Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1, 8, and 15 are objected to because of the following informalities:
Claims 1, 8, and 15 recite “maximal number of users”. It appears that this should recite “a maximal number of users”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 11805091 B1 to Rapaport et al. (“Rapaport”).
MPEP 2163.07(b) states that “Instead of repeating some information contained in another document, an application may attempt to incorporate the content of another document or part thereof by reference to the document in the text of the specification. The information incorporated is as much a part of the application as filed as if the text was repeated in the application, and should be treated as part of the text of the application as filed.” As such, any and all such incorporations by reference and their relevant citations within are reflected in the mappings as shown below. They will be considered as being part of the teachings of the cited references for the purposes of anticipation under 35 USC 102.
In the instant case, Rapaport incorporates by reference US 8539359 B2 to Rapaport et al. at column 1, lines 40-51 and column 2, lines 28-56 and will be referred to in the instant action as “Rapaport ‘359’”.
Regarding claim 1, Rapaport taught a computer implemented method for managing virtual spaces, the computer implemented method comprising:
monitoring, by a processor set, a number of users in a virtual environment to generate historical user activities and real-time activities for the number of users, wherein the virtual environment is a collective computer-generated space designed to simulate imaginary settings, and wherein the virtual environment is accessible to the number of users through use of virtual reality and augmented reality (consider column 37, lines 18-41 and column 125, lines 3-14 regarding “topic space augmented reality and/or virtuality” using, i.e., “GoogleGoggles” and also wherein “corresponding, topic-related feedbacks (e.g., on-topic invitations/suggestions) are returned from the STAN_3 system 410 to the user's device 199 where the topic-related feedbacks are displayed on a back-facing screen 211 of the device (or otherwise presented to the user 201A) together with the camera captured imagery (or a revised/transformed version of the captured imagery). This provides the user 201A with a virtually augmented reality wherein real life (ReL) objects/persons (e.g., 198) are intermixed with experience augmenting data produced by the STAN_3 topic space mapping mechanism 413′ (see FIG. 4D, to be explained below)”); (consider column 47, lines 23-48, “Jason Rose (a.k.a. Jr. 492) may not know it, but his father, Dr. Samuel Rose (a.k.a. Sr. 491) enjoys playing in a virtual reality domain, say in the SecondLife™ domain (e.g., 460a of FIG. 4A) or in Zygna's Farmville™ and/or elsewhere in the virtual reality universe. When operating in the SecondLife™ domain 494a (or 460a, and this is purely hypothetical), Dr. Samuel Rose presents himself as the young and dashing Dr. Marcus U. R. Wellnow 494 where the latter appears as an avatar who always wears a clean white lab coat and always has a smile on his face. By using this avatar 494, the real life (ReL) personage, Dr. Samuel Rose 491 develops a set of relationships (490.14) as between himself and his avatar. In turn the avatar 494 develops a related set of relationships (490.45) as between itself and other virtual social entities it interacts with within the domain 494a of the virtual reality universe (e.g., within SecondLife™ 460a). Those avatar-to-others relationships reflect back to Sr. 491 because for each, Sr. may act as the behind the scenes puppet master of that relationship. Hence, the virtual reality universe relationships of a virtual social entity such as 494 (Dr. Marcus U. Welcome) reflect back to become real world relationships felt by the controlling master, Sr. 491. In some applications it is useful for the STAN_3 system 410 to track these relationships so that Sr. 491 can keep an eye on what top topics are being currently focused-upon by his virtual reality friends.”) (consider Rapaport ‘359, consider column 19, line 64-column 20, line 16 further regarding a “Personal Emotions Expression Profile” such that “Each user is different and thus may indicate heightened interest in one form or another of informational content that is being displayed and/or otherwise output to the user by his local machine by means of a different set of local user activities. Accordingly, in one embodiment, the local client software 105 includes a local Personal Emotion Expression Profile (L-PEEP) 107 which correlates different ones of locally expressible and detectable physical activities of the user with different kinds of interest and/or emotional indications, be they negative, positive or neutral. The meanings of detected user state may also change as a function of user mood or user surroundings. Alternatively or additionally, the Remote Client Access and Accessible-Resources Monitoring Service (AARMS) 150a in the cloud 150 may link to a client account database that stores an online or remote Personal Emotion Expression Profile (rPEEP) where the latter is made responsible for carrying out, or for supplementing the correlations made for different ones of locally expressed and detected physical activities of the user with different kinds of interest or emotion indications”) (consider further Rapaport ‘359, column 24, line 54-column 25, line 16, “Aside from using the locally stored browser use history 105d and/or in-cloud stored history of the user's browsing activities (and/or mouse use activities 105g) to determine probable topic of interest, automated determination of the user's probable topic of interest may be based on a locally stored history of the user's chat room activities (e.g., stored in local chat history file 105f) and/or in-cloud stored history of the user's chat room activities, where the latter histories may contain information about the identities of the chat rooms that the given user had recently entered into (whether invited or not) and that information may include duration of stay in the room, level of contribution to room content and indications of positive or negative reactions by the user to the contributions of others within that chat room. The latter gathered information can be used to automatically infer certain preferences of the client user (e.g., 121). In one embodiment, one or more adaptive neural networks and/or statistical analysis models are established in the cloud 150 for each user for determining from a host of input parameters, that user's current, most probable topics of interest; that user's current, most probable emotional state; and that user's current, most probable voting intentions (inferred ones rather than explicitly stated ones). The host of input parameters may include data provided in current CFi's, data obtained from the user's most recent or earlier browsing, searching and chatting histories and data regarding apparent success of earlier guesses made by the neural network or other models (for example, based on the user accepting an invitation into a chat room and the user demonstrating satisfaction with the choice, such as by the user participating for a relatively long time in that chat room).”)
