Azure provides Speech Services that let developers add advanced speech features to achieve complex functionality, including Speech-to-Text. With Azure Speech Services, we can convert speech to text.

 In this blog post, let us check how the conversion from speech to text using Azure Speech Service in a Power Automate flow is done. The following steps can help achieve this requirement.

Recently, we got a requirement to transcribe audio attachments when an email is received with attachments in the shared mailbox and send back transcribed text.

Step 1: Create an ‘Automated’ flow

Create an ‘Automated’ flow using the trigger “When a new email arrives in a shared mailbox (V2)”

Step 2: Initialize the String variable

Add the ‘Initialize variable’ action to store the transcribed text of the audio attachment for further use.

Step 3: Initialize the Object variable

Add the ‘Initialize variable’ action to store the content of the audio attachment for further use.

Step 4: Get Attachments

Now, proceed to get the attached attachments of the received email. Utilize an ‘Apply to Each’ action to iterate through each attachment of an email and within that add a ‘Get Attachments (V2)’ action to get the current attachment of an email.

Expressions from the above image:

• triggerOutputs()?[‘body/attachments’]
• triggerOutputs()?[‘body/id’]
• items(‘Apply_to_each_Attachment ‘)?[ ‘id’]

Step 5: Validate the content type of the audio attachment

Add the ‘Condition’ action to validate that the content type of the audio attachment must be in WAV format. Inside this set the value of the ‘Body Content’ variable using the content type and content of the current attachment audio file.

Expressions from the above image:

• outputs(‘Get_Attachment_(V2)’)?[‘body/contentType’]
• outputs(‘Get_Attachment_(V2)’)?[‘body/contentBytes’]

Step 6: Convert Speech-To-Text

Add ‘HTTP’ action and do the following configurations,

• Method: Post
• URL: https://eastus.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/ v1?language=en-US

Note: Make sure to use your region code at the highlighted in the URL.

• Headers:
  • Accept: application/json;text/xml
  • Content-Type: audio/wav; codecs=audio/pcm; samplerate=16000;
  • Ocp-Apim-Subscription-Key: Azure API Key
  • Host: eastus.stt.speech.microsoft.com
  • Transfer-Encoding: Chunked
  • Expect: 100-continue
  • Body: ‘Body Content’ variable

Step 7: Get the text of the audio attachment

Add a ‘Parse JSON’ action to get the text of the audio attachment.

Schema:

 { "type": "object", "properties": { "type": { "type": "string" }, "properties": { "type": "object", "properties": { "RecognitionStatus": { "type": "object", "properties": { "type": { "type": "string" } } }, "Offset": { "type": "object", "properties": { "type": { "type": "string" } } }, "Duration": { "type": "object", "properties": { "type": { "type": "string" } } }, "DisplayText": { "type": "object", "properties": { "type": { "type": "string" } } } } } } } 

Expressions from the above image:

• body(‘HTTP_Azure_SpeechToText’)

Step 8: Append audio text in the variable

Add the ‘Append to string variable’ step and append the audio file text along with the file name.

We can use the above output for further use.

Conclusion:

By following these steps, you can effectively convert speech to text using Azure Speech Service in a Power Automate flow.

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In Dynamics, managing access to sensitive data is handled through security measures such as role-based permissions. Security roles and access management play a significant role in ensuring that the right individuals have access to the appropriate information. To grant access to particular records, users with Share permissions typically navigate to the respective record and click on the “Share” button as shown below.

A common scenario arises when a sales manager needs to grant specific record access to a salesperson who doesn’t have the necessary permissions. The changes made by the sales manager may leave the salesperson unaware of their newly granted access, potentially leading to a loss in productivity. To resolve this issue, we can use access triggers introduced in Power Automate. These triggers activate whenever there is a change in user access for a particular record.

To improve this process, we can automate it by sending access notifications through email messages. By setting up a Power Automate flow, we can establish a notification system that triggers whenever access is granted, modified, or revoked for a particular user regarding specific records.

In this blog, we will utilize the example of a sales manager granting/modifying/revoking the permission of a salesperson in a lead record. To achieve this, we will need to create three different Power Automate flows, each of which is explained below.

