WO2022155051A1 - Means for defining mental aspects of user digital activity - Google Patents

Abstract

A computer-based method is provided for defining and leveraging a user's digital activity. The method includes identifying a first digital event and creating a record of the first digital event in a database and identifying a second digital event and creating a record of the second digital event in the database. The method then proceeds with identifying at least one value of one of several defined mental components of the first digital event that is identical to a value for a corresponding defined mental component of the second digital event and comparing at least one additional mental component of the first digital event to a corresponding additional mental component of the second digital event to establish a proximity metric between the first and second digital events. A present digital event is then identified at the user interface and a recommendation is generated based at least partially on the proximity metric.

Description

Means for defining mental aspects of user digital activity

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Application 63/136,778, filed January 13, 2021, the contents of which are incorporated herein by reference.

FIELD OF THE INVENTION

[0002] Systems and methods are described for utilizing a computer for defining mental aspects of a user's digital activity, representing such digital activity in a virtual mental net, and incorporating present and later digital activity.

BACKGROUND

[0003] Computers and current technological solutions do not acknowledge and consider the user as a human being with personality and a complex mental subjective entity. It is as if the computer is communicating with another computer or machine, delivering objective information and operating applications.

[0004] As technology makes progress in the service of personal devices and social interactions, great amounts of mental and subjective information are placed in each user's digital applications and accounts. This is what makes our smartphones and personal gadgets practically an extension of our body and mind.

[0005] Thanks to all this precious subjective information stored in our devices, computers can assist the human being in exploring and linking the never-ending experiences of our complex mind.

[0006] In addition, the mental capacity of the mind is challenged these days with endless and intense interactions in the virtual reality of the internet and social media.

[0007] There is a need for systems and methods that can evaluate new variables based on the dynamics and subjectivity of the mind. There is a need for such systems and methods that can then explore and interact with a whole human being, while considering the internal reality of the person inside the user. SUMMARY

[0008] This disclosure relates to defining mental aspects of a user's digital activity, gathering in an associative net of experiences, and connecting with future activity to acknowledge and integrate the subjectivity of the user in devices and apps.

[0009] Mental and subjective reality is defined here as a complex network of never-ending experiences, far beyond our full understanding, yet accounting for who we are.

[0010] Systems and methods are provided for a computer's defining the mental and subjective aspects of the user as reflected from digital activity, represented in a Virtual Mental Net (VMN), and contributing to later digital events with the combination of the user's mental reality. The embodiments discussed rely on a conception of the mental and subjective reality of any user as a complex network of never-ending experiences, far beyond our full understanding, yet accounting for who we are. To start with, Digital Events (DE) are disassembled into components of mental representations such as Time, Location, Digital Source, Self, Other/s, Emotional Experience, Action, and Inanimate. Then the computer links between same components of different DE, to create an ongoing VMN (Virtual Mental Net) of the user. The computer explores a New Digital Event (NDE) by comparing its mental components to other segments on the VMN, enabling insights of the mental links of which the present digital activity addresses. At last, the system uses the Selected Link (SL) on the VMN to adjust later digital activity in a near-experience and subjective manner.

[0011] Systems and methods are provided to define mental components of a user's digital activity, build an ongoing representation of a Virtual Mental Net as reflected from the user's activity, and finally assimilating the user's mental states in later digital activity.

[0012] Defining the mental components of the user's digital activity rely on computer's categorizations to objective parameters such as Time (T), Location (L), and Digital Source (DS), and subjective parameters such as Emotional Experience (EE), Self (S), Other/s (O), Action (A), and Inanimate subject or object (I).

[0013] The computer classifies the subjective parameters by using key words, key symbols, and key behaviors, each fitting to one category.

[0014] Once a digital event is disassembled into mental components, the computer creates links between all the components, to be called Internal Links of a Digital Event, ILDE, or ILX for Internal Links of digital event signed as X.

