Patent Application
20240037605
Personal communication, productivity, and entertainment devices such as cellular phones, PDAs, portable email devices, tablet computers, e-books, hand-held games, portable media players, etc. (all referred to hereafter as “smart devices”) are known to include features such as graphical user interfaces on color touch screens and/or voice-enabled interfaces, Bluetooth and/or WiFi capability, etc. Increasingly, such smart devices also incorporate support for ancillary applications (hereafter referred to as “apps,” “skills,” etc.) for example calendars, email, maps and navigation, etc. Such ancillary applications may be pre-installed in a smart device or may be made available for download by a consumer.
Portable controlling devices capable of commanding the operation of multiple consumer appliances of different type and/or manufacture, such as universal remote controls, and the features and functions offered by such devices are also well known in the art. Sophisticated implementations of these devices incorporate technologies such as color touch screens, wireless home network compatibility, user configurable graphical user interfaces, slave relay stations positioned to control appliances not situated in line of sight of the controlling device, etc. In some cases such controlling device functionality may be offered in the form of an app for installation on an existing smart device, said app comprising a GUI to be used in conjunction with supplemental hardware and/or firmware, built-in or external to the smart device, suitable for the generation of appliance command signals. In other cases, such controlling devices may be self-contained units specific to that purpose such as for example Nevo® brand products from Universal Electronics Inc., or Harmony® brand products from Logitech Inc. Yet further, such controlling devices may be in the form of voice-enabled devices, such as the “NEVO BUTLER.”
Regardless of the exact manner in which universal controlling device functionality is implemented, in general such devices or apps may require configuration or “set up” prior to use, i.e., an appropriate set of command data from within a library of command data sets must be associated with each of the specific appliances to be controlled, for example by entry of data that serves to identify each intended target appliance by its make, and/or model, and/or type; by testing various command formats sequentially, via command transmissions, until an appliance response is observed; by sampling signals of original equipment remote controls; etc.; all as known in the art. Since systems and methods for setting up universal controlling devices to command the operation of specific home appliances are well-known, these will not be described in greater detail herein. Nevertheless, for additional information pertaining to setup procedures, the reader may turn, for example, to U.S. Pat. Nos. 4,959,810, 5,872,562, 7,093,003, 7,653,212, 7,612,685, or U.S. application Ser. No. 16/717,546, all of which are incorporated herein by reference in their entirety.
Systems and methods for using information obtained from a universal controlling device are also known in the art. For example, U.S. application Ser. No. 13/118,682, filed on May 31, 2011, which application is incorporated herein by reference in its entirety, describes a system wherein, once such controlling device setup has been successfully performed, information regarding a consumer's appliance configuration gathered thereby may be advantageously used to provide additional services to the consumer, such as advice in the selection of additions or replacements to an existing equipment configuration, recommendations for preferred interconnections, etc.
This disclosure relates generally to the configuration of home appliance systems, and in particular to methods for recommending equipment expansions, additions and/or substitutions; interconnections; supplemental capabilities; features; services; etc. to retailers and/or consumers based upon a knowledge of a consumer's current equipment configuration and usage.
This disclosure also relates to systems and methods that function to link a completion status of seller's initial-setup process with targeted advertisements. The systems and methods thereby provide a novel way to announce onboarding status of newly setup device/app to specified target market segments.
A better understanding of the objects, advantages, features, properties and relationships of the disclosure will be obtained from the following detailed description and accompanying drawings which set forth illustrative embodiments and which are indicative of the various ways in which the principles of the disclosure may be employed.
For a better understanding of the various aspects of the disclosure, reference may be had to preferred embodiments shown in the attached drawings in which:
The following describes systems and methods for making recommendations to a retailer and/or a consumer concerning additions to, modifications of, and/or usage of an existing system of electronic consumer appliances.
