Session identification and transfer between devices

Abstract

A system for transferring a session between devices includes a first device including a first software development kit for collecting data and behaviors related to viewing digital media on the first device, a second device including a second SDK for collecting data and behaviors related to viewing digital media on the second device, and an intermediary device coupled to the first and second devices, including a collection of the data and behavior received from the first and second devices, and a session transfer function operating to use the collection of data and behavior to identify a use of an application on the second device to view a digital media representation present in an application on the first device, determine a session of the application on the first device, and transfer the session to the second device without requiring authentication for the use of the second device.

Description

Attorney Docket: MVG.7959.WOU2 SESSION IDENTIFICATION AND TRANSFER BETWEEN DEVICES FIELD

[0001] The disclosed embodiments generally relate to creating an inference from data during use of a device and using that inference to transfer a session between devices. BACKGROUND

[0002] As the adoption of technology becomes more commonplace, especially in regard to the continued development of wearable devices, it may be desirable to transfer use of a computer program on one device to another device. Examples of this may include transferring digital media (such as images, videos, and audio clips), written information (such as documents), gameplay status, and other data between devices which is usually accomplished by providing credentials, such as login information, on each device. When building an application such as a computer game within a platform, it may not be practical to leverage the authentication system in another program outside of the platform. In this scenario, logging into each computer programs may not be possible as the different platforms generally do not use the same authentication provider and other centralized authentication systems are generally not available. A solution is required where a session may be transferred between devices without requiring credentials. SUMMARY

[0003] In at least one embodiment, a system for transferring a session between devices includes a first device including a first software development kit (SDK) configured to collect data and behaviors related to viewing digital media on the first device, a second device including a second SDK configured to collect data and behaviors related to viewing digital media on theAttorney Docket: MVG.7959.WOU2 second device, and an intermediary device coupled to the first and second devices, the intermediary device including a collection of the data and behavior received from the first and second devices, and a session transfer function operating to use the collection of data and behavior to identify a use of an application on the second device to view a digital media representation present in an application on the first device, determine a session of the application on the first device, and transfer the session to the second device without requiring authentication for the use of the application on the second device.

[0004] In another embodiment, a method of transferring a session between first and second devices includes using a first software development kit (SDK) to collect data and behaviors related to viewing digital media on the first device, using a second SDK configured to collect data and behaviors related to viewing digital media on the second device, and using an intermediary device coupled to the first and second devices to collect the data and behavior received from the first and second devices, operate a session transfer function to use the collection of data and behavior to identify a use of an application on the second device to view a digital media representation present in an application on the first device, determine a session of the application on the first device, and transfer the session to the second device without requiring authentication for the use of the application on the second device.

[0005] The disclosed embodiments represent an improvement over present procedures requiring a login or other authentication procedures when operation of various applications moves from one device to another. A seamless session transfer between devices may significantly enhance user experience and productivity by allowing uninterrupted workflows. Tasks may be transferred with minimal interruption, for example, drafting emails, editing presentations, or extending gameplay, without repeated authenticate requirements as they are operated on oneAttorney Docket: MVG.7959.WOU2 device and then operation is transferred to another device. Additionally, this approach enhances security and simplifies onboarding by removing repeated credential exposure and streamlining device adoption. Because authentication is not required on each device, fewer vulnerabilities like phishing or keylogging attacks may be encountered. The result is safer, more efficient transitions between devices, ensuring that tasks resume effortlessly and securely wherever the task activities may be transferred. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The foregoing aspects and other features of the embodiments are explained in the following description, taken in connection with the accompanying drawings, wherein:

[0007] Figure 1 illustrates a system for transferring a session between first and second devices according to the disclosed embodiments;

[0008] Figure 2 shows a general block diagram of the operation of one or more software development kits in the first and second devices of the system;

[0009] Figure 3 shows a general block diagram of the operation of an intermediary device in the system; and

[0010] Figure 4 shows a general block diagram of a session transfer function according to the disclosed embodiments. DETAILED DESCRIPTION

[0011] For purposes of the disclosed embodiments, a session may be defined as an interactive connection with a device where information is exchanged and may include applicationAttorney Docket: MVG.7959.WOU2 data sufficient to resume operation of an application after transfer of the session from one device to another.

[0012] For purposes of the disclosed embodiments, digital media refers to content delivered electronically across digital devices and platforms. Digital media may include web2 services, for example, social media, digital ads, video platforms such as YouTube, immersive virtual worlds within metaverse platforms such as Decentraland or Roblox, and interactive gaming experience; including console systems like Microsoft Xbox and Sony PlayStation.