1. Notify the user when Access is Granted for Lead records:

The GrantAccess action is triggered whenever full access, including Read, Write, Append, Append To, Delete, Share, and Assign permissions, is granted to the user for a specific record.

Let’s see how we can create a Power Automate flow for that:

Step 1: Create a Power Automate flow and select the trigger point as “When an action is performed”. Select the Catalog and Category as All, and Table as Lead. Select the Action name as GrantAccess.

Initialize the AccessMask and leadID Variable by clicking on add action and searching for initialize variable. Set the value for each variable as shown below:

• AccessMask = triggerOutputs()?[‘body/InputParameters/PrincipalAccess/AccessMask’]
• LeadID = triggerOutputs()?[‘body/InputParameters/Target/Id’]

Step 2: Validate the above variable values and retrieve Lead, Access Modified User, and Target User Information by using the Get a Row By ID action in Microsoft Dataverse connector as shown below:

• Lead = variables(‘leadID’)
• Access Modified User = triggerOutputs()?[‘body/InputParameters/PrincipalAccess/Principal/Id’]
• Target User Information = triggerOutputs()?[‘body/RunAsSystemUserId’]

Step 3: As we have retrieved all the necessary details, we can now send an email to the user informing them that they have full access to the record, as shown below. To send the email, we are using the Outlook connector. Essentially press on “Include an activity” and look for “Send an e-mail,” as illustrated underneath.

Now, enter the To as the primary email address of the target user and the Subject and Body as shown below.

2. Notify the user when Access is Modified for Lead records:

This flow triggers whenever specific access i.e. Read, Write, etc. is granted or revoked for a particular user under specific records.

When we look at the JSON output of the trigger action from the power automate, we can see the AccessMask in JSON data, which is the addition of all access values which has been given to the user as shown below.

Following are the access values for all permissions:

• Read: 1
• Write: 2
• Append: 4
• Append To: 16
• Delete: 65536
• Share: 262144
• Assign: 524288

Let’s say if we give a user Read and Write access, the AccessMask value will be 3. To check if the user has any specific permissions, we can use a basic calculation. First, divide the AccessMask value by the permission value (either Read or Write shown above). At that point, apply the modulo operation by 2 (which is nothing but the leftover portion returned after isolating by 2). If the remainder is 1 or greater than 1, the permission is granted. If the remainder is 0, the permission is not granted.

Imagine you have a user with Read and Write access, you would get the Access Mask as 3. Here’s how you can identify if specific permissions (Read or Write) are granted to the user.

1. Access Mask Value: 3 (this means both Read and Write permissions are granted)
2. Permission Values:
   • Read: 1
   • Write: 2

To check if each permission is granted, follow these steps:

Step-by-Step Calculation

For Read Permission:

1. Divide the Access Mask Value by the Read Permission Value:

• 3÷1=3

2. Apply the Modulo Operation (remainder when divided by 2):

• 3 modulo 2=1

3. Interpret the Result:

• As the result is 1, the Read permission will be granted.

For Write Permission:

1. Divide the Access Mask Value by the Write Permission Value:

• 3÷2=1.5

2. Apply the Modulo Operation (remainder when divided by 2):

• 5 modulo 2=1.5

3. Interpret the Result:

• Since the result is greater than 1, the Write permission is granted.

So, in this case, both Read and Write permissions are granted to the user based on the calculated values. This process helps us determine whether specific access permissions are granted or not for a given Access Mask value. Let’s implement this in the Power Automate and see how we can check if the permission is granted or not.

Step 1: The triggered step and variable declaration will be the same as we have used in Step 1 of GrantAccess flow; the only change is that the action name will be ModifyAccess.

Step 2: Initialize boolean variables for all access permissions and set the respective expressions as shown below:

• Read Permission Check Equation:

IsReadPermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 1), 2), 1), true, false)

• Write Permission Check Equation:

IsWritePermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 2), 2), 1), true, false)

• Append Permission Check Equation:

IsAppendPermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 4), 2), 1), true, false)

• Append To Permission Check Equation:

IsAppendToPermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 16), 2), 1), true, false)

• Delete Permission Check Equation:

IsDeletePermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 65536), 2), 1), true, false)

• Share Permission Check Equation:

IsSharePermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 262144), 2), 1), true, false)

• Assign Permission Check Equation:

