DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/17/2025 has been entered.
Status of Claims
The following is a Non-Final Office Action in response to applicant’s response filed on 12/17/2025.
Claims 2, 3, 5, 6, 12, 13, 15, and 16 are cancelled. Claims 1 and 11 are amended. Claims 1, 3, 4, 7-11, 14, and 17-20 are considered in this Office Action. Claims 1, 3, 4, 7-11, 14, and 17-20 are currently pending.
Response to Arguments
Applicant’s amendments necessitated new grounds of rejections set forth in this Office Action.
Applicant’s amendments and arguments have been considered however applicant’s arguments are primarily raised in light of applicant’s amendments, and therefore are moot. An updated 35 U.S.C. 103 rejection will address applicant’s amendments.
Claim Rejections - 35 USC § 103
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 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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 7-11, 14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Setlla Guimaraes (US 2010/0228633 A1, hereinafter “Guimaraes”) in view of Hillel Felman (US 2022/0132214 A1, hereinafter “Felman”) in view of Yao (US 20150088541 A1, hereinafter “Yao”) in view of Theodore Kozlowski III (US 2023/0120897 A1, hereinafter “Kozlowski”) in view of Ying Wang (US 2018/0361203 A1, hereinafter “Wang”) in view of Nicola Barbieri (US 2016/0026918 A1, hereinafter “Barbieri”).
Claim 1/11
Guimaraes describes:
An apparatus for analyzing a communication datum in a …, the apparatus comprising ([0016]): receive a communication datum from a user in a metaverse related to the user ([0052] and Fig. 6 step 162 illustrate receiving a user action in the metaverse related to user A); wherein the communication datum comprises a user request ([0052] and Fig. 6 step 162 illustrate receiving a user action in the metaverse related to user A),
identify a plurality of communication providers as a function of the communication datum ([0052] user A and user B as represented by their respective avatars are shown co-exiting within the metaverse. [0057] When this environment involves ads, it presents them in a new and interactive way to the user. System hosters can even buy ads and campaigns from the system marketplace);
classify the communication datum to the user profile, wherein classifying communication datum to the user profile comprises generating a communication request (Fig. 6 and [0052] a user action is taken and the action sent to the virtual web server 125. The sequence moves from step 162 to step 164 where the virtual web server informs user B of the action taken and, representative avatars display the action on the screen);
display the identified plurality of communication providers to the user using a graphical user interface (GUI) in a metaverse operated on a decentralized platform for user selection of at least one of the identified plurality of communication providers to carry out the communication datum, wherein the metaverse is a simulated digital environment using virtual reality and augmented reality for user interaction([0020] Navigation is facilitated by the system allowing the user to host the metaverse environment within a webpage, and wherein the system further comprises a web-based server having a plurality of web pages accessible therefrom. There is provided a first interface between the plurality of web pages and a user data processing system. Further, there is a second interface embedded in a web page wherein the user can create an avatar. The avatar is capable of moving over a virtual reality street capable of being acquired from the metaverse environment, and wherein the virtual reality street is associated with a particular URL. The virtual reality street is placed on the bottom of a webpage where it is embedded on the site content of the web page. [0058] For the system user, web navigation becomes social navigation, functioning as a social network within regular web pages. System streets can also be placed within social networking system profiles (such as FACEBOOK, then further developed into applications with extra features and games. All this makes the metaverse experience very accessible, lowering the barriers and creating a global, communal marketplace. [0060] There will be a marketplace with advertising campaigns that can be placed in the various system streets);
and output the communication request to a communication provider device (fig. 6 and [0052] both clients see the same end result which is shown in Client user B interface (communication provider device)).
