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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 12/15/2021, Applicant, on 11/26/2025, amended claims 1, 8 and 16. Claims 1-20 are pending in this application and have been rejected below. Information Disclosure Statement (IDS) filed 1/21/2026 is acknowledged.
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has been withdrawn pursuant to 37 CFR 1.114.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
11/26/2025 has been entered.
Response to Arguments
Applicant’s arguments filed November 26, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed November 26, 2025.
On Pgs. 10-13 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the claims are not directed to the alleged abstract idea or any other abstract idea. Rather, like the patent-eligible claims in Enfish, claim 1 is "directed to an improvement to computer functionality" rather than "being directed to an abstract idea" and is thus properly treated as patent- eligible "even at the first step of the Alice analysis." See Enfish, 118 USPQ2d at 1689. In response, Examiner respectfully disagrees. The aforementioned procedures are not improvements to a problem in the software arts, a technology or technological field. The electronic records data analysis and grouping is a judicial exception (i.e. abstract idea). The claimed invention is executed by generic computer elements performing generic computer functions (see par. 0014-0015). Enfish recited claims that asserted improvements to the configuration of computer memory in accordance with a self-referential table with sufficient support in the specification that the claims were directed to a specific implementation of a solution to a problem in the software arts. Which shows the claimed invention made improvements in computer related technology. In contrast, the present claims recite generic computer elements to perform the generic functions, such as a memory storing modules comprising instructions executed by a processor. Examiner asserts, regardless of the complexity of the data analysis and/or processing, without recitation of improvements to the functioning of the technology, technological field and/or computer-related technology (i.e. software), the steps outlined in the claimed invention to create competency learning maps amount to no more than mere instructions to implement the idea on a general purpose computer. Applicant has not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves. Specifically the training of the machine learning model are not included the abstract idea. The artificial intelligence/machine learning is solely used a tool to perform the instructions of the abstract idea.
On Pgs. 14-16 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states claims recites features that contain an "inventive concept" amounting to "significantly more" than any such abstract idea. In response, As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “application computing system”, “data repository”, “computing platform”, “processor”, “memory”, “user application”, “user device”, “user interface generator”; “user interface” and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to retrieving records and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). With regards to the artificial intelligence / machine learning model and step 2B- it is a tool to perform the instructions of the abstract idea. Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
On Pgs. 16-17 of the Remarks, regarding 35 U.S.C. § 103 rejections. Applicant states prior art does not teach amended claim language. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Wren is now applied for Claims 1, 8 and 16. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1, 8 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent Publication 18,218,808. Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are drawn to the same invention of grouping micro-communities.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to segmentation analysis.
Claim 1 recites a system for micro-community analysis, Claim 8 recites a method for micro-community analysis and Claim 16 recites an article of manufacture for micro-community analysis, which include retrieving a plurality of electronic data records; identifying, based on analysis of the plurality of electronic data records, a plurality of actions performed by a plurality of users; determining, based on the plurality of actions performed by the plurality of users, whether a particular user activity was performed based on a level of expertise in the particular user activity; grouping users into user tiers, wherein each tier corresponds to one of an experience level of a particular activity; generating a plurality of micro- communities of users associated with activities identified from the plurality of electronic data records, wherein each micro-community of the plurality of micro-communities corresponds to identification of a combination of experience level, activity type, and whether a particular activity is performed as a profession, as a hobby, or as a new practitioner; generating, for each user of the plurality of users, a likelihood score that a corresponding user may join each micro-community of the plurality of micro-communities; facilitating electronic communication within the plurality of micro- communities and between a plurality of user devices associated with members of the micro- communities; sending, based on the likelihood score, an invitation message to join one or more micro-communities to user devices associated with corresponding users; generating based on a user tier and a micro-community associated with a first user, a customized user interface screen corresponding to the user tier and the micro-community, wherein customized information is presented associated with the first user in context of an identified user experience level in the micro- community;
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing interactions. The recitation of “system”, “application computing system”, “data repository”, “computing platform”, “processor”, “memory”, “user application”, “user device”, “user interface generator”; “user interface”, “screen” and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation and “Methods of Organizing Human Activity”- managing interactions. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The of “system”, “application computing system”, “data repository”, “computing platform”, “processor”, “memory”, “user application”, “user device”, “user interface generator”; “user interface”; “screen” and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 8, and claim 16 recite using one or more artificial intelligence/ machine learning analysis techniques (training and retraining). The specification discloses the artificial intelligence/machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of artificial intelligence/machine learning does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the artificial intelligence/machine learning is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in segmentation analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “application computing system”, “data repository”, “computing platform”, “processor”, “memory”, “user application”, “user device”, “user interface generator”; “user interface” “screen” and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to retrieving records and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). With regards to the artificial intelligence / machine learning model and step 2B- it is a tool to perform the instructions of the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-7, 9-15 and 17-20 recite aggregating historical electronic data records from each of the plurality of application computing system; anonymizing each electronic data record from the plurality electronic data records aggregated from each of the plurality of application computing system by removing financial and/or personal information from the data record; each micro-community corresponds to an identified creative interest and where on or more is a member of multiple micro-communities; a first micro-community comprises a same experience level for each of the members; each micro-community comprises a different experience levels for each member of the micro-community; determining, based on monitored first micro-community activities, a participation level for each member of the first micro-community; and communicating, an electronic reward communication to at least one member of the first micro-community based on the participation level for each member of the first micro- community; receiving feedback concerning the micro- community; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 8 and 16. Regarding Claims, 2-3, 6-7, 9-10, 15, 17-19 and the additional elements of “computing platform”; “application computing system”; “application”; “user device”- it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 7, claim 15 and claim 19 and the additional element of artificial intelligence/machine learning model - the artificial intelligence/ machine learning is solely used a tool to perform the instructions of the abstract idea.
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.
