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 .
Status of the Claims
Claims 1-20 are all the claims pending in the application.
Claims 1-5 8-11, 31, 14, and 16-20 are amended.
Claims 1-20 are rejected.
The following is a Final Office Action in response to amendments and remarks filed June 26, 2025.
Response to Arguments
Regarding the 112(a) rejections, the rejections are withdrawn in light of the amendments to the claims.
Regarding the 101 rejections, the rejections are withdrawn because Examiner finds the additional elements reflect a practical application. That is, under Step 2A Prong 1, Examiner finds the claims recite an abstract idea (e.g., providing an annual performance review to an employee, providing feedback from customers, providing performance metrics to a business entity, etc.). Under Step 2A Prong 2, Examiner finds the additional elements of receiving feedback on the recommendations, training the artificial intelligence system based on the feedback and providing a new recommendation, as claimed, reflects a practical application (i.e., a process for improving the automated recommendation system). Accordingly, the 101 rejections are withdrawn.
Regarding the 103 rejections, the rejections are withdrawn at least because the cited references do not teach receiving feedback and training an artificial intelligence system as claimed. Please see below for the new rejections of the claims as amended.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding the independent claims, claims 1, 17, and 20 are rejected under 112(a) because the claims recite the newly amended limitation “…training, based on the feedback from the user relating to the effectiveness of augmenting at least one interaction of the first avatar in the first future event, the second future event, or a combination thereof, the artificial intelligence system…” however there is no discussion, throughout the entirety of the specification and drawings, of training the artificial intelligence system based on feedback from the user relating to the effectiveness. For example, ¶[0095] of the Specification as filed discusses training based on feedback data but does not contemplate using feedback from the user relating to the effectiveness as training data.
As such, the Examiner asserts this as evidence that the newly amended claims are new matter. Accordingly claims 1, 17, and 20 are rejected under 112(a). Claims 2-16 and 18-20 do not correct this issue and accordingly are rejected due to their dependencies.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaddam et al, US Pub. No. 2016/0134633, herein referred to as "Gaddam" in view of Siebers, Peer-Olaf, et al. "A multi-agent simulation of retail management practices." arXiv preprint arXiv:0803.1598 (2008), herein referred to as “Siebers”, Dinu et al, US Pub. No. 2018/0096251, herein referred as “Dinu”, further in view of Rice, US Pub. No. 2011/0082746, herein referred to as "Rice".
Regarding claim 1, Gaddam teaches:
a memory that stores instructions; and a processor that executes the instructions to perform operations, the operations comprising (memory, instructions, and processor, ¶¶ [0182]- [0183] and Fig. 11):
generating a first avatar with a mapping to a first identifier (users register an account with the social network, and a social network provider computer stores a social network user account identifier, ¶ [0112]. Please note, the user's account in a social media network is within the scope of an "avatar" because the broadest reasonable interpretation of "avatar" includes a digital representation or handle of a user, i.e., a social media account)
receiving, from a first device associated with the first avatar, information related to effectiveness of the first avatar with regard to participation by the first avatar in a first virtual event also participated in by a second avatar (users provide feedback about transaction with a resource provider after verifying they conducted a transaction, with resource provider e.g. ¶¶[0021], [0080], [0113], and Abstract; see also ¶¶[0029], [0038], [0039] defining feedback, user, and resource provider; see also e.g. ¶¶[0083], [0118] discussing receiving a request for permission to submit feedback),
wherein at least a portion of the information is provided by at least one sensor or input device that measures interaction information associated with at least one interaction of the first avatar with the second avatar (receives user input via keyboard, ¶[0055]);
assigning the encoded information to the first avatar mapped to a first user identifier of the first user (user submits feedback via the user's account in a social network, e.g., ¶ [0112], [0118]; see also ¶ [0090] noting user feedback is contained in user's profile),
generating and storing, based on an analysis of the information and the interaction information, first condition state data for the first avatar and second condition state data for the second avatar (confirms user interacted with resource provider e.g., ¶¶ [0087], [0113], [0124]; see also ¶¶ [0115]- [0131] and Fig. 7 explaining example confirmation process; and e.g., ¶¶ [0064], [0111], [0112] discussing storing user data),
