DETAILED ACTION
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 .
Response to Amendment
All pending claims 1-7, 9, 10 and 12-18 filed April 3, 2026 are examined in this final office action necessitated by amendment. Claims 8 and 11 were canceled in the preliminary amendment.
Response to Arguments
35 USC 112
Rejection is withdrawn necessitated by amendment.
35 USC 101
Rejection is withdrawn necessitated by amendment.
35 USC 103
Applicant’s arguments, see remarks filed April 3, 2026 with respect to the rejection(s) of claim(s) under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made. Although Morgan-Dutta establish avatar setting conditions by the user, Morgan-Dutta do not expressly mention determining whether the XR salesclerk behaves based on artificial intelligence or behavior of a performer. Leise on the other hand would have taught Morgan-Dutta such techniques. In Leise, rules determine whether artificial intelligence is relied upon to develop questions for the customer service representative, i.e. performer, or pushes questions/responses from AI directly to the customer.
(Leise: D13: col. 3, line 63-col. 4, line 2) As used herein, the terms “customer service representative” or “call agent,” may refer to a person who handles incoming or outgoing customer calls for an organization. The call agent may handle account inquiries, customer complaints, support issues, etc. For example, if the organization is an insurance provider, the call agent may handle insurance-related inquiries from customers.
(Leise: D45: col. 10, lines 51-59) In some embodiments, the set of rules for a requested action may be pre-stored in a rules database and in other embodiments, the set of rules may automatically be learned. The avatar generation server 102 may call upon the machine learning module 146 which may use various machine learning techniques to learn the most likely insurance-related information for responding to the requested action and/or the set of rules for identifying the most likely insurance-related information.
(Leise: D101: col. 20, line 62-col. 21, line 7) In any event, once a requested action is determined, the avatar response generation module 142 may identify a set of rules associated with the requested action for determining insurance-related information corresponding to the requested action. In some embodiments, the set of rules for a requested action may be pre-stored in a rules database and in other embodiments, the set of rules may automatically be learned. The avatar generation server 102 may call upon the machine learning module 146 which may use various machine learning techniques to learn the most likely insurance-related information for responding to the requested action and/or the set of rules for identifying the most likely insurance-related information.
35 USC § 101-Subject Matter Eligibility
The instant claims are executed within an extended reality environment. The extended reality (XR) mall per the instant specification can be an Augmented Reality (AR) and/or Virtual Reality (VR) mall. Using broadest reasonable interpretation of the independent claims as a whole, the system can merge a live view with virtual content which renders claims a practical application under Step 2A (second prong).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-7, 9, 10 and 12-18 are rejected under 35 USC 103 as being unpatentable over Morgan et al., US 2022/0351281 “Morgan,” in view of Dutta et al., US 2022/0200934 “Dutta,” further in view of Leise et al., US 10,387,963 “Leise.”
In Morgan see at least (underlined text is for emphasis):
Regarding claim 17: (Currently Amended) A method for controlling an extended reality (XR) mall generating device, comprising:
generating, using the XR mall generating device an XR mall including a plurality of XR stores in each of which a user is capable of experiencing shopping with use of an XR technology; and
[Definition: Instant specification: 0004] The XR technology is also referred to as xR or cross reality, and is a generic term for virtual reality (VR), augmented reality (AR), mixed reality (MR), and substitutional reality (SR).
[Morgan: 0007] In various embodiments, methods and a system for Virtual Reality (VR) shopping are presented. Please note: Extended Reality (XR) and Virtual Reality (VR) are interchangeable.
[Morgan: 0014] As will be discussed herein and below, methods and a system 100 are provided for dynamically generated and customized VR stores and VR shopping sessions within the VR stores. Item codes are dynamically obtained from store catalogues and mapped to corresponding item images. Object images for structures, floors, shelves, tables, chairs, walls, carts, baskets, terminals, checkout stations, wall hangings, decorations, etc. are obtained. A model of a store is obtained, and the object images and item images are custom arranged within a dynamically rendered VR store according to the model. The items that populate the VR store and layout of the items within the VR store can be customized based on consumer-selected filters, consumer transaction history, and/or a known store (using a known planogram for the store).
[Morgan: 0021] A “VR session” is an interactive network simulation of a real-world shopping trip/journey of a given consumer within a given dynamically rendered VR store through the VR interface. The VR store may comprise the customer visiting or interacting within one or more customer selected VR rooms. Each customer may be shopping in their own independent VR session through their own customized VR store but upon entering a common VR room the customers visually and audibly see and hear the same environment associated with the common VR room and the customers become visible to one another within the common VR room. Please note: The system provides provisions multiple VR stores which qualifies as a XR mall.
providing, using the XR mall generating device, the XR mall to the user, …
[Morgan: 0022] The system 100 comprises a cloud/server 110, a plurality of retailer/delivery servers 120, and a plurality of user-operated devices 130. Please note: Morgan’s system architecture is similar to Applicant’s system architecture and provides access to multiple retailers via a VR session.