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entities as illustrated in figure 6, it does not explicitly teach the following, however analogous reference Felman teaches:
at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to ([0124] processor, memory, and instruction);
and wherein the user profile comprises physiological state data and includes user entered psychological symptoms, wherein the physiological state data is determined from a biological extraction of the user comprising [factors](fig. 1 and [0038] the method begins at step 101 where user information is received via manual entry by a user and/or automatically from a connected third-party system. [0040] User information may generally comprise physiological or motor-behavior information (e.g., electroencephalogram (“EEG”), electrical potentials from electrodes; medical information (e.g., medical history (a psychological assessment), medical conditions (psychological symptoms), medical providers, medication, etc.). Figure 4 illustrates database of users. While paragraph [0067] describes that system may retrieve activity information stored by a user device, such as but not limited to: historical, current and/or scheduled physical location(s) of the user, health and exercise information, website browsing history, social media account information, etc. [0185] physiological data can be abstracted out and stored as SRT data. Paragraph [0098] discloses that the interface may measure or determine information about a user's engagement with one or more of the displayed users (e.g., real-time users or recorded users). Such engagement information may include eye contact, facial and other micro-expressions, hand or body movements, voice volume, voice tone, attention level, emotional state, mood, physiological indicators such as heart rate, blood pressure and/or others);
real time browsing data ([0041] It will be appreciated that user information may include social activities information, such as status updates, reactions/comments, uploads and other postings. Exemplary postings may include, for example, third-party content, user-created content, content relating to the user's real-world or virtual activity, historical content, real-time or live content, and/or any of the above content relating to groups (e.g., groups created by the user and/or groups to which the user belongs)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes incorporate the teachings of Felman to include an apparatus, processor, memory, the user profile comprises physiological state data and includes user entered psychological symptoms, wherein the physiological state data is determined from a biological extraction of the user comprising a psychological assessment, and gather real-time browsing information, because the references are analogous and compatible since they are both directed managing interaction. Doing so would improve system runtime and accurately use real-time information to generate recommendation and allow users to connect, interact and share various personal information with one another, based on customizable events and/or filters [0002].
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entities as illustrated in figure 6 and Felman teaches fig. 1 and [0038] the method begins at step 101 where user information is received via manual entry by a user and/or automatically from a connected third-party system. [0040] User information may generally comprise physiological or motor-behavior information (e.g., electroencephalogram (“EEG”), electrical potentials from electrodes; medical information (e.g., medical history (a psychological assessment), medical conditions (psychological symptoms), medical providers, medication, etc.). Figure 4 illustrates database of users. While paragraph [0067] describes that system may retrieve activity information stored by a user device, such as but not limited to: historical, current and/or scheduled physical location(s) of the user, health and exercise information, website browsing history, social media account information, etc. [0185] physiological data can be abstracted out and stored as SRT data. Paragraph [0098] discloses that the interface may measure or determine information about a user's engagement with one or more of the displayed users (e.g., real-time users or recorded users). Such engagement information may include eye contact, facial and other micro-expressions, hand or body movements, voice volume, voice tone, attention level, emotional state, mood, physiological indicators such as heart rate, blood pressure and/or others, Guimaraes and Felman do not explicitly teach the following, however analogous reference Yao teaches:
genetic testing results obtained from a previously collected biopsy sample ([0042] Health data comprise variables selected from the group consisting of demographic variables (e.g., ethnicity, household income level, education), clinical data (e.g., symptoms, findings on physical examination, medical history, past treatments, past surgeries, medications), and laboratory data. Laboratory data includes without limitation, lab test results pertaining to any bodily fluids, tissue-level data, imaging results, and biomarker test results, which may or may not include genetic markers, genomics, or genetic data extracted from discovery platforms);
and wherein the compatible element comprises a service provider related to a geographical location close to the user and is based on the physiological state data and the biological extraction of the user ([0051] the MTPS/CPMS offers features to refine search to provide recommendations that are personalized by the health, demographic and/or financial profiles of the consumer; his/her preferences such as distance from medical providers and geographic region).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes and Felman incorporate the teachings of Yao to include genetic testing results obtained from a previously collected biopsy sample as part of the user profile information and include compatible element comprising a service provider related to a geographical location close to the user and is based on the physiological state data and the biological extraction of the user, because the references are analogous and compatible since they are both directed managing interaction. Doing so would improve system runtime and accurately use real-time information to generate recommendation based on the derived biological/genetic information and altering identified recommendation-based personalization by matching simple or complex health profile data specific to the consumer, based on simple references of demographics or advanced analytics [0004].