Claims 1- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Polleri, US Publication No. 20210081377 A1, [hereinafter Polleri], in view of Hazy et al., US Publication No 20210042854 A1, [hereinafter Hazy] and in further view of Wren et al., US Patent No 10511642B1, [hereinafter Wren].
Regarding Claim 1,
Polleri teaches
A system comprising: a plurality of application computing systems, each application computing system comprising a data repository storing electronic data records corresponding to electronic transactions for a plurality of users; a computing platform, comprising: at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: (Polleri- Fig. 1; Par. 44-45; Par. 209-210;Par. 51-“The model execution engine 108 can execute the machine learning application 112 on infrastructure 128 using one or more the infrastructure interfaces 124. The infrastructure 128 can include one or more processors, one or more memories, and one or more network interfaces, one or more buses and control lines that can be used to generate, test, compile, and deploy a machine learning application 112.”)
retrieve a plurality of electronic data records from the plurality of application computing systems (Polleri Par. 89-90- “At 306, the functionality includes gathering data in streams (with chunking) or in batches. Chunking is a term referring to the process of taking individual pieces of information (chunks) and grouping them into larger units. By grouping each piece into a large whole, you can improve the amount of information you can remember. The model composition engine can access the data storage to gather the data for generating the machine learning model. The data can be stored locally or in cloud-based network.”);
train an artificial intelligence/machine learning (AI/ML) model based on a plurality of electronic data records retrieved from the plurality of application computing systems (Polleri Par. 45-46- “Machine learning configuration and interaction with the model composition engine 132 allows for selection of various library components 168 (e.g., pipelines 136 or workflows, micro services routines 140, software modules 144, and infrastructure modules 148) to define implementation of the logic of training and inference to build machine learning applications 112. Different parameters, variables, scaling, settings, etc. for the library components 168 can be specified or determined by the model composition engine 132. The complexity conventionally required to create the machine learning applications 112 can be performed largely automatically with the model composition engine 132.”);
facilitate, via a user application, electronic communication within the one or more micro-communities and between a plurality of user devices associated with members of the micro-communities (Polleri Par. 169-170; Par. 176-“ Enterprise service 525 may communicate with connector 530 in a manner similar to messaging application system 515. Enterprise service 525 may send content to connector 530 to be associated with one or more end users. Enterprise service 525 may also send content to connector 530 to cause bot system 520 to perform an action associated with an end user. Action engine 560 may communicate with enterprise service 525 to obtain information from enterprise service 525 and/or to instruct enterprise service 525 to take an action identified by action engine 560.”);
retrain, based on analysis of micro-community communications, the AI/ML model. (Polleri Par.191-“ Database 640 may be used to store data for the bot system, such as data for the classification models, logs of conversation, and the like. Management APIs 650 may be used by an administrator or developer of the bot system to manage the bot system, such as retraining the classification models, editing intents, or otherwise modifying the bot system. The administrator or developer may use user interface 654 and UI server 652 to manage the bot system.”)
Polleri teaches learning analysis and the feature is expounded upon by Hazy:
identify, based on analysis of the plurality of electronic data records, a plurality of actions performed by a plurality of users of the plurality of application computing systems (Hazy Par. 49-“ The processing performed by the sDAASS 306, which includes, but is not limited to, data mining, item or vote counting, individual or decision ranking, dynamical systems processing, statistical analysis, scientific studies such as, but not limited to, theory-based hypothesis testing, may be used to analyze user and system data related to, but not limited to, user interactions, organization state information, or environmental data. The processing conducted by the sDAASS 306, may also include, but is not limited, to conducting and executing various scenarios and simulations. Together, these analyses may serve as inputs for user reports, feedback and recommendations generated by the system 100.”)
determine, based on the plurality of actions performed by the plurality of users, whether a particular user activity was performed based on a level of expertise in the particular user activity (Hazy Par. 20; Par. 30-“ In certain embodiments, the situational context a first user may experience can be classified as a User-Interest and each of these may be assigned a User-Interest-Code. ... In certain embodiments, the professional development plan may include but is not limited to the individual progressive development of a portfolio of skills, behaviors or competencies, such as but not limited to those that might be signified as one or more of the categories Expert Contributor, Agile Contribution, Organizing Contributor, or Authentic Contributor or a combination thereof, to one or more of but not limited to Autonomous Contributor or Collaborative Contributor or a combination there of, and then to a single portfolio that might be called, but is but not limited to, Transformational Contributor. In certain embodiments, a User-Interest-Code can be inferred by the system 100, by experts, through artificial intelligence or through machine learning algorithms and assigned to the first user for the purpose of identifying the coaching or media content or translated scholarly or practical research or material, to be pushed to the user by the system. ...”);
group, by the trained AI/ML model, users into user tiers, wherein each tier corresponds to an experience level (Hazy Par. 11-“ Additionally, the system may perform an operation that includes assigning the information related to the effectiveness of the second user to a first avatar mapped to a first user identifier of the first user and/or to a second avatar mapped to a second user identifier of the second user. In certain embodiments, the first and second avatars may be anonymized virtual representations of the first user and second users within a virtual social network of the system. Furthermore, the system may perform an operation that includes generating, based on an analysis of the information, first condition state data for the first avatar and second condition state data for the second avatar. In certain embodiments, the first condition state data and the second condition state data may be time series variables for an instance of time or a plurality of instances of time, which may include a representation of a relationship of the first and second avatars to each other in the virtual social network of the system. “)
