Tag Archives: Google Assistant

Smartphone AI Digital Assistant Encroaching on the Virtual Receptionist

Businesses already exist which have developed and sell Virtual Receptionist , that handle many caller needs (e.g. call routing).

However, AI Digital Assistants such as Alexa, Cortana, Google Now, and Siri have an opportunity to stretch their capabilities even further.  Leveraging technologies such as Natural language processing (NLP) and Speech recognition (SR), as well as APIs into the Smartphone’s OS answer/calling capabilities, functionality can be expanded to include:

  • Call Screening –  The digital assistant asks for the name of the caller,  purpose of the call, and if the matter is “Urgent
    • A generic “purpose” response, or a list of caller purpose items can be supplied to the caller, e.g. 1) Schedule an Appointment
    • The smartphone’s user would receive the caller’s name, and the purpose as a message back to the UI from the call, currently in a ‘hold’ state,
    • The smartphone user may decide to accept the call, or reject the call and send the caller to voice mail.
  • Call / Digital Assistant Capabilities
    • The digital assistant may schedule a ‘tentative’ appointment within the user’s calendar.  The caller may ask to schedule a meeting, the digital assistant would access the user’s  calendar to determine availability.  If calendar indicates availability, a ‘tentative’ meeting will be entered.  The smartphone user would have a list of tasks from the assistant, and one of the tasks is to ‘affirm’ availability of the meetings scheduled.
    • Allow recall of ‘generally available’ information.  If a caller would like to know the address of the smartphone user’s office, the Digital Assistant may access a database of generally available information, and provide it.  The Smartphone user may use applications like Google Keep, and any note tagged with a label “Open Access” may be accessible to any caller.
    • Join the smartphone user’s social network, such as LinkedIn. If the caller knows the phone number of the person, but is unable to find the user through the social network directory, an invite may be requested by the caller.
    • Custom business workflows may also be triggered through the smartphone, such as “Pay by Phone”.

Beyond Google Search of Personal Data – Proactive, AI Digital Assistant 

As per previous Post, Google Searches Your Personal Data (Calendar, Gmail, Photos), and Produces Consolidated Results, why can’t the Google Assistant take advantage of the same data sources?

Google may attempt to leapfrog their Digital Assistant competition by taking advantage of their ability to search against all Google products.  The more personal data a Digital Assistant may access, the greater the potential for increased value per conversation.

As a first step,  Google’s “Personal”  Search tab in their Search UI has access to Google Calendar, Photos, and your Gmail data.  No doubt other Google products are coming soon.

Big benefits are not just for the consumer to  search through their Personal Goggle data, but provide that consolidated view to the AI Assistant.  Does the Google [Digital] Assistant already have access to Google Keep data, for example.  Is providing Google’s “Personal” search results a dependency to broadening the Digital Assistant’s access and usage?  If so, these…

interactions are most likely based on a reactive model, rather than proactive dialogs, i.e. the Assistant initiating the conversation with the human.

Note: The “Google App” for mobile platforms does:

“What you need, before you ask. Stay a step ahead with Now cards about traffic for your commute, news, birthdays, scores and more.”

I’m not sure how proactive the Google AI is built to provide, but most likely, it’s barely scratching the service of what’s possible.

Modeling Personal, AI + Human Interactions

Starting from N number of accessible data sources, searching for actionable data points, correlating these data points to others, and then escalating to the human as a dynamic or predefined Assistant Consumer Workflow (ACW).  Proactive, AI Digital Assistant initiates human contact to engage in commerce without otherwise being triggered by the consumer.

Actionable data point correlations can trigger multiple goals in parallel.  However, the execution of goal based rules would need to be managed.  The consumer doesn’t want to be bombarded with AI Assistant suggestions, but at the same time, “choice” opportunities may be appropriate, as the Google [mobile] App has implemented ‘Cards’ of bite size data, consumable from the UI, at the user’s discretion.

As an ongoing ‘background’ AI / ML process, Digital Assistant ‘server side’ agent may derive correlations between one or more data source records to get a deeper perspective of the person’s life, and potentially be proactive about providing input to the consumer decision making process.