identifying, by the processor set, a number of trending topics for the number of users based on the historical user activities and the real-time user activities for the number of users; classifying, by the processor set, the number of users into a number of clusters based on common interests between the number of users identified from the historical user activities and the real-time activities, wherein each cluster in the number of clusters represents a trending topic from the number of trending topics; (consider Rapaport ‘359, column 94, lines 33-37, “As mentioned above, chat rooms operating under the root catch-all node or under the catch-all node of a domain typically have a specific topic assigned to them. However, over time, participants in the catch-all room may begin to cluster around a specific topic.”) (consider further Rapaport ‘359, column 95, lines 6-40, “When step 441 is entered via entry path 441a, the process points to the root's catch-all domain node and sorts the chat rooms (similar to 479) running under that node according to current room population, current room activity level and/or current intensity of user engagement with that room. In one embodiment sorting of chat rooms running under a catch-all also scores the amount of topic space clustering (both recent clustering and over-time trended clustering) that takes place in that room. The concept of topic space clustering has already been described above with reference to FIG. 6. To recap, recently uploaded CFi hints of Sam, Sally and Larry (users 611, 621, 651) are processed by a DLUX service and the DLUX outputs are averaged or otherwise combined to provide a mapping of each user's apparent points of most likely interests on top of the topic space (600). Then closeness of mapped topic (e.g., distance 625) is automatically determined as between users. This determination of closeness (625) between users can indicate that some users are more closely clustered to one another and to a specific point in topic space than they are to other users or to the point locations (cross hairs in FIG. 6) of nearby nodes. When a predetermined proportion of users within a given catch-all room are determined to be closely clustered to one another in topic space (e.g. 600) for one or both of recent CFi values and long terms CFi values, the room receives a high clustering score. If users within a given catch-all room are determined to not be closely clustered to one another in topic space (e.g. 600), the room receives a relatively lower clustering score. The top of the sorted list (whose sort can be based on topic clustering and/or other sort keys) then identifies a room with greatest recent or longer term focus on a specific topic, greatest population, significant room activity and/or significant user intensity and thus one that may warrant being migrated sooner rather than later to a more specific node rather than being kept under the auspices of a catch-all node.”)
determining, by the processor set, a time period for scheduling an event for a first trending topic from the number of trending topics based on historical user activities and real-time user activities from users classified in the first cluster for the first trending topic, wherein the time period is selected based on a time slot that is available for maximal number of users that are classified in the first cluster; and automatically creating, by the processor set, a virtual space in the virtual environment for the event for the first trending topic at the time period. (consider Rapaport, column 6, line 8-column 7, line 19 regarding “During operation of the STAN systems, a variety of different kinds of informational signals may be collected by a STAN system in regard to the current states of its users; including but not limited to, the user's geographic location, the user's transactional disposition (e.g., at work? at a party? at home? etc.); the user's recent online activities; the user's recent biometric states; the user's habitual trends, behavioral routines, and so on. The purpose of this collected information is to facilitate automated joinder of like-minded and co-compatible persons for their mutual benefit. More specifically, a STAN-system-facilitated joinder may occur between users at times when they are in the mood to do so (to join in a so-called Notes Exchange session) and when they have roughly concurrent focus on same or similar detectable content and/or when they apparently have approximately concurrent interest in a same or similar particular topic or topics and/or when they have current personality co-compatibility for instantly chatting with, or for otherwise exchanging information with one another or otherwise transacting with one another….Proper reading of each individual's body-language expressions may require access to a Personal Emotion Expression Profile (PEEP) that has been pre-developed for that individual and for certain contexts in which the person may find themselves. Example structures for such PEEP records are disclosed in at least one of the here incorporated U.S. Ser. No. 12/369,274 and Ser. No. 12/854,082. Appropriate PEEP records for each individual may be activated based on automated determination of time, place and other context revealing hints or clues (e.g., the individual's digitized calendar or recent email records which show a plan, for example, to attend a certain friend's “Superbowl™ Sunday Party” at a pre-arranged time and place, for example 1:00 PM at Ken's house).”) (consider further Rapaport ‘359, column 3, lines 11-20, “However, heretofore there was no automated and easy-to-use mechanism available for bringing such isolated online users together at the moment of their concurrent focus on same or similar content and at the moment of their concurrent substantial involvement (e.g., emotional or other involvement) with that content, where for example, the mechanism invites both of them to join a mutually acceptable, real time online chat or another such online informational exchange forum that focuses on the content and/or topic of their concurrent mutual focus.”) (consider further Rapaport ‘359, column 6, lines 18-57, “In one embodiment, structures and methods for collecting content-identifying reports and/or reports about a given user's level of focused interest in that content, operate under the auspices of a distributed and automated match-making and invitations-generating system (MM-IGS). The MM-IGS collects (e.g., uploads) information (Current Focus information, also referred to herein as CFi data) about the content that participating individual users are currently focusing on, about their emotional reactions to the focused upon content, and also additional personhood information about their backgrounds and preferences. Using this information, the MM-IGS automatically generates clustering maps which cluster together in an appropriate co-compatibility space, various ones of plural users who appear to be co-focused on same or similar content, and/or who appear to have same or similar topics on their minds and/or who appear to have co-compatible personalities and/or who appear to have same or similar emotional reactions regarding the topic or content being focused upon. The MM-IGS automatically generates invitations and sends the invitations to closely clustered together ones of its users to thereby invite them to join each other in an online chat room (or other network supported information exchange forum, e.g., a restricted access blog, a peer-to-peer forum) whose occupants are or are expected to be focused upon the same or similar content, on the same or similar topic and/or to exhibit same or similar general emotional reactions to the topic and/or to the focused upon content. Users who accept the invitations are automatically linked to the real time informational exchange forums (e.g., chat rooms) that contain other, similarly invited persons. In this way, people who are interested in same or similar things and who may have not otherwise met each other, are automatically given opportunities to meet and chat in real time in online rooms and perhaps benefit from the exchanges. In one embodiment, if an invitation/recommendation confidence score assigned to a given invitation (or corresponding recommendation) exceeds a predefined threshold, the linking to the chat room or other real time informational exchange forum (or linking to recommended further search results) is performed instantly without waiting for acceptance of the invitation/recommendation.”) (consider further Rapaport ‘359, column 95, lines 40-60, “An example of how this might come to be, may be helpful. Suppose a new and previously unknown celebrity pops into the public consciousness. Let's call him, Harry the Hairdresser (fictitious name here). He became famous overnight because a politician mentioned him and/or because he won a popular talent show (e.g., American Idol.TM.) or for whatever reason. Suddenly everyone is talking about Harry the Hairdresser, about how well he sings or dances, about a new hair style he just introduced, about how he affects an ongoing political debate, etc. Yesterday, there were no nodes in the system hierarchy tree regarding the topic of Harry the Hairdresser. Today, the catch-all domain node is swamped with new chat rooms, all talking about Harry the Hairdresser. It behooves the system to move at least the more populated and more active ones of these rooms whose topic of discussion clusters around Harry the Hairdresser to specific domains and/or topic or subtopic nodes or to create new nodes (with node ID tags) for these rooms so as to better serve the growing numbers of users logging in to talk about Harry the Hairdresser.”) (consider further Rapaport, column 28, lines 5-27, “GoogleWave™ (447) is a project collaboration system that is believed to be still maturing at the time of this writing. Microsoft Outlook™ provides calendaring and collaboration scheduling services whereby a user can propose, declare or accept proposed meetings or other events to be placed on the user's computerized schedule. It is within the contemplation of the present disclosure for the STAN_3 system to periodically import calendaring and/or collaboration/event scheduling data from a user's Microsoft Outlook™ and/or other alike scheduling databases (irrespective of whether those scheduling databases and/or their support software are physically local within a user's computer or they are provided via a computing cloud) if such importation is permitted by the user, so that the STAN_3 system can use such imported scheduling data to infer, at the scheduled dates, what the user's more likely environment and/or contexts are. Yet more specifically, in the introductory example given above, the hypothetical attendant to the “Superbowl™ Sunday Party” may have had his local or cloud-supported scheduling databases pre-scanned by the STAN_3 system 410 so that the latter system 410 could make intelligent guesses as to what the user is later doing”) (consider further Rapaport, column 29, line 39-column 30, line 3, specifically “Since STAN systems such as the ones disclosed in here incorporated U.S. application Ser. No. 12/369,274 and Ser. No. 12/854,082 as well as the present disclosure are persistently testing or sensing for change of user mood (and thus change of active PEEP and/or other profiles), the same mood determining algorithms may be used for automatically formulating group invitations based on mood. Since STAN systems are also persistently testing for change of current user location or current surroundings, the same user location/context determining algorithms may be used for automatically formulating group invitations based on current user location and/or other current user context. Since STAN systems are also persistently testing for change of user's current likely topic(s) of interest, the same user topic(s) determining algorithms may be used for automatically formulating group invitations based on user topic(s) being currently focused-upon. Since STAN systems are also persistently checking their users' scheduling calendars for open time slots and pressing obligations, the same algorithms may assist in the automated formulating of group invitations based on open time slots and based on competing other obligations. In other words, much of the underlying data processing is already occurring in the background for the STAN systems to support their primary job of delivering online invitations to STAN users to join on-topic (or other) online forums. It is thus a relatively small extension to add other types of group offers to the process, where the other types of offers can include invitations to join in a real world social interactions (e.g., lunch, dinner, movie, show, bowling, etc.) or to join in on a real world or virtual world business oriented venture (e.g., group discount coupon, group collaboration project).”)