As to providing recommendations to a consumer,
In the illustrative example of
As illustrated in
With reference now to the flowchart of
In addition, as part of the login process a user may be offered an opportunity to link to a social networking account such as for example, without limitation, a “FACEBOOK” brand social networking account as illustrated at 310. Selecting “Yes” 312 may take the user to a screen wherein the desired account information is entered. Where the user already has a linked account, at step 204 screen 310 may be substituted by a display indicating whether or not there are unread friend comments pending at the social networking site. If there are, at the request of the user these comments 502 may be displayed as illustrated in the exemplary computer screen 500 of
Once login is complete, at step 206 the current equipment configuration data associated with that user account may be retrieved by the product recommendation app in preparation for the steps which are to follow. As will be appreciated, such configuration data may be stored locally on smart device 102, on a local PC 132, on a remote server 124, or a combination thereof as appropriate for a particular embodiment. Next, at step 208 the user is offered a choice of a product recommendation (where “products” may include apps as well as physical devices) or a product compatibility check, as illustrated at screen 320. In this context, a product recommendation comprises a review of the items in a user's current equipment configuration with the objective of generally suggesting improvements and/or additions to the user's current equipment configuration; while a compatibly check comprises a review of a particular user-specified product which is not currently part of an equipment configuration, with the objective of determining if this item is compatible with the existing equipment as currently configured. As illustrated by checkboxes 326, a user may be provided with an opportunity to further limit these reviews to only certain products or functionalities, for example audio or video appliances or functionalities as illustrated (or both, if more than one box is checked.)
Considering first the product recommendation mode, at step 210 the existing equipment configuration may be retrieved and displayed to the user as illustrated for example at screen 400. Once a user has verified that the retrieved configuration is correct, for example by selecting “Start” 402, the listed configuration may be reviewed for adequacy and compatibility. In this regard, it will be appreciated that the steps comprising the review algorithm may be performed locally on the smart device, performed remotely at an associated server, cloud-based and/or local, or a combination thereof as appropriate for a particular embodiment. Similarly, it will be understood that data indicative of the current equipment configuration and data used for reference during the review process may be either locally resident on the smart device or hosted by a server, in any convenient combination.
In determining the adequacy of an existing configuration an exemplary review algorithm may, for instance when applied to the illustrative AV system configuration 100, consider factors such as:
Once any inadequacies or inconsistencies have been identified, at step 212 recommended improvements for a device/the system may be determined and presented to the user, for example as illustrated in screen shot 410. In this regard, factors that may be considered in identifying suggested replacements or additions to the device(s) and/or system configuration may include:
In certain embodiments, user-specified filtering parameters may also be applied during this identification process, for example upper limits on price and/or dimensions, brand preference, etc. Input of such parameters may be solicited from a user at the start of the recommendation process (i.e., in conjunction with steps 210 and 212), or may be provided during initial installation and setup of the product recommendation app, as appropriate.
In addition, where a database of device command code sets is available for reference, for example where the product recommendation app is provided by or hosted by a manufacturer of universal controlling devices or of universal remote control apps for smart devices, the suitability of an appliance's command set may also be taken into account, for example:
By way of further example and without limitation, a product recommendation method and associated database are illustrated in
With reference to
Once a set of qualifying products has been selected, at step 706 a weighing factor may be assigned to each of the remaining non-mandatory features based on that feature's relative importance to the known equipment configuration in which it is to be used. In some embodiments, some or all of such weighing factors may also be user-adjustable according to personal preferences, e.g., cost. After weighing factors are established, at step 708 a first product record from the set of eligible records is retrieved, and at steps 712, 714, and 716 a product score may be accumulated, calculated in the illustrative example as the sum of the products of each participating feature's rating 808 and the weighing factor established in step 706. Thereafter, at step 718 the total score for that product may be saved, and at steps 720 and 722 the process repeated until all eligible products have been scored. Upon completion of score calculations, at step 724 the highest scoring product may be returned as a recommendation and the process is complete.
Once suggested replacement or add-on products (which may include apps and/or services) have been thus identified, these may be displayed to the user of the smart device, for example as illustrated in display 410. Returning to
Considering now the product compatibility or “shopping companion” mode of system usage, a consumer may wish to use the smart device app of the current disclosure to verify the compatibility of a particular electronic appliance with their existing configuration, based upon for example an advertisement, a recommendation from a friend or a salesperson, a store display, etc. In such cases, after initiating the product recommendation app as described previously, at step 208 the compatibility check mode 324 may be selected. Initially, at step 216 a listing of the user's currently configured electronic appliances may be displayed as illustrated at screen 600 of
At step 220, a compatibility check algorithm may be performed. The factors considered in this process may be similar to those previously enumerated above, but excluding for example cost and dimensions since these are no longer variables. In addition, the compatibility check may incorporate further steps such as verifying that a sufficient number of suitable connections and input/output ports are available to allow optimal integration of the proposed appliance in the system, etc. Once compatibility checking is complete, at step 220 the result may be displayed to the user as illustrated at screen 620. An exemplary display may include a summary 622 of the salient points considered in determining compatibility. Some embodiments may include an option for the display of additional information screens containing, for example, recommended interconnection schemes and methods, etc., which in the illustrative example may be accessible via icon 624. In addition, options for posting to social media 424 and locating a merchant 428 may be offered as previously described.