Bass Fishing Trip
Bass Fishing Trip

For example,

  • The proactive Google Assistant may suggest to book your annual fishing trip soon.  Elevated Interaction to Consumer / User.
  • The Assistant may search Gmail records referring to an annual fishing trip ‘last year’ in August. AI background server side parameter / profile search.   Predefined Assistant Consumer Workflow (ACW) – “Annual Events” Category.  Building workflows that are ‘predefined’ for a core set of goals/rules.
  • AI Assistant may search user’s photo archive on the server side.   Any photo metadata could be garnished from search, including date time stamps, abstracted to include ‘Season’ of Year, and other synonym tags.
  • Photos from around ‘August’ may be earmarked for Assistant use
  • Photos may be geo tagged,  e.g. Lake Champlain, which is known for its fishing.
  •  All objects in the image may be stored as image metadata. Using image object recognition against all photos in the consumer’s repository,  goal / rule execution may occur against pictures from last August, the Assistant may identify the “fishing buddies” posing with a huge “Bass fish”.
  • In addition to the Assistant making the suggestion re: booking the trip, Google’s Assistant may bring up ‘highlighted’ photos from last fishing trip to ‘encourage’ the person to take the trip.

This type of interaction, the Assistant has the ability to proactively ‘coerce’ and influence the human decision making process.  Building these interactive models of communication, and the ‘management’ process to govern the AI Assistant is within reach.

Predefined Assistant Consumer / User Workflows (ACW) may be created by third parties, such as Travel Agencies, or by industry groups, such as foods, “low hanging fruit” easy to implement the “time to get more milk” .  Or, food may not be the best place to start, i.e. Amazon Dash

 

AI Digital Assistant verse Search Engines

Aren’t AI Digital Assistants just like Search Engines? They both try to recognize your question or human utterance as best as possible to serve up your requested content. E.g.classic FAQ. The difference in the FAQ use case is the proprietary information from the company hosting the digital assistant may not be available on the internet.

Another difference between the Digital Assistant and a Search Engine is the ability of the Digital Assistant to ‘guide’ a person through a series of questions, enabling elaboration, to provide the user a more precise answer.

The Digital Assistant may use an interactive dialog to guide the user through a process, and not just supply the ‘most correct’ responses. Many people have flocked to YouTube for instructional type of interactive medium. When multiple workflow paths can be followed, the Digital Assistant has the upper hand.

The Digital Assistant has the capability of interfacing with 3rd parties (E.g. data stores with API access). For example, there may be a Digital Assistant hosted by Medical Insurance Co that has the ability to not only check the status of a claim, but also send correspondence to a medical practitioner on your behalf. A huge pain to call the insurance company, then the Dr office, then the insurance company again. Even the HIPPA release could be authenticated in real time, in line during the chat.  A digital assistant may be able to create a chat session with multiple participants.

Digital Assistants overruling capabilities over Search Engines are the ability to ‘escalate’ at any time during the Digital Assistant interaction. People are then queued for the next available human agent.

There have been attempts in the past, such as Ask.com (originally known as Ask Jeeves) is a question answering-focused e-business.  Google Questions and Answers (Google Otvety, Google Ответы) was a free knowledge market offered by Google that allowed users to collaboratively find good answers, through the web, to their questions (also referred as Google Knowledge Search).

My opinions are my own, and do not reflect my employer’s viewpoint.

AI Personal Assistant Needs Remedial Guidance for their Users

Providing Intelligent ‘Code’ Completion

At this stage in the application platform growth and maturity of the AI Personal Assistant, there are many commands and options that common users cannot formulate due to a lack of knowledge and experience.  Using Natural Language to formulate questions has gotten better over the years, but assistance / guidance formulating the requests would maximize intent / goal accuracy.

A key usability feature for many integrated development environments (IDE) are their capability to use “Intelligent Code Completion” to guide their programmers to produce correct, functional syntax. This feature also enables the programmer to be unburdened by the need to look up syntax for each command reference, saving significant time.  As the usage of the AI Personal Assistant grows, and their capabilities along with it, the amount of commands and their parameters required to use the AI Personal Assistant will also increase.

