GPT-5: Family Operations Assistant

— Automate logistics, enrich conversation.

One‑line summary: The AI doesn’t speak on behalf of your family. It creates time and prompts so you can talk—every morning.

TL;DR

• The two biggest reasons family conversation shrinks are logistical load and last‑minute scramble.
• The fix is to design time: deliver a structured Morning Card at a fixed hour (e.g., 5:00 AM) in a format that’s easy to talk from.
• Core stack: Scheduler → Fetchers → Normalizer → Composer → Notifier.
• Principles: the AI does not speak for you, does not decide, only creates conversation triggers.
• This article includes MVP steps, templates, and evaluation metrics.

Chapter 1: Background: why conversation fades

• Family information is scattered across school emails, family chats, shopping notes, weather, and inventory. When we only remember at the last minute, tension and scolding rise.
• Chat‑first AI helps the person who asks—but if no one asks, nothing happens. That’s the limit of reactive help.
• Shift to a time‑first approach: automatically issue one structured Morning Card. Preparation moves earlier, and conversation returns to a calmer space.

Chapter 2: The solution: Morning Card + Conversation Triggers

Include these standard blocks in the Morning Card (e.g., every day at 5:00 AM):

1. Whole‑family schedule (who / where / when / items)
2. Weather × chore suitability (laundry, bedding, outdoor drying)
3. Dinner options (×3) + missing ingredients
4. Message digest (deadline / action / owner)
5. Conversation Triggers (a 30‑second question, a mini‑poll, and a “praise cue” draft)

Conversation Triggers don’t let the AI “speak.” They ignite family talk.

• 30‑sec question: “What’s the one thing you’re most excited about today?”
• Mini‑poll: pick dinner from 3 options → share results in the evening
• Praise cue draft: “Started revising homework proactively (child)” → makes it easy for parents to verbalize appreciation

Chapter 3: A day in the life (scenario)

05:00 Morning Card delivery

• schedule / weather × chores / dinner options / message digest / conversation triggers

07:30 Short pre‑departure check

• “All items packed?” “Today’s 30‑sec question”

17:00 Catch‑up card

• re‑list high‑priority deadlines, dinner poll results, prep hints for tomorrow morning

Night (optional)

• 10‑minute family meeting script (3 topics, 1 praise, 1 item for next week)

Tone examples (no commands; suggestions only)
• “15 minutes until departure. If helpful, I can assist with the water bottle.”
• “Now is a good window for laundry to dry quickly. Shall we start a load?”

Chapter 4: Design principles (Conversation‑First)

1. Don’t speak for people. No guessing or voicing feelings.
2. No summaries without consent. Opt‑in only for private chats/calls.
3. Don’t decide. The AI proposes; people decide.
4. Short and talkable. One‑line topics + choices to spark dialogue.
5. Positive bias. Avoid scolding triggers; emphasize preparation, praise, and preview.

Chapter 5: Technical architecture (5 layers)

5.1 Layering

Scheduler

• RRULE (e.g., daily 05:00 / weekly Sat 09:00) for fixed runs.

Fetchers

• Calendar / Weather / Mail (school, municipality) / Inventory via least privilege.

Normalizer

• Normalize to YAML/JSON; compute priority scores from deadline × importance × people affected.

Composer

• Template fill. Conversation Triggers generated via rules (“facts → compression → choices”), with minimal LLM assistance.

Notifier

• Per‑person routing (email, messaging app, print) + mini‑poll links.

5.2 Data contract (example)

card:
  date: 2025-08-13
  schedules:
    - who: Child
      when: "08:15-15:30"
      where: School
      items: ["Art smock", "Indoor shoes"]
  weather:
    laundry: "◎"
    note: "Est. dry time ~2h; morning outdoor drying recommended"
  dinner:
    options:
      - "Ginger pork" 
      - "Salmon meunière" 
      - "Taco rice"
    shortages: ["Ginger", "Butter"]
    poll_url: "https://family.link/poll/dinner-YYYYMMDD"
  notices:
    - due: "2025-08-20"
      title: "Art project submission"
      action: "Mount on A4 board; add name"
      owner: "Parent"
      score: 0.86
  conversation_triggers:
    question: "What’s the one thing you’re most excited about today?"
    praise_hint: "Started revising homework proactively (yesterday)"

5.3 Priority scoring (example)

• score = w1×deadline_urgency + w2×importance + w3×people_affected − w4×uncertainty
• Use thresholds to order display and prevent misses on critical items.

Chapter 6: Privacy & safety

• Least‑privilege scopes for APIs with time‑limited tokens.
• Audit trail across source → transform → delivery.
• Short data retention unless the family explicitly chooses to save.
• Misinformation control: when data is missing, show last known value + warning; treat critical items rules‑first.
• Children‑aware tone: no commands or shaming; encourage autonomy via praise and choices.

Chapter 7: Evaluation (KPIs)

Conversation—quantity & quality

• Morning positive utterances (“thanks,” “that helps,” etc.)
• Reduced scolding rate (count of “Hurry up!”)
• Minutes of small talk before dinner / poll participation rate

Operational efficiency

• Count of forgotten items / prep lead time / “search time” reduction
• Delivery latency / read rates / recall (missed items) / precision (false picks)

Chapter 8: Implementation guide (MVP → V1 → V2)

MVP (weekend build)

1. Ship one Morning Card only (schedule / weather × chores / message digest / conversation triggers).
2. Normalize the digest into deadline / action / owner.
3. Keep one delivery channel (don’t multiply pathways).

V1 (next month)

• Add the 17:00 catch‑up card, dinner poll, and semi‑automated inventory checks.

V2 (extensions)

• Disaster templates (typhoon/earthquake), PTA/neighborhood templates, caregiving templates.
• Accessibility: optimize for text‑to‑speech (numbers first, short lines, bulleting).

Chapter 9: Complementing chat‑first models

• “Answer‑on‑ask” chat models and the “time‑based” Morning Card serve different roles.
• In family ops, the core value is preparation over reaction.
• The Morning Card is the map; chat is the guide. Together, the experience completes.

Chapter 10: FAQ

Q. Won’t the AI replace family conversations?

A. No. The AI only structures facts and offers prompts. Decisions and feelings stay with people.

Q. Isn’t 5:00 AM too early?

A. Adjust with RRULE to match your routine (e.g., 06:30).

Q. What data do you access?

A. Only least‑necessary, consented sources. You can review, change, or stop access at any time.

Conclusion

• The goal is to increase family conversation and strengthen bonds.
• The method is automating logistics and delivering conversation triggers in the morning.
• The AI doesn’t talk instead of you; it designs so you can talk.

This proposal uses information design to give families more time to speak—human to human.

card:
  date: 2025-08-13
  schedules:
    - who: 子
      when: "08:15-15:30"
      where: 学校
      items: ["図工スモック", "上履き"]
  weather:
    laundry: "◎"
    note: "乾燥見込み2h／午前外干し推奨"
  dinner:
    options:
      - "豚の生姜焼き" 
      - "鮭のムニエル" 
      - "タコライス"
    shortages: ["生姜", "バター"]
    poll_url: "https://family.link/poll/dinner-YYYYMMDD"
  notices:
    - due: "2025-08-20"
      title: "図工作品提出"
      action: "A4台紙に貼付、記名"
      owner: "親"
      score: 0.86
  conversation_triggers:
    question: "今日いちばん楽しみなことは？（ひとつ）"
    praise_hint: "宿題の見直しを自分から始めた（昨日）"