wherein the first condition state data for the first avatar is generated based at least in part on an input associated with the first avatar generated by the second avatar and the virtual interaction of the first node of the first avatar with the second node of the second avatar in the virtual social network (transaction processing computer generates feedback token that is associated with a transaction, e.g., ¶¶ [0088], [0103]- [0106]);
providing, based on the first condition state data for the first avatar, a recommendation, media content, or a combination thereof, to the first avatar related to a potential for improving effectiveness of the first avatar with regard to a first future event participated in by the second avatar, a second future event without the second avatar, or a combination thereof, wherein the recommendation, media content, the first condition state data, or a combination thereof, are accessible by the first avatar (provides feedback to resource provider record, ¶¶ [0128]- [0129]; see also ¶ [0029] noting feedback is used to improve merchant. Please note, the limitation "a potential for improving effectiveness of the first avatar with regard to a first future event participated in by the second avatar, a second future event without the second avatar" does not further limit the scope of the claim because it is only the intended use of providing, a recommendation, or a media content, see MPEP 2103.I.C.).
However, Gaddam does not teach but Siebers does teach:
wherein the first avatar is embodied as a first computer program in the system (uses Agent-Based Modeling and Simulation, e.g. Abstract, pg.1, and Sect. 2. WHY AGENT-BASED SIMULATION?, pg. 2, to model various individuals including customers, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 2)
and the second avatar is embodied as a second computer program (models various individuals including sales staff and managers, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 3);
facilitating, via the mapping of the first avatar to the first identifier, a virtual interaction of the first node of the first avatar with the second node of the second avatar in the virtual social network based on interaction between the first and second computer programs (models interactions of customer, staff, and manager agents, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 3);
generating, based on an analysis of the information and the interaction information first condition state data for the first avatar and second condition state data for the second avatar (generates agents internal states, pg. 2 Sect. 2. WHY AGENT-BASED SIMULATION?)
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam with the retail simulation of Siebers because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., merchants) would likely wish to improve customer satisfaction and accordingly would have used the modeling of Siebers to do so.
However, the combination of Gaddam and Siebers does not teach but Dinu does teach:
wherein the first avatar serves as a first node of a plurality of nodes of a virtual social network of the system (social network includes users, etc. represented by nodes and connections as edges, ¶¶[0080]-[0081])
wherein the first avatar comprises the first node of the virtual social network and the second avatar is embodied and comprises a second node of the virtual social network (social network includes users, etc. represented by nodes and connections as edges, ¶¶[0080]-[0081]);
wherein the first avatar a virtual representation of the first node within the virtual social network of the system that includes the second avatar mapped to a second identifier (social network includes users, etc. represented by nodes and connections as edges, ¶¶[0080]-[0081]; see also ¶[0085] discussing identifiers for user profiles);
providing, by utilizing the artificial intelligence system and based on the first condition state data for the first avatar, a recommendation (uses machine learning to recommend content to users, e.g., ¶[0052])
receiving, based on the recommendation, the media content, or a combination thereof, feedback from a user relating to effectiveness of augmenting at least one interaction of the first avatar in the first future event, the second future event, or a combination thereof (receives feedback on content items user, ¶[0055]);
training, based on the feedback from the user relating to the effectiveness of augmenting at least one interaction of the first avatar in the first future event, the second future event, or a combination thereof, the artificial intelligence system (retrains machine learning model based on feedback, ¶[0055]);
and providing, by utilizing the artificial intelligence system trained based on the feedback, a new recommendation, new media content, or a combination thereof, for the first avatar, for improving effectiveness of the first avatar at a third future event (uses machine learning to recommend content to users, e.g., ¶¶[0052], [0055]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the feedback system of Gaddam and Siebers with user profile pages as nodes in a social network and machine learning as taught by Dinu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have understood user information in a social network (e.g., user profile pages of Gaddam) would likely be stored as nodes in the network and would likely benefit from a machine learning based analysis, i.e., as taught by Dinu.