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entities as illustrated in figure 6 and Fig. 4 illustrates database #127 users accounts related to users i.e., 110 user A and 112 user B. [0054] and Fig. 8 is a log-in screen for the system user. Returning users can simply enter their username and password (profile). New users will be asked to register at this point, it does not explicitly teach the following, however analogous reference Kozlowski teaches:
a user request comprising an online order placed by the user ([0032] and Figure 1 describe decentralized platform 168 may include a decentralized exchange platform. A “decentralized exchange platform,” contains digital technology, which allows buyers and sellers of securities such as NFTs to deal directly with each other instead of meeting in a traditional exchange. The decentralized platform 168 may include an NFT marketplace. [0043]-[0045] [0044] With continued reference to FIG. 1, user profile 112 includes a temporal resource request 124. A “temporal resource request,” is a conditional denoting a user’s desire to perform a cryptographic exchange. A “cryptographic exchange,” is any form of exchange involving cryptographic assets, where a cryptographic exchange may include a buy, sell, trade, borrow, loan, or the like thereof, wherein the principal asset is a cryptographic asset. As shown in FIG. 7, an NFT page 700 may include a plurality of information related to an NFT. NFT page 700 may be a representation of a decentralized platform and/or decentralized exchange platform for which creators, buyers, sellers, and/or any user may interface with. [0094] Still referring to FIG. 7, an NFT may be deployed via a digitally signed smart contract which conforms to some standard 704);
receive a user profile related to the user from an immutable sequential listing associated with the metaverse, wherein the immutable sequential listing continuously updates the user profile ([0050] computing device 100 may generate and/or update user profile 112 as a function of a user chain data 140. A “user chain data,” as used in this disclosure, is any user data involved in any cryptographic exchange and/or transaction recorded on any blockchain such as an immutable sequential listing);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes, Felman, and Yao to incorporate the teachings of Kozlowski to include an online order placed by the user as part of the datum received from the user as taught in Guimaraes and receive a user profile related to the user from an immutable sequential listing associated with the metaverse, wherein the immutable sequential listing continuously updates the user profile, because the references are analogous and compatible since they are both directed managing interaction. Doing so would improve system’s safety and accuracy where the use of immutable sequential listing will provide tamper-free data.
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entities as illustrated in figure 6, it does not explicitly teach the following, however analogous reference Wang teaches:
generate a provider classifier using provider training data ([0018] train a classification model utilizing specific inputs related to a group of users/user profiles. Once trained, input data may be provided to the classification model. The classification model may produce output indicating at least a classification for the input data. [0026] The assigned classifications may be then utilized as training data to train a classification model with a supervised machine-learning algorithm);
classify the communication datum to the plurality of communication providers using the provider classifier ([0018] train a classification model utilizing specific inputs related to a group of users/user profiles. Once trained, input data may be provided to the classification model. The classification model may produce output indicating at least a classification for the input data);
wherein the provider classifier is an iteratively-trained machine-learning model([0045] and [0063] describe the machine-learning model 202 may be updated. Such information may be utilized as input to the updated machine-learning model in order to provide input with which to classify the user 312. Accordingly, the information associated with the user 302 may be utilized by the machine-learning model to classify the user 312. Examiner notes machine learning is inherently iterative, e.g., train with a training set, observe the output, repeat/retain with updated data, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes, Felman, Yao, and Kozlowski to incorporate the teachings of Wang to include generate a provider classifier using provider training data and classifying data, because the references are analogous and compatible since they are both directed managing interaction. Doing so would improve and present a more accurate classification of data captured [0019].
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entity as illustrated in figure 6, and Wang teaches in [0063] the machine-learning model 202 may be updated. Such information may be utilized as input to the updated machine-learning model in order to provide input with which to classify the user 312. Accordingly, the information associated with the user 302 may be utilized by the machine-learning model to classify the user 312, Guimaraes illustrates in figure 13 “a plurality of user-specific product”, Wang teaches “iteratively-trained machine learning model” in paragraph [0062], where it discloses any of suitable information received above may be utilized to update (e.g., retrain) the machine-learning model, Guimaraes and Wang do not explicitly teach the following, however analogous reference Barbieri teaches:
wherein iteratively training the machine-learning model comprises ad-hoc categorization of communication datum to associate the communication datum with descriptors of data elements corresponding to communication providers (0066] At step 806, the engine 802 obtains user information and social network information. [0067] At step 808, the engine 802 generates social graph, with nodes representing users, and features associated with each node. [0068] At step 810, the engine 802 generates a machine learning-based stochastic model. [0098] The system gathers information about the users, the links between the users, along with link directionality information, and user related information. User related information may include user content information and user adding, monitoring, or sharing activity information, for example, as related to user content. User content related information may be collected, including for example, user content, and related topics, groups, lists, such as ad hoc lists, and the like, along with the related rate or trend, which may, for example, indicate or identify features or interests related to the user or content, or group or classify user content, among other things. [0100] The model is trained by splitting the dataset into training data and test data. The dataset can be split in various ways, for example randomly or based on features such as, for example, user, network, or dataset features);
the […] machine-learning model is configured to output a compatible element; ( [0050] the engine, using a computational model, incorporating collected user and social networking information, determines topical and social user groupings and linkages, [0051] the engine determines one or more users with a high degree or strength of linkage to a particular user, [0052] the engine determines and displays a recommendation to the particular user to follow at least one of the other users that have high linkage to the particular user).