IsAssignPermissionGranted = if(equals(mod(div(variables(‘AccessMask’), 524288), 2), 1), true, false)

Step 3: Now we have all permission values as Boolean. We can check if the value is true, and then the access is granted; otherwise, the access is revoked as shown below:

• Read: @{if(variables(‘IsReadPermissionGranted’), ‘Granted’,’ Revoked’)}
• Write: @{if(variables(‘IsWritePermissionGranted’), ‘Granted’,’ Revoked’)}
• Append: @{if(variables(‘IsAppendPermissionGranted’), ‘Granted’,’ Revoked’)}
• Append To: @{if(variables(‘IsAppendToPermissionGranted’), ‘Granted’,’ Revoked’)}
• Delete: @{if(variables(‘IsDeletePermissionGranted’), ‘Granted’,’ Revoked’)}
• Share: @{if(variables(‘IsSharePermissionGranted’), ‘Granted’,’ Revoked’)}
• Assign: @{if(variables(‘IsAssignPermissionGranted’), ‘Granted’,’ Revoked’)}

3. Notify the user when Access is Revoked for Lead records:

This flow triggers whenever all access of the user has been removed from the specific record.

The flow is almost similar to the Grant Access flow, apart from some modifications mentioned below:

1. The Action name would be RevokeAccess
2. Retrieve the Target user record by providing the Table name as “User” and Row Id from Trigger Outputs i.e. “triggerOutputs()? [‘body/InputParameters/Revokee/Id’” as shown below
3. The email needs to be modified as shown below

Conclusion:

By following the steps for creating Power Automate flows to notify users about access changes, organizations can not only improve productivity but also strengthen security measures by maintaining clear visibility and control over data access.

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Receipt processing is a prebuilt artificial intelligence model available in Power Automate. It’s designed to analyze receipts using cutting-edge optical character recognition (OCR) technology. This model can understand both printed and handwritten text on receipts. Once it reads a receipt, it extracts important information such as the store’s name and address, the transaction’s date and time, and a detailed list of items purchased. For each item, it identifies the name, price, quantity, and total cost. Additionally, it calculates the subtotal (the total before tax), the tax amount, any tip included, and the final total spent. This helps streamline tasks like expense tracking and management.

Let’s delve deeper into this practical example for a better understanding:

At a local general store, the owners use Dynamics 365 CRM to keep customer information. However, keeping track of customers’ purchases and calculating daily profits has been difficult. They used to manually enter each purchase into a record, export it to Excel, and then figure out the daily profit. This process took a lot of time and often had mistakes.

To make things easier, they decided to use the receipt processing feature in Power Automate. Now, when a customer buys something, they just upload the receipt into the system. The receipt processing model automatically extracts important details like items bought, their prices, and the total amount spent. This data is then automatically entered into an Excel spreadsheet.

Implementing the described scenario using Power Automate

A custom entity named “Customers” has been established, containing fields for customer details such as name, phone number, and address. Additionally, a “Receipt” field has been incorporated for uploading receipt images or files.

Currently, our Power Automate flow is triggered by a modification of a row, as the receipt upload in the form can only occur after the record is created. We’ve specified the customer’s table name in the parameters.

After retrieving the record, we need to download the associated file. To accomplish this, we will add a “Download a file or an image” action immediately after the trigger action. We’ll pass the extracted record ID to this action and specify “receipt” as the column name from which the file will be downloaded.

After getting the file, we’ll use a tool to extract information from the receipts. If the document is big and has just one receipt, we’ll tell the tool which page the receipt is on. This enhances cost-effectiveness and improves operational efficiency.

Note: Suppose there are receipts on different pages: one on page 2 and another spanning pages 3 and 4. If you specify ‘2’, the tool will only gather information from the first receipt. Choosing ‘3-4’ means it will only extract info from the first page of the second receipt. If you choose ‘2-4’, it will collect details solely from the first receipt, ignoring the second one.

To enter the extracted information into an Excel sheet, we’ll link the output parameters to the columns of our created table in Excel Online. The output parameters from the Receipt extraction model include the purchased item name, quantity, price, merchant’s name, address, total amount, subtotal, tip, tax, and more. We’ll organize this data in the Excel sheet according to our table columns.