However, the combination of Gaddam, Siebers, and Dinu does not teach but Rice does teach:
and encoding the information assigned to the first avatar mapped to the first identifier by utilizing a secure mapping key (encrypts feedback, e.g., with random character code for user identifier, ¶¶[0102]-[0103]);
wherein the recommendation, media content, the first condition state data, or a combination thereof, are accessible by the first avatar by utilizing the secure mapping key (encrypts private data, e.g., with random character code for user identifier, ¶¶[0102]-[0103]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam, Siebers, and Dinu with the random character code as a user identifier of Rice because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., customers) would may wish to be anonymous or have privacy concerns and accordingly would have modified Gaddam to hide or delete information identifying the customer, e.g., as taught by Rice.
Regarding claim 2, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Gaddam further teaches:
wherein the information related to the effectiveness of the first avatar comprises data gathered by a surveillance technology, data associated with transportation, logistics, operations, inventory management or guidance systems, data associated with audio content, data associated with video content, data associated with text analysis, data associated with a global positioning system, data associated with biometric data collection, or a combination thereof (users check-in when they are physically at or near resource provider location, e.g. ¶¶[0058], [0060], [0086], [0119]; see also ¶[0059] discussing using GPS to determine user location).
Regarding claim 3, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Gaddam further teaches:
wherein the operations further comprise ensuring a privacy of the information associated with the first avatar, wherein the privacy is ensured, at least in part, via confidential computing technology, a trusted execution environment, a system for securing privacy, the mapping of the first avatar to the first user identifier, or a combination thereof (allows user to submit feedback anonymously, ¶ [0179]. Please note, since the system allows the user to submit feedback anonymously, the system is a system for securing privacy).
Regarding claim 4, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Siebers further teaches:
wherein the user comprises an intelligent machine, a program, a humanoid, a robot, a drone, a wearable device, a function, a process, a device, or a combination thereof (uses Agent-Based Modeling and Simulation, e.g. Abstract, pg.1, and Sect. 2. WHY AGENT-BASED SIMULATION?, pg. 2, to model various individuals including customers, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 2).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam with the retail simulation of Siebers because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., merchants) would likely wish to improve customer satisfaction and accordingly would have used the modeling of Siebers to do so.
Regarding claim 5, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Gaddam further teaches:
wherein the first virtual event is associated with a plurality of virtual events, or wherein the plurality of events is organized into event types, projects, project types, institutions, institution types, or a combination thereof (transactions are associated with other transactions stored in the transaction processing module, ¶¶ [0072], [0133]).
Regarding claim 6, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Gaddam further teaches:
wherein the operations further comprise providing the information to a third-party user (allows other users to view feedback, ¶¶ [0080]),
wherein the information and data are provided anonymously or not anonymously (allows user to submit feedback anonymously, ¶ [0179]).
Regarding claim 7, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 6 and Gaddam further teaches:
wherein the third-party user is a subscriber to a plan, an account, or a combination thereof (the other users access the social network, ¶ [0058]. Please note, Examiner finds that the limitations specifying the user is a subscriber does not substantially further limit the scope of the claim because information about the users does not functionally alter or relate to the system and merely labeling the information does not patentably distinguish the claimed invention, see MPEP 2111.05.)
