38 results about "Demographic Accounting" patented technology

This disclosure relates to systems and techniques that can be implemented by content platforms to optimize (a) demographic-based digital component distribution used to categorize each user into a particular demographic so as to appropriately target that user for purposes of maximizing the efficacy of digital components shown to that user, and (b) demographic reporting used to report to digital component providers the effectiveness of the digital component.

The invention relates to the technical field of power systems and artificial intelligence, in particular to a virtual power plant personalized incentive method based on psychological account representation learning. Comprising the following steps: S1, acquiring and processing user feature data which comprises a demographic feature, a historical behavior feature and a context feature; s2, processing the user characteristic data by using a self-calibration high-order transform psychological account representation learning mechanism to obtain user representation including four psychological accounts of economy, society, environment and convenience; s3, capturing mutual influence among accounts through a psychological account cross enhancement mechanism; s4, calculating dynamic weights of the four psychological accounts of the user; s5, generating and distributing personalized incentives for the four psychological accounts; and S6, predicting a user response probability and optimizing excitation configuration.

A system and method for evaluating narrative, entertainment, or message-based content using a multi-axis diagnostic framework. The system processes a media input—such as a screenplay, advertisement, website, short-form video, or branded communication—through modular scoring layers including artistic merit, commercial potential, demographic alignment, genre fidelity, and ideological sensitivity. Optional components include symbolic tagging, AI-origin detection, and cultural volatility indices. The results are compiled into a structured Media Scorecard comprising numerical scores, qualitative diagnostics, quadrant resonance maps, and role-specific summaries. The system may be deployed as desktop software, cloud platform, or API-integrated service, and optionally incorporates machine learning to refine scoring and forecast performance. Outputs are used to inform development, marketing, investment, and acquisition decisions across entertainment and media ecosystems.

The present disclosure provides a system for quantifying drivers of growth of niche categories. The system includes a data collection module configured to receive consumer data collected using surveys, an analysis module configured to identify consumer segments using factor analysis of the received consumer data, a market size estimation module configured to estimate market size for each identified consumer segment based on current consumption, a demographic categorization module configured to create sub-groups based on demographics, a growth potential determination module configured to determine growth potential by regressing consumption behavior on the identified consumer segments and consumer demographics using multinomial regression, and a market size prediction module configured to predict potential market size based on model weights and predicted probability of switching between consumer classes.

Disclosed herein are system, apparatus, article of manufacture, method and / or computer program product embodiments, and / or combinations and sub-combinations thereof, for classifying media accounts of a media system into different demographic buckets based on media content data associated with the media accounts, where media content associated with a media account can be accessed by a plurality of members in a household of the media account. A demographic classifier can receive a plurality of media accounts, a plurality of sets of probability scores corresponding to the plurality of media accounts, and a plurality of demographic buckets. The demographic classifier can further estimate, based on public available information, an approximate number of media accounts of the plurality of media accounts belonging to a specific demographic bucket. The demographic classifier can further select the approximate number of media accounts to be included in a set of media accounts assigned to the specific demographic bucket.

The invention relates to an infant asthma three-branch classification auxiliary judgment method and a medium, and relates to the technical field of data processing. The method comprises the following steps: collecting and preprocessing tidal respiration data and demographic data of infants, and performing heterogeneous feature extraction and grouping log-likelihood ratio acquisition on the preprocessed tidal respiration data and demographic data to obtain a log-likelihood ratio corresponding to each group; and performing hierarchical Bayesian fusion on each log-likelihood ratio to obtain a fusion result, and performing asthma risk auxiliary judgment of three-branch classification based on the fusion result. Compared with the prior art, the method can assist in judging the infant asthma more objectively and accurately based on the tidal respiration data.

Included in the present disclosure is a method, including obtaining data associated with a metric of a user. In some embodiments, the method includes comparing the data to a benchmark associated with the metric, thereby determining a benchmark score. According to some embodiments, the method includes applying artificial intelligence (AI) to determine a course of action based on the benchmark score and demographic information. The course of action may include i) calibrating the benchmark, ii) determining an activity to adjust future data associated with the metric, or iii) both, so as to modify the benchmark score.

The subject technology predicts consumer sentiment based on demographics and other static features of the consumer as well as dynamic features generated based on engagement of the consumer with previously presented targeted content. The sentiment predictions are used to recommend and generate new targeted content that is published to the consumer.