Now that our flow is set up, we’ll test it by uploading the following receipt in the customer’s entity form.

The flow was activated when a modification was made to a customer entity’s record. The information from the receipt image was successfully extracted and entered into the system according to the flow’s instructions. You can now compare the values of those entries with the details from the receipt image provided above.

Conclusion

Our flow successfully automated the process of extracting information from a receipt uploaded to a customer entity’s record. By triggering the flow upon modification of the record, we ensured that the receipt details were accurately captured and entered into the system. This streamlined the data entry process and provided a reliable way to compare the entered values with the original receipt image. Overall, the flow showcased the efficiency and precision of automating repetitive tasks with Power Automate.

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Microsoft provides Power BI Report Builder to design paginated reports within the Power BI ecosystem. Paginated reports can create highly formatted, pixel-perfect reports suitable for printing or sharing as PDFs. With Power BI Report Builder, users can design these reports with precise control over layout, formatting, and data presentation, complementing the interactive and exploratory capabilities of Power BI Desktop.

Power BI Report Builder supports various data sources, allowing users to connect to different data types to create paginated reports. Some of the commonly used data sources include Microsoft SQL Server, Analysis Services, Power BI Datasets, Azure SQL Database, Oracle, Excel, XML, and CSV/Text Files.

In this blog post, our primary emphasis will be on integrating Power BI Datasets within Power BI paginated reports. We will consider one of the use case scenarios where I have a Power BI report that displays an invoice report specific to each user. This report includes numerous calculated columns and measures. To facilitate exporting the report as a PDF, we use paginated reports. Instead of recreating all the columns and measures manually in Power BI Report Builder, we can leverage the functionality of adding a Power BI semantic model.

Demonstration: Add a Power BI Semantic Model in the Power BI Report Builder.

Step 1: Opening Power BI Report Builder

When you open Power BI Report Builder for the first time, you will be greeted with a screen where you can choose to open an existing report or start a new one.

Above the design surface is the toolbar, where you have various tabs to change the formatting and setup of your report. To the left of the design surface is the Report Data pane, which displays all the built-in fields (automatically available in any paginated report), parameters, images, data sources, and datasets included in your report.

Step 2: Creating a New Report

You can also have the option to create a report from a wizard or a blank report.

Click on the data source and select the add power bi semantic model connection.

Step 3: Selecting the Semantic Model

Select your workspace and choose the appropriate semantic model.

Step 4: Adding a Dataset

After selecting the semantic model, add a dataset. A dataset comprises the query to be executed, a list of fields with their data types to be included in the report, and a connection to a data source.

Step 5: Designing the Query

Select your data source and click on Query Designer. The Query Designer is a visual representation of the families, fields, and criteria that the query is configured to return. While designing a query, it can be tailored to return particular records based on predetermined criteria and formatting.

In the screenshot below, you can see the measures developed in Power BI. You can also add tables to the query by simply dragging them into the design area.

Step 6: Select “OK”

Now you see your new dataset with a list of its fields in the Report Data pane, along with your calculated Columns and Measures.

By following these steps, you can effectively create and customize paginated reports in Power BI Report Builder, leveraging the Power BI semantic model to incorporate rich data visualizations and insights.

Note: To utilize a Power BI semantic model with DirectQuery in Power BI Report Builder, your dataset must include a measure, even if you don’t intend to display it in your report. Without the measure, the dataset won’t provide accurate data results.

Conclusion:

This way, you will be able to use the Power BI Semantic model incorporated within Power BI Paginated Reports.

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As Microsoft is providing us more flexibility with Power Automate (MS Flow), now we can retrieve List of solution details and their components directly within Power Automate flows.

Recently, we got a requirement to retrieve the list of solutions and their components from specific environments and store this information in an Excel file on SharePoint.

We can achieve this using Solution and Solution Components entity records in Dynamics CRM.

Below are the steps to achieve this requirement.

Step 1: Initialize the Array variable

This array variable should include the details of the solution component types you want to retrieve from the solution, as shown in the screenshot below.

The above array contains the following details of the solution component types:

• ComponentType – The type value of the component from the documentation.
• EntityName – The logical name of the component type.
• FilterRowName – The field name to filter the component type.
• SelectColumn – The field name to filter the component type.