Regarding claim 8, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Gaddam further teaches:
wherein the operations further comprise maintaining a quality of data associated with the information, a quantity of data associated with the information, or a combination thereof (system stores various information including user payment information, ¶ [0053], interaction records, ¶ [0071]),
wherein the information is provided to or obtained from the first avatar, the second avatar, or a combination thereof (users provide feedback about transaction with a resource provider after verifying they conducted a transaction, with resource provider e.g., ¶¶ [0021], [0080], [0113], and Abstract; see also ¶¶ [0029], [0038], [0039] defining feedback, user, and resource provider; see also e.g., ¶¶ [0083], [0118] discussing receiving a request for permission to submit feedback).
Regarding claim 9, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Rice further teaches:
determining an amount of the feedback to determine a quantity of the feedback received, a type of feedback received, or a combination thereof (sorts feedback and generates statistics e.g., average ratings, ¶ [0074]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam, Siebers, and Dinu with the location-based feedback of Rice because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., merchants) would likely be interested in knowing what types of feedback they are receiving and accordingly would have modified Gaddam to sort the feedback for the users, e.g., as taught by Rice.
Regarding claim 10, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Gaddam further teaches:
aggregating the information with other data, and wherein the aggregated information and the other data is provided for use to a first user, a second user, a third user, or a combination thereof (aggregates all feedback for a resource provider and publicly displays feedback and overall ratings, ¶ [0129]).
Regarding claim 11, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Rice further teaches:
deleting, after assigning the information to the first avatar mapped to the first identifier, the information from a mapping server utilized to facilitate the assigning of the information to the first avatar (uses a random character code as a user identifier for feedback to provide anonymity, ¶ [0103], and deletes feedback to address privacy concerns, ¶ [0102]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam, Siebers, and Dinu with the random character code as a user identifier of Rice because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., customers) would may wish to be anonymous or have privacy concerns and accordingly would have modified Gaddam to hide or delete information identifying the customer, e.g., as taught by Rice.
Regarding claim 12, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Gaddam further teaches:
wherein the first condition state data further comprises self- assessment or self-development data, a complete or partial history of all events, a representation of a portion of network connections among the first avatar, the second avatar, and other avatars, an avatar identifier, a table of events and event reports, a table of connected avatar identifiers and interaction events, or a combination thereof (a confirmation the user has actually conducted a transaction with a resource provider, e.g. ¶[0113] would be a partial history of all events because it would be a confirmation of a transaction).
Regarding claim 13, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Gaddam further teaches:
processing and projecting the first condition state data as at least one input onto a first condition state type as at least one output so as to classify a need of a first user, an interest of the first user, media content to be presented to the first user, a connectivity option to be presented to the first user, or a combination thereof, and/or wherein the operations further comprise processing and projecting the second condition state data as at least one input onto a second condition state type as at least one output so as to classify a need of a second user, an interest of the second user, media content to be presented to the second user, or a combination thereof (analyzes purchase data to determining spending trends and preferences (i.e. an interest of the user), ¶[0176])).
However, the combination of Gaddam and Siebers does not teach but Dinu does teach:
by utilizing the artificial intelligence system (uses machine learning to recommend content to users, e.g., ¶[0052])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the feedback system of Gaddam and Siebers with user profile pages as nodes in a social network and machine learning as taught by Dinu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have understood user information in a social network (e.g., user profile pages of Gaddam) would likely be stored as nodes in the network and would likely benefit from a machine learning based analysis, i.e., as taught by Rubinstein.
Regarding claim 14, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 13 and Gaddam further teaches:
wherein the operations further comprise mapping the first condition state type to a first interest state, and/or wherein the operations further comprise mapping the second condition state type to a second interest state (analyzes purchase data to determining spending trends and preferences (i.e., an interest of the user), ¶ [0176]).
However, the combination of Gaddam and Siebers does not teach but Dinu does teach:
by utilizing the artificial intelligence system (uses machine learning to recommend content to users, e.g., ¶[0052])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the feedback system of Gaddam and Siebers with user profile pages as nodes in a social network and machine learning as taught by Dinu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have understood user information in a social network (e.g., user profile pages of Gaddam) would likely be stored as nodes in the network and would likely benefit from a machine learning based analysis, i.e., as taught by Rubinstein.