Regarding claim 2, Rapaport taught the computer implemented method of claim 1, wherein the creating, by the processor set, a virtual space for the event for the first trending topic at the time period in the virtual environment comprises:
feeding, by the processor set, information associated with the event for the first trending topic to a ticketing system; registering, by the processor set using the ticketing system, the users classified in the first cluster for the first trending topic; and creating, by the processor set, the virtual space for the event for the first trending topic at the time period based on number of participants registered for the event. (again, consider Rapaport ‘359, column 6, lines 18-57, “In one embodiment, structures and methods for collecting content-identifying reports and/or reports about a given user's level of focused interest in that content, operate under the auspices of a distributed and automated match-making and invitations-generating system (MM-IGS). The MM-IGS collects (e.g., uploads) information (Current Focus information, also referred to herein as CFi data) about the content that participating individual users are currently focusing on, about their emotional reactions to the focused upon content, and also additional personhood information about their backgrounds and preferences. Using this information, the MM-IGS automatically generates clustering maps which cluster together in an appropriate co-compatibility space, various ones of plural users who appear to be co-focused on same or similar content, and/or who appear to have same or similar topics on their minds and/or who appear to have co-compatible personalities and/or who appear to have same or similar emotional reactions regarding the topic or content being focused upon. The MM-IGS automatically generates invitations and sends the invitations to closely clustered together ones of its users to thereby invite them to join each other in an online chat room (or other network supported information exchange forum, e.g., a restricted access blog, a peer-to-peer forum) whose occupants are or are expected to be focused upon the same or similar content, on the same or similar topic and/or to exhibit same or similar general emotional reactions to the topic and/or to the focused upon content. Users who accept the invitations are automatically linked to the real time informational exchange forums (e.g., chat rooms) that contain other, similarly invited persons. In this way, people who are interested in same or similar things and who may have not otherwise met each other, are automatically given opportunities to meet and chat in real time in online rooms and perhaps benefit from the exchanges. In one embodiment, if an invitation/recommendation confidence score assigned to a given invitation (or corresponding recommendation) exceeds a predefined threshold, the linking to the chat room or other real time informational exchange forum (or linking to recommended further search results) is performed instantly without waiting for acceptance of the invitation/recommendation.”) (again, consider further Rapaport, column 28, lines 5-27 and column 29, line 39-column 30, line 3)
Regarding claim 3, Rapaport taught the computer implemented method of claim 1, further comprising:
identifying, by the processor set, a speaker for the event for the first trending topic based on speakers’ expertise (“level of proficiency”/”reputation”/”subject matter proficiency”) in the first trending topic. (consider Rapaport ‘359, column 10, line 62-column 11, line 4, “While personality-based co-compatibility may be one attribute tested for prior to inviting users to join into a live chat room, it is within the contemplation of the disclosure to generate invitations that are filtered on the basis of other co-compatibility factors such as level of proficiency in a given topic or reputation regarding a hierarchically categorized subtopic within a hierarchal tree having predefined domain nodes, topic nodes and subtopic nodes, etc., as will be detailed below.”) (consider further Rapaport ‘359, column 14, lines 18-33, “The system also adaptively changes individual knowledge base rules for personality and/or topic co-compatibility processing based on accumulated trending information. The current chat compatibility profiles (e.g., CpCCp's) of one embodiment further include reputation files or pointers to such files where the corresponding reputations are ones that are built over time for the local user by votes (e.g., biometrically inferred votes or explicit votes) cast by other online users. The current chat compatibility profiles (CpCCp's) of one embodiment further include credential files or pointers to such files where corresponding credentials (including those indicating proficiencies for specific topics or subject areas) are ones that are initially declared by their users but can afterwards be validated or invalidated by system operators and/or by challenges or affirmations by other users of the chat rooms spawning system.”) (consider further Rapaport ‘359, column 54, line 62-column 55, line 26 regarding “reputation data” and “credentials data” which “are not directly controllable or specifiable by user 121'' (e.g., Sally) to whom those reputation and credential indicators are attributed” and “Instead they are developed over time through community determinations such as by votes cast by trusted other users in the system (when those other users are of sound mind) and/or by validation by the system operator” including “3 free reputation files which define various reputation attributes of the user when seen as a general person (G) or as a professional person (P) acting in their primary professional occupation or as an alleged expert or novice in a particular first domain and topic area (Topic A)” and also that “By way of example one reputation value assigned to user 121'', in her general person role (G), by other users who have voted on this attribute indicate that user 121'' likes to argue a lot. This is recorded in her general personality reputation file (G).”) (consider further Rapaport ‘359, column 56, line 8-column 57, line 21, specifically regarding “credentials” wherein “While reputation is generally earned as a matter of popularity, (voted on by either credentialed voters or non-credentialed voters and/or or by either voters with high reputations or with low reputations when in trustworthy states of mind), credentials and subject matter proficiency are another matter” and “For some users, credentials (row 173) may be the most important type of preferences to indicate for being invited into a given chat room or not. Some users may prefer to deal only with chat rooms that contain other persons who are equally credentialed in a specific topic (e.g. topic B) and to have validated educations at the masters' degree level or higher” and that “each user may control the type of chat room invitations they receive from the cloud 150 based on how they fill out their current personality-based chat compatibility profiles 105h.1, based on what reputations they have earned through general use or in topic-specific arenas, and based on what credentials and/or proficiencies they have established on an unvalidated basis or on a community-validated basis”) (consider further Rapaport ‘359, column 162, lines 45-57, “In one embodiment, a potential chat partner client profile score is calculated as follows: (a) A starting score of 50 (neutral) is assumed; (b) The DsMS compares each of the chat room preferences (i.e. number of participants, conversation tone, etc.) declared in the CpCCp or DsCCp values for both the first user and the potential chat partner client; (c) The DsMS compares each first user's CpCCp or DsCCp preference file attributes for a chat partner (i.e. age range, level of domain expertise, etc.) to the corresponding demographic settings (i.e. Actual age, declared or verified level of domain expertise, etc.) from the potential chat partner and vise versa; (c) Favorable comparisons (e.g., declared level of expertise matches requested level of expertise) will increase the score.”)
Regarding claim 4, Rapaport taught the computer implemented method of claim 3, wherein expertise for the speaker is updated after the event is completed. (again, consider Rapaport ‘359, column 14, lines 18-33, “The system also adaptively changes individual knowledge base rules for personality and/or topic co-compatibility processing based on accumulated trending information. The current chat compatibility profiles (e.g., CpCCp's) of one embodiment further include reputation files or pointers to such files where the corresponding reputations are ones that are built over time for the local user by votes (e.g., biometrically inferred votes or explicit votes) cast by other online users. The current chat compatibility profiles (CpCCp's) of one embodiment further include credential files or pointers to such files where corresponding credentials (including those indicating proficiencies for specific topics or subject areas) are ones that are initially declared by their users but can afterwards be validated or invalidated by system operators and/or by challenges or affirmations by other users of the chat rooms spawning system.”) (again, consider further Rapaport ‘359, column 54, line 62-column 55, line 26 regarding “reputation data” and “credentials data” which “are not directly controllable or specifiable by user 121'' (e.g., Sally) to whom those reputation and credential indicators are attributed” and “Instead they are developed over time through community determinations such as by votes cast by trusted other users in the system (when those other users are of sound mind) and/or by validation by the system operator” including “3 free reputation files which define various reputation attributes of the user when seen as a general person (G) or as a professional person (P) acting in their primary professional occupation or as an alleged expert or novice in a particular first domain and topic area (Topic A)” and also that “By way of example one reputation value assigned to user 121'', in her general person role (G), by other users who have voted on this attribute indicate that user 121'' likes to argue a lot. This is recorded in her general personality reputation file (G).”)