[0015] The computer then searches for identical mental components, to connect between digital activities. For example, if the component of Other is the same in digital events X and Y, then the computer links these events together, ILX — ILY, with all their components. The components are compared to determine the mental distance based on resemblance, ranging between 0-10, where 0 represents identical and 10 represents wholly different.

[0016] In an ongoing linking of identical components and their accompanying components, the computer creates a Virtual Mental Net (VMN) of the user, as reflected from digital activities.

[0017] The VMN is evolving and changing all the time, as the digital activity of the user is always in motion.

[0018] In the Present Digital Event (PDE), the computer is searching for links between the mental components of the PDE and the components on the VMN by: (i) Quantity of references between the mental components of the NDE and the relevant components on the VMN. For example, if ILX has 3 identified mental components - OX, EEX, and TX - and the computer finds on the VMN - 6 times of the same O, 4 times of the same EE, and 1 reference to T. (ii) Proximity between the mental components of the NDE to the relevant components on the VMN. For example, OY (VMN)=0X and EEZ(VMN)=EEX are the closet to each other on the VMN.

[0019] The computer then weighs all the results to determine what should be the Selected Link (SL) on the VMN to the present digital activity. In the example above, the computer finds digital activities Y and Z as the most relevant to the present digital activity, and Y is the SL because it represents O, which has more connections on the VMN than the Z's event which represents the EE.

[0020] Connecting the mental components of the Present Digital Event (PDE) with the SL establishes a new link in the VMN.

[0021] The computer connects the mental components on the SL with the PDE, to integrate the mental reality of the user, as composed of the VMN, with the user's digital activity (represented as PDE). For example, PDE A (PDEA) is composed of OA and TA, and SL Y (SLY) is composed of OY (the same as OA) and EEY. Now the computer adds the EEY's component to create a PDEA+EEY.

[0022] Adding mental components from the VMN to the PDE, is to take into consideration the user's mental reality when using technology.

[0023] Accordingly, in some embodiments, a computer-based method is provided for defining and leveraging a user’s digital activity.

[0024] The method comprises identifying a first digital event at a user interface, creating a record of the first digital event in a database, the record comprising a plurality of defined mental components, wherein a value for each defined mental component is extracted from details of the first digital event.

[0025] The method then proceeds to identify a second digital event at the user interface, creating a record of the second digital event in the database, the record comprising the plurality of defined mental components, wherein a value for each defined mental component is extracted from details of the second digital event.

[0026] The method then proceeds with identifying at least one value of one of the defined mental components of the first digital event that is identical to a value for a corresponding defined mental component of the second digital event and comparing at least one additional mental component of the first digital event to a corresponding additional mental component of the second digital event to establish a proximity metric between the first and second digital events. [0027] The method then proceeds with identifying a present digital event at the user interface and identifying values for the plurality of defined mental components for the present digital event, wherein the value for each defined mental component is extracted from details of the present digital event.

[0028] The method then compares the plurality of defined mental components of the present digital event to each of a plurality of digital events, including the first digital event and the second digital event, stored in the database to identify at least one matching mental component.

[0029] For each matching mental component identified, the method defines a similarity metric between the corresponding digital event and the present digital event. The method then identifies, based on the similarity metric, the digital event of the plurality of digital events in the database most similar to the present digital event and generates a recommendation at the user interface based on a secondary digital event distinct from the most similar digital event, wherein the secondary digital event is selected at least partially based on a value for the proximity metric between the most similar digital event and the secondary digital event.

[0030] In some embodiments, the plurality of digital events in the database each comprise the plurality of defined mental components, and each digital event forms a node of a virtual mental net. Each node in the virtual mental net is then associated with at least one adjacent node by an identical value for a corresponding defined mental component and a proximity metric.

[0031] In some such embodiments, the secondary digital event is selected based on a cumulative proximity metric based on values for at least two proximity metrics for consecutive nodes forming a chain in the virtual mental net.