Regarding claim 15, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 14 and Gaddam further teaches:
wherein the first interest state, the second interest state, or a combination thereof, comprise environmental state variables, organizational state variables, leadership activity variables, behavioral variables, interaction variables, relationship variables, social network variables, communication variables, attitudinal variable, emotional state variables, cognitive state variables, competence variables, knowledge variables, management activity variables, or a combination thereof (confirms user interacted with resource provider e.g. ¶¶[0087], [0113], [0124], which would be interaction variables; see also ¶¶[0115]-[0131] and Fig. 7 explaining example confirmation process).
Regarding claim 16, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 1 and Rice further teaches:
utilizing a first secure mapping key to secure a relationship between the first identifier and the first avatar (uses a random character code or encryption as a user identifier for feedback to provide anonymity, ¶ [0103], and deletes feedback to address privacy concerns, ¶ [0102]),
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam, Siebers, and Dinu with the random character code as a user identifier of Rice because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., customers) would may wish to be anonymous or have privacy concerns and accordingly would have modified Gaddam to hide or delete information identifying the customer, e.g., as taught by Rice.
However, the combination of Gaddam, Siebers, Dinu and Rice does not explicitly teach:
and wherein the operations further comprise utilizing a second secure mapping key to secure a relationship between the second identifier and the second avatar.
Nevertheless, it would have been obvious at the time of filing to have multiple secure mapping keys because duplication of parts is obvious unless a new and unexpected result is produced, see MPEP 2144.04.VI.B. That is, Examiner finds no evidence using a second random character code or encryption as a user identifier for another user would produce new and unexpected results, and accordingly finds claim 16 obvious in light of the combination of Gaddam, Siebers, Dinu, and Rice.
Regarding claim 17, Gaddam teaches:
generating a first avatar with a mapping to a first identifier (users register an account with the social network, and a social network provider computer stores a social network user account identifier, ¶ [0112]. Please note, the user's account in a social media network is within the scope of an "avatar" because the broadest reasonable interpretation of "avatar" includes a digital representation or handle of a user, i.e., a social media account),
transmitting a query to a first user device associated with the first avatar user, wherein the query requests information related to an effectiveness of the first avatar with regard to participation by the first avatar in a first virtual event also participated in by a second avatar (prompts user to submit feedback (e.g., after visiting merchant), e.g., ¶¶ [0127], [0145]);
obtaining, from the first device associated with the first avatar, the information related to the effectiveness of the first avatar with regard to participation by the first avatar in the first virtual event also participated in by the second avatar (users provide feedback about transaction with a resource provider after verifying they conducted a transaction, with resource provider e.g. ¶¶[0021], [0080], [0113], and Abstract; see also ¶¶[0029], [0038], [0039] defining feedback, user, and resource provider; see also e.g. ¶¶[0083], [0118] discussing receiving a request for permission to submit feedback),
wherein at least a portion of the information is provided by at least one sensor that measures interaction information associated with at least one interaction of the first avatar with the second avatar (receives user input via keyboard, ¶[0055]);
associating the information to the first avatar mapped to the first identifier of the first avatar (user submits feedback via the user's account in a social network, e.g., ¶ [0112], [0118]; see also ¶ [0090] noting user feedback is contained in user's profile),
generating and storing, and based on an analysis of the information and the interaction information, first condition state data for the first avatar and second condition state data for the second avatar (confirms user interacted with resource provider e.g., ¶¶ [0087], [0113], [0124]; see also ¶¶ [0115]- [0131] and Fig. 7 explaining example confirmation process; and e.g., ¶¶ [0064], [0111], [0112] discussing storing user data),
wherein the first condition state data for the first avatar is generated based at least in part on an input associated with the first avatar generated by the second avatar (transaction processing computer generates feedback token that is associated with a transaction, e.g., ¶¶ [0088], [0103]- [0106]);
and providing, by utilizing the artificial intelligence system and based on the first condition state data for the first avatar, a recommendation, media content, or a combination thereof, to the first avatar for improving the effectiveness of the first avatar with regard to a first future event to be participated in by the second avatar user, a second future event without the second avatar, or a combination thereof (provides feedback to resource provider record, ¶¶ [0128]- [0129]; see also ¶ [0029] noting feedback is used to improve merchant. Please note, the limitation "for improving effectiveness of the first avatar with regard to a first future event participated in by the second avatar, a second future event without the second avatar" does not further limit the scope of the claim because it is only the intended use of providing, a recommendation, or a media content, see MPEP 2103.I.C.).