You can find all solution component types information in Microsoft Documentation

Step 2: Retrieve solutions

Add the ‘List rows from selected environment’ step to retrieve the solutions from the specific environment. Use a filter query to retrieve the visible solutions and exclude the default solution.

Step 3: Retrieve Solution Components

Now, proceed to retrieve the solution component for the previously obtained solution. Utilize an ‘Apply to Each’ step to iterate through each solution, and within that, add a ‘List rows’ action to retrieve the solution component using the filter rows.

Step 4: Filter Array step to check the Component Type

For each solution component, implement an ‘Apply to Each’ loop. Inside this loop, use a Filter Array step to check that the Component Type of the current solution component matches the type specified in the array variable we set up earlier.

Expressions from the above image:

• variables(‘SolutionCompentSummary’)
• @item()[‘ComponentType’]
• items(‘Apply_to_each_Solution_Components’)?[‘componenttype’]

Step 5: Retrieve Solution Components Summary

Add the ‘List rows’ step to retrieve the solution component if the Component Type matches and add a row into the Excel table.

Expressions from the above image:

• length(body(‘Filter_Solution_Component_Summaries’))
• body(‘Filter_Solution_Component_Summaries’)[0][‘EntityName’]
• body(‘Filter_Solution_Component_Summaries’)[0][‘FilterRowName’]

Expressions from the above image:

• items(‘Apply_to_each_Solutions’)?[‘uniquename’]
• items(‘Apply_to_each_Solutions’)?[‘createdon@OData.Community.Display.V1.FormattedValue’]
• items(‘Apply_to_each_Solutions’)?[‘version’]
• items(‘Apply_to_each_Solutions’)?[‘ismanaged@OData.Community.Display.V1.FormattedValue’]}
• outputs(‘List_of_Solution_Component_Summary’)?[‘body/value’][0]?[body(‘Filter_Solution_Component_Summaries’)[0][‘SelectColumn’]]
• concat(toUpper(take(replace(string(outputs(‘List_of_Solution_Component_Summary’)?[‘body/value’][0]?[‘@odata.type’]), ‘#Microsoft.Dynamics.CRM.’, ”), 1)), toLower(skip(replace(string(outputs(‘List_of_Solution_Component_Summary’)?[‘body/value’][0]?[‘@odata.type’]), ‘#Microsoft.Dynamics.CRM.’, ”), 1)))

Conclusion:

By following these steps, you can retrieve Solutions and their component details using Power Automate flow.

From customizing Microsoft Dynamics 365 to developing Copilot-enabled Power Platform Apps, our outsourced development services ensure top-notch solutions tailored to your needs. Let our experts handle the technicalities while you focus on your core business. Email us at crm@inogic.com to get started today.

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The tools of AI Builder help you find important information in text. They organize this info into categories that make sense for your business needs.

There are two types: prebuilt and custom. Prebuilt ones are ready to use without any extra work. They’re great for lots of common tasks. Custom ones need you to train them with your data and settings. This way, you can make a model that’s perfect for what you need.

Practical Example: Making Appointments Easier with Automated Data Handling

Imagine running a small doctor’s office. Patients fill out a form on your website with their name, phone number, city, email, and when they want an appointment. With AI Builder in Power Automate, the system automatically collects and saves this info from the forms present on the website.

As patients send their forms, Power Automate quickly grabs the important details and stores them in a file or your CRM system. Later, when it’s time to schedule appointments, you can easily find the patient’s info, contact them, and sort out their appointments.”

Extract Data from Text and Input into Excel Spreadsheet

We’ll create a manual-triggered flow in Power Automate to demonstrate using AI Builder’s entity extraction model. This flow will include a Text Input parameter.

Now, next to the initial triggered action, add an AI Builder action. From the available AI Builder actions, select “Extract entities from text with the standard model.”

This action requires two parameters: the first one is the language, and the second one is the text. You can pass a file as well from the trigger action, but documents cannot exceed more than 5,000 characters.

As we proceed to input data into the Excel file, a table has been prepared with two columns: one for the type of entity and the other for its respective value. This model will provide outputs such as entity value, entity type (including person’s name, phone number, email, city, date, etc.), confidence score, character length, and starting location.