[0032] In some embodiments, the present digital event is added to the virtual mental net as a node. [0033] In some embodiments, the recommendation is based on a semantic analysis of the present digital event combined with a semantic analysis of the secondary digital event.

[0034] In some embodiments, the plurality of defined mental components include at least one of a time, location, and digital source derived from metadata of the digital event and at least one of emotional experience, self, other party, action, and inanimate subject or object derived from a semantic analysis of contents of the digital event.

[0035] In some such embodiments, the similarity metric is based on a quantity of the defined mental components that correspond between two events and the proximity of the values of any corresponding mental components between the two events.

[0036] In some embodiments, a method is provided in which a database is provided containing a prepared virtual mental net. In such an embodiment, a method may first identify a present digital event at a user interface.

[0037] The method may then retrieve, from a database, a virtual mental net comprising a plurality of prior digital events, with each prior digital event comprising values for a plurality of defined mental components, and where each prior digital event forms a node of the virtual mental net.

[0038] The method then defines values for the present digital event corresponding to the plurality defined mental components of each of the prior digital events amd compares the values for the present digital event to each of the prior digital events in the virtual mental net to identify at least one matching mental component.

[0039] For each matching mental component identified, the method then defines a similarity metric between the corresponding prior digital event and the present digital event and identifies, based on the similarity metric, the prior digital event in the virtual mental net most similar to the present digital event.

[0040] The method then generates a recommendation at the user interface device based on a secondary digital event of the prior digital events distinct from the most similar digital event, wherein the secondary digital event is a prior digital event in the virtual mental net and is selected at least partially based on a value for a proximity metric between the most similar digital event and the secondary digital event.

[0041] In some such embodiments, the virtual mental net is created by comparing each pair of prior digital events and determining if the pair has at least one value of one of the defined mental components that is identical. For each pair having at least one identical value of a defined mental component, the method compares values for at least one additional defined mental component and defines a proximity metric between the pair based on the comparison of the at least one additional defined mental component.

[0042] In such embodiments, each pair having at least one identical value is an adjacent node of the virtual mental net.

[0043] In some such embodiments, the secondary digital event is selected based on a cumulative proximity metric based on values for at least two proximity metrics for consecutive nodes forming a chain in the virtual mental net.

[0044] In some embodiments, the present digital event is added to the virtual mental net as a node either prior to or following the generation of the recommendation.

[0045] In some embodiments, the recommendation is based on a semantic analysis of the present digital event combined with a semantic analysis of the secondary digital event.

[0046] In some embodiments, the plurality of defined mental components include at least one of a time, location, and digital source derived from metadata of the digital event and at least one of emotional experience, self, other party, action, and inanimate subject or object derived from a semantic analysis of contents of the digital event.

[0047] In some such embodiments, the similarity metric is based on a quantity of the defined mental components that correspond between two events and the proximity of the value of the corresponding mental components between the two events.

BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 shows the disassembling of digital event to mental components by key words, key behavior, and key symbols. [0049] Figure 2 shows the creation of Internal Links (IL) between mental components of a Digital Event (DE) - ILDE

[0050] Figure 3 shows the connection of two digital events by proximity of the mental components to each other, the identical components to be the link.

[0051] Figure 4 shows a Virtual Mental Net (VMN) of a user based on the digital Activity (DE).

[0052] Figure 5 shows a method calculating and linking a Present Digital Event (PDE) with the Virtual Mental Net of the user (VMN) - a model for the computer's interaction with the mental states of the user.