However, Gaddam does not teach but Siebers does teach:
wherein the first avatar is embodied as a first computer program in the system (uses Agent-Based Modeling and Simulation, e.g. Abstract, pg.1, and Sect. 2. WHY AGENT-BASED SIMULATION?, pg. 2, to model various individuals including customers, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 2);
and the second avatar is embodied as a second computer program (models various individuals including sales staff and managers, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 3);
facilitating, via the mapping of the first avatar to the first identifier, a virtual interaction of first node of the first avatar with the second node of the second avatar in the virtual social network based on interaction between the first and second computer programs (models interactions of customer, staff, and manager agents, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 3);
generating, based on an analysis of the information and the interaction information, first condition state data for the first avatar and second condition state data for the second avatar (generates agents internal states, pg. 2 Sect. 2. WHY AGENT-BASED SIMULATION?)
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam with the retail simulation of Siebers because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., merchants) would likely wish to improve customer satisfaction and accordingly would have used the modeling of Siebers to do so.
However, the combination of Gaddam and Siebers does not teach but Dinu does teach:
wherein the first avatar serves as a first node of a plurality of nodes of a virtual social network of the system (social network includes users, etc. represented by nodes and connections as edges, ¶¶[0080]-[0081])
wherein the first avatar is a virtual representation of the first node within the virtual social network of a system that includes the second avatar mapped to a second identifier of the second avatar (social network includes users, etc. represented by nodes and connections as edges, ¶¶[0080]-[0081]; see also ¶[0085] discussing identifiers for user profiles);
receiving, based on the recommendation, the media content, or a combination thereof, feedback from a user relating to effectiveness of augmenting at least one interaction of the first avatar in the first future event, the second future event, or a combination thereof (receives feedback on content items user, ¶[0055]);
training, based on the feedback from the user relating to the effectiveness of augmenting at least one interaction of the first avatar in the first future event, the second future event, or a combination thereof, the artificial intelligence system (retrains machine learning model based on feedback, ¶[0055]);
and providing, by utilizing the artificial intelligence system trained based on the feedback, a new recommendation, new media content, or a combination thereof, for the first avatar, for improving effectiveness of the first avatar at a third future event (uses machine learning to recommend content to users, e.g., ¶¶[0052], [0055]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the feedback system of Gaddam and Siebers with user profile pages as nodes in a social network and machine learning as taught by Dinu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have understood user information in a social network (e.g., user profile pages of Gaddam) would likely be stored as nodes in the network and would likely benefit from a machine learning based analysis, i.e., as taught by Dinu.
However, the combination of Gaddam, Siebers, and Dinu does not teach but Rice does teach:
and encoding the information assigned to the first avatar mapped to the first identifier by utilizing a secure mapping key (encrypts feedback, e.g., with random character code for user identifier, ¶¶[0102]-[0103]),
wherein the recommendation, media content, the first condition state data, or a combination thereof, are accessed by utilizing the secure mapping key (encrypts private data, e.g., with random character code for user identifier, ¶¶[0102]-[0103]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam, Siebers, and Dinu with the random character code as a user identifier of Rice because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., customers) would may wish to be anonymous or have privacy concerns and accordingly would have modified Gaddam to hide or delete information identifying the customer, e.g., as taught by Rice.