Apparatus and associated methods relate to a group specific language application (GSLA). In an illustrative example, a GSLA may, for example, include a slang dictionary configured to store informal expressions (IBs) with metadata such as origin, usage context, and demographic relevance. For example, the GSLA may include a viral threshold engine that evaluates engagement metrics — likes, shares, usage frequency — to determine whether an IE qualifies for inclusion. The GSLA may further include a pronunciation engine that generates voice renderings using selectable voice profiles, and a promotion module that distributes validated IBs across social media platforms based on user selection and demographic targeting. Various embodiments may advantageously enable crowd-validated slang creation and dissemination tailored to particular cultural or generational groups.

A financial planning tool for personalized optimization of retirement income, supported by artificial intelligence, comprising a data acquisition interface configured to collect user-specific and environmental financial data; a knowledge modeling unit designed to identify relationships between financial, demographic, and macroeconomic variables; a predictive simulation module trained to generate probabilistic retirement income trajectories; and a decision optimization unit based on reinforcement learning logic that calculates individual investment strategies and withdrawal policies that maximize retirement income adequacy while minimizing financial risk.

A system and method for determining and managing reputation of an entity or industry includes determining a sample size of the population that provides at least a pre-defined percent confidence interval, and which has the predefined level of familiarity (unique group); determining a measure of likelihood that the targeted population will perform a positive action on behalf of an entity (behavioral connection score), wherein determining the measure of the likelihood that the targeted population will perform a positive action on behalf of an entity comprises: receiving survey ratings from behavioral connection survey questions where each survey rating is provided by a party within the unique group; weighting the received survey ratings to accommodate for at least one of the group consisting of cultural bias and missed demographic quotas; and aggregating the ratings within each individual question to provide a single aggregated behavioral connection score for each behavioral connection survey question.

Methods and apparatus to apply household-level weights to audience measurement data at a household-member level are disclosed. An example method to determine demographics of populations to measure media audiences of populations includes determining demographics for members of a first household of a sub-population. First demographics of a first member of the first household are different than second demographics of a second member of the first household. The example method includes calculating a first household-level weight for the first household based on a demographics distribution of the sub-population and aggregate demographics of a population. The example method includes applying the first household-level weight to the first demographics of the first member, applying the first household-level weight to the second demographics of the second member, and estimating a demographics distribution of the population to measure a media audience of the population based on the weighted first demographics and the weighted second demographics.

Systems and methods for obtaining anonymous demographics from gestures, where the method includes capturing information on one or more gestures performed by a user on a touchscreen of a device, along with motion data of the device. The gesture information and motion data in the form of deltas or deviations is provided to a machine learning (ML) model trained to analyze gestures and motion data and output predicted demographics. The predicted demographics from the ML model are then provided to an advertising provider, which send the device one or more ads targeted to the user based on the demographics. The device then displays the ads. Other embodiments are discussed herein.

A computing system for generating handicap-adjusted health scores processes multi-source health metric data collected from heterogeneous devices and activity modalities. The system normalizes and calibrates unstructured health data using schema-conformant transformation logic and device-specific correction parameters. User-specific baseline values are computed over configurable rolling windows and compared to cohort-level reference parameters derived from demographic and device-based population segmentation. Deviation values are corrected using demographic and device normalization models to generate structured adjustment vectors. The system applies context-aware scoring logic to compute normalized health scores that reflect individual performance patterns relative to statistically similar user cohorts. Scoring outputs are configured for competitive presentation via a user interface and may support leaderboard ranking, team-based scoring, or context-specific evaluations across diverse activity types.

A computer-implemented method is disclosed for detecting advertisement fraud in real-time using a multi-modal analysis framework. The method involves receiving user data and user action data from a media device, including demographic information and sensor-based interaction metrics such as accelerometer, gyroscope, and touch sensor data. These inputs are processed by an advertisement fraud detection system equipped with a processor and hardware-run algorithms, including a multi-modal machine learning model that distinguishes human from non-human interactions. Fraudulent actions are identified by detecting deviations from predefined behavioral baselines enriched with campaign-level intelligence. The system further analyzes historical ad performance to identify downtime periods characterized by low human activity and high fraud probability. During these periods, fake advertisements are adaptively inserted based on contextual mismatches to confirm fraudulent behavior. Upon detection, notifications are dispatched to advertisers via multiple communication mediums.