generate, based on the user tiers and by a trained AI/ML model, one or more micro-communities of users associated with activities identified from the plurality of electronic data records, wherein each micro-community of a plurality of micro- communities corresponds to identification of a combination of experience level, activity type, and whether a particular activity is performed as a profession, as a hobby, or as a new practitioner (Hazy Par. 6-“ In particular, the system and methods provide a software platform that facilitates the obtaining of higher quality feedback, which may be analyzed and enhanced so as to provide reports and/or recommendations to users in order to improve organization's performance as well as the users' performance in interactive situations, such as, but not limited to, team-based projects, multiteam systems, social interactions, working groups at a job,; Par. 30-“ n certain embodiments, a User-Interest-Code can be inferred by the system 100, by experts, through artificial intelligence or through machine learning algorithms and assigned to the first user for the purpose of identifying the coaching or media content or translated scholarly or practical research or material, to be pushed to the user by the system. In certain embodiments, pushed content might include comparison with but not limited to benchmarks, other team scores, other individual scores along with but not necessarily awards, accommodations, points or other reward whether monetary or otherwise. In certain embodiments, these can be determined in a secure manner for the first user by mapping the condition-state-type of the User's associated avatar to a User-Interest code and assigning it to the first User. “; Par. 56-user access code Par. 65-“ User Experience (UX) Perspective: Based upon this processing, the focal user (i.e. first user 101) is presented with an isolated real-world event and asked to join the event and provide anonymous feedback to the other participants. .. Any relevant formal and informal teams, multiteam systems, associations, affiliations, identity groups or special interest groups, or other social structures that may be identified by the user, other users or the system through its machine learning, artificial intelligence or interfaces with human experts Par. 95)
generate, by a user interface generator and based on a user tier and a micro- community associated with a first user, a customized user interface screen corresponding to the user tier and the micro-community, wherein customized information is presented via a first user device associated with the first user in context of an identified user experience level in the micro-community (Hazy Par. 66-“Users Can Establish A Virtual-Event in the Virtual Network: In the application, a user can decide to establish a virtual event in conjunction with a real-life event. In addition to providing and receiving feedback for professional improvement through the application, establishing the virtual event also helps the system to virtually augment interactions in the user's real-life Social Network in future interactions. For example, prior to a meeting, the user can be reminded of social interaction dynamics that have occurred during prior meetings of this type with some or all of the same actors and provide some coaching to enable greater effectiveness. A user can add a virtual event (that is correlated with a real-life event) by, but not limited to, doing the following with the user interface of the application: 1. Select a “create an event” function; 2. Select the event type; 3. Enter some of the date, start time, end time, or other data. 4. The study or studies to be carried out before, during or after the event can be specified, otherwise the system could, but would not necessarily assign a default study. 5. Select a team or otherwise identify participants. 6. Optionally, select a project to which event pertains. 7. Optionally, the user might assign roles to various participants.”);
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Polleri in view of Hazy teach data analysis and the feature is expounded upon by Wren:
generate, for each user of the plurality of users, a likelihood score that a corresponding user may join each micro-community of the plurality of micro- communities; (Wren Col. 11-12“ The rating server 220 rates the generated account references/micro-communities in relation to their relevance to the object reference. In one embodiment, the rating score of the account references generated by the ratings server 221 is used by the micro-community fetcher 211 to decide which account references to associate with the micro-community. In another embodiment, when a user inputs a search query, the generated account references/micro-communities are presented to a user based on the rating scores. In another embodiment, a user rates the generated account references/micro-communities. The ratings server 221 stores these user ratings in memory 237 for future retrieval related to generating account references/micro-communities… The micro-community fetcher 211 then populates the created micro-community with the generated account references (subject to user consent) or the notification module 219 transmits an invitation to the account references. A person of ordinary skill in the art will recognize that the account references can be pre-configured or manually inputted to the micro-community engine 130. A person with ordinary skill in the art will also recognize that the micro-community fetcher 211 retrieves and/or creates not just one, but a plurality of micro-communities relevant to the object reference and/or account references. The micro-community fetcher 211 is described in further detail with reference to FIG. 2B….Col 13 In another embodiment, the implicit fetcher 290 infers that a user is not interested in an object reference if the user rejected an invitation sent by the notification module 219 to join a micro-community related to the object reference. Thus, for future invitations the implicit fetcher 290 does not include the account reference as relevant to the object reference. In a similar embodiment, the implicit fetcher 290 makes inferences based on user ratings received by the ratings server 221.)
send, based on the likelihood score and via the user application, an invitation message to join one or more micro-communities to user devices associated with corresponding users (Wren Col 4-6; Col 11-12 The notification module 219 is software including routines for transmitting a notification to a user. In one embodiment, the notification module 219 is a set of instructions executable by the processor 235 to generate a notification of an account reference list, an invitation to join a micro-community, a change in account references associated with the micro-community, a request to rate a micro-community, etc. In another embodiment, the notification module 219 is stored in memory 237 of the computing device 200 and is accessible and executable by the processor 235. In either embodiment, the notification module 219 is adapted for cooperation and communication with the processor 235, the micro-community fetcher 211, the ratings server 221, the account reference fetcher 207 and other components of the computing device 200 via signal line 228.) ;
Polleri and Hazy are directed to user segment analysis. Wren improves upon the analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri in view of Hazy, as taught by Wren, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri in view of Hazy with the motivation of associating users with a micro-community that is relevant. (Wren Abstract).
Regarding Claim 2, Claim 9 and Claim 17,
wherein the instructions cause the computing platform to aggregate historical electronic data records from each of the plurality of application computing systems. (Polleri Par.212 - “A machine learning algorithm could be used to analyze the employee records, identify information regarding the performance of each employee, and conduct comparative analysis of historical reviews to determine performance trend of each of the employees.”)