Regarding claim 18, Gaddam, Rowland, Dinu and Rice teaches all the limitations of claim 17 and Dinu further teaches:
determining, by utilizing a machine a relative weighting for the information to be utilized in the analysis (assigns different weights to different relationships, ¶[0080]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the feedback system of Gaddam and Siebers with user profile pages as nodes in a social network and machine learning as taught by Dinu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have understood user information in a social network (e.g., user profile pages of Gaddam) would likely be stored as nodes in the network and would likely benefit from a machine learning based analysis, i.e., as taught by Dinu.
Regarding claim 19, the combination of Gaddam, Siebers, Dinu and Rice teaches all the limitations of claim 17 and Gaddam further teaches:
wherein the information related to the effectiveness of the first avatar comprises preparation information, authenticity information, clarify information, evidence information, relevance information, respect information, openness information, contribution information, engagement information, credibility information, value information, any attribute information, any effectiveness information, or a combination thereof (users provide feedback about transaction with a resource provider after verifying they conducted a transaction, with resource provider e.g. ¶¶[0021], [0080], [0113], and Abstract, which would be within the scope of several these claim element, e.g. evidence information, relevance information, value information, any attribute information, any effectiveness information, etc.).
Regarding claim 20, Gaddam teaches:
A non-transitory computer-readable device comprising instructions, which when loaded and executed by a processor, cause the processor to perform operations comprising (memory, instructions, and processor, ¶¶ [0182]- [0183] and Fig. 11):
generating a first avatar with a mapping to a first identifier (users register an account with the social network, and a social network provider computer stores a social network user account identifier, ¶ [0112]. Please note, the user's account in a social media network is within the scope of an "avatar" because the broadest reasonable interpretation of "avatar" includes a digital representation or handle of a user, i.e., a social media account),
generating a query to a first user device associated with the first avatar user, wherein the query requests information related to an effectiveness of the first avatar with regard to participation by the first avatar in a first virtual event also participated in by a second avatar (prompts user to submit feedback (e.g., after visiting merchant), e.g., ¶¶ [0127], [0145]);
receiving, from the first device associated with the first avatar, the information related to the effectiveness of the first avatar with regard to participation by the first avatar in the first virtual event also participated in by the second avatar (users provide feedback about transaction with a resource provider after verifying they conducted a transaction, with resource provider e.g. ¶¶[0021], [0080], [0113], and Abstract; see also ¶¶[0029], [0038], [0039] defining feedback, user, and resource provider; see also e.g. ¶¶[0083], [0118] discussing receiving a request for permission to submit feedback),
wherein at least a portion of the information is provided by at least one sensor that measures interaction information associated with at least one interaction of the first avatar with the second avatar (receives user input via keyboard, ¶[0055]);
providing, by utilizing the artificial intelligence system and based on the first condition state data for the first avatar, a recommendation, media content, or a combination thereof, to the first avatar for improving the effectiveness of the first avatar with regard to a first future event to be participated in by the second avatar user, a second future event without the second avatar, or a combination thereof (provides feedback to resource provider record, ¶¶ [0128]- [0129]; see also ¶ [0029] noting feedback is used to improve merchant. Please note, the limitation "for improving effectiveness of the first avatar with regard to a first future event participated in by the second avatar, a second future event without the second avatar" does not further limit the scope of the claim because it is only the intended use of providing, a recommendation, or a media content, see MPEP 2103.I.C.).
providing, based on an analysis of the information and the interaction information, first condition state data for the first avatar and second condition state data for the second avatar (confirms user interacted with resource provider e.g., ¶¶ [0087], [0113], [0124]; see also ¶¶ [0115]- [0131] and Fig. 7 explaining example confirmation process; and e.g., ¶¶ [0064], [0111], [0112] discussing storing user data),
and generating, based on at least the first condition state data for the first avatar a recommendation, media content, or a combination thereof, for the second user for improving the effectiveness of the first avatar with regard to a first future event to be participated in by the second avatar, a second future event without the second avatar, or a combination thereof (provides feedback to resource provider record, ¶¶ [0128]- [0129]; see also ¶ [0029] noting feedback is used to improve merchant. Please note, the limitation "for improving effectiveness of the first avatar with regard to a first future event participated in by the second avatar, a second future event without the second avatar" does not further limit the scope of the claim because it is only the intended use of providing, a recommendation, or a media content, see MPEP 2103.I.C.).