The present disclosure relates to techniques for generating a ranked list of a set of subjects by predicting their potential health benefit from an intervention to prioritize subjects that may be at a risk of a negative outcome and likely to benefit from a proposed intervention. Additionally, the ranking may further account for potential cost-savings associated with early intervention to avoid acute-care utilization by applying a cost-modeling technique. The disclosed techniques may include analyzing subject-specific data, including demographic, clinical, and historical information, to compute a total net-benefit score by combining a predicted benefit probability with cost and revenue metrics. The benefit probability may be calculated using causal inference models to estimate a potential improvement in health outcomes from the proposed intervention or treatment. The disclosed techniques may further facilitate personalized subject care by dynamically updating rankings based on real-time data, enhancing clinical decision-making, and optimizing resource allocation.

According to the present embodiments, provided are a digital health literacy-based content service device and method, wherein the device receives an input of a demographic factor, a health-related factor, and a digital information-related factor to predict a digital health literacy level, recommends user-customized health information on the basis of a prediction result, performs binary classification of a literacy level of a user by using a classification model and a recommendation model, and provides personalized health information on the basis of metadata and interaction data.

Methods and systems to train and use an ensemble of artificial intelligence / machine learning (AI / ML) models to extract information from social determinants of health (SDoH), including training each of multiple dimensionality reduction models to reduce dimensionality of socio-demographic variables associated with a respective one of multiple SDoH categories, training a predictive model to predict a patient behavior for a geographic region (e.g., risk of non-adherence to treatment regimens) based on dimensionally reduced SDoH (alone or in combination with selected socio-demographic variables and / or other data), training a patient classification model to classify patients based on prescription transactions, and / or training a regional similarity model to determine a measure of similarity between geographic regions based on SDoH and / or dimensionally reduced SDoH. Also disclosed are techniques to visually represent outputs of the models on a user-interactive display.

Systems and methods for obtaining anonymous demographics from gestures, where the method includes capturing information on one or more gestures performed by a user on a touchscreen of a device, along with motion data of the device. The gesture information and motion data in the form of deltas or deviations is provided to a machine learning (ML) model trained to analyze gestures and motion data and output predicted demographics. The predicted demographics from the ML model are then provided to an advertising provider, which send the device one or more ads targeted to the user based on the demographics. The device then displays the ads. Other embodiments are discussed herein.

Systems, devices, and methods are described herein for searching for entities based on trust score and geography. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, or businesses / corporations and may take into account a variety of factors, including verification data, network connectivity, publicly available information, ratings data, group / demographic information, location data, and transactions to be performed, among others. A user may search for entities within a certain geographic location that meet a desired trust score. The results of the search may be generated for display on a user device, for example, by generating a map that shows the current location of the user device and the identified entities. The search may be filtered by entering an anticipated activity or transaction to be performed or desired by the user, and thereby returning entities that are associated with the activity or transaction.

The present disclosure relates to a demographic estimation system, method, and non-transitory computer-readable storage medium. Example methods, devices, systems, and articles of manufacture (e.g., physical storage media) are disclosed that implement neural network processing of set-top box return path data to estimate household demographics. The example demographic estimation system disclosed herein includes a feature generator that generates features based on return path data reported from a set-top box associated with a return path data household. The disclosed example demographic estimation system also includes a neural network that processes the features generated based on the return path data to predict demographic classification probabilities for the return path data household, the neural network being trained based on panel data reported from a meter that monitors media devices associated with panelist households. The disclosed example demographic estimation system also includes a demographic assignment engine that assigns one or more demographic categories to the respective return path data household in the return path data household based on the predicted demographic classification probabilities.

Systems and methods may provide for generating, maintaining, and using an inventory for insurance purposes. An optimized inventory analysis server may use a demographic classification for a given user along with data related to items owned by other users to determine one or more items for recommendation to the given user. Details associated with existing insurance coverage levels may also be used to recommend new insurance products to the user. In addition, the optimized inventory analysis server may determine a depreciated value of each item within an inventory. These values may be used to assess loss and settle an insurance claim for damage to one or more of the items in the inventory. Finally, the optimized inventory analysis server may recommend incentives on products / services sold by third party entities and may further recommend incentives on insurance products for insuring items purchased by a given user.