Regarding Claim 3, Claim 10 and Claim 18,
Polleri teaches user analysis and the feature is expounded upon by Hazy:
wherein the instructions cause the computing platform to anonymize each electronic data record from the plurality electronic data records aggregated from each of the plurality of application computing system by removing financial and/or personal information from the data record (Hazy Par. 6- “Based on the gathered performance and feedback information and/or data, the system and methods may include components and subsystems for aggregating and analyzing situational and/or feedback data in an anonymous and secure data environment ; Par. 9-10)(Duggirala Par. 56-“ The scrubbing circuit(s) 306 may be or include any device, component, element, or hardware designed or configured to cleanse, clean, or otherwise scrub the response generated by the AI circuit(s) 302 according to the policy (or policies) of the policy circuit as applied to the particular user. In some embodiments, the scrubbing circuit(s) 306 may be configured to scrub the response, to remove protected or secured data”);
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Regarding Claim 4, Polleri in view of Hazy teach The system of claim 1,…
Polleri teaches knowledge management and the feature is expounded upon by Hazy:
wherein each micro-community corresponds to an identified creative interest. (Hazy Par.56-“ The information used by the system 100 to construct this instrument may but not necessarily be associated with a conceptual model that considers one or more, attributes, behaviors or other observable metrics in the context of their hypothesized statistical relationships first order, second order third order, or other level latent variables or factor some or all of which may or may not be validated empirically. In certain embodiments, observed attributes, behaviors, other observable metrics, or latent variables used to but not limited to construct the instrument, report or to identify User Interest code, may be one of or be some combination of: accountability, active listening, administrative activities, adaptability, agility, approachability, authenticity, authority, autonomy, autonomous contribution, consistency, contribution, balanced decision-making, balanced processing, collaborative contribution, community building, competence in areas such as but not limited to industry, technical, functional, cultural, information and communications technology, information elaboration, a metric of innovation, creativity, or physicality, citizenship behaviors, committed, confident, clarity (clear communication), clear thinking, comradery, consideration, convergent, curiosity, divergent, followership, emotional intelligence, empathy, enabling, engagement, evidence, generative activities, humor, initiative, innovative, intellectual stimulation, leadership, mental toughness, clear values or moral compass, climate, openminded, organizing, a metric of performance, predictable, preparedness, proactive, psychological safety, self-awareness, relational transparency, relevance, respectfulness, self-awareness, self-regulation, supportiveness, team norm strength, transformational contribution transparency, trust, generalized, trust, individualized trust, or workplace climate.
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Regarding Claim 5, Polleri in view of Hazy teach The system of claim 1,…
Polleri teach segment analysis and the feature is expounded upon by Hazy:
wherein a first micro-community comprises a same experience level for each of the members; wherein a second micro-community comprises a different experience level for each member of the micro-community (Hazy Par. 56; Par. 65-“ Any relevant formal and informal teams, multiteam systems, associations, affiliations, identity groups or special interest groups, or other social structures that may be identified by the user, other users or the system through its machine learning, artificial intelligence or interfaces with human experts; Organization_State_Variables such as, but not limited to: internal projects, multiteam systems, initiatives, departments, budget item, activity type, position in organization structure, topic area, function, category of event, or other items that might be associated with the event.”)
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Regarding Claim 6 and Claim 14, Polleri teach The system of claim 1, herein the instructions cause the computing platform to:,… and The method of claim 8, further comprising:,…
Polleri teaches user analysis and the feature is expounded upon by Hazy:
determine, based on monitored first micro-community activities, a participation level for each member of the first micro-community (Hazy Par. 39-“ The system 100 may also include a computing device 120, which may be utilized to monitor the users, such as during an event.; Par 9”)
and communicate, an electronic reward communication to at least one member of the first micro-community based on the participation level for each member of the first micro- community. (Hazy Par. 24“ In certain embodiments, the FFSS 302 may prompt and/or trigger a user's participation by, but not limited to, utilizing queries, such as survey questions or by providing opportunities for “likes”, comments, ranking systems, or audio/video capture or by providing rewards; Par. 30”)
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Regarding Claim 7, Claim 15 and Claim 19, Polleri teaches The system of claim 1 wherein the instructions cause the computing platform to:,…, The method of claim 8, further comprising:… and The method of claim 8, further comprising:,…
Polleri teaches user analysis and the feature is expounded upon by Hazy:
receive, via an application on a user device, feedback concerning the micro-community; (Hazy Par 6-“ In particular, the system and methods may include components and subsystems for gathering performance and individual contribution feedback information from users about other users. Based on the gathered performance and feedback information and/or data, the system and methods may include components and subsystems for aggregating and analyzing situational and/or feedback data in an anonymous and secure data environment..; Par 11”)
and retrain, based on the feedback, the AI/ML model. (Hazy Par. 95-“ For example, by training the system 100 over time based on the feedback, data, and/or other information provided and/or generated in the system 100, a reduced amount of computer operations need to be performed by the devices in the system 100 using the processors and memories of the system 100 than compared to traditional methodologies... For example, operations associated with the avatars and/or media content operations may be performed on the graphics processors, and, in certain embodiments, as the system 100 learns over time various actions conducted in the system 100, artificial intelligence and/or machine learning algorithms facilitating such learning may also be executed on graphics processors and/or application specific integrated processors”)
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Regarding Claim 8,
Polleri teaches
A method comprising: retrieving a plurality of electronic data records from a plurality of application computing systems (Polleri Par. 89-90- “At 306, the functionality includes gathering data in streams (with chunking) or in batches. Chunking is a term referring to the process of taking individual pieces of information (chunks) and grouping them into larger units. By grouping each piece into a large whole, you can improve the amount of information you can remember. The model composition engine can access the data storage to gather the data for generating the machine learning model. The data can be stored locally or in cloud-based network.”);
training an artificial intelligence/machine learning (AI/ML) model based on a plurality of electronic data records retrieved from a plurality of application computing systems (Polleri Par. 45-46- “Machine learning configuration and interaction with the model composition engine 132 allows for selection of various library components 168 (e.g., pipelines 136 or workflows, micro services routines 140, software modules 144, and infrastructure modules 148) to define implementation of the logic of training and inference to build machine learning applications 112. Different parameters, variables, scaling, settings, etc. for the library components 168 can be specified or determined by the model composition engine 132. The complexity conventionally required to create the machine learning applications 112 can be performed largely automatically with the model composition engine 132.”);
facilitating, via a user application, electronic communication within the plurality of micro-communities and between a plurality of user devices associated with members of the micro-communities (Polleri Par. 169-170; Par. 176-“ Enterprise service 525 may communicate with connector 530 in a manner similar to messaging application system 515. Enterprise service 525 may send content to connector 530 to be associated with one or more end users. Enterprise service 525 may also send content to connector 530 to cause bot system 520 to perform an action associated with an end user. Action engine 560 may communicate with enterprise service 525 to obtain information from enterprise service 525 and/or to instruct enterprise service 525 to take an action identified by action engine 560.”);
retraining, based on analysis of micro-community communications, the AI/ML model. (Polleri Par.191-“ Database 640 may be used to store data for the bot system, such as data for the classification models, logs of conversation, and the like. Management APIs 650 may be used by an administrator or developer of the bot system to manage the bot system, such as retraining the classification models, editing intents, or otherwise modifying the bot system. The administrator or developer may use user interface 654 and UI server 652 to manage the bot system.”)