In a further example, the subject recommendation system may be used by consumers who are planning to upgrade their smart home or current set of connected smart devices with the intention of enhancing their smart home experience. The recommender will provide ideas to consumers which will help them with decision making before the consumer buys and/or installs an additional smart device or smart device related product, e.g., an app or a skill, for use in their home. The system can recommend the most popular smart devices, brands, and models, the device or brand combination that is most popular, most often bought and sought after, additional smart devices/products most commonly used by the customers having similar configuration, etc. which will also ensure interoperability of the recommended products with the current setup. In some circumstances, the consumer's geo location, zip code, or other region identifier can be used such that recommendations can be narrowed down or clustered based on availability of product/service providers in the consumer's neighborhood making the recommendations more accurate and customized for the consumer.
For determining the current configuration of the consumer's system (e.g. installed appliances, accessories, apps, and/or the like), the recommendation system can use one or more of the discovery processes described in U.S. application Ser. No. 13/657,176, which application is incorporated herein by reference in its entirety. For example, the current configuration of the consumer's system/devices can be determined via use of a process that functions to autodetect connected IOT devices in a home network. The information collected during such a process will feed the recommender with the user's current system and/or device setup, the inclination of the user towards buying a specific category or brand of product, etc. This information, along with the history of recommendations the user has previously requested, if any, can be stored in a backend database, such as an “Azure” SQL database, and the stored information may be incorporated into future recommendations which will resonate even more with what a user may want. Furthermore, the stored user information can be clustered based on a neighborhood (or other geographic region) and the clustered information can be used by retailers to provide targeted advertisements or specific offerings in the clustered area by studying the choices made by consumers in that pocket, area, or zip code.
For providing the recommendations, the system will use a device knowledge base, e.g., device identity and attribute information collected from product manuals, product inspections, manufacturer inquiries, and the like as well as marketing information, and a level of analytics that is performed on the device knowledge base. In a preferred embodiment, these analytics will employ association rule learning, for example using Apriori (a machine learning modeling technique), to find associations of interest, such as the associations between most bought brands, devices, and/or models, attribute features of devices, and/or the like in the smart home category. The rule learning will help to collect and link associations between devices (e.g., associations as to which devices in similar configurations consumers across the globe use) thereby feeding the recommender with the data that indicates what product(s) to suggest to the user. Data from Web crawlers can also be fed to the association rule learning algorithm as an alternate input to ensure that the recommendation encompasses any new brands, models, and devices/products being released in the market. This information will further strengthen the ability of the recommender to make recommendations that are supremely useful for consumers. Thus, the subject recommender system (which learns from actual customer's preferences and which provides a robust machine learning engine that will adjust and change the recommendations to a customer based on the latest trends learned from the market) will eliminate the need for customers to spend a hefty amount of time researching the statistics about different devices (which most of the time is not from a reliable source) in order to decide what device/model/brand to purchase.
Turning to
In combination with the database of user system information created as above, the recommender system will additionally utilize a database having data for most popular devices and the currently popular devices based on geographical locations (which locations can be of any desired size, e.g., neighborhoods, cities, states, countries, etc.). Such a database can be created using well-known market information gathering/research techniques. Preferably, this marketing data is cleaned of any unwanted information and inconsistencies and structured for further analysis such that the data can then be analyzed to see correlations between different features and geographic clustering to get more insights on most popular devices on a region-by-region bases.
As will additionally be appreciated by those of skill in the art, the captured data may be split into training and test sets, for example using scikit learn's train_test_split, for the purpose of validating and tuning the performance of the system. In addition, the support and confidence values associated with the machine learning algorithm may be tweaked until a satisfactory recommendation of devices from the machine learning algorithm is achieved.
In use, the recommender system, including the machine learning algorithm that is particularly adapted for frequent item set mining and association rule learning over relational databases and for identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database, is used to learn associations between smart home devices/products (e.g., TV, smart switch, smart plug, smart bulb, smart door lock, sound bar, etc.) and through these learned associations arrive at device(s)/product(s) to recommend to a consumer and/or retailer based on a user's current setup of devices/products whereupon the recommendations may be presented to the user and/or the retailer for consideration by being displayed in a device, spoken by a voice assistant, etc.