determine a user activity intention by comparing […] user browsing to a browsing or purchasing history of the user ([0046]user profiles specific to each user are generated to model user behavior, for example, by tracking each user's path through a web site or network of sites, and then compiling a profile based on what pages and recommendations on who to follow were delivered to the user. Using aggregated data, a correlation develops between users in a certain target audience and the products that those users purchase. The correlation then is used to target potential purchasers by targeting content or recommendations on who to follow to the user at a later time);
display the identified plurality of communication providers to the user using a graphical user interface (GUI) for user selection of at least one of the identified plurality of communication providers to carry out the communication datum based on the determined user activity intention(Fig. 7 step 716 and [0046] user profiles specific to each user are generated to model user behavior, for example, by tracking each user's path through a web site or network of sites, and then compiling a profile based on what pages and recommendations on who to follow were delivered to the user. Using aggregated data, a correlation develops between users in a certain target audience and the products that those users purchase. The correlation then is used to target potential purchasers by targeting content or recommendations on who to follow to the user at a later time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes, Felman, Yao, Kozlowski, and Wang to incorporate the teachings of Barbieri to include iteratively training the machine learning model comprises ad-hoc categorization of communication datum to associate the communication datum with descriptors of data elements corresponding to communication provider, the trained machine learning model is configured to output a compatible element; and wherein the compatible element comprises a plurality of user-specific elements ranked in an order of compatibility for the user, and determine a user activity intention by comparing user browsing to a browsing or purchasing history of the user to use the model of Barbieri to produce a user specific product elements as illustrated in Guimaraes, because the references are analogous and compatible since they are both directed managing interaction. Doing so would improve and present a more accurate classification of data captured and recommend user content/provider based on user past interaction which will positively contribute to gaining users' trust and satisfaction [0107].
Claim 4/14
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entity as illustrated in figure 6, par. [0052] user A and user B as represented by their respective avatars are shown co-exiting within the metaverse, and par. [0057] teaches when this environment involves ads, it presents them in a new and interactive way to the user and system hosters can even buy ads and campaigns from the system marketplace, it does not explicitly teach the following, however Felman teaches:
The apparatus of claim 1, wherein the user profile comprises activity data (Fig. 4 illustrates database of users and user reaction. [0067] system may retrieve activity information stored by a user device, such as but not limited to: historical, current and/or scheduled physical location(s) of the user, health and exercise information, website browsing history, social media account information, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes incorporate the teachings of Felman to include user activity data as part of user account data. Doing so would improve user interaction and experience by allowing users to connect, interact and share various personal information with one another.
Claim 7/17
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entity as illustrated in figure 6, par. [0052] user A and user B as represented by their respective avatars are shown co-exiting within the metaverse, and par. [0057] teaches when this environment involves ads, it presents them in a new and interactive way to the user and system hosters can even buy ads and campaigns from the system marketplace, it does not explicitly teach the following, however Felman teaches:
The apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to train the machine-learning model with training data comprising at least physiological state data and activity data related to the user([0067] system may retrieve activity information stored by a user device, such as but not limited to: historical, current and/or scheduled physical location(s) of the user, health and exercise information, website browsing history, social media account information, etc. [0068]-[0069] describes the use of machine learning models to analyze pattern of user activities and identify a mapping interface may include a list of users, where information relating to each user in the user list is displayed, “the system may employ machine learning and/or artificial intelligence processes to determine activity information and/or predict future activity information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes incorporate the teachings of Felman to include train the machine-learning model with training data comprising at least physiological state data and activity data related to the user. Doing so would improve user interaction between two entity and experience by allowing users to connect, interact and share various personal information with one another.