Polleri teaches learning analysis and the feature is expounded upon by Hazy:
identifying, based on analysis of the plurality of electronic data records, a plurality of actions performed by a plurality of users of the plurality of application computing systems; (Hazy Par. 49-“ The processing performed by the sDAASS 306, which includes, but is not limited to, data mining, item or vote counting, individual or decision ranking, dynamical systems processing, statistical analysis, scientific studies such as, but not limited to, theory-based hypothesis testing, may be used to analyze user and system data related to, but not limited to, user interactions, organization state information, or environmental data. The processing conducted by the sDAASS 306, may also include, but is not limited, to conducting and executing various scenarios and simulations. Together, these analyses may serve as inputs for user reports, feedback and recommendations generated by the system 100.”)
determining, based on the plurality of actions performed by the plurality of users, whether a particular user activity was performed based on a level of expertise in the particular user activity; (Hazy Par. 20; Par. 30-“ In certain embodiments, the situational context a first user may experience can be classified as a User-Interest and each of these may be assigned a User-Interest-Code. ... In certain embodiments, the professional development plan may include but is not limited to the individual progressive development of a portfolio of skills, behaviors or competencies, such as but not limited to those that might be signified as one or more of the categories Expert Contributor, Agile Contribution, Organizing Contributor, or Authentic Contributor or a combination thereof, to one or more of but not limited to Autonomous Contributor or Collaborative Contributor or a combination there of, and then to a single portfolio that might be called, but is but not limited to, Transformational Contributor. In certain embodiments, a User-Interest-Code can be inferred by the system 100, by experts, through artificial intelligence or through machine learning algorithms and assigned to the first user for the purpose of identifying the coaching or media content or translated scholarly or practical research or material, to be pushed to the user by the system. ...”);
grouping, by the trained AI/ML model, users into user tiers, wherein each tier corresponds to one of an experience level of a particular activity; (Hazy Par. 11-“ Additionally, the system may perform an operation that includes assigning the information related to the effectiveness of the second user to a first avatar mapped to a first user identifier of the first user and/or to a second avatar mapped to a second user identifier of the second user. In certain embodiments, the first and second avatars may be anonymized virtual representations of the first user and second users within a virtual social network of the system. Furthermore, the system may perform an operation that includes generating, based on an analysis of the information, first condition state data for the first avatar and second condition state data for the second avatar. In certain embodiments, the first condition state data and the second condition state data may be time series variables for an instance of time or a plurality of instances of time, which may include a representation of a relationship of the first and second avatars to each other in the virtual social network of the system. “)
generating, based on the user tiers and by a trained AI/ML model, a plurality of micro- communities of users associated with activities identified from the plurality of electronic data records, wherein each micro-community of the plurality of micro-communities corresponds to identification of a combination of experience level, activity type, and whether a particular activity is performed as a profession, as a hobby, or as a new practitioner; (Hazy Par. 6-“ In particular, the system and methods provide a software platform that facilitates the obtaining of higher quality feedback, which may be analyzed and enhanced so as to provide reports and/or recommendations to users in order to improve organization's performance as well as the users' performance in interactive situations, such as, but not limited to, team-based projects, multiteam systems, social interactions, working groups at a job,; Par. 30-“ n certain embodiments, a User-Interest-Code can be inferred by the system 100, by experts, through artificial intelligence or through machine learning algorithms and assigned to the first user for the purpose of identifying the coaching or media content or translated scholarly or practical research or material, to be pushed to the user by the system. In certain embodiments, pushed content might include comparison with but not limited to benchmarks, other team scores, other individual scores along with but not necessarily awards, accommodations, points or other reward whether monetary or otherwise. In certain embodiments, these can be determined in a secure manner for the first user by mapping the condition-state-type of the User's associated avatar to a User-Interest code and assigning it to the first User. “; Par. 56-user access code Par. 65-“ User Experience (UX) Perspective: Based upon this processing, the focal user (i.e. first user 101) is presented with an isolated real-world event and asked to join the event and provide anonymous feedback to the other participants. .. Any relevant formal and informal teams, multiteam systems, associations, affiliations, identity groups or special interest groups, or other social structures that may be identified by the user, other users or the system through its machine learning, artificial intelligence or interfaces with human experts Par. 95)
generating, by a user interface generator and based on a user tier and a micro- community associated with a first user, a customized user interface screen corresponding to the user tier and the micro-community, wherein customized information is presented via a first user device associated with the first user in context of an identified user experience level in the micro-community; (Hazy Par. 66-“Users Can Establish A Virtual-Event in the Virtual Network: In the application, a user can decide to establish a virtual event in conjunction with a real-life event. In addition to providing and receiving feedback for professional improvement through the application, establishing the virtual event also helps the system to virtually augment interactions in the user's real-life Social Network in future interactions. For example, prior to a meeting, the user can be reminded of social interaction dynamics that have occurred during prior meetings of this type with some or all of the same actors and provide some coaching to enable greater effectiveness. A user can add a virtual event (that is correlated with a real-life event) by, but not limited to, doing the following with the user interface of the application: 1. Select a “create an event” function; 2. Select the event type; 3. Enter some of the date, start time, end time, or other data. 4. The study or studies to be carried out before, during or after the event can be specified, otherwise the system could, but would not necessarily assign a default study. 5. Select a team or otherwise identify participants. 6. Optionally, select a project to which event pertains. 7. Optionally, the user might assign roles to various participants.”);