[0013] Figure 1 shows a schematic illustration of an exemplary system 100 on which the disclosed embodiments may be implemented. The system 100 may include one or more first and second devices 1021-102n, 2021-202nand at least one intermediary device 302. The first and second devices 1021-102n, 2021-202n may include a software development kit (SDK) 1041-104n, 2041-204nthat may operate to collect data and behaviors and provide that data and behaviors to the intermediary device 302, for example, through an external device interface 1061-106n, 2061- 206n. In some embodiments, the external device interfaces 1061-106n, 2061-206n may include a wireless interface, for example, IEEE 802.11, IEEE 802.15, cellular 2G-6G, or any other suitable wireless interface, a wired interface, for example, Ethernet, Universal Serial Bus (USB), or any other suitable wired interface, and may include a web browser. The first and second devices 1021- 102n, 2021-202n may be embodied as, for example, desktop computers, laptops, tablets, mobile phones, or any other computing devices capable of performing the functions of the disclosed embodiments.

[0014] Each first and second device 1021-102n, 2021-202n may include computer readable program code 1081-108n, 2081-208nstored on at least one non-transitory computer readable medium for carrying out and executing the implementation of the session transfer between devicesAttorney Docket: MVG.7959.WOU2 as described herein. In at least one embodiment, the computer readable program code 1081-108n, 2081-208nmay invoke or operate the SDKs 1041-104n, 2041-204n.

[0015] As shown in Figure 2, the SDKs 1041-104n, 2041-204n generally operate to collect data and behaviors (block 402) and communicate the data and behaviors to the intermediary device 302 (block 404).

[0016] The computer readable medium may include memories 1101-110n, 2101-210n and in alternate aspects, the computer readable program code 1081-108n, 2081-208nmay be stored in memories external to, or remote from, the first and second devices 1021-102n, 2021-202n, for example, in memory 304 of intermediary device 302. Memories 1101-110n, 2101-210n may include magnetic media, semiconductor media, optical media, or any media which is readable and executable by a computer. Each first and second device 1021-102n, 2021-202n may also include a processor 1121-112n, 2121-212nfor executing the computer readable program code 1081-108n, 2081-208n. One or more applications 1141-114n, 2141-214n may operate on the first and second devices 1021-102n, 2021-202n.

[0017] Each SDK 1041-104n, 2041-204nmay be specifically tailored to a particular operating environment and a particular one of the first and second devices 1021-102n, 2021-202n.

[0018] The SDKs 1041-104n, 2041-204nmay operate to collect, without limitation, SDK data and behaviors 310, including session data and information related to the first and second devices 1021-102n, 2021-202n, for example, one or more of IP addresses, geographic locations, for example, countries, Geographic Information System (GIS) or location data, applications installed or running, including any session identifiers, game identifiers, device identifiers, language settings, time of use, digital media viewed, times when digital media was viewed, digital media scanned,Attorney Docket: MVG.7959.WOU2 times when digital media was scanned, digital media that was possible to scan, applications within which digital media is present, digital media associated with an application, or times when digital media was selected. It should be understood that the SDK data and behaviors 310 may include any data and behaviors suitable for use or operation by the disclosed embodiments.

[0019] The SDK data and behaviors 310 collected by the SDKs 1041-104n, 2041-204nmay further include real-time behaviors and data, including, for example, one or more of application- specific context and interactions within an application, such as items viewed and actions performed, and information related to activity within one or more applications operating on the first and second devices 1021-102n, 2021-202n.

[0020] The SDKs 1041-104n, 2041-204nmay still further operate to collect SDK data and behaviors 310 including, without limitation, one or more of application start time, exit time, digital spaces entered / exited, items viewed, distance from items, interaction with items, for example, selection or movement, or a present scanning of an item. It should be understood that behaviors may include any behaviors suitable for use or operation by the disclosed embodiments.

[0021] The SDKs 1041-104n, 2041-204nmay package all, or a portion of, the SDK data and behaviors 310, into a payload, which may be a schemaless payload, the package including identification of data types and current state, value or any other characteristic. The SDKs 1041- 104n, 2041-204n may operate to transmit these payloads in real time to the intermediary device 302 using a data communication protocol. For example, a mobile device may send payloads using TCP over cellular or Wi-Fi networks.

[0022] The intermediary device 302 may operate to aggregate data from multiple SDKs, analyze the data to identify and match behavioral patterns, identify related sessions, and store theAttorney Docket: MVG.7959.WOU2 current session in order to enable seamless session transfers across devices. The intermediary device 302 may include a processor 304 and memory 306 storing computer program code 308 for generally operating the intermediary device 302 to provide the implementation of the session transfer between devices as described herein. The intermediary device 302 may include an external interface 314 for communication with the first and second devices 1021-102n, 2021-202n, similar to the external device interfaces 1061-106n, 2061-206n .