[0053] Figure 6 shows the combining of Present Digital Activity with the user's mental reality as composed of the Selected Link (SL).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0054] The description of illustrative embodiments according to principles of the present invention is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description. In the description of embodiments of the invention disclosed herein, any reference to direction or orientation is merely intended for convenience of description and is not intended in any way to limit the scope of the present invention. Relative terms such as “lower,” “upper,” “horizontal,” “vertical,” “above,” “below,” “up,” “down,” “top” and “bottom” as well as derivative thereof (e.g., “horizontally,” “downwardly,” “upwardly,” etc.) should be construed to refer to the orientation as then described or as shown in the drawing under discussion. These relative terms are for convenience of description only and do not require that the apparatus be constructed or operated in a particular orientation unless explicitly indicated as such. Terms such as “attached,” “affixed,” “connected,” “coupled,” “interconnected,” and similar refer to a relationship wherein structures are secured or attached to one another either directly or indirectly through intervening structures, as well as both movable or rigid attachments or relationships, unless expressly described otherwise. Moreover, the features and benefits of the invention are illustrated by reference to the exemplified embodiments. Accordingly, the invention expressly should not be limited to such exemplary embodiments illustrating some possible non-limiting combination of features that may exist alone or in other combinations of features; the scope of the invention being defined by the claims appended hereto.

[0055] This disclosure describes the best mode or modes of practicing the invention as presently contemplated. This description is not intended to be understood in a limiting sense, but provides an example of the invention presented solely for illustrative purposes by reference to the accompanying drawings to advise one of ordinary skill in the art of the advantages and construction of the invention. In the various views of the drawings, like reference characters designate like or similar parts.

[0056] Figure 1 shows the disassembling of a digital event into mental components by key words, key behaviors, and key signals. The first step of some embodiments of the method is to define mental components out of the user's digital activity, as shown in FIG. 1. The computer is using key words, key behaviors, and key symbols to categorize Digital Events (DE) in mental components. In the context of the computerized system and method disclosed herein, each defined mental component may be included in a record of the digital event, such as in a database, such that each defined mental component may have a value, or may be null, for each digital event.

[0057] In some embodiments, such as that shown, the defined mental components comprise a standard set of mental components. As shown, the defined mental components may thereby comprise time (T), location (L), digital source (DG), emotional experience represented by the event (EE), references to, and the form of references to, the user’ s self (S), references to others (O), references to action (A), and references to inanimate objects (I).

[0058] Some of these categories are objective, such as: time (T) of the activity, and any reference to a time in the past or the future. Location (L) of the activity, indicating where physically the user is during the Digital Event, and sometimes the mentioning of where the user was or will be. The Digital Source (DG) of where virtually the digital event took place, and sometimes the mentioning of where virtually the digital event took place or will take place. The digital source may include, for example, a reference to the platform or platforms on which a particular digital event occurred.

[0059] Alternatively, several of the defined mental components of a digital event are subjective, such as: Category of the user's Self (S) which contains references to the user by oneself or others such as I, me, one's name or nick names, you (mentioned by another), and if the user is includes with others such as we, my class/family, and so on. The category of other/s (O) can contain names of other people, references by pronouns such as you (by the user), they, and we. The category of Emotional Experience (EE) may parse contents of the digital event for key words denoting feelings such as "glad" or "afraid," symbols such as emojis of feelings or punctuations such as exclamation marks (and sometimes many of them), and behaviors such as nervous typing or driving. The category of Action (A) may evaluate and capture any verbs, symbols such as drawings of action (e.g., bicycle or swimming), and behaviors such as fast transitions between applications without a stop. The category of inanimate objects (I) may evaluate the contents of the digital event to capture references to nature, organizations, concepts and so on, such as ball, cinema, weather.

[0060] The digital event may take a wide variety of forms, and may include, for example, sending emails, posting on social media platforms, engaging with particular applications on a user interface device, switching between applications, and many other potential digital events. The subjective categories may evaluate a particular digital event by deriving some value from user action or based on the contents of media being sent, created, or posted. For example, the subjective categories may be populated based on a semantic analysis of a particular social media post comprising the digital event.

[0061] As one example, a user may be at the office late in the evening, sending an e-mail to one's brother and partner. The subject of the email is “NEW OFFER,” and it is about a dilemma whether to take a business offer from M, a “successful company,” or stay in the same position. “I don’t know what to do,” writes the user with a confused emoji.