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Polleri in view of Hazy teach data analysis and the feature is expounded upon by Wren:
generating, for each user of the plurality of users, a likelihood score that a corresponding user may join each micro-community of the plurality of micro- communities; (Wren Col. 11-12“ The rating server 220 rates the generated account references/micro-communities in relation to their relevance to the object reference. In one embodiment, the rating score of the account references generated by the ratings server 221 is used by the micro-community fetcher 211 to decide which account references to associate with the micro-community. In another embodiment, when a user inputs a search query, the generated account references/micro-communities are presented to a user based on the rating scores. In another embodiment, a user rates the generated account references/micro-communities. The ratings server 221 stores these user ratings in memory 237 for future retrieval related to generating account references/micro-communities… The micro-community fetcher 211 then populates the created micro-community with the generated account references (subject to user consent) or the notification module 219 transmits an invitation to the account references. A person of ordinary skill in the art will recognize that the account references can be pre-configured or manually inputted to the micro-community engine 130. A person with ordinary skill in the art will also recognize that the micro-community fetcher 211 retrieves and/or creates not just one, but a plurality of micro-communities relevant to the object reference and/or account references. The micro-community fetcher 211 is described in further detail with reference to FIG. 2B….Col 13 In another embodiment, the implicit fetcher 290 infers that a user is not interested in an object reference if the user rejected an invitation sent by the notification module 219 to join a micro-community related to the object reference. Thus, for future invitations the implicit fetcher 290 does not include the account reference as relevant to the object reference. In a similar embodiment, the implicit fetcher 290 makes inferences based on user ratings received by the ratings server 221.)
sending, based on the likelihood score and via the user application, an invitation message to join one or more micro-communities to user devices associated with corresponding users (Wren Col 4-6; Col 11-12 The notification module 219 is software including routines for transmitting a notification to a user. In one embodiment, the notification module 219 is a set of instructions executable by the processor 235 to generate a notification of an account reference list, an invitation to join a micro-community, a change in account references associated with the micro-community, a request to rate a micro-community, etc. In another embodiment, the notification module 219 is stored in memory 237 of the computing device 200 and is accessible and executable by the processor 235. In either embodiment, the notification module 219 is adapted for cooperation and communication with the processor 235, the micro-community fetcher 211, the ratings server 221, the account reference fetcher 207 and other components of the computing device 200 via signal line 228.) ;
Polleri and Hazy are directed to user segment analysis. Wren improves upon the analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri in view of Hazy, as taught by Wren, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri in view of Hazy with the motivation of associating users with a micro-community that is relevant. (Wren Abstract).
Regarding Claim 11, Polleri in view of Hazy teach The method of claim 8,…
Polleri teaches user analysis and the feature is expounded upon by Hazy:
wherein each micro-community corresponds to an identified creative interest and each user may be a member of multiple micro-communities The information used by the system 100 to construct this instrument may but not necessarily be associated with a conceptual model that considers one or more, attributes, behaviors or other observable metrics in the context of their hypothesized statistical relationships first order, second order third order, or other level latent variables or factor some or all of which may or may not be validated empirically. In certain embodiments, observed attributes, behaviors, other observable metrics, or latent variables used to but not limited to construct the instrument, report or to identify User Interest code, may be one of or be some combination of: accountability, active listening, administrative activities, adaptability, agility, approachability, authenticity, authority, autonomy, autonomous contribution, consistency, contribution, balanced decision-making, balanced processing, collaborative contribution, community building, competence in areas such as but not limited to industry, technical, functional, cultural, information and communications technology, information elaboration, a metric of innovation, creativity, or physicality, citizenship behaviors, committed, confident, clarity (clear communication), clear thinking, comradery, consideration, convergent, curiosity, divergent, followership, emotional intelligence, empathy, enabling, engagement, evidence, generative activities, humor, initiative, innovative, intellectual stimulation, leadership, mental toughness, clear values or moral compass, climate, openminded, organizing, a metric of performance, predictable, preparedness, proactive, psychological safety, self-awareness, relational transparency, relevance, respectfulness, self-awareness, self-regulation, supportiveness, team norm strength, transformational contribution transparency, trust, generalized, trust, individualized trust, or workplace climate.
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Regarding Claim 12, Polleri in view of Hazy teach The method of claim 8,…
Polleri teaches user analysis and the feature is expounded upon by Hazy:
wherein a first micro-community comprises a same experience level for each of the members... community (Hazy Par. 56; Par. 65-“ Any relevant formal and informal teams, multiteam systems, associations, affiliations, identity groups or special interest groups, or other social structures that may be identified by the user, other users or the system through its machine learning, artificial intelligence or interfaces with human experts; Organization_State_Variables such as, but not limited to: internal projects, multiteam systems, initiatives, departments, budget item, activity type, position in organization structure, topic area, function, category of event, or other items that might be associated with the event.”)
