode forming a hollow area at the center; through forming hollow area on the reflective electrode of the anode, the light can be emitted simultaneously for the cathode and the hollow area to achieve transparent display with a simple and stable structure, high yield rate and low production cost.

Abstract: A fabrication method of an organic electroluminescent device includes: providing a substrate configured to an anode of the device; fabricating a blue pixel emission layer on one side of the substrate with a universal mask plate; and fabricating a red pixel emission layer and a green emission layer successively on one side of the blue pixel emission layer which backs toward the substrate. The blue pixel emission layer includes an effective emission area and a non-effective emission area. The red pixel emission layer and the green pixel emission layer both are the same layer and arranged on the non-effective emission area. The present disclosure can reduce equipment expenditure in fabricating the emission layers and the complexities of technology.

Abstract: The invention provides a transparent OLED display panel and manufacturing method thereof, the OLED display panel comprising: a transparent substrate and a plurality of sub-pixels disposed on the substrate arranged in an array; each sub-pixel comprising: an anode disposed on the substrate, an organic light-emitting layer disposed on the anode, and a transparent cathode disposed on the organic light-emitting layer; the anode comprising: a first transparent electrode, a reflective electrode stacked on the first transparent electrode, a second transparent electrode stacked on the reflective electrode, the reflective electrode forming a hollow area at the center; through forming hollow area on the reflective electrode of the anode, the light can be emitted simultaneously for the cathode and the hollow area to achieve transparent display with a simple and stable structure, high yield rate and low production cost.

Abstract: A method includes obtaining data related to a plurality of historical transactions, where each historical transaction is associated with a label based on a click stream created by the first user, generating a vector of features from the data related to each historical transaction, training, using the vectors and labels, a multinomial classifier to generate a probability that a specific transaction belongs to a specific classification with respect to income, obtaining data related to a new transaction from a financial stream for a second financial account of a second user of the financial service, generating a new vector of features from the data related to the new transaction, determining a classification with respect to income for the new transaction, and presenting the classification to the second user for review in a view of a graphical user interface.

Abstract: The present disclosure provides a composite machine-learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine-learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine-learning model is updated based on the descriptive string and the label. The machine-learning model is then trained against the updated set of training data.

Abstract: Financial transaction data representing a current financial transaction is processed and divided into financial transaction data segments of one of more words or symbols. A financial transaction data segment in the current financial transaction is assigned a financial transaction data segment score based on an analysis of historical financial transaction categorizations of historical financial transactions containing the same financial transaction data segment. The calculated financial transaction data segment score is then compared with a defined threshold financial transaction data segment score and, if the calculated financial transaction data segment score is greater than the threshold financial transaction data segment score, the financial transaction containing the financial transaction data segment is categorized, at least temporarily, as being a first financial transaction category financial transaction.