It will be further appreciated that the recommender system may be utilized to incentivize users to add more smart devices/products into their home and/or to use more devices/products already incorporated into their system. For example, the system may be adapted to check whether the user is qualified to receive a benefit/reward should the user add a particular device/product to their current system configuration, use an existing device, etc. Furthermore, by use of the aforementioned automatic device/product discovery processes, the system may automatically verify user compliance with the conditions of the offer, e.g., that a product has been purchased and installed and, if needed, the date and time of such installation and continued usage of the product/device. Thus, fulfillment of a connectivity criteria could result in the computation and/or provisions of extra user benefits in accordance with agreements.
By way of further example with reference to
As further illustrated in
In a preferred example, the relational database(s) will not only include data that is indicative of the automatically detected devices and services within the household but will also include data that may be associated with information that a user manually provided to the system (e.g., data indicative of devices and/or services manually identified to the system as a part of a controlling device configuration process). Yet further, the relational database(s) may include data for those devices/services with varying levels of trust/reliability. Still further, the collected data may include data indicative of all interactions with the recommendation and/or configuration services offered by the system, usage of the devices, usage of services supported by the devices, etc. This data, which would be preferably collected regardless of which device is used to provide a service in the case where multiple service enabled devices exist in a single household, would then be merged to provide comprehensive snapshot(s) of the user's household. In addition, the captured and merged data, e.g., the appliance/service identifying data noted above as well as data indicative of content that is being access via use a device, a change in a state for a device/service, a user interaction with a device/service, and the like type of usage/telemetry data as can be or as desired to be captured, may be timestamped so that the system may be informed as to when a device/service was first seen, last seen, used, etc., for use in connection with at least the various purposes described herein.
It will also be appreciated that the above described, comprehensive collection of data will also allow the system to be trained in a manner that will particularly improve the prediction, compliance checking, etc. capabilities of the system as the system is able to reprocess/update the household profiles upon repeated usage of the various data collection processes. Thus, it will be further appreciated that this data would be well suited to train models (or in some cases simple rules) for data expansion to add to household profile data in place, e.g., models for use in deriving brand preferences in a household, household categories (such as gamers and streamers), predicting likely churn candidates, deriving time of use, etc. Similarly, such data would be well suited to train models for runtime predictions, e.g., models for predicting patterns such as first action taken in the afternoon (entertainment or smart home) on their TV, basic thermostat temp setting, purchase intention (what would/could they buy next?), etc.
It will be further understood that the recommender system may also be utilized to come up with new hypothesis and reports considering models trained to discern habits and trends. Likewise, the recommender system may be used to train models that are intended to personalize the experience of a household no matter through what device a third party service may be accessed. To this end, the system may function to merge a household view across enabled devices, independent of the devices explicitly being linked together, whereupon the experience of the household on other third party services may be personalized based on derived household profiles that can be retrieved for the household by any such third party services.
While it is contemplated that the system may analyze usage data on the recommender (or device setup/configuration) services to obtain insight into the household, e.g., by monitoring API calls to a device configuration service, such as UEI's “QUICKSET” brand service, by monitoring API calls to a streamer service to determine what media is being accessed/watched, by capturing data such as when a TV is turned on and when it was first installed, etc., in a preferred example any such data will not, if provided to a service outside of the household, contain user identifiable data. To this end, such data may be abstracted as required to avoid violating privacy laws or may otherwise be maintained on and be accessible only by devices within a secure local area network. In this use case, it will also be appreciated that any services, such as the above-described recommender services, any device configuration services, etc., may be performed locally where the required “comparison” data is also locally maintained and, to the extent any cloud services are required to be used, any data that is provided to such services is again abstracted to avoid the dissemination of any user identifiable data. Thus, a household profile may exist in the cloud in an abstracted manner and/or may also reside on a device such that the information remains within the user's network as a decentralized system and each device within the user's network may share data within the home network (e.g., acting as a smart edge device) for synchronizing the profile across all of the local area network connected devices.
When data is stored in the relational database, a household identifier, such as an IP address, may be used to cross-reference appliance and/or usage data to a given household. It may, however, be desirable to use an alternative identifier as it is recognized that IP addresses can change over time. Thus, in some instances, a service identifier, such as a device ID for a device supporting a configuration service, can be used once a profile is in place to remap new public IPs to an existing household as they evolve/change on an ongoing basis. Furthermore, because public IPs correlated to user recognizable data may also provide a security concern, in some instances such data may be kept private through use of hashing schemes that never have access to originals, key vaults limiting personal access to data outside of runtime environments, etc.