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Regarding Claim 13 and Claim 20, Polleri in view of Hazy teach The method of claim 8,… and The non-transitory computer readable media of claim 16,…
Polleri teach user analysis and the feature is expounded upon by Hazy:
wherein each micro-community comprises a different experience levels for each member of the micro-community (Hazy Par. 65-“ Any relevant formal and informal teams, multiteam systems, associations, affiliations, identity groups or special interest groups, or other social structures that may be identified by the user, other users or the system through its machine learning, artificial intelligence or interfaces with human experts; Organization_State_Variables such as, but not limited to: internal projects, multiteam systems, initiatives, departments, budget item, activity type, position in organization structure, topic area, function, category of event, or other items that might be associated with the event.”)
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Regarding Claim 16,
Polleri teaches
Non-transitory computer readable media storing instructions that, when executed by a processor, cause a computing platform to:
: retrieve a plurality of electronic data records from a plurality of application computing systems (Polleri Par. 89-90- “At 306, the functionality includes gathering data in streams (with chunking) or in batches. Chunking is a term referring to the process of taking individual pieces of information (chunks) and grouping them into larger units. By grouping each piece into a large whole, you can improve the amount of information you can remember. The model composition engine can access the data storage to gather the data for generating the machine learning model. The data can be stored locally or in cloud-based network.”);
train an artificial intelligence/machine learning (AI/ML) model based on a plurality of electronic data records retrieved from a plurality of application computing systems: (Polleri- Fig. 1; Par. 39; Par. 44-45; Par. 209-210;Par. 51-“The model execution engine 108 can execute the machine learning application 112 on infrastructure 128 using one or more the infrastructure interfaces 124. The infrastructure 128 can include one or more processors, one or more memories, and one or more network interfaces, one or more buses and control lines that can be used to generate, test, compile, and deploy a machine learning application 112.; Par. 45-46- “Machine learning configuration and interaction with the model composition engine 132 allows for selection of various library components 168 (e.g., pipelines 136 or workflows, micro services routines 140, software modules 144, and infrastructure modules 148) to define implementation of the logic of training and inference to build machine learning applications 112. Different parameters, variables, scaling, settings, etc. for the library components 168 can be specified or determined by the model composition engine 132. The complexity conventionally required to create the machine learning applications 112 can be performed largely automatically with the model composition engine 132.”);
facilitate, via a user application, electronic communication within the one or more micro- communities and between a plurality of user devices associated with members of the micro- communities (Polleri Par. 169-170; Par. 176-“ Enterprise service 525 may communicate with connector 530 in a manner similar to messaging application system 515. Enterprise service 525 may send content to connector 530 to be associated with one or more end users. Enterprise service 525 may also send content to connector 530 to cause bot system 520 to perform an action associated with an end user. Action engine 560 may communicate with enterprise service 525 to obtain information from enterprise service 525 and/or to instruct enterprise service 525 to take an action identified by action engine 560.”);
retrain, based on analysis of micro-community communications, the AI/ML model. (Polleri Par.191-“ Database 640 may be used to store data for the bot system, such as data for the classification models, logs of conversation, and the like. Management APIs 650 may be used by an administrator or developer of the bot system to manage the bot system, such as retraining the classification models, editing intents, or otherwise modifying the bot system. The administrator or developer may use user interface 654 and UI server 652 to manage the bot system.”)
Polleri teaches learning analysis and the feature is expounded upon by Hazy:
identify, based on analysis of the plurality of electronic data records, a plurality of actions performed by a plurality of users of the plurality of application computing systems; (Hazy Par. 49-“ The processing performed by the sDAASS 306, which includes, but is not limited to, data mining, item or vote counting, individual or decision ranking, dynamical systems processing, statistical analysis, scientific studies such as, but not limited to, theory-based hypothesis testing, may be used to analyze user and system data related to, but not limited to, user interactions, organization state information, or environmental data. The processing conducted by the sDAASS 306, may also include, but is not limited, to conducting and executing various scenarios and simulations. Together, these analyses may serve as inputs for user reports, feedback and recommendations generated by the system 100.”)
determine, based on the plurality of actions performed by the plurality of users, whether a particular user activity was performed based on a level of expertise in the particular user activity (Hazy Par. 20; Par. 30-“ In certain embodiments, the situational context a first user may experience can be classified as a User-Interest and each of these may be assigned a User-Interest-Code. ... In certain embodiments, the professional development plan may include but is not limited to the individual progressive development of a portfolio of skills, behaviors or competencies, such as but not limited to those that might be signified as one or more of the categories Expert Contributor, Agile Contribution, Organizing Contributor, or Authentic Contributor or a combination thereof, to one or more of but not limited to Autonomous Contributor or Collaborative Contributor or a combination there of, and then to a single portfolio that might be called, but is but not limited to, Transformational Contributor. In certain embodiments, a User-Interest-Code can be inferred by the system 100, by experts, through artificial intelligence or through machine learning algorithms and assigned to the first user for the purpose of identifying the coaching or media content or translated scholarly or practical research or material, to be pushed to the user by the system. ...”);
group, by the trained AI/ML model, users into user tiers, wherein each tier corresponds to one of an experience level of a particular activity; (Hazy Par. 11-“ Additionally, the system may perform an operation that includes assigning the information related to the effectiveness of the second user to a first avatar mapped to a first user identifier of the first user and/or to a second avatar mapped to a second user identifier of the second user. In certain embodiments, the first and second avatars may be anonymized virtual representations of the first user and second users within a virtual social network of the system. Furthermore, the system may perform an operation that includes generating, based on an analysis of the information, first condition state data for the first avatar and second condition state data for the second avatar. In certain embodiments, the first condition state data and the second condition state data may be time series variables for an instance of time or a plurality of instances of time, which may include a representation of a relationship of the first and second avatars to each other in the virtual social network of the system. “)