[0062] The computer then deconstructs the digital event, which is the sending of the email, into the mental components of location (L) - the office; time (T) - 8:30 pm, 22 of December, 2020, Tuesday; digital source (DG) - email; other person (O) - the user's brother and partner; self (S) - 1; emotional experience (EE) - confused, do not know, dilemma, successful; action (A) - what to do, take or stay?; inanimate objects (I) - M company, new offer.

[0063] After dividing the Digital Event (DE) into the defined mental components, the computer create links between all the components of the event, as shown in FIG. 2. These links shall be called Internal Links (IL) of Digital Event (ILDE).

[0064] From the example above, now the office is linked to Tuesday the 22 of December 8:30 pm, and both are linked to email, and three of them are linked to the user's brother and partner, and five of them are linked to “I,” and six of them are linked to confused, do not know, dilemma, and successful; and ten of them are linked to “what to do,” “take or stay?;” and twelve of them are linked to M company and new offer. In this way, all fourteen mental components are linked with each other.

[0065] In an ongoing and never-ending process, the computer divides all digital events identified into mental components. In some embodiments, the digital events identified reflect digital events at the user’s user interface. In other embodiments, all digital events that can be identified as associated with the user may be incorporated across multiple user interfaces. This may include, for example, a user’s smartphone and laptop, among other devices.

[0066] When a system implementing the methods described finds identical components from different digital events, it may create a link between the events as shown in FIG. 3. From the example above, the user's partner may respond 5 minutes later, with a message “I really wonder about this thing because the last time with Z company wasn't so good. But I am also proud and excited for you! Maybe it will be better this time.”

[0067] Now, as shown in FIG. 3, these two digital events, namely the outgoing and incoming emails, are connected, with the component of O - partner as being the link.

[0068] As described, the mental components of the linked events are compared to determine the mental distance between matched components. In some embodiments, the distance is based on resemblance, ranging between 0-10. In such an embodiment, 0 represents identical values for a particular mental component across two digital events, 10 represents wholly different values. The decisions are made by objective proximity as in time or emotional relatedness (e.g. happy and glad), and subjective proximity as known by the user's personality, e.g., confusion and sadness usually come together, pictures are usually followed by social media. In such a way, the method may then generate and establish a proximity metric between a first and second digital event linked by at least one component.

[0069] In this example, the two events are connected as O (partner) gets the 0, T - is 1 (5 minutes between two events), EE 1 (successful - repair) I - 2 (M company - Z company), DG - 2 (email-message), EE - 3 (confused-wasn't so good), A - 5 what to do- wonder, EE - 5 (successful - excited). EE - 5 (dilemma - was not so good), EE- 9 (successful - was not so good).

[0070] The process of disassembling digital events into mental components, linking between them and between the digital events, creates an ongoing Virtual Mental Net (VMN), as shown in FIG. 4. The VMN of the user is typically stored in a database and is updated constantly as new digital events are identified. The virtual mental net is then used as the database from which the computer is appreciating and interacting with the present digital activity of the user.

[0071] In the example above, the next digital activity registered as "confusion", is going to be linked to the previous activities, as reflected by the connections between the mental components. For example, the user reacts to one of his friends, AV, on social media: "I am confused. What is your suggestion then?" with an emoji of a wink. Now confusion is the link between the events, and all the mental components relate to each other by grading their resemblance as was explained before.

[0072] Figure 5 shows a method for defining and leveraging digital activity in accordance with this disclosure. As shown, and as discussed above, the method first identifies (500) a first digital event at a user interface. Once the first digital event is identified (at 500), the method creates a record of the first digital event in a database (510). The database may be to catalog all digital events identified in a virtual mental net, such as that discussed above.