In view of the foregoing, it will be appreciated that the described recommender service has, among other advantages, the advantage of providing a real-time view of what different types of households are currently doing (or not doing) in specific timeframes, e.g., what devices/products are installed in a user's home network and how the installed devices/products are being used. Accordingly, it will also be appreciated that this real-time view can be used to provide targeted advertising via the use of retargeting tags, e.g., derived data that is used to select advertising that is to be presented to a user. For example, when a user accesses a particular website, cloud service, or the like that supports advertising, through retargeting tags, the system can map a household to certain attributes and automatically adjust the advertising within (or advertising to be added to) the content that is being accessed based on the household profile, e.g., the system can tag the household to a “PlayStation” brand gaming household that has a 6 year old “Sony” brand TV and a household that is therefore likely to buy a new TV, and more likely to buy a “Sony” brand TV such that the advertising in the website or cloud service is adjusted to present an advertisement for at least a “Sony” branded TV. As noted above, this advertising service may be performed locally to ensure compliance with privacy laws and, to this end, may require a plurality of advertisements to be pre-stored in a local device for selection of a desired advertisement based on a locally determined retargeting tag.
In a further example, when the system tags a household as using a particular brand or service the system can promote different and compatible devices as desired. To this end, when a user visits a support/help website and/or interacts with a virtual agent on any device, the system will be provided with information about what devices and/or services a user has at home and what the user could be looking for, e.g., the knowledge could be gleaned from a user asking to setup/troubleshoot an issue with a particular device and/or service. In such an instance, the system can use the gleaned knowledge to contact a third party provider, third party retailer, etc. whereupon the third party can target the user for an offer, benefit, etc. Thus, the system can be used to proactively send notices to pay-tv operators on possibility of churn in a household, so they can offer new incentives before a churn occurs, target a push notification to user of a specific model/brand and year of TV that has a specific new security flaw that needs to be corrected, to offer purchasing opportunities, to offer upgrade possibilities, and the like without limitation.
By way of still further example, the system can repeatedly use a device discovery process to automatically determine a configuration of a home network system of the user, e.g., the devices/apps installed on the network and/or the services that are installed on the devices. The repeatedly determined configuration of the home network system of the user can then be examined by the machine learning algorithm(s), using historical data captured from other households, to determine if a change in the system has occurred and to determine if the detected system change is indicative of the user being likely to drop a service that is being accessed via the home network system of the user, i.e., that the user is likely to churn. Such a change can be a detected disconnection of a STB in favor of a streaming device, the simple addition of a streaming device to the user's home network, the installation of a particular service on a streaming device, the use of a streaming device more frequently than a STB, etc. as determined via use of the noted machine learning processes. As before, when the system determines that a user is likely to churn, the system can cause an appropriate notification to be sent to the service provider.
As noted above, when suggested replacement or add-on products (which may include apps and/or services) have been identified, these products, apps, and or services may be called to the attention of a retailer whereupon the information can be used by the retailer to direct market to the consumer. In this manner, retailers, such as smart device manufacturers and service providers, can utilize the generated information to, among other things, increase market penetration in the smart home environment.
By way of further example, a retailer, a manufacturer, or the like (referred to herein simply as a “retailer” or “seller”), such as a retailer of smart TVs, might want to specifically identify opportunities to increase penetration into living rooms of users. In such case, the smart TV retailer could register one or more target business opportunities (e.g. requesting an opportunity to target users with no smart TVs in living rooms) in a connectivity opportunity database associated with the recommender system. Thus, using the auto detection and location-aware features discussed about and a reverse-marketing approach, the subject systems would allow smart device retailers to conveniently receive notifications or reverse advertisements from the system when their target business opportunities have been identified.
More particularly, it is contemplated that the described systems could be utilized to provide a reverse advertisement capability. For example, the system can use device, app, owner, and the like information obtained as described above to identify an exploitable household or room configuration of a home network system of the user to a business entity/third party. The previously described device discovery process could be used to automatically determine a room, home theater, or other location or interconnectability identifiable configuration of a home network system of the user and the determined grouping of devices and/or configuration of the devices of the user would be used to automatically verify a compliance by the opportunity settings of a business entity with a condition for receiving the reverse advertisement. The reverse advertisement would be provided to the business entity when the determined room and/or household, and/or home theater, and/or other identifiable configuration of the home network system of the user is determined to be in compliance with the condition for receiving the reverse advertisement.