AI Leveraging Intelligent Command Completion

For each command parameter [level\tree], a drop down list may appear giving users a set of options to select for the next parameter. A delimiter such as a period(.) indicates to the AI Parser another set of command options must be presented to the person entering the command. These options are typically in the form of drop down lists concatenated to the right of the formulated commands.  Vocally, parent / child commands and parameters may be supplied in a similar fashion.

AI Personal Assistant Language Syntax

Adding another AI parser on top of the existing syntax parser may allow commands like these to be executed:

  • Abstraction (e.g. no application specified)
    • Order.Food.Focacceria.List123
    • Order.Food.FavoriteItalianRestaurant.FavoriteLunchSpecial
  • Application Parser
    • Seamless.Order.Food.Focacceria.Large Pizza

These AI command examples uses a hierarchy of commands and parameters to perform the function. One of the above commands leverages one of my contacts, and a ‘List123’ object.  The ‘List123’ parameter may be a ‘note’ on my Smartphone that contains a list of food we would like to order. The command may place the order either through my contact’s email address, fax number, or calling the business main number and using AI Text to Speech functionality.

All personal data, such as Favorite Italian Restaurant,  and Favorite Lunch Special could be placed in the AI Personal Assistant ‘Settings’.  A group of settings may be listed as Key-Value pairs,  that may be considered short hand for conversations involving the AI Assistant.

A majority of users are most likely unsure of many of the options available within the AI Personal assistant command structure. Intelligent command [code] completion empowers users with visibility into the available commands, and parameters.

For those without a programming background, Intelligent “Command” Completion is slightly similar to the autocomplete in Google’s Search text box, predicting possible choices as the user types. In the case of the guidance provided by an AI Personal Assistant the user is guided to their desired command; however, the Google autocomplete requires some level or sense of the end result command. Intelligent code completion typically displays all possible commands in a drop down list next to the constructor period (.). In this case the user may have no knowledge of the next parameter without the drop down choice list.  An addition feature enables the AI Personal Assistant to hover over one of the commands\parameters to show a brief ‘help text’ popup.

Note, Microsoft’s Cortana AI assistant provides a text box in addition to speech input.  Adding another syntax parser could be allowed and enabled through the existing User Interface.  However, Siri seems to only have voice recognition input, and no text input.

Is Siri handling the iOS ‘Global Search’ requests ‘behind the scenes’?  If so, the textual parsing, i.e. the period(.) separator would work. Siri does provide some cursory guidance on what information the AI may be able to provide,  “Some things you can ask me:”

With only voice recognition input, use the Voice Driven Menu Navigation & Selection approach as described below.

Voice Driven, Menu Navigation and Selection

The current AI personal assistant, abstraction layer may be too abstract for some users.  The difference between these two commands:

  • Play The Rolling Stones song Sympathy for the Devil.
    • Has the benefit of natural language, and can handle simple tasks, like “Call Mom”
    • However, there may be many commands that can be performed by a multitude of installed platform applications.

Verse

  • Spotify.Song.Sympathy for the Devil
    • Enables the user to select the specific application they would like a task to be performed by.
  • Spotify Help
    • A voice driven menu will enable users to understand the capabilities of the AI Assistant.    Through the use of a voice interactive menu, users may ‘drill down’ to the action they desire to be performed. e.g. “Press # or say XYZ”
    • Optionally, the voice menu, depending upon the application, may have a customer service feature, and forward the interaction to the proper [calling or chat] queue.

Update – 9/11/16

  • I just installed Microsoft Cortana for iOS, and at a glance, the application has a leg up on the competition
    • The Help menu gives a fair number of examples by category.  Much better guidance that iOS / Siri 
    • The ability to enter\type or speak commands provides the needed flexibility for user input.
      • Some people are uncomfortable ‘talking’ to their Smartphones.  Awkward talking to a machine.
      • The ability to type in commands may alleviate voice command entry errors, speech to text translation.
      • Opportunity to expand the AI Syntax Parser to include ‘programmatic’ type commands allows the user a more granular command set,  e.g. “Intelligent Command Completion”.  As the capabilities of the platform grow, it will be a challenge to interface and maximize AI Personal Assistant capabilities.