[0073] In creating the record of the first digital event (at 510), the method defines values (520) for a plurality of defined mental components associated with the first digital event. As discussed above, the values are extracted from details of the first digital event. In some embodiments, the plurality of defined mental components includes at least one of a time, location, and digital source derived from metadata of the digital event and at least one of emotional experience, self, other party, action, and inanimate subject or object derived from a semantic analysis of contents of the digital event.

[0074] After the record for the first digital event is created (at 510), the method identifies an additional digital event (530), such as a second digital event. A record of the second digital event is then created (at 510), and values are defined for defined mental components associated with the second digital event (at 520). The method continuously identifies digital events (at 500, 530) in this manner and continuously creates records (at 510) for each digital event identified.

[0075] Once multiple digital events are stored in the virtual mental net, the method proceed to identify (540) at least one value of one of the defined mental components of the first digital event that is identical to a value for a corresponding defined mental component of the second digital event. This step links two digital events, as discussed above and shown in FIG. 3. Once such linked digital events are identified, the method proceeds to compare (550) at least one additional mental component of the first digital event to a corresponding additional mental component of the second digital event. This comparison, typically across multiple additional mental components is discussed above, in reference to FIG. 3, and is used to define a proximity metric (560) between the first and second digital events.

[0076] This definition of a proximity metric (at 560) defining a relationship between two digital events may be repeated for any pair of digital events in the virtual mental net in the database in which at least one value of one of the defined mental components are determined to be identical.

[0077] Once a virtual mental net is defined in this way, the method continues to evaluate digital events as they occur and incorporate them into a virtual mental net (570). When such an event occurs, it may be considered to be a present digital event identified (580) at the user interface. The method may then identify values (590) for the plurality of mental components discussed above as they relate to the present digital event. This may be done with or without creating a record at this time. As discussed above with respect to the first and second digital events, the values for each defined mental component (identified at 590) may be extracted from details of the present digital event.

[0078] Once the values are identified (at 590), those values are then compared (600) to the existing plurality of digital events previously incorporated into the virtual mental net. In this way, at least one matching mental component is identified (610) among the records of digital events.

[0079] Once at least one mental component is identified (at 610) as matching the value of the corresponding mental component of the present digital event, the method proceeds to define, for each matching mental component identified, a similarity metric (620) for a relationship between the identified digital event and the present digital event.

[0080] The method then identifies (630), based on the similarity metric, the digital event of the plurality of digital events in the database, or in the virtual mental net, most similar to the present digital event. In some embodiments, the similarity metric may be based on a quantity of the defined mental components that correspond between two events and the proximity of the values of any corresponding mental component between the two events.

[0081] The method then generates a recommendation (640) at the user interface based on a secondary digital event distinct from the most similar digital event, where the secondary digital event is selected at least partially based on a value for the proximity metric between the most similar digital event and the secondary digital event.

[0082] Accordingly, each of the plurality of digital events in the database generally comprise the plurality of defined mental components, and each digital event then forms a node of the virtual mental net built by the method. Each node in the virtual mental net is then associated with at least one adjacent node by an identical value for a corresponding defined mental component and a proximity metric.

[0083] The secondary digital event selected to inform the recommendation (at 640) may then be selected based on a cumulative proximity metric based on values for at least two proximity metrics for consecutive nodes forming a chain in the virtual mental net.

[0084] The method is described in terms of first identifying a plurality of individual events at a user interface, forming a virtual mental net, and then monitoring present digital events. It will be understood that the method may alternatively begin with the identification of a present digital event and rely on a previously formed virtual mental net. Such a virtual mental net may be maintained at the database, and it may comprise prior digital events from additional user interfaces.

[0085] It will further be understood that the method may be an ongoing process. As such, after each present digital event, whether the method generates a recommendation or not, the digital event may be incorporated into the virtual mental net as a digital event, and may be linked to adjacent nodes by way of a proximity metric calculated in the same way discussed above.

