Recent advances in user modeling make it feasible to conduct open-ended inference over a person's everyday computer use. Despite longstanding visions of systems that deeply understand our actions and the purposes they serve in our lives, existing systems only capture what a person is doing in the moment -- not why they are doing it -- limiting these systems to surface-level support. We introduce striving co-creation, a process for inferring broader life goals from unstructured observations of computer use. Grounded in Activity Theory and Emmons' personal strivings framework, our system progressively constructs a hierarchical representation of a person's activities. Crucially, strivings are difficult to fully resolve from observation alone, as the same action can be driven by many different goals. Our system therefore supports an editing interface that gives people agency over how they are understood by the system, feeding their corrections back into subsequent rounds of striving induction. In a week-long field deployment (N=14), we find that our co-creation process produces strivings that are representative of participants' long-term goals and gives them greater agency than baseline methods.

翻译：近期用户建模的进展使对个体日常计算机使用进行开放式推理成为可能。尽管长久以来存在关于系统能深度理解我们的行为及其生活目的的愿景，现有系统仅能捕捉个体当下行为，却无法理解其行为动机，导致支持局限于表层层面。我们提出"奋斗共创"（striving co-creation）方法，通过非结构化的计算机使用观测推断更广泛的人生目标。本系统基于活动理论与埃蒙斯个人奋斗框架，逐步构建个体活动的层次化表征。关键挑战在于：仅凭观测难以完全解析奋斗动机——相同行为可能源于截然不同的目标。为此，系统提供编辑界面，赋予个体对系统理解方式的自主权，并将其修正反馈至后续奋斗归纳循环中。通过为期一周的实地部署实验（N=14），我们发现该共创过程生成的奋斗目标能有效反映参与者长期目标，且相较于基线方法赋予个体更强的自主性。