Now that the flow is set up, we can proceed with testing it. To do this, I’m passing the content of an email as input to the manually triggered action. In the email content, you’ll find information such as the patient’s name, phone number, address, and preferred date, as shown in the screenshot below.

After executing this flow, you’ll find the results in the Excel file. Unlike the key phrase extraction model, this model is more advanced as it allows you to categorize the detected text. In contrast, key phrase extraction simply extracts key phrases from the text or file. For further understanding of the key phrase extraction model and its application, you can refer to the blog titled “Understanding key phrase extraction in power automate for text analysis”.

Conclusion

We’ve successfully demonstrated the implementation of a Power Automate flow utilizing an entity extraction model. Through a practical example of appointment scheduling at a medical clinic, we showcased how this technology efficiently extracts and organizes relevant data from text inputs. By automating the extraction process, businesses can streamline their workflows, enhance data accuracy, and improve overall efficiency. With the ability to categorize extracted information, AI Builder’s entity extraction model offers a powerful solution for various text analysis tasks.

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A standout feature of Power BI Desktop is its mobile layout view, which enables users to fine-tune their reports and dashboards specifically for mobile devices. This ensures that visualizations remain crisp, responsive, and user-friendly when accessed on smartphones or tablets.

Within the mobile layout view, users can leverage a suite of tools to customize their content for mobile consumption. This includes resizing visuals, optimizing spacing and alignment for smaller screens, and prioritizing key information for mobile users. By harnessing these tools effectively, users can create immersive and intuitive mobile experiences that effectively communicate insights and data to users on the move.

In this blog post, we will undertake a comprehensive exploration of this feature, delving into its intricacies through a meticulously structured step-by-step process.

Steps to open the Mobile view within Power BI Desktop:

Here are two ways to open the mobile view,

1. From the taskbar, as shown in the below screenshot
   2. From the ‘View’ option located on the Ribbon bar
   

Additionally, users can seamlessly transition back to the desktop view by simply clicking on the desktop icon located at the bottom of the interface.

Use case Scenario:

Let’s consider a scenario for a better understanding the mobile view.

Here is an example of the browser view, and we will convert it into the mobile view.

To switch the current browser view to a mobile-friendly layout, simply follow these steps:

Step 1: Click on the Mobile Icon in order to switch to the Mobile layout.

The screenshot below illustrates the same.

Step 2: It will be presented with two alternatives, as listed below:

1. Auto Create
2. Design Manually

Opting for “Auto Create” entails Power BI Desktop automatically generating a mobile-friendly version of your report, simplifying the process for you.

So, a single click on the Auto-Create Mobile Layout option will design the visuals on the mobile layout view, as shown in the below screenshot.

If you prefer to do it yourself as manual customization, simply drag and drop the tiles from the page visuals to craft your desired mobile layout.

Page Visuals

This feature lets us set up the mobile version of a report by adding visuals to it. It is visible in the page visuals on the right-hand side, as shown in the screenshot below:

Conclusion:

The enhancement improves the user experience by allowing Power BI to simplify the design process with its one-click auto creation of mobile views. This feature saves time and ensures a smooth user experience across various devices.

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To utilize this prebuilt model within Power Automate, refer to the documentation titled “Using the Key Phrase Extraction Prebuilt Model in Power Automate” for detailed instructions and guidance on implementation.

Note: The format for data input is limited to a maximum of 5,120 characters per document when using the key phrase extraction prebuilt model.

Use Case: Extracting Key Phrases from Email for Meeting Preparation

Scenario:

You have sent an email to a client detailing your product features that can fulfill their requirements. Now, you want to automatically extract key phrases from that email and store them in a spreadsheet to prepare for an upcoming meeting or call with the client.

Solution Overview:

When the client responds to your email or when you manually trigger the process, Power Automate will retrieve the email content, extract key phrases using AI Builder’s Key Phrase Extraction model, and then update a spreadsheet with the extracted key phrases for easy reference during the scheduled meeting or call.

Let’s explore how we can implement this functionality using Power Automate.

To trigger the Power Automate flow when a new email message record is created in Microsoft Dynamics 365 CRM, set the change type to ‘added’, the table name to ’email message’, and configure the scope at the ‘organization’ level based on your specific needs.