[0086] Additional details are now provided for how the computer determines which of the mental components of the present digital event (PDE) is linked with which of the matched components already present in the VMN, and where exactly on the VMN it is then located. First the computer divides the present digital event into the defined mental components, then the method searches for matched components on the VMN. After collecting all matches, the computer calculates their importance and relevance by quantity, resemblance of components, and the proximity on the VMN, to define a similarity metric between the present digital event and the digital events already present in the virtual mental net. The similarity metric may then be used to identify a Selected Link (SL) between the VMN and the PDE.

[0087] In this rolling example, the PDE is the user's reaction to AV on social media about "confusion" with a wink. The mental components of the event are DG - social media, O - AV, EE - confusion and wink, T - 22 of December 1 pm, L - the Park near the office. The computer finds 1000 references to social media on the VMN, 50 to AV, EE confusion 34, EE wink 64, 22 of December 2020 - 20, the park near the office 50. There are 5 DE on the VMN which combines confusion and social media, 10 combines wink and the park.

[0088] The computer then calculates the similarity metric to determine the overall resemblance of the mental components of the present digital event PDE to every link on the VMN, by grading each connection 0-10. For example, the most resembled event on the VMN got the score of 10, and the least got the score 80. The most resembled event on the VMN is the Selected Link (SL). In this example, the SL is the user's listening to “Glory Days” of Bruce Springsteen at the park, sharing with one's partner and quoting: “Glory Days, in the wink of a young girl eye.”

[0089] The next application of the method is to enrich and adjust the user's digital activity to one's mental states and reality as shown in figure 6. By using the SL, the computer can now recognize and connect the Present Digital Activity (PDE) of the user with a relevant link inside oneself.

[0090] Accordingly, the recommendation discussed above (provided at 640) may be based on a semantic analysis of the present digital event combined with a semantic analysis of the secondary digital event. Accordingly, the recommendation may be to incorporate components of the selected link SL into any action taken with respect to the present digital event PDE.

[0091] In the example, the SL is serving the user in linking between the dilemma the user is considering staying in the same job or take a new offer, in and the mental involvement with glory. Now if the user searches in the internet for something to clarify this dilemma, as in looking for a documentary movie or search for information about the new company, the adding of “Glory” to the search can find the user better results for something that is in one's motivations, maybe far beyond what the user can see by oneself.

[0092] The methods according to the present disclosure may be implemented on a computer as a computer implemented method, or in dedicated hardware, or in a combination of both. Executable code for a method according to the present disclosure may be stored on a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc. Preferably, the computer program product may include non-transitory program code stored on a computer readable medium for performing a method according to the present disclosure when said program product is executed on a computer. In an embodiment, the computer program may include computer program code adapted to perform all the steps of a method according to the present disclosure when the computer program is run on a computer. The computer program may be embodied on a computer readable medium.

[0093] While the present disclosure has been described at some length and with some particularity with respect to the several described embodiments, it is not intended that it should be limited to any such particulars or embodiments or any particular embodiment, but it is to be construed with references to the appended claims so as to provide the broadest possible interpretation of such claims in view of the prior art and, therefore, to effectively encompass the intended scope of the disclosure.

[0094] All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the disclosure and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the disclosure, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.

Claims

What is claimed is:

1. A computer-based method for defining and leveraging digital activity comprising: identifying a first digital event at a user interface; creating a record of the first digital event in a database, the record comprising a plurality of defined mental components, wherein a value for each defined mental component is extracted from details of the first digital event; identifying a second digital event at the user interface; creating a record of the second digital event in the database, the record comprising the plurality of defined mental components, wherein a value for each defined mental component is extracted from details of the second digital event; identifying at least one value of one of the defined mental components of the first digital event that is identical to a value for a corresponding defined mental component of the second digital event; comparing at least one additional mental component of the first digital event to a corresponding additional mental component of the second digital event to establish a proximity metric between the first and second digital events; identifying a present digital event at the user interface; identifying values for the plurality of defined mental components for the present digital event, wherein the value for each defined mental component is extracted from details of the present digital event; comparing the plurality of defined mental components of the present digital event to each of a plurality of digital events, including the first digital event and the second digital event, stored in the database to identify at least one matching mental component; for each matching mental component identified, defining a similarity metric between the corresponding digital event and the present digital event; identifying, based on the similarity metric, the digital event of the plurality of digital events in the database most similar to the present digital event; and generating a recommendation at the user interface based on a secondary digital event distinct from the most similar digital event, wherein the secondary digital event is selected at least partially based on a value for the proximity metric between the most similar digital event and the secondary digital event.

