4 results about "Demographic Accounting" patented technology

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.

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.

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.

The embodiment of the specification discloses a multi-modal depression sentiment analysis system based on sensitive attribute label constraint. The system comprises a feature extraction module configured to obtain sentiment features under each modality; a feature preprocessing module configured to output each modality feature from a multi-modal data processing model; a feature alignment module configured to construct a text modality-centered feature pair based on each modality feature, and calculate the contrastive loss of each feature pair to determine each target modality feature; a contrastive decoupling module configured to sequentially encode and decode each target modality feature to obtain sensitive attribute-independent features; a representation fusion module configured to perform cross-modal attention fusion on the sensitive attribute-independent features to obtain fusion features, and determine sentiment information according to the fusion features. In this embodiment, individualized and accurate emotion recognition and depression sentiment analysis can be performed according to the user's social media data while eliminating the generalization influence of demographic bias.