Next, we’ll incorporate the Key Phrase Extraction prebuilt model in our flow to extract key phrases from the email. We’ll map the email message description to the text field of the Key Phrase model for analysis.

To store the extracted key phrases along with email details in an Excel sheet using the “Excel Online (Business)” connector, I’m creating a table with columns for Subject, Description, and Key Phrases.

Now that the table is set up, we’ll proceed to add it as the next action in our flow and map the fields accordingly to populate the Excel sheet with the email subject, description, and extracted key phrases.

Upon adding this action, Power Automate will automatically incorporate a “For Each” loop in the flow, which will iterate through each extracted key phrase from the email description. This loop ensures that the subsequent actions are applied to each key phrase individually. With this setup, our flow is now complete. Let’s save it and proceed to testing.

To test our flow, we’ve created an email activity record in Dynamics 365 CRM. Below is the record details and its description. This email contains points about the product features we plan to discuss with the client during an upcoming scheduled meeting, focusing on fulfilling the client’s requirements.

Once we create the email, it will trigger our flow to extract key phrases from its description and add them to our Excel table. This automated process helps organize and capture important topics discussed in the email for further analysis or action.

Conclusion

We’ve successfully implemented an automated workflow using Power Automate and Dynamics 365 CRM to extract key phrases from email descriptions and store them in an Excel table. This streamlined process allows us to efficiently capture and analyze important topics discussed in client communications, enabling better preparation and organization for client meetings and engagements.

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Utilize the prebuilt business card model to extract details from images of business cards. Upon detecting a business card in the image, the AI model automatically extracts key information, including the individual’s name, email, address, company, job title, and contact numbers. Once extracted, this data can be used efficiently for various purposes, streamlining processes and eliminating manual data entry tasks.

Note: Currently, the prebuilt business card reader supports extraction in English and Japanese languages exclusively.

Additionally, images must be in JPG, PNG, BMP, or PDF format. Furthermore, the size of the image file should not exceed 50 MB. By ensuring that your images meet these requirements, you can effectively utilize the business card model for accurate data extraction.

Using Business Cards to Extract Data

To create a flow that utilizes the business card reader with a basic example, we’ll set up a manual trigger and include an action to upload a sample card file. Then, we’ll check if it can successfully create a contact record in our Dynamics 365 CRM.

First, let’s set up the flow:

1. Manual Trigger: Start by adding a trigger that is manually initiated. This will allow you to trigger the flow whenever you’re ready to upload the sample card file.
2. Upload File: Add an action to upload the image file. You can use a suitable action depending on where your file is stored, such as “Get file content” if it’s stored in a cloud storage service like OneDrive or SharePoint.
3. Business Card Reader: Next, add an action to use the business card reader model. Choose the appropriate action provided by AI Builder for extracting information from the uploaded image.
4. Create a Record in Dynamics 365 CRM: Finally, add an action to create a contact record in your Dynamics 365 CRM. Map the extracted information from the business card reader action to the corresponding fields in the contact record.
5. Save and Test: Once you’ve set up the flow, save it and give it a name. Then, manually trigger the flow and upload the sample card file. Check if the flow successfully extracts the information and creates a contact record in your Dynamics 365 CRM.

To integrate the extraction of information from a business card into your flow, simply search for the “Extract information from business card” action in the actions list and add it after triggering your flow.

By keeping the file type detection set to automatic, the action will efficiently process the uploaded card file. Ensure to specify the uploaded card file in the “Business card” field, typically sourced from the previous action where the file was uploaded.

It’s advisable to implement validations before creating a record in your Dynamics 365 CRM, especially considering scenarios where the AI might not detect all the required data. In the screenshot provided, we will validate the last name data from the card, recognizing it as a mandatory field for creating a contact record. This precautionary step ensures that essential information is present before proceeding with record creation, reducing the risk of incomplete or inaccurate record creation in your Dynamics 365 CRM system.

Next, connect all the field values you wish to fill after the AI model detects them. This includes name, contact details, email, job title, company name, and address details.

Now that the flow is complete, we can test it by manually uploading an image file in the Card Input field, as shown in the below screenshot.

Although it may take some time for the AI model to detect and extract data from the uploaded file, the process ultimately saves a significant amount of time compared to manual entry.