2. The method of claim 1, wherein the plurality of digital events in the database each comprise the plurality of defined mental components, and each digital event forms a node of a virtual mental net, wherein each node in the virtual mental net is associated with at least one adjacent node by an identical value for a corresponding defined mental component and a proximity metric.

3. The method of claim 2, wherein the secondary digital event is selected based on a cumulative proximity metric based on values for at least two proximity metrics for consecutive nodes forming a chain in the virtual mental net.

4. The method of claim 2, wherein the present digital event is added to the virtual mental net as a node.

5. The method of claim 1, wherein the recommendation is based on a semantic analysis of the present digital event combined with a semantic analysis of the secondary digital event.

6. The method of claim 1, wherein the plurality of defined mental components include at least one of a time, location, and digital source derived from metadata of the digital event and at least one of emotional experience, self, other party, action, and inanimate subject or object derived from a semantic analysis of contents of the digital event.

7. The method of claim 6, wherein the similarity metric is based on a quantity of the defined mental components that correspond between two events and the proximity of the values of any corresponding mental components between the two events.

8. A computer-based method for defining and leveraging digital activity comprising: identifying a present digital event at a user interface; retrieving, from a database, a virtual mental net comprising a plurality of prior digital events, each prior digital event comprising values for a plurality of defined mental components, wherein each prior digital event forms a node of the virtual mental net; defining values for the present digital event corresponding to the plurality defined mental components of each of the prior digital events; comparing the values for the present digital event to each of the prior digital events in the virtual mental net to identify at least one matching mental component; for each matching mental component identified, defining a similarity metric between the corresponding prior digital event and the present digital event; identifying, based on the similarity metric, the prior digital event in the virtual mental net most similar to the present digital event; generating a recommendation at the user interface device based on a secondary digital event of the prior digital events distinct from the most similar digital event, wherein the secondary digital event is a prior digital event in the virtual mental net and is selected at least partially based on a value for a proximity metric between the most similar digital event and the secondary digital event.

9. The method of claim 8 wherein each of the prior digital events correspond to prior events at the user interface, and wherein the virtual mental net is created by: comparing each pair of prior digital events and determining if the pair has at least one value of one of the defined mental components that is identical; for each pair having at least one identical value of a defined mental component, comparing values for at least one additional defined mental component; and defining a proximity metric between the pair based on the comparison of the at least one additional defined mental component, wherein each pair having at least one identical value is an adjacent node of the virtual mental net.

10. The method of claim 9, wherein the secondary digital event is selected based on a cumulative proximity metric based on values for at least two proximity metrics for consecutive nodes forming a chain in the virtual mental net.

11. The method of claim 9, wherein the present digital event is added to the virtual mental net as a node.

12. The method of claim 8, wherein the recommendation is based on a semantic analysis of the present digital event combined with a semantic analysis of the secondary digital event.

13. The method of claim 8 wherein the plurality of defined mental components include at least one of a time, location, and digital source derived from metadata of the digital event and at least one of emotional experience, self, other party, action, and inanimate subject or object derived from a semantic analysis of contents of the digital event.

14. The method of claim 13, wherein the similarity metric is based on a quantity of the defined mental components that correspond between two events and the proximity of the value of the corresponding mental components between the two events.

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