Claim 8/18
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entity as illustrated in figure 6, par. [0052] user A and user B as represented by their respective avatars are shown co-exiting within the metaverse, and par. [0057] teaches when this environment involves ads, it presents them in a new and interactive way to the user and system hosters can even buy ads and campaigns from the system marketplace, it does not explicitly teach the following, however Felman teaches:
The apparatus of claim 1, wherein the machine-learning model is configured to output a compatible element([0067] system may retrieve activity information stored by a user device, such as but not limited to: historical, current and/or scheduled physical location(s) of the user, health and exercise information, website browsing history, social media account information, etc. [0068]-[0069] describes the use of machine learning models to analyze pattern of user activities and identify a mapping interface may include a list of users, where information relating to each user in the user list is displayed, “the system may employ machine learning and/or artificial intelligence processes to determine activity information and/or predict future activity information”, [0068], wherein a user may select a user from the displayed user list to view that user's publicly available profile and/or to send a message to that user (when permitted) (i.e., compatible element)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes incorporate the teachings of Felman to include output a compatible element based on machine learning model used to analyze data. Doing so would improve user interaction between two entity and experience by allowing users to connect, interact and share various personal information with one another.
Claim 9/19
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entity as illustrated in figure 6, par. [0052] user A and user B as represented by their respective avatars are shown co-exiting within the metaverse, and par. [0057] teaches when this environment involves ads, it presents them in a new and interactive way to the user and system hosters can even buy ads and campaigns from the system marketplace, it does not explicitly teach the following, however Felman teaches:
The apparatus of claim 1, wherein classifying the communication datum to the user profile comprises receiving a user selection of a compatible element([0067] system may retrieve activity information stored by a user device, such as but not limited to: historical, current and/or scheduled physical location(s) of the user, health and exercise information, website browsing history, social media account information, etc. [0068]-[0069] describes the use of machine learning models to analyze pattern of user activities and identify a mapping interface may include a list of users, where information relating to each user in the user list is displayed, “the system may employ machine learning and/or artificial intelligence processes to determine activity information and/or predict future activity information”, [0068], wherein a user may select a user from the displayed user list to view that user's publicly available profile and/or to send a message to that user (when permitted) (i.e., compatible element)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes incorporate the teachings of Felman to include receiving a user selection of a compatible element. Doing so would improve user interaction between two entity and experience by allowing users to connect, interact and share various personal information with one another.
Claim 10/20
While Guimaraes teaches a method and system for hosting a metaverse environment within a webpage and navigating within that environment and managing interaction between two entity as illustrated in figure 6, par. [0052] user A and user B as represented by their respective avatars are shown co-exiting within the metaverse, and par. [0057] teaches when this environment involves ads, it presents them in a new and interactive way to the user and system hosters can even buy ads and campaigns from the system marketplace, it does not explicitly teach the following, however Felman teaches:
The apparatus of claim 1, wherein outputting the communication request comprises transmitting the communication request to a communication provider device outside the metaverse([0067] system may retrieve activity information stored by a user device, such as but not limited to: historical, current and/or scheduled physical location(s) of the user, health and exercise information, website browsing history, social media account information, etc. [0068]-[0069] describes the use of machine learning models to analyze pattern of user activities and identify a mapping interface may include a list of users, where information relating to each user in the user list is displayed, “the system may employ machine learning and/or artificial intelligence processes to determine activity information and/or predict future activity information”, [0068], wherein a user may select a user from the displayed user list to view that user's publicly available profile and/or to send a message to that user (when permitted) (i.e., compatible element), while fig. 2 illustrates a communication between user device e.g. inside the metaverse and a third-party system e.g. outside the metaverse via network).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Guimaraes incorporate the teachings of Felman to include transmitting the communication request to a communication provider device outside the metaverse t. Doing so would improve user interaction between two entity and experience by allowing users to connect, interact and share various personal information with one another.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to REHAM K ABOUZAHRA whose telephone number is (571)272-0419. The examiner can normally be reached M-F 7:00 AM to 5:00 PM.
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/REHAM K ABOUZAHRA/ Examiner, Art Unit 3625