Regarding claim 5, Rapaport taught the computer implemented method of claim 1, wherein the number of trending topics are identified based on the historical user activities and the real-time activities for the number of users using natural language processing. (again, consider Rapaport ‘359, column 24, line 54-column 25, line 16, “Aside from using the locally stored browser use history 105d and/or in-cloud stored history of the user's browsing activities (and/or mouse use activities 105g) to determine probable topic of interest, automated determination of the user's probable topic of interest may be based on a locally stored history of the user's chat room activities (e.g., stored in local chat history file 105f) and/or in-cloud stored history of the user's chat room activities, where the latter histories may contain information about the identities of the chat rooms that the given user had recently entered into (whether invited or not) and that information may include duration of stay in the room, level of contribution to room content and indications of positive or negative reactions by the user to the contributions of others within that chat room. The latter gathered information can be used to automatically infer certain preferences of the client user (e.g., 121). In one embodiment, one or more adaptive neural networks and/or statistical analysis models are established in the cloud 150 for each user for determining from a host of input parameters, that user's current, most probable topics of interest; that user's current, most probable emotional state; and that user's current, most probable voting intentions (inferred ones rather than explicitly stated ones). The host of input parameters may include data provided in current CFi's, data obtained from the user's most recent or earlier browsing, searching and chatting histories and data regarding apparent success of earlier guesses made by the neural network or other models (for example, based on the user accepting an invitation into a chat room and the user demonstrating satisfaction with the choice, such as by the user participating for a relatively long time in that chat room).”) (consider also Rapaport ‘359, column 27, line 65-column 29, 42 regarding “emotional expression profiles” in “PEEPs” wherein “The personal emotion expression profile(s) Peep 107 and/or rpeep's (not shown in 1A but can be same as in FIG. 1E) may be adjusted with use of neural networks, statistical modeling, knowledge-base rules sets, trend detecting software or the like to adaptively learn how the user expresses his or her emotions via the various detectable mechanisms such as auditory, visual, biometric, etc. Correlations and/or rules provided in the PEEPs may be adjusted accordingly.”)
Regarding claim 6, Rapaport taught the computer implemented method of claim 1, wherein the historical user activities and real-time activities for the number of users comprise at least one of voices from the number of users, interactions between the number of users and virtual objects in the virtual environment, and messages between the number of users. (again, consider Rapaport ‘359, column 24, line 54-column 25, line 16, “Aside from using the locally stored browser use history 105d and/or in-cloud stored history of the user's browsing activities (and/or mouse use activities 105g) to determine probable topic of interest, automated determination of the user's probable topic of interest may be based on a locally stored history of the user's chat room activities (e.g., stored in local chat history file 105f) and/or in-cloud stored history of the user's chat room activities, where the latter histories may contain information about the identities of the chat rooms that the given user had recently entered into (whether invited or not) and that information may include duration of stay in the room, level of contribution to room content and indications of positive or negative reactions by the user to the contributions of others within that chat room. The latter gathered information can be used to automatically infer certain preferences of the client user (e.g., 121). In one embodiment, one or more adaptive neural networks and/or statistical analysis models are established in the cloud 150 for each user for determining from a host of input parameters, that user's current, most probable topics of interest; that user's current, most probable emotional state; and that user's current, most probable voting intentions (inferred ones rather than explicitly stated ones). The host of input parameters may include data provided in current CFi's, data obtained from the user's most recent or earlier browsing, searching and chatting histories and data regarding apparent success of earlier guesses made by the neural network or other models (for example, based on the user accepting an invitation into a chat room and the user demonstrating satisfaction with the choice, such as by the user participating for a relatively long time in that chat room).”) (again, consider Rapaport ‘359, column 27, line 65-column 29, 42 regarding “emotional expression profiles” in “PEEPs” wherein “The personal emotion expression profile(s) Peep 107 and/or rpeep's (not shown in 1A but can be same as in FIG. 1E) may be adjusted with use of neural networks, statistical modeling, knowledge-base rules sets, trend detecting software or the like to adaptively learn how the user expresses his or her emotions via the various detectable mechanisms such as auditory, visual, biometric, etc. Correlations and/or rules provided in the PEEPs may be adjusted accordingly.”)