It will also be appreciated that, by leveraging the ability of the system to support voice commands, the voice commands can be used to discern device, apps, and location information and associations as desired. For example, in a scenario where a user requests a specific voice enabled platform to “turn on the lights in the living room” or “lock the backdoor” an association between a location within a home, a controllable device, and a voice enabled platform (and/or app) can be established. Thus, to provide smart device retailers with increased opportunities, the subject system could be adapted to combine the use of auto detection and location-aware features to define the room/position (e.g. living room, backdoor, home entertainment center) of each connected smart device based on the application of Natural Language Understanding (NLU) feature.
Turning to
When the process illustrated in
When determining if a reverse advertising opportunity exists for a retailer, the system will access device information 1510, e.g. from the devices directly and/or from the administration database, and the collected information will be compared against the conditions for various retailer opportunities 1512 defined in the connectivity opportunity database. The connectivity opportunity database represents a multitude of smart device retailers' target business opportunities based on users' household room configurations such that smart device retailers would be provided with reverse advertisements when an opportunity condition for receiving the reverse advertisement is identified.
As noted, the system may maintain a device data table which may include information such as legal entity IDs, applicable product category IDs, applicable room/position IDs, and applicable country IDs for the purpose of business-opportunity searches by smart device retailers. The administration system may then track the status of rooms/positions of the users' connected smart devices against the connectivity opportunity database. In addition, retailers may be provided with access to users' home network system configuration information and related analytics to thereby allow the retailers to review such information for possibly defining still further opportunities. The user's home network system configuration information may be abstracted when accessed by third parties to thereby ensure privacy of a given user or a user may be required to opt-in to this feature.
When the conditions of an opportunity are determined to be met by the system 1514, e.g., if smart TVs are not detected in the users' living rooms, the administration system could send automatic notifications or reverse advertisements 1516 to one or more smart TV retailers about exploitable opportunities that exist in the living rooms of specified users. By utilizing the opportunity information on the users' household room settings, administration system could send notifications or reverse advertisements to one or more smart device retailers, which in turn would allow these smart device retailers to send targeted advertisements to potential buyers who already possess a connected smart home system. The contact with a buyer by a retailer could be direct or it may be required to be through the system administrator to provide some degree of separation between system users and retailers. In some instances, the system can facilitate the providing of advertising to a user via use of their connected smart device.
In sum, by utilizing the powerful device discovery features of the subject systems, the described systems may allow smart device retailers to at least semi-automate routine marketing and sales processes to identify target business opportunities (e.g. target users who already possess a connected smart home system but not the smart devices on sale), set the product selling price, send targeted advertisements to these target users, and arrange online purchases by these target users.
To facilitate the automation of the marketing and sales processes, the system will leverage the application of artificial intelligence (e.g. machine learning) and rule-based expert systems whereby some basic marketing and sales expertise are captured in a collection of rules that are implemented under the connected service environment through the collective intelligence of an administration system and smart device manufacturers. These rules and learning processes may take into consideration the users' unique configurations in their home network systems, users' online behaviors, market conditions, and smart device manufacturers' motivations to maximize sales Periodic data curation by the system administrator and automatic implementation of a Frequent Pattern Growth Algorithm by the administration system is preferably utilized with the rules and learning processes to highlight specific business opportunities for smart device manufacturers. An association rule learning principle is also preferably utilized to discover strong relations among the variables stored in the databases (e.g., relations among general device/service category IDs such as Television, Smart thermostat, and Smart bulb). Via use of these techniques and processes, auto-detected devices/services in the users' household configurations may be captured by the administration system as a collection of device/product category IDs and service category IDs that could be linked to existing knowledge graphs and the autodetected device/service dataset may then be curated in accordance with the predefined settings of basic key parameters of association rule learning principle such as minimum support threshold, support, confidence, and lift.
It will also be appreciated by those of skill in the art that system administrators could manually adjust the key parameters and apply data curation and/or that the data-curation process itself could be automated by tracking and optimizing these key parameters such that the total number of new auto detections is maximized in the system. Furthermore, it is to be understood that using an Association Rule Learning principle, Frequent Pattern Growth Algorithm to automatically discover frequent patterns/combinations of specified device/service category IDs provides a time and cost improvement as compared to using a conventional Apriori Algorithm for this same purpose.
