About this Agent

The Trademark Renewal Reminder Agent is an AI digital worker designed to streamline and automate the process of managing trademark renewals. This intelligent agent tracks trademark portfolios and sends timely reminders for upcoming renewal deadlines, taking into account jurisdiction-specific renewal timelines. By ensuring that trademark renewals are never missed, this agent helps protect valuable intellectual property rights and reduces the administrative burden on legal departments.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Trademark Renewal Reminder Agent:

Trademark reminders input

Trademark Name	Jurisdiction	Registration Date	Renewal Interval	Contact Email	Next Renewal Date	Reminder Date	Reminder Status
TechPro	USA	2014-06-15	10	legal@techpro.com	2024-06-12	2023-12-15	Sent
GreenWave	EU	2016-09-30	10	info@greenwave.eu	2026-09-28	2026-04-01	Pending
EcoFuel	India	2015-12-01	7	support@ecofuel.in	2022-11-29	2022-06-02	Sent
InnoVision	Canada	2017-04-20	15	contact@innovision.ca	2032-04-16	2031-10-19	Pending

Deliverable Example

Sample output delivered by the Trademark Renewal Reminder Agent:

Trademark Renewal Reminder

Dear [TechPro],

We hope this message finds you well. This is a friendly reminder that the renewal deadline for your trademark, [Trademark Name], registered in [Jurisdiction], is approaching.

- Next Renewal Date: 2024-06-12

- Jurisdiction: [USA]

To ensure the continued protection of your trademark rights, please complete the renewal process before the deadline. If you have any questions or require assistance, feel free to reach out.

Thank you for your prompt attention to this matter.

Sincerely,

Trademark Management Team

ZBrain credits and their usage

A credit is a unit of usage on ZBrain. Credits are consumed whenever you perform actions such as embedding documents or querying an app. Here’s how credits are utilized:

Document Embedding:

When you upload a document, it is processed and embedded to create a searchable vector representation. This step consumes credits based on the size of the document and the embedding model used.

Input Processing:

When you ask a question, the relevant context is retrieved from the embeddings and processed by the Large Language Model (LLM). This step incurs a credit cost based on the model and the number of input tokens.

Generating Output

The LLM then generates a response based on the processed input, consuming additional credits based on the number of output tokens.

The cost in credits for various models and processes is detailed below:

Models and Credit Consumption:

Model	Input Cost	Output Cost
GPT-4o	2,500 credits / 1M tokens	7,500 credits / 1M tokens
GPT-4	15,000 credits / 1M tokens	30,000 credits / 1M tokens
GPT-4-32k	30,000 credits / 1M tokens	60,000 credits / 1M tokens
GPT-3.5 Turbo	250 credits / 1M tokens	750 credits / 1M tokens

Embedding Models:

Model	Input Cost
text-embedding-3-small	20 credits / 1M tokens
text-embedding-3-large	130 credits / 1M tokens
ada v2	100 credits / 1M tokens

When you create a knowledge base or query an app, credits are deducted based on the embedding, input, and output token usage. This ensures you only pay for what you use while leveraging the full power of advanced AI models.

Trademark Renewal Reminder Agent

About this Agent

Input Data Set

Deliverable Example

Trademark Renewal Reminder

Company

Product

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