generate, based on the user tiers and by a trained AI/ML model, a plurality of micro- communities of users associated with activities identified from the plurality of electronic data records, wherein each micro-community of the plurality of micro-communities corresponds to identification of a combination of experience level, activity type, and whether a particular activity is performed as a profession, as a hobby, or as a new practitioner; (Hazy Par. 6-“ In particular, the system and methods provide a software platform that facilitates the obtaining of higher quality feedback, which may be analyzed and enhanced so as to provide reports and/or recommendations to users in order to improve organization's performance as well as the users' performance in interactive situations, such as, but not limited to, team-based projects, multiteam systems, social interactions, working groups at a job,; Par. 30-“ n certain embodiments, a User-Interest-Code can be inferred by the system 100, by experts, through artificial intelligence or through machine learning algorithms and assigned to the first user for the purpose of identifying the coaching or media content or translated scholarly or practical research or material, to be pushed to the user by the system. In certain embodiments, pushed content might include comparison with but not limited to benchmarks, other team scores, other individual scores along with but not necessarily awards, accommodations, points or other reward whether monetary or otherwise. In certain embodiments, these can be determined in a secure manner for the first user by mapping the condition-state-type of the User's associated avatar to a User-Interest code and assigning it to the first User. “; Par. 56-user access code Par. 65-“ User Experience (UX) Perspective: Based upon this processing, the focal user (i.e. first user 101) is presented with an isolated real-world event and asked to join the event and provide anonymous feedback to the other participants. .. Any relevant formal and informal teams, multiteam systems, associations, affiliations, identity groups or special interest groups, or other social structures that may be identified by the user, other users or the system through its machine learning, artificial intelligence or interfaces with human experts Par. 95)
generate, by a user interface generator and based on a user tier and a micro- community associated with a first user, a customized user interface screen corresponding to the user tier and the micro-community, wherein customized information is presented via a first user device associated with the first user in context of an identified user experience level in the micro-community; (Hazy Par. 66-“Users Can Establish A Virtual-Event in the Virtual Network: In the application, a user can decide to establish a virtual event in conjunction with a real-life event. In addition to providing and receiving feedback for professional improvement through the application, establishing the virtual event also helps the system to virtually augment interactions in the user's real-life Social Network in future interactions. For example, prior to a meeting, the user can be reminded of social interaction dynamics that have occurred during prior meetings of this type with some or all of the same actors and provide some coaching to enable greater effectiveness. A user can add a virtual event (that is correlated with a real-life event) by, but not limited to, doing the following with the user interface of the application: 1. Select a “create an event” function; 2. Select the event type; 3. Enter some of the date, start time, end time, or other data. 4. The study or studies to be carried out before, during or after the event can be specified, otherwise the system could, but would not necessarily assign a default study. 5. Select a team or otherwise identify participants. 6. Optionally, select a project to which event pertains. 7. Optionally, the user might assign roles to various participants.”);
Polleri and Hazy are directed to user segment analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri, as taught by Hazy, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri with the motivation of obtaining feedback and improving business outcomes. (Hazy Par. 4).
Polleri in view of Hazy teach data analysis and the feature is expounded upon by Wren:
generate, for each user of the plurality of users, a likelihood score that a corresponding user may join each micro-community of the plurality of micro- communities; (Wren Col. 11-12“ The rating server 220 rates the generated account references/micro-communities in relation to their relevance to the object reference. In one embodiment, the rating score of the account references generated by the ratings server 221 is used by the micro-community fetcher 211 to decide which account references to associate with the micro-community. In another embodiment, when a user inputs a search query, the generated account references/micro-communities are presented to a user based on the rating scores. In another embodiment, a user rates the generated account references/micro-communities. The ratings server 221 stores these user ratings in memory 237 for future retrieval related to generating account references/micro-communities… The micro-community fetcher 211 then populates the created micro-community with the generated account references (subject to user consent) or the notification module 219 transmits an invitation to the account references. A person of ordinary skill in the art will recognize that the account references can be pre-configured or manually inputted to the micro-community engine 130. A person with ordinary skill in the art will also recognize that the micro-community fetcher 211 retrieves and/or creates not just one, but a plurality of micro-communities relevant to the object reference and/or account references. The micro-community fetcher 211 is described in further detail with reference to FIG. 2B….Col 13 In another embodiment, the implicit fetcher 290 infers that a user is not interested in an object reference if the user rejected an invitation sent by the notification module 219 to join a micro-community related to the object reference. Thus, for future invitations the implicit fetcher 290 does not include the account reference as relevant to the object reference. In a similar embodiment, the implicit fetcher 290 makes inferences based on user ratings received by the ratings server 221.)
send, based on the likelihood score and via the user application, an invitation message to join one or more micro-communities to user devices associated with corresponding users (Wren Col 4-6; Col 11-12 The notification module 219 is software including routines for transmitting a notification to a user. In one embodiment, the notification module 219 is a set of instructions executable by the processor 235 to generate a notification of an account reference list, an invitation to join a micro-community, a change in account references associated with the micro-community, a request to rate a micro-community, etc. In another embodiment, the notification module 219 is stored in memory 237 of the computing device 200 and is accessible and executable by the processor 235. In either embodiment, the notification module 219 is adapted for cooperation and communication with the processor 235, the micro-community fetcher 211, the ratings server 221, the account reference fetcher 207 and other components of the computing device 200 via signal line 228.) ;
Polleri and Hazy are directed to user segment analysis. Wren improves upon the analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Polleri in view of Hazy, as taught by Wren, by utilizing additional user analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Polleri in view of Hazy with the motivation of associating users with a micro-community that is relevant. (Wren Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20240289746A1 to Goldsmith et al.- Abstract-“ The disclosed computer-implemented system facilitates collaboration and information management in space exploration and technology development. It includes an Information Technology (IT) core for website management and a Project Moon Habitat (PMH) integration module for PMH activities. The system features a participant classification module utilizing the PMH Classification System for categorizing individuals and organizations based on their roles in the space ecosystem. Other modules handle data collection, social interaction, community leveraging, alliance facilitation, government involvement, and comprehensive data collection. The system aims to enhance collaboration and innovation in space exploration and technology development”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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