Regarding claim 7, Rapaport taught the computer implemented method of claim 1, wherein the number of trending topics are identified by performing temporal analysis on the historical user activities to determine trends of topics over time. (again, consider Rapaport ‘359, column 95, lines 6-40, “When step 441 is entered via entry path 441a, the process points to the root's catch-all domain node and sorts the chat rooms (similar to 479) running under that node according to current room population, current room activity level and/or current intensity of user engagement with that room. In one embodiment sorting of chat rooms running under a catch-all also scores the amount of topic space clustering (both recent clustering and over-time trended clustering) that takes place in that room. The concept of topic space clustering has already been described above with reference to FIG. 6. To recap, recently uploaded CFi hints of Sam, Sally and Larry (users 611, 621, 651) are processed by a DLUX service and the DLUX outputs are averaged or otherwise combined to provide a mapping of each user's apparent points of most likely interests on top of the topic space (600). Then closeness of mapped topic (e.g., distance 625) is automatically determined as between users. This determination of closeness (625) between users can indicate that some users are more closely clustered to one another and to a specific point in topic space than they are to other users or to the point locations (cross hairs in FIG. 6) of nearby nodes. When a predetermined proportion of users within a given catch-all room are determined to be closely clustered to one another in topic space (e.g. 600) for one or both of recent CFi values and long terms CFi values, the room receives a high clustering score. If users within a given catch-all room are determined to not be closely clustered to one another in topic space (e.g. 600), the room receives a relatively lower clustering score. The top of the sorted list (whose sort can be based on topic clustering and/or other sort keys) then identifies a room with greatest recent or longer term focus on a specific topic, greatest population, significant room activity and/or significant user intensity and thus one that may warrant being migrated sooner rather than later to a more specific node rather than being kept under the auspices of a catch-all node.”)
Claims 8-14 recite a computer system that contain substantially the same limitations as recited in claims 1-7 respectively and are also rejected under 35 USC § 102(a)(1) as being anticipated by the same teachings of Rapaport.
Claims 15-20 recite a computer program product that contain substantially the same limitations as recited in claims 1-3 and 5-7 respectively and are also rejected under 35 USC § 102(a)(1) as being anticipated by the same teachings of Rapaport.
Response to Arguments
Applicant's arguments filed in the instant response have been fully considered but they are not persuasive.
Applicant states that “Applicant respectfully submits that the claim language has been amended to clarify that the virtual environment in Applicant's claim in fact directed to a collective computer-generated space designed to simulate imaginary settings, and is accessible to the number of users through use of virtual reality and augmented reality.”
In response to these limitations, the rejection has been updated and emphasized to reflect the added limitations, therefore, Examiner points to the rejections as updated that teach these limitations.
Applicant also states that “Applicant has amended the claim language to clarify that the identified time period is in fact a time slot that is available to the maximal number of users within a particular cluster” since “[n]one of the cited references discloses such selection mechanisms that are based on the availability of users within a cluster”.
Again, in response to these limitations, the rejection has been updated and emphasized to reflect the added limitations, therefore, Examiner points to the rejections as updated that teach these limitations.
The crux of Applicant’s arguments against the other teachings of Rapaport appear to be that Rapaport primarily teaches certain aspects but is also somehow deficient in also teaching other aspects and that the rejection itself is improper despite its showings. Examiner respectfully disagrees with these characterizations of Rapaport and the rejection’s interpretations and citations.
For instance, Applicant argues that “Applicant respectfully submits that US8539359B2 (the "359 patent") does not teach or suggest the claimed invention directed to virtual environment management or virtual reality environment management (e.g., metaverse systems). The '359 patent is directed to a fundamentally different technological field, namely topic-based social networking and contextual information systems, and does not disclose or suggest management of virtual environments or the scheduling and hosting of events within such environments.”
Applicant further augments these arguments by arguing that “Applicant respectfully submits that the Office Action reflects an improper and overly broad interpretation of the claim terms "virtual space" and "virtual environment" wherein “the Examiner appears to equate general communication spaces, forums, or user interaction contexts disclosed in the '359 patent with the claimed "virtual spaces" and "virtual environments." Such an interpretation is not reasonable in light of the specification, which makes clear that a "virtual environment" refers to a virtual environment created through the use of virtual reality and augmented reality. The '359 patent does not disclose or suggest such a virtual reality environment. Instead, the '359 patent describes topic-based communication systems, including chat rooms, forums, and context-aware information presentation. Even where the '359 patent references a "virtual reality domain" (See '359, at Col. 19:64 - Col. 20:16), it is merely illustrative of a context in which users may interact, and is not directed to the structure, operation, or management of the virtual environment itself. The patent does not disclose the creation, control, or lifecycle management of a virtual space within such virtual environment. Rather, it simply acknowledges that user relationships and topical interests may be tracked even when users interact through avatars in an external virtual reality platform. The Examiner's apparent interpretation of "virtual space" as encompassing any communication session, forum, or chat room disclosed in the '359 patent is therefore improper.”
Applicant also similarly continues to argue that “the '359 patent does not disclose or suggest any mechanism for creating a virtual space within a virtual environment, as required by the claims”, “Applicant further submits that interpreting "virtual environment" to include any software environment or communication context, as appears to be the case in the Office Action, is inconsistent with the specification and the understanding in the art” wherein “The '359 patent lacks any disclosure of such features and therefore cannot reasonably be interpreted as teaching the claimed limitations”.
Applicant also continues to argue that “the '359 patent is primarily directed to identifying, organizing, and presenting information based on user context and topics” by allegedly only teaching “identifying, organizing, and presenting information based on user context and topics”, “determining user context and facilitating communication or content delivery based on those topics (See '359, at Col. 3: 5-25)” which “are limited to analyzing and presenting information, and do not involve any temporal event scheduling or virtual environment management”, “focuses on facilitating communication among users based on topical relevance” which “[w]hile such disclosures may involve grouping users or enabling interactions, they do not disclose determining a time period for scheduling an event, nor do they disclose creating or managing any virtual space in which such an event would occur”.
First, Examiner notes that Applicant’s arguments only reference Rapaport ‘359’s teachings. Rapaport (aka. US 11805091 B1) is the main reference relied upon in the § 102 rejection and the rejection contains citations to the main reference that are relevant to the claimed invention. Therefore, given that the rejection contains particular citations to Rapaport to which Applicant fails to contest, Applicant’s arguments are unpersuasive as they fail to consider Rapaport and therefore fail to consider the full scope of the teachings of Rapaport as they apply to the claimed invention, especially as amended.