However, Gaddam does not teach but Siebers does teach:
wherein the first avatar is embodied as a first computer program in the system (uses Agent-Based Modeling and Simulation, e.g. Abstract, pg.1, and Sect. 2. WHY AGENT-BASED SIMULATION?, pg. 2, to model various individuals including customers, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 2);
and the second avatar is embodied as a second computer program (models various individuals including sales staff and managers, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 3);
facilitating, via the mapping of the first avatar to the first identifier, a virtual interaction of first node of the first avatar with the second node of the second avatar in the virtual social network based on interaction between the first and second computer programs (models interactions of customer, staff, and manager agents, Sect. 3.1. Model Concepts, pg. 3 and Figs. 1 and 3);
generating, based on an analysis of the information and the interaction information, first condition state data for the first avatar and second condition state data for the second avatar (generates agents internal states, pg. 2 Sect. 2. WHY AGENT-BASED SIMULATION?)
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam with the retail simulation of Siebers because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., merchants) would likely wish to improve customer satisfaction and accordingly would have used the modeling of Siebers to do so.
However, the combination of Gaddam and Siebers does not teach but Dinu does teach:
wherein the first avatar serves as a first node of a plurality of nodes of a virtual social network of the system (social network includes users, etc. represented by nodes and connections as edges, ¶¶[0080]-[0081])
wherein the first avatar is a virtual representation of the first node within the virtual social network of a system that includes the second avatar mapped to a second identifier of the second avatar (social network includes users, etc. represented by nodes and connections as edges, ¶¶[0080]-[0081]; see also ¶[0085] discussing identifiers for user profiles);
receiving, based on the recommendation, the media content, or a combination thereof, feedback from a user relating to effectiveness of augmenting at least one interaction of the first avatar in the first future event, the second future event, or a combination thereof (receives feedback on content items user, ¶[0055]);
training, based on the feedback from the user relating to the effectiveness of augmenting at least one interaction of the first avatar in the first future event, the second future event, or a combination thereof, the artificial intelligence system (retrains machine learning model based on feedback, ¶[0055]);
and providing, by utilizing the artificial intelligence system trained based on the feedback, a new recommendation, new media content, or a combination thereof, for the first avatar, for improving effectiveness of the first avatar at a third future event (uses machine learning to recommend content to users, e.g., ¶¶[0052], [0055]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the feedback system of Gaddam and Siebers with user profile pages as nodes in a social network and machine learning as taught by Dinu because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have understood user information in a social network (e.g., user profile pages of Gaddam) would likely be stored as nodes in the network and would likely benefit from a machine learning based analysis, i.e., as taught by Dinu.
However, the combination of Gaddam, Siebers, and Dinu does not teach but Rice does teach:
and encoding the information assigned to the first avatar mapped to the first identifier by utilizing a secure mapping key (encrypts feedback, e.g., with random character code for user identifier, ¶¶[0102]-[0103]),
wherein the recommendation, media content, the first condition state data, or a combination thereof, are accessed by utilizing the secure mapping key (encrypts private data, e.g., with random character code for user identifier, ¶¶[0102]-[0103]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the reputation feedback system of Gaddam, Siebers, and Dinu with the random character code as a user identifier of Rice because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Gaddam (e.g., customers) would may wish to be anonymous or have privacy concerns and accordingly would have modified Gaddam to hide or delete information identifying the customer, e.g., as taught by Rice.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626