[0023] In some embodiments, the intermediary device 302 may be implemented by a cloud computing service, and the session transfer between devices may be provided in the form of a software as a service (SaaS). The various components of the system 100, including first and second devices 1021-102n, 2021-202nand the intermediary device 302 may communicate over a network 400.

[0024] The computer program code 308 on the intermediary device 302 may operate to receive data and behaviors 310 from the SDKs 1041-104n, 2041-204n on the first and second devices 1021-102n, 2021-202n, store the SDK data and behaviors 310, and invoke or operate a session transfer function 312. The session transfer function 312 may be implemented in hardware, software or a combination of hardware and software.

[0025] As shown in Figure 3, the session transfer function 312 may operate to use the SDK data and behaviors 310 received from the SDKs 1041-104n, 2041-204n to identify a use of an application on a second device 2021-202n (block 502), determine a related session on a first device 1021-102n(block 504), and transfer the session to the second device 2021-202n(block 506).

[0026] The session transfer function 312 may operate to use the SDK data and behaviors 310 to identify the use on the second device 2021-202nutilizing one or more exemplary methods,Attorney Docket: MVG.7959.WOU2 separately or in combination. Such methods may include digital media recognition, filtering, and various artificial intelligence techniques and may include multiple types of each method.

[0027] Referring to Figure 4, the session transfer function 312 may include an artificial intelligence engine 316 trained using a specific set of training data 318. The artificial intelligence engine 316 may include a convolutional neural network or other artificial intelligence network. In at least one embodiment, the training data may include a digital media dataset of multiple perspectives of possible digital media items that may be viewed when operating the one or more applications 1141-114n, 2141-214n when using the first and second devices 1021-102n, 2021-202n. In some embodiments, the digital media items may be collected by the SDKs 1041-104n, 2041-204n , transferred to the SDK data and behaviors 310, and used by the artificial intelligence engine 316 for unsupervised learning training. In other embodiments, the digital media items may be collected by the SDKs 1041-104n, 2041-204n, transferred to the SDK data and behaviors 310, reviewed and labeled, and used by the artificial intelligence engine 316 for supervised learning training.

[0028] The trained artificial intelligence engine 316 may be used to identify a digital media representation when that digital image representation is in one application and scanned or viewed using another application. For example, multiple perspectives of digital media representations within a gaming application in combination with the collected SDK data and behaviors 310 may be used as a training set for the artificial intelligence engine 316. When an application, for example, a gaming application, on one of the first devices 1021-102n includes a digital media representation, and a use of the application on one of the second devices 2021-202nis detected by the trained artificial intelligence engine 316, including viewing the digital media representation on the one of the second devices 2021-202n, the trained artificial intelligence engine 316 may operateAttorney Docket: MVG.7959.WOU2 to automatically identify the application session on the first device 1021-102n and transfer the session to the second device 2021-202n.

[0029] The digital media representation recognition may be used in combination with a filtering technique. The filtering technique may use a data set of a combination of the digital media of the scanned item and one or more of the data and behaviors 310 and use the data set to search for the application session. As an example, the digital media of the scanned item may be used in combination with a specific time period in which the data set is or had been registered.

[0030] The digital media recognition algorithm may incorporate a self-correcting mechanism driven by activity following a session transfer. When application activity continues following a session transfer, the algorithm may implicitly mark the transfer as successful. If application activity does not continue, the session transfer may be flagged for further evaluation and may be explicitly marked as unsuccessful. These activity driven outcomes, both successes and failures, are then fed back into the machine learning model, continuously improving its accuracy in matching user sessions.

[0031] In some embodiments, another artificial intelligence algorithm may be utilized to analyze the SDK data and behaviors 310 to identify a use on a second device 2021-202n related to a session on a first device 1021-102n. For example, the SDKs 1041-104n, 2041-204nmay also collect a time series of data events of operations within each application, and a time and manner in which interaction occurs with items of the application, and this data may be grouped with the data and behaviors collected by the SDKs 1041-104n, 2041-204ndescribed above, and stored as the SDK data and behaviors 310. This time series of data events grouped with the data and behaviors may be used as a training set for an artificial intelligence algorithm, for example, a Long-Term Short Memory (LTSM) neural network model.Attorney Docket: MVG.7959.WOU2

[0032] In order to improve the session identification process, a sparse matrix may be used to assemble some or more of the time series of data events and data and behaviors 310. By definition the sparse matrix may include a large number of zero entries at least because operations may not include the same applications and the applications may not be operated in the same manner. The sparse matrix may be used in a standard Principal Component Analysis (PCA) by first removing at least a portion of the zero entries using Singular Vector Decomposition to compute the smallest singular values and corresponding singular vectors of the sparse matrix, thus reducing the size of the matrix while retaining most of the important information. Supervised linear models using logistic regression from the data may be used to create a model using the training data.