After targeted advertisements are sent out by business entity to target household users, it is contemplated that there is a realistic possibility that these household users would still like to consider alternative options for purchase. To address this possibility, when a pre-determined criteria/conditions established by one retailer is fulfilled, this fulfillment could also trigger the administration system to monitor for still further conditions established for sending one or more reverse advertisements to one or more other relevant business entities. By way of example these further criteria/conditions may include: a determined inaction by target household users (no auto detection of advertised device/service) for a pre-determined period of time (e.g. 10 days); a determination that another relevant business entity would be able to sell devices/services whose categories are the same as the advertised ones (e.g. smart bulb); a determination that another relevant business entity has at least one device/service that is already installed in the target household configuration (brand loyalty scenario); a determination that an original targeted advertisement sent to users does not include any discount/rewards; etc.
It will also be appreciated that in some instances the system may utilize an estimated probability (P) or relevant predictive modeling information to identify still further marketing opportunities. For example, the system will automatically discover various frequently occurring category patterns/combinations, e.g., households having a smart television, smart thermostat, and a smart bulb. When the system determines that a household partially fulfills a frequent-pattern-based household configuration criteria, e.g., has a smart television and a smart thermostat, the system may automatically send reverse advertisements to retailers of a high probability missing component, e.g., to smart bulb manufacturers Y & Z, about opportunities to target households that could potentially follow similar purchasing patterns. Each frequent pattern fulfillment preferably carries a confidence value, determined using a Binary Classifier model or the like. The confidence value, for example a value that indicates the estimated probability (P) that the missing component(s), e.g., a smart bulb, will become automatically detected in a household after providing a targeted advertising to that household, can further be utilized by the system, retailers, or the like to define discounts/rewards, etc. for target households.
It is also contemplated that the various concepts described above can be utilized to incentivize retailers/sellers, including business entities, independent developers, and other external parties, to directly carry out the initial-setup process, thereby ensuring immediate readiness of their newly setup devices, apps, and/or services (simply referred to hereinafter as a “device”) for digital marketing and selling. The initial-setup process, e.g., to adapt a device for use within a network using, for example, information from the administration system as needed, can be performed prior to the product being provided to/shipped to/installed at the customer and/or can be performed by the installer/user (or automatically by the device) when the device is introduced into the home eco-system. When performed on site, the seller may be notified of the completion of the setup process and, upon completing this initial-setup process (whether done remotely or at the seller), sellers could automatically send initial targeted advertisements to their specified target market segments (such as customer, other sellers, or the like). The target market segment information is stored in a connectivity opportunity database for the newly setup device and the administration system will then function to automatically send B2C/B2B/C2C initial targeted advertisements related to the seller's newly setup device to the specified target market segments. For the initial targeted advertisements that are sent to specified target market segments, some or all of them may be free of charge for the seller as an initial incentive (certain restrictions may apply in the initial targeted advertisements) or fees can be charged as desired. The marketing related information may also be provided to the administration system prior to the setup of a device and linked by the administration system to the device upon the administration system being used to perform the initial setup for the device. In summary, the system and methods described herein can thus be utilized to link the seller's completion status of an initial-setup process with initial, targeted advertisements, thereby providing a novel way to announce the onboarding status of newly setup device to specified target market segments.
The administration system, e.g., the system that, among other things, monitors API calls that are made to a configuration database, is preferably responsible for confirming a completion of the initial setup process by the seller for the new device. To this end, the administration system may provide, and the seller may utilize, the UEI “QUICK SET” Features described previously. Once the setup of the new device is confirmed in the administration system, the administration system can perform maintenance of the device. The administration system may, for example, automatically perform device updates as the administration system learns of other devices being added to/removed from a home. Such updates can include providing a device with sets of command data, formats, and signals for use in controlling/communicating with such other devices in the home ecosystem.
After a seller completes this initial setup for their devices, the administration system may utilize the previously described auto detection capabilities of the administration system to assist the seller in implementing a customized, connectivity-based market opportunity search (or market segmentation). As noted, the seller will initially specify to the administration system, e.g., via the information that is stored in a connectivity opportunity database, the biographical and home network system characteristics of their custom target group of potential buyers (or target market segments) and, when the administration system detects that one or more of the criteria set in the searchable datasets of the connected smart home ecosystem are met, the administration system can send one or more targeted advertisements relevant to the auto-detectable devices to this custom target group. The targeted advertisements may be delivered through one or more channels that have been pre-established by the seller, which may include delivery directly to a device newly detected in the home ecosystem, a third party via email, etc.