The embodiment of the invention discloses a multi-mode depression sentiment analysis system based on sensitive attribute tag constraints. The system comprises a feature extraction module which is configured to obtain emotion features in each mode; the feature preprocessing module is configured to output each modal feature by a multi-modal data processing model; the feature alignment module is configured to construct feature pairs with the text modality as the center based on the modal features and calculate the comparison loss of the feature pairs so as to determine the target modal features; the comparison decoupling module is configured to sequentially encode and decode each target modal feature to obtain a sensitive attribute irrelevant feature; and the representation fusion module is configured to perform cross-modal attention fusion on the sensitive attribute irrelevant features to obtain fusion features and determine emotion information according to the fusion features. In the embodiment of the invention, individualized and precise emotion recognition and depression emotion analysis can be carried out according to the social media data of the user under the condition of eliminating generalization influence of demographic deviation.

Systems and methods create new viewership estimates to drive linear ad schedule optimizations. The systems use machine learning techniques to predict granular-level television viewership metrics for any consumer segment measurable at a national level. The systems provide capabilities beyond merely estimating and forecasting television viewership for age- and gender-based demographic segments. The systems accurately estimate viewership for any consumer segment, including behavioral, demographic, and other segmentation techniques and provide reliable viewership predictions. The systems model viewership by training an ensemble of machine learning models using historical consumer segments data and TV viewership data. The models work in concert to create viewership predictions, which are ingested, transformed, and processed and then used for determining and pricing advertising sales based on predicted viewership for advanced segments, for pacing forecasts and pre-actuals in a campaign stewardship program, and for further model training in an optimization engine.

A method may include obtaining demographic data from multiple households in a cleanroom. The method may include obtaining viewership data associated with displayed content from one or more data sources in the cleanroom. The viewership data may be generated by at least a portion of the multiple households. The method may include generating demographic scores using the viewership data and the demographic data with respect to viewers in the multiple households. The method may include estimating a first count of the viewers and a second count of the viewers of the displayed content. The method may include determining an impression score using the demographic scores and the first count of the viewers. The method may include determining a reach score using the demographic scores and the second count of the viewers. The method may include providing the impression score and the reach score in the cleanroom to a requesting entity.

Methods, apparatus, systems and articles of manufacture to determine demographic classifications for census level impression counts and unique audience sizes are disclosed. In an example, the apparatus includes media tag format circuitry to generate a reformatted media tag corresponding to an impression request. The example apparatus also includes model execution circuitry to execute a machine learning model based on the reformatted media tag to generate outputs, the outputs including at least a value representative of a probability of an occurrence of a demographic classification. The example apparatus further includes audience counting circuitry to assign an identification of ones of audience members in a group to the demographic classification based at least on the outputs.

Systems, methods, and computer program products are provided for predicting consumer behavior based on demographics and new product features using machine learning models. An example method includes receiving spending behavior data, payment device feature data, and transaction data associated with existing payment devices and proposed spending behavior data and proposed payment device feature data associated with a proposed payment device. A machine learning model (MLM) is trained to predict a transaction metric associated with the transaction data based on the spending behavior data and the payment device feature data for the existing payment devices. The trained MLM predicts a predicted transaction metric based on the proposed spending behavior data and the proposed payment device feature data associated with the proposed payment device. A communication is transmitted based on the predicted transaction metric.

The present invention relates to the field of marketing technology, specifically an artificial intelligence-based advertising method and apparatus for equipment sales, comprising the following steps: collecting social media behavior data and purchase records of consumers in the target market, obtaining standardized consumer data through data cleaning and formatting; and performing user portrait analysis based on the standardized consumer data. In the present invention, the collection of user social media behavior data and purchase records, combined with data cleaning and formatting, makes the construction of user portraits more dynamic and accurate. Consumer segmentation not only relies on basic demographic information, but also introduces purchase frequency, interest tags, and historical interactive behaviors to form a more hierarchical user classification. The adjustment of advertising content is not limited to static text optimization, but also dynamically adjusts visual elements, language style, and information presentation methods based on the results of sentiment analysis to make the content more in line with the preferences of different consumer groups.

Methods, apparatus, systems and articles of manufacture to determine demographic classifications for census level impression counts and unique audience sizes are disclosed. In an example, the apparatus includes media tag format circuitry to generate a reformatted media tag corresponding to an impression request. The example apparatus also includes model execution circuitry to execute a machine learning model based on the reformatted media tag to generate outputs, the outputs including at least a value representative of a probability of an occurrence of a demographic classification. The example apparatus further includes audience counting circuitry to assign an identification of ones of audience members in a group to the demographic classification based at least on the outputs.