Examiner submits that Rapaport’s teachings including those incorporated by reference to Rapaport ‘359 are relevant for all they teach. See MPEP § 2123, subsection I. Also, disclosed examples and preferred embodiments do not constitute a teaching away from a broader disclosure. See Id., subsection II. Not only does Rapaport make clear that the “virtual environment” and its other teachings regarding the consolidated “STAN” system are meant to functionally interrelate, it is also clear that these taught embodiments are relevant prior art in that they are taught as embodiments within Rapaport and the citations cited within the rejection show these interrelationships.
Rapaport teaches at at least column 37, lines 18-41, column 47, lines 23-48, and column 125, lines 3-14 as provided in the updated rejection prompted by Applicant’s amendments to the “virtual environment”, the “STAN” system utilizes “topic space augmented reality and/or virtuality” using, i.e., “GoogleGoggles” and also “the user” is provided with “a virtually augmented reality wherein real life (ReL) objects/persons (e.g., 198) are intermixed with experience augmenting data produced by the STAN_3 topic space mapping mechanism 413′” (Examiner’s emphasis added.) Therefore, not only is such reasonably equivalent to the “virtual environment” as claimed, in fact, within these selections, Rapaport makes clear the synergy between these systems as they functionally interrelate (“intermixed”) and also as the system relates to other well-known virtual environments such as “SecondLife™”, “Farmville™” “and/or elsewhere in the virtual reality universe”.
Therefore, Applicant’s characterization that Rapaport is “directed to a fundamentally different technological field” and that the rejection “reflects an improper and overly broad interpretation of the claim terms ‘virtual space’ and ‘virtual environment’” is unpersuasive and does not consider the full scope of Rapaport’s teachings and its clear teachings about how the “STAN” system functionally interrelates and/or operates within a “virtual environment” as defined in the claims.
Rapaport nonetheless contemplates such to the extent that the consolidated “STAN” platform does in fact instantiate a plurality of “virtual spaces” within the contemplated and taught “virtual environment”. Rapaport’s teachings are extensive and detailed with respect to these features and to characterize the teachings of Rapaport as not being a consolidated platform in which these taught features are integrated together is simply unpersuasive.
Applicant also argues that “The '359 patent also consistently focuses on facilitating communication among users based on topical relevance. For instance, it describes enabling users to participate in communications such as messaging, forums, or discussions based on shared interests (See '359, at Col. 4:15-40 and Col. 6:18-57). While such disclosures may involve grouping users or enabling interactions, they do not disclose determining a time period for scheduling an event, nor do they disclose creating or managing any virtual space in which such an event would occur. The communication mechanisms described are non-spatial and are not tied to any managed virtual environment.”
However, as the updated rejection states, Rapaport taught that “During operation of the STAN systems, a variety of different kinds of informational signals may be collected by a STAN system in regard to the current states of its users; including but not limited to, the user's geographic location, the user's transactional disposition (e.g., at work? at a party? at home? etc.); the user's recent online activities; the user's recent biometric states; the user's habitual trends, behavioral routines, and so on. The purpose of this collected information is to facilitate automated joinder of like-minded and co-compatible persons for their mutual benefit. More specifically, a STAN-system-facilitated joinder may occur between users at times when they are in the mood to do so (to join in a so-called Notes Exchange session) and when they have roughly concurrent focus on same or similar detectable content and/or when they apparently have approximately concurrent interest in a same or similar particular topic or topics and/or when they have current personality co-compatibility for instantly chatting with, or for otherwise exchanging information with one another or otherwise transacting with one another….Appropriate PEEP records for each individual may be activated based on automated determination of time, place and other context revealing hints or clues (e.g., the individual's digitized calendar or recent email records which show a plan, for example, to attend a certain friend's “Superbowl™ Sunday Party” at a pre-arranged time and place, for example 1:00 PM at Ken's house).”
As previously pointed to, Rapaport also taught within column 29, line 39-column 30, line 3 that “Since STAN systems are also persistently testing for change of current user location or current surroundings, the same user location/context determining algorithms may be used for automatically formulating group invitations based on current user location and/or other current user context. Since STAN systems are also persistently testing for change of user's current likely topic(s) of interest, the same user topic(s) determining algorithms may be used for automatically formulating group invitations based on user topic(s) being currently focused-upon. Since STAN systems are also persistently checking their users' scheduling calendars for open time slots and pressing obligations, the same algorithms may assist in the automated formulating of group invitations based on open time slots and based on competing other obligations.”
As also pointed to previously, Rapaport ‘359 at column 3, lines 11-20 further taught that “However, heretofore there was no automated and easy-to-use mechanism available for bringing such isolated online users together at the moment of their concurrent focus on same or similar content and at the moment of their concurrent substantial involvement (e.g., emotional or other involvement) with that content, where for example, the mechanism invites both of them to join a mutually acceptable, real time online chat or another such online informational exchange forum that focuses on the content and/or topic of their concurrent mutual focus.”)
Again, the consolidated “STAN” system is able to use a “time period” that uses, inter alia, “open time slots” from “the individual's digitized calendar” to determine a time slot that is available to the users that are classified in the first cluster as claimed.
Applicant alleges that Examiner equated the “maximal number of users” to the teachings of Rapaport ‘359 at column 119, lines 13-18 during the interview prior to the filing of the instant response. However, the claims fail to particularly specify the context of what the invention would contemplate to be a “maximal” number of users or how such a number is critically “maximal” for the “selection” of a “time period”, only that “maximal number of users” be “available”. The example provided by Examiner is only one example of a particular threshold.