[0033] The artificial intelligence algorithm using the time series of data events may also incorporate the self-correcting mechanism driven by activity following a session transfer.

[0034] As mentioned above, one or more of the exemplary methods may be used, separately or in combination in order to identify the session. Once identified, the intermediary device may operate to transfer the session to the second device where the use related to the session is detected.

[0035] It is noted that the embodiments described herein can be used individually or in any combination thereof. It should be understood that the foregoing description is only illustrative of the embodiments. Various alternatives and modifications can be devised by those skilled in the art without departing from the embodiments. Accordingly, the present embodiments are intended to embrace all such alternatives, modifications and variances that fall within the scope of the appended claims.

Claims

Attorney Docket: MVG.7959.WOU2 CLAIMS 1. A system for transferring a session between devices comprising: a first device including a first software development kit (SDK) configured to collect data and behaviors related to viewing digital media on the first device; a second device including a second SDK configured to collect data and behaviors related to viewing digital media on the second device; and an intermediary device coupled to the first and second devices, the intermediary device including: a collection of the data and behavior received from the first and second devices; and a session transfer function operating to use the collection of data and behavior to: identify a use of an application on the second device to view a digital media representation present in an application on the first device; determine a session of the application on the first device; and transfer the session to the second device without requiring authentication for the use of the application on the second device.

2. The system of claim 1, wherein the collection of data and behavior comprises session data and information related to the first and second devices, including one or more of IP addresses, geographic locations, location data, applications installed, language settings, time of use, digital media viewed, and interactions within an application, 3. The system of claim 1, wherein the session transfer function comprises: an artificial intelligence engine trained using a digital media dataset of multiple perspectives of digital media items viewed when operating the application, wherein the artificial intelligence engine is configured to:Attorney Docket: MVG.7959.WOU2 automatically identify the session on the first device when an application on the first device includes a digital media representation, and a use of the application on the second device is detected by the artificial intelligence engine; and transfer the session to the second device.

4. The system of claim 3, wherein the digital media dataset of multiple perspectives of digital media items viewed when operating the application is used for unsupervised learning when training the artificial intelligence engine.

5. The system of claim 3, wherein the digital media dataset of multiple perspectives of digital media items viewed when operating the application is labelled and used for supervised learning when training the artificial intelligence engine.

6. The system of claim 3, wherein the artificial intelligence engine comprises a convolutional neural network.

7. The system of claim 3, wherein the artificial intelligence engine comprises a long term short memory neural network model.

8. The system of claim 7, wherein a time series of data events of operations within the application, and a time and manner in which interaction occurs with items of the application, grouped with the collection of data and behavior is used as a training set for the long term short memory neural network model.

9. A method of transferring a session between first and second devices comprising: using a first software development kit (SDK) to collect data and behaviors related to viewing digital media on the first device; using a second SDK configured to collect data and behaviors related to viewing digital media on the second device; and using an intermediary device coupled to the first and second devices to: collect the data and behavior received from the first and second devices; operate a session transfer function to use the collection of data and behavior to:Attorney Docket: MVG.7959.WOU2 identify a use of an application on the second device to view a digital media representation present in an application on the first device; determine a session of the application on the first device; and transfer the session to the second device without requiring authentication for the use of the application on the second device.

10. The method of claim 9, wherein the collection of data and behavior comprises session data and information related to the first and second devices, including one or more of IP addresses, geographic locations, location data, applications installed, language settings, time of use, digital media viewed, and interactions within an application, 11. The method of claim 9, wherein the session transfer function comprises an artificial intelligence engine trained using a digital media dataset of multiple perspectives of digital media items viewed when operating the application, and the artificial intelligence engine: automatically identifies the session on the first device when an application on the first device includes a digital media representation, and a use of the application on the second device is detected by the artificial intelligence engine; and transfers the session to the second device.

12. The method of claim 11, comprising using the digital media dataset of multiple perspectives of digital media items viewed for unsupervised learning when training the artificial intelligence engine.

13. The method of claim 11, comprising labelling the digital media dataset of multiple perspectives of digital media items viewed when operating the application and using the labelled the digital media dataset for supervised learning when training the artificial intelligence engine.

14. The method of claim 11, wherein the artificial intelligence engine comprises a convolutional neural network.

15. The method of claim 11, wherein the artificial intelligence engine comprises a long term short memory neural network model.

16. The method of claim 15, comprising training the long term short memory neural network model using a time series of data events of operations within the application, and a time and mannerAttorney Docket: MVG.7959.WOU2 in which interaction occurs with items of the application, grouped with the collection of data and behavior.

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