Conclusion

We have successfully created a Microsoft Power Automate flow to extract information from a business card and generate a contact record from it. This efficient process not only saves time but also allows end-users to focus on more critical tasks. Importantly, creating this flow doesn’t require the user to be a technical expert, showcasing the accessibility and user-friendly nature of Power Automate. With this automated workflow, tedious manual data entry tasks are minimized, enabling users to allocate their time and resources more effectively.

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Azure OpenAI is a cloud-based service offered by Microsoft that provides access to advanced artificial intelligence models developed by OpenAI. It empowers developers to integrate these powerful models into their applications without requiring extensive expertise in AI development or infrastructure management.

Inogic aims to transform the way businesses function by developing advanced AI-driven applications with integrated AI Models provided by Azure OpenAI for Dynamics 365 and Power Platform. The Team offers to develop functional applications for businesses helping with interactive client conversations, virtual assistance, content generation and translation, report analysis, buying assistance, end-to-end management, and more!

What Inogic Offers?

Inogic Offers –

Consultation & Development of Applications

Team Inogic’s AI experts craft bespoke AI apps that integrate seamlessly with your business, boosting sales and efficiency. They unlock hidden potential and transform it into powerful engines driving conversions and optimizing workflows.

Configuration Setup to accommodate them

Our proactive team closely monitors and maintains your AI app, guaranteeing optimal performance and adapting to evolving business needs. We’re reliable partners throughout the app’s lifecycle, readily available for any questions or fine-tuning.

Reporting and Analysis Assistance

Inogic leverages Azure OpenAI’s power to create cutting-edge reporting applications. These applications utilize predictive analytics and intelligent visualizations to anticipate market shifts, automate reports, and extract valuable insights from complex data.

Decision-Making Setup

Team Inogic harnesses Azure OpenAI’s algorithms to build AI-powered decision engines. These applications analyze complex data, uncover hidden patterns, predict trends, and suggest optimal actions, paving the way for automated and data-driven strategic decisions.

Azure Open AI Application developed by Inogic – Buyer Behaviour Analysis

In the bustling world of business, understanding your customers is key to success. But deciphering their true feelings can be like trying to read tea leaves. That’s where Team Inogic stepped in, armed with a revolutionary solution: an AI-powered application that listens beyond words, analyzing sentiment for enhanced sales and deeper customer relationships.

Imagine a world where every customer interaction, from emails to chat messages, becomes a window into their emotions. This is the magic of Inogic’s application. Powered by the invisible hand of Power Automate, it seamlessly extracts comments from client conversations, feeding them into the Azure OpenAI Services. This powerhouse of AI then analyzes the sentiment hidden within, categorizing it as negative, positive, or neutral.

The results? A treasure trove of insights displayed right within your CRM and individual client accounts. Each interaction is visualized on a timeline, painting a clear picture of the customer’s emotional journey. This allows you to see not just the “what” of their communication, but the crucial “why.”

Armed with this newfound understanding, sales teams can now predict with greater accuracy whether a client is likely to convert and become a long-term partner. This empowers them to tailor their approach, addressing concerns, fostering positive interactions, and ultimately strengthening relationships.

But the benefits extend far beyond sales. By identifying negative sentiments early on, customer service teams can proactively address issues, preventing frustration and fostering loyalty. This proactive approach builds trust and strengthens the bond between your business and your customers.

Inogic’s sentiment analysis application is a testament to the power of AI in transforming customer relationships. It’s not just about technology; it’s about harnessing the power of listening to truly understand the hearts and minds of your customers. In a world where genuine connection is paramount, Inogic’s innovation paves the way for a future of deeper, more meaningful interactions, ultimately leading to sustained success.

What’s More?

Inogic Services division is also popular as a Power Apps Dedicated Offshore Developer, an expert in Dynamics 365 Development Services, Power Platform Technical Support and Consultation, and much more!

The Professional Services Division of Inogic designs and develops AI applications for clients using Copilot in Dynamics 365 Sales, Dynamics 365 Customer Service, Dynamics 365 Field Service, Dynamics 365 Marketing, and Copilot Power Apps. The division also provides services to create AI-enabled conversational chatbots using Copilot Studio.

Check out our Website or write to the experts at crm@inogic.com

Be progressive with Inogic Professional Services!

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