In view of the teachings of Rapaport, it is clear that a “maximal number” may be reasonably interpreted, but not limited to, as being the full amount of users available at a “open time slot” and “based on competing other obligations”. Rapaport taught that this is “based on current user location and/or other current user context” wherein “a variety of different kinds of informational signals may be collected by a STAN system in regard to the current states of its users; including but not limited to, the user's geographic location, the user's transactional disposition (e.g., at work? at a party? at home? etc.); the user's recent online activities; the user's recent biometric states; the user's habitual trends, behavioral routines” such that “[t]he purpose of this collected information is to facilitate automated joinder of like-minded and co-compatible persons for their mutual benefit”. Rapaport also makes clear the “STAN” systems are also “persistently testing” for “change of current user location or current surroundings”, “change of user's current likely topic(s) of interest” and “checking their users' scheduling calendars for open time slots and pressing obligations” when the users in the cluster “have roughly concurrent focus on same or similar detectable content and/or when they apparently have approximately concurrent interest in a same or similar particular topic or topics and/or when they have current personality co-compatibility for instantly chatting with, or for otherwise exchanging information with one another or otherwise transacting with one another”.
Therefore, in view of the rest of Rapaport’s teachings, Examiner finds that Rapaport reasonably taught wherein the “time period” is “selected” “based on a time slot that is available for maximal number of users that are classified in the first cluster” in that the “maximal” number of users are the users determined to be necessary to be invited at the particular time of determination/selection of the “time period” “based on historical user activities and real-time user activities from users classified in the first cluster for the first trending topic” as claimed and required.
While this appears to treat many of Applicant’s other arguments which, again, center around that Rapaport taught certain things but then somehow fails to teach the claimed invention, they also unpersuasively generally allege that Rapaport fails to teach the claimed invention without making a specific correlation to the actual claimed language and makes blanket generalizations about what Rapaport teaches and, to an extent, alleging that the rejection is improper based on these generalizations.
For example, Applicant argues that “Importantly, the cited portions of the '359 patent lack any disclosure of the technical characteristics ordinarily associated with virtual spaces in a virtual reality environment. The reference does not disclose rendering or generating three-dimensional environments, spatial positioning of users or avatars, navigation within a virtual world, placement of virtual objects, synchronization of users in a shared immersive environment, or management of persistent virtual world states. Instead, the reference consistently focuses on identifying topics of interest, clustering users according to those interests, and facilitating communication among those users through forums and chat rooms”. Further, Applicant argues that “In other words, Applicant respectfully submits that the '359 patent lacks any disclosure of core virtual environment management functionality. It does not describe persistent virtual spaces, spatial positioning of users or objects, synchronization of multiple users within a shared virtual environment, or instantiation of environments for hosting events. Instead, the patent consistently operates at the level of topic-based social context, user interaction, and information presentation. These functions are fundamentally different from the claimed invention, which requires both temporal scheduling based on trending topics and spatial creation of virtual environments for hosting events.”
Applicant fails to correlate the “technical characteristics ordinarily associated with virtual spaces in a virtual reality environment” allegedly including “rendering or generating three-dimensional environments, spatial positioning of users or avatars, navigation within a virtual world, placement of virtual objects, synchronization of users in a shared immersive environment, or management of persistent virtual world states” or a “core virtual environment management functionality” which allegedly involves “persistent virtual spaces” and “spatial positioning of users or objects” with the invention as claimed, therefore, Examiner can only point to the teachings in the rejection which are found to anticipate the claimed invention as presented and required.
Again, Examiner submits that the rejection clearly provides teachings of embodiments meant to work in conjunction with each other. This is not inconsistent with the relevant art of computerized systems as these systems are, by nature, controlled by logic and software that are meant to communicate with each other. Rapaport is clearly reflective of the particular level of knowledge and skill of one of ordinary skill in the taught computerized relevant art and to characterize Rapaport’s teachings as being incomplete or focused on particular embodiments does not diminish its other teachings which, again, is relevant for all it teaches. The rejection provides a reasonable correspondence between the teachings of Rapaport and the claimed invention as required.
Applicant also argues that “The claims require a system that creates a virtual space in the virtual environment for an event, which inherently requires a structured, spatial, and persistent environment that has characteristics associated with virtual reality or metaverse systems”. However, the claims fail to require a “a structured, spatial, and persistent environment” or any sort of “metaverse” system. The only limitation with regards to and Applicant’s characterization that Rapaport fails to teach “virtual environment” or “virtual space” as claimed is unpersuasive.
In another argument, Applicant appears to admit that “Even where the '359 patent references more advanced interaction modalities, such as virtual reality or augmented reality contexts”, Applicant dismisses them as “merely illustrative and do not relate to management of a virtual environment”. Since Applicant fails to explain what “management of a virtual environment” translates to any specific claim language regarding such “management”, Examiner can only point to what is cited in the rejection regarding the required limitations in response.
Again, while Rapaport’s teachings are voluminous with regards to the consolidated “STAN” system, Rapaport is clearly directed to the context of the claimed invention and Applicant’s arguments to the contrary are unpersuasive.
Therefore, Examiner finds Applicant’s arguments with regards to Rapaport to be unpersuasive. The rejection, while updated to reflect the instant amendments, is otherwise maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to G. C. Neurauter, Jr. whose telephone number is (571)272-3918. The examiner can normally be reached Monday-Friday 9am-5pm Eastern Time.
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/G. C. Neurauter, Jr./Primary Examiner, Art Unit 2459
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