When establishing criteria for the target advertisements, the sellers can specify the data for one or more searchable data fields of a household user account that is maintained/monitored by the administration system. Such data fields can include, as desired for any particular purpose, country, longitude/latitude coordinates, age, occupation, education level, family profile, interests, hobbies, recency/frequency of purchases, usage rates, brand loyalty, etc. The data fields are intended to provide an opportunity for sellers to characterize their custom target group in terms of household user account data and are intended to provide a broad range of possibilities to create custom target groups based on different market segmentation scenarios that may include, but need not be limited to, geographic, demographic, psychographic, and behavioral segmentation (or a combination thereof as appropriate).
In addition to the data fields noted, sellers can characterize their custom target group in terms of home network system configurations. In this manner and, based on the core premise that the auto-detection capability of the administration system also establishes what might be missing from a home, sellers can use the information to identify and exploit new business opportunities for their auto-detectable devices and overall business. Sellers can thus specify one or more searchable categories/brands, etc. of devices as either automatically detected or not detected by the administration system from the connectivity-based market opportunity search and the administration system can then send targeted advertisements to the customer and/or notify the seller or other sellers/retailers of the opportunity as desired.
By way of more particular examples, a marketing scenario may include: B2C geographic segmentation (e.g. smart thermostat manufacturer X intends to advertise its smart thermostat to a custom target group of consumers who live within Seattle area and do not already own a smart thermostat of brand X); B2C demographic segmentation (e.g. video game developer Y intends to advertise its video game to a custom target group of consumers who have family members between the ages of 10 to 18 and do not already own a video game of brand Y); B2C psychographic segmentation (e.g. video game developer Y intends to advertise its video game to a custom target group of consumers who are interested in baseball, already own a smart TV, and do not already own a video game of brand Y); B2C behavioral segmentation (e.g. security service provider C intends to advertise its home security service to a custom target group of consumers who made a new purchase of a device within one year, own at least two devices of brand C, and do not already use the home security service of brand C); C2C psychographic/behavioral segmentation (e.g. independent developer F intends to advertise his/her video game to a custom target group of consumers who are interested in racing, made a new purchase of a device within one year, already own a smart TV, and do not already own a video game); etc.
Furthermore, in a B2B context, it will be appreciated that the auto-detectable devices and/or apps of product/service providers may be initially utilized by other product/service providers, retailers/sellers, or the like for further integration, modification, reselling, etc. Thus, it will be understood that product/service providers may utilize the customization capability of the administration system to characterize their custom target groups in terms of household user account data and related home network system configurations from the searchable datasets of the connected smart home ecosystem and thereafter advertise their devices and/or apps to other relevant product/service providers, retailers, or the like.
While various concepts have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those concepts could be developed in light of the overall teachings of the disclosure. For example, while the user interface portion of the illustrative product recommendation system and method described takes the form of a smart device app, it will be appreciated that other embodiments are possible, for example in the form of a PC or Web tablet application, either locally resident or server-based. Additionally, while the databases used for storing setup and configuration information, command code sets, and product feature/function reference may for simplicity be illustrated herein as co-located on a single Web server, it will be appreciated that individual data sets may be located across a multiplicity of servers as long as all are accessible to the product recommendation application. Accordingly, it will be appreciated that the method described herein could be implemented in general as computer-executable software associated with one or more network servers, i.e., a hardware platform, with the software being stored on a computer-readable media embodied in a physical device such as a hard disk drive, memory card, and the like.
Further, while described in the context of functional modules and illustrated using flowcharts and/or block diagrams, it is to be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or a software module, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an enabling understanding of the disclosure. Rather, the actual implementation of such modules would be well within the routine skill of an engineer, given the disclosure herein of the attributes, functionality, and inter-relationship of the various functional modules in the system. Therefore, a person skilled in the art, applying ordinary skill, will be able to practice the disclosure set forth in the claims without undue experimentation. It will be additionally appreciated that the particular concepts disclosed are meant to be illustrative only and not limiting as to the scope of the disclosure which is to be given the full breadth of the appended claims and any equivalents thereof.
All patents and patent applications cited within this document are hereby incorporated by reference in their entirety.
This application claims the benefit of and is a continuation-in-part of U.S. application Ser. No. 17/560,333, filed on Dec. 23, 2021, which application claims the benefit of and is a continuation-in-part of U.S. application Ser. No. 17/539,847, filed on Dec. 1, 2021, which application claims the benefit of U.S. Provisional Application No. 63/134,468, filed on Jan. 6, 2021, the disclosures of which are incorporated herein by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63134468 | Jan 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17560333 | Dec 2021 | US |
| Child | 18231924 | US | |
| Parent | 17539847 | Dec 2021 | US |
| Child | 17560333 | US |
