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iLearningEngines (iLearningEngines) Gross-Profit-to-Asset % : 372.66% (As of Jun. 2023)


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What is iLearningEngines Gross-Profit-to-Asset %?

Gross-Profit-to-Asset % is calculated as Gross Profit divided by its average Total Assets over a certain period of time. iLearningEngines's annualized Gross Profit for the quarter that ended in Jun. 2023 was $267.2 Mil. iLearningEngines's average Total Assets over the quarter that ended in Jun. 2023 was $71.7 Mil. Therefore, iLearningEngines's annualized Gross-Profit-to-Asset % for the quarter that ended in Jun. 2023 was 372.66%.

iLearningEngines Gross-Profit-to-Asset % Historical Data

The historical data trend for iLearningEngines's Gross-Profit-to-Asset % can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

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iLearningEngines Gross-Profit-to-Asset % Chart

iLearningEngines Annual Data
Trend Dec20 Dec21 Dec22
Gross-Profit-to-Asset %
- 375.81 412.94

iLearningEngines Semi-Annual Data
Dec20 Dec21 Jun22 Dec22 Jun23
Gross-Profit-to-Asset % - - 494.01 360.99 372.66

Competitive Comparison of iLearningEngines's Gross-Profit-to-Asset %

For the Software - Infrastructure subindustry, iLearningEngines's Gross-Profit-to-Asset %, along with its competitors' market caps and Gross-Profit-to-Asset % data, can be viewed below:

* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.

iLearningEngines's Gross-Profit-to-Asset % Distribution in the Software Industry

For the Software industry and Technology sector, iLearningEngines's Gross-Profit-to-Asset % distribution charts can be found below:

* The bar in red indicates where iLearningEngines's Gross-Profit-to-Asset % falls into.


iLearningEngines Gross-Profit-to-Asset % Calculation

iLearningEngines's annualized Gross-Profit-to-Asset % for the fiscal year that ended in Dec. 2022 is calculated as:

Gross-Profit-to-Asset %=Gross Profit (A: Dec. 2022 )/( (Total Assets (A: Dec. 2021 )+Total Assets (A: Dec. 2022 ))/ count )
=215.28/( (40.721+63.545)/ 2 )
=215.28/52.133
=412.94 %

iLearningEngines's annualized Gross-Profit-to-Asset % for the quarter that ended in Jun. 2023 is calculated as:

Gross-Profit-to-Asset %=Gross Profit (Q: Jun. 2023 )/( (Total Assets (Q: Dec. 2022 )+Total Assets (Q: Jun. 2023 ))/ count )
=267.16/( (63.545+79.835)/ 2 )
=267.16/71.69
=372.66 %

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

In the calculation of annual Gross-Profit-to-Asset %, the Gross Profit of the last fiscal year and the average total assets over the fiscal year are used. In calculating the quarterly data, the Gross Profit data used here is two times the semi-annual (Jun. 2023) data. Gross-Profit-to-Asset % is displayed in the 30-year financial page.

iLearningEngines Gross-Profit-to-Asset % Related Terms

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iLearningEngines (iLearningEngines) Business Description

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GURUFOCUS.COM » STOCK LIST » Technology » Software » iLearningEngines Inc (NAS:AILE) » Definitions » Gross-Profit-to-Asset %
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Traded in Other Exchanges
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Address
6701 Democracy Boulevard, Suite 300, Bethesda, MD, USA, 20817
Website
https://www.ilearningengines.com
iLearningEngines Inc is an AI and automation platform that empowers its customers to productize their institutional knowledge by transforming it into actionable intellectual property that enhances outcomes for employees, customers and other stakeholders. Its platform enables enterprises to build intelligent Knowledge Clouds that incorporate large volumes of structured and unstructured information across disparate internal and external systems and to automate organizational processes that leverage these Knowledge Clouds to improve performance. The company combines its offerings with vertically focused capabilities and data models to operationalize AI and automation to effectively and efficiently address critical challenges facing its customers.