A machine learning platform that analyzes a user's historical purchase activity to identify patterns, anticipates the user's next transaction, matches it to a vendor's current sales offering, and delivers a real-time pairing notification — connecting a buyer's predicted intent with a seller's live inventory before the purchase is made.
Existing recommendation systems analyze purchase history to surface products similar to past purchases. They are fundamentally retrospective: they use what you did to predict more of the same. They don't model when a purchase is likely to occur, don't cross-reference live vendor inventory against predicted intent, and don't generate a matched pairing when a user's anticipated purchase aligns with a specific vendor's current offering.
The platform derives a pattern from the user's historical activity, uses that pattern to identify an anticipated purchase — a specific transaction the user is likely to make based on behavioral signals — then determines whether a vendor currently has a sales offering that matches the anticipated purchase. When a match is found, the platform generates and transmits a pairing notification to the user's computing device.
The key innovation is the temporal alignment: the anticipated purchase is identified at a moment when it's actionable, and the matching is done against current vendor offerings — not historical ones. The notification is generated specifically because both sides of the match exist simultaneously: a buyer who is about to buy, and a seller who currently has what they need.
The platform maintains two live data sources: user activity histories and vendor sales offerings. A pattern extraction layer analyzes each user's history to maintain a current behavioral model. When the model identifies an anticipated purchase, a matching engine scans current vendor offerings for alignment. A notification generator produces the pairing message when a match is confirmed.
The vendor data integration is continuous — vendor offerings are updated in real time (new sales, expiring deals, inventory changes). This keeps the matching engine's vendor side current, ensuring the pairing notification reflects a live offer rather than a historical one. The user's behavioral model updates as new transactions occur, keeping anticipated purchase identification accurate over time.
The pattern extraction layer analyzes historical user activity across multiple dimensions: purchase category, frequency, timing (day of week, season, life events), purchase amount range, and co-occurrence with other activities. The output is not just a category preference — it's a behavioral pattern that includes temporal components, enabling the model to anticipate when a purchase is likely to occur, not just that it will occur eventually.
The 20 claims cover both the pattern derivation process and the anticipated purchase identification — distinguishing between a general preference (user likes coffee) and an anticipated purchase (user typically buys coffee on Tuesday mornings, and today is Tuesday morning). The temporal specificity is what makes the notification timely rather than generic.
How often the user purchases in a given category — daily, weekly, monthly, or event-triggered. Establishes the purchase rhythm per category.
Time of day, day of week, and seasonal patterns associated with each category. A morning coffee buyer and a weekend grocery shopper have different temporal patterns.
Typical transaction amount for each category — enables matching to vendor offerings in the right price tier rather than any offering in the category.
Purchases that typically occur together or in sequence — user who buys gym clothes often buys protein supplements within 2 weeks. Pattern includes cross-category anticipation.
The platform identifies an anticipated purchase when the user's current behavioral context (time, location, recent activity) aligns with the historical pattern in a way that predicts a specific transaction is about to occur. The anticipated purchase has specificity: a category, an approximate amount range, and a temporal window during which it is most likely to occur.
This specificity is what enables effective vendor matching. A generic "user likes electronics" preference produces too many potential matches; an anticipated purchase of "user typically buys a new phone every 18 months, and it's been 17 months since their last phone purchase" narrows the match to current phone offerings in the relevant price tier — making the notification precise enough to be useful.
The vendor matching layer receives the anticipated purchase specification (category, amount range, timing window) and scans current vendor sales offerings for matches. A vendor offering matches when its category aligns with the anticipated purchase category, its price range overlaps with the anticipated amount range, and the offering is currently active (not expired, not future-dated).
Multiple vendors may match a single anticipated purchase. The platform's match selection logic determines which match — or matches — to include in the notification, considering factors like vendor relationship priority, offer quality (discount depth), and geographic relevance. The 20 claims cover single-match and multi-match notification scenarios.
Vendor's offering category must match the anticipated purchase category — both broad (athletic footwear → running shoes) and specific (specific brand/model) matching is supported.
Vendor's offering price must fall within or overlap with the user's anticipated purchase amount range — prevents irrelevant matches outside the user's expected spend tier.
Vendor offering must be currently active — not upcoming or expired. Matching is against live inventory and live sales, not historical or scheduled offers.
Vendor offering should be accessible to the user — online or within their typical purchase geography. Prevents surfacing offers the user cannot act on.
The pairing notification communicates both sides of the match: the anticipated purchase (what the platform believes the user is about to buy, derived from their own behavioral pattern) and the vendor offering (what the specific vendor currently has available that matches it). This framing makes the notification feel like a match rather than a promotion — it's grounded in the user's own behavior, not in the vendor's desire to sell.
The notification is transmitted to the user's computing device at the moment of match identification — when both the anticipated purchase signal and the vendor offering are simultaneously active. Timing is part of the value: the match is surfaced when it's actionable, not days before or after the user's anticipated purchase window.
The ML customer-business pairing platform creates a new category of commercial notification — one grounded in behavioral prediction rather than demographic targeting, timed to the user's anticipated purchase window rather than the vendor's marketing schedule.
No forward citations on record as of June 2026. US12001970B2 was granted in June 2024 — citations are expected to accumulate as the behavioral pairing model attracts attention in the recommendation and fintech sectors.