[Morgan: 0028] VR session manager 115 interacts with VR interface 133 for purposes of defining a VR session, determining item codes of a customized VR store from an appropriate catalogue service 123 and activating store population manager 113 to interact with object image manager 113 to dynamically render an initial and starting state for the VR store within the VR session. Each image within the VR store is associated with an object and each object or type of object has functions that can be activated during the session based on customer input or action during the VR session.
[Morgan: 0029] VR interface 133 allows the customer to navigate the VR store (or any VR room) through a pointer object that is rendered within the animated stream of the store and controlled by the customer.
… wherein:
an XR salesclerk is capable of being disposed in each of the plurality of XR stores,
[Morgan: 0028] … VR session manager 115 processes the functions to cause the location of the images to change within the store and to cause actions, such as add item to cart, remove item from cart, move to a VR room, show item nutrition information, show item price, show item ingredients, activate an item search, display item discounts, show customer loyalty account information, activate an assistant avatar or chatbot through chatbot manager 117,
setting information concerning the XR salesclerk is set by the user and store-side setting information concerning a behavior of the XR salesclerk is set by a store side of each of the plurality of XR stores, …
[Morgan: 0037] When the customer activates an assistant option within the VR session, an automated avatar or chat text box is presented to the customer for a natural language dialogue between the customer and a chatbot. VR session manager 115 activates chatbot manager 117 and chatbot manager 117 initiates the natural language chatbot for customer assistance. The customer can type natural language questions or can speak through voice (which is captured by a microphone of device 130 and translated to text for processing by the chatbot). Whether the chatbot appears as a chat text box or as an avatar within the VR store is a configurable option selected by the user. In some cases, a wake-up word may be used to activate the chatbot during the VR session through the VR interface 133. For example, VR interface 133 listens for the customer to say, “can you help me,” “I need help,” “help,” “assistance is needed,” etc. The automated assistant can also be always active and listening or monitor activity of the customer for providing assistance as well. For example, the user may be holding within the VR session an item for over a predetermined amount of time with no detectable activity, in this case the automated chatbot may autonomously engage the customer asking if the customer needs assistance on the item; the customer may be aimless wondering throughout the VR store without having selected an item for a predetermined period of time, the automated chatbot may autonomously ask the customer if the customer is looking for a specific item or needs help locating the specific item within the VR store; etc. Please note: Setting activities for the chatbot include a) predetermined amount of time with no detectable activity, b) wake-up words or phrases.
… wherein, the setting information concerning the XR salesclerk is set by the user regardless of the store-side setting information concerning the behavior of the XR salesclerk, and
Rejection is based in part upon the teachings applied to claim 17 by Morgan and further upon the combination of Morgan-Dutta. Although Morgan implements setting activity for a chatbot, Morgan does not expressly mention the user providing setting activity concerning the behavior of the chatbot, i.e. XR salesclerk. Dutta on the other hand would have taught Morgan such techniques.
In Dutta see at least:
[Dutta: 0011] FIG. 4 is a flowchart illustrating an example operation of a computing system for training a machine learning (ML) model in accordance with one or more techniques of this disclosure.
[Dutta: 0017] Similarly, user interaction device 104 may comprise one or more computing devices. In examples where user interaction device 104 includes two or more computing devices, the computing devices of user interaction device 104 may act together as a system. User interaction device 104 may include a computing device, such as a mobile telephone, tablet, personal computer, wearable device, smart speaker device, augmented/mixed/virtual reality headset, smart eyewear, ambient computing device, special-purpose computing device, projection device, or other type of computing device. In general, user 110 may use user interaction device 104 to conduct chatbot interaction sessions.
[Dutta: 0074] In the example of FIG. 3, storage device(s) 206 of computing system 102 may store a score for each chatbot profile of a plurality of chatbot profiles 124 (300). Each of the chatbot profiles corresponds to a different persona. For each chatbot profile of the plurality of chatbot profiles, computing system 102 may collect biometric response data 214 for user 110 while user 110 has an interaction session with the chatbot profile (302).
[Dutta: 0075] Furthermore, in the example of FIG. 3, scoring system 116 of computing system 102 may update the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile (304). FIG. 7, which is described in detail below, shows an example operation for updating the scores for chatbot profiles.
[Dutta: 0076] Additionally, scoring system 116 may determine a ranking of the chatbot profiles based on the scores for the chatbot profiles (306). Computing system 102 (e.g., conversation engine 118 of computing system 102) may select a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles (308). In some examples, computing system 102 (e.g., conversation engine 118 of computing system 102) may use the selected chatbot profile for the subsequent interaction session with user 110 without user 110 wearing biometric collection device(s) 106.
[Dutta: 0077] In some examples, scoring system 116 may also receive persona preference data from user 110 and may determine the ranking of the chatbot profiles based on the scores for the chatbot profiles and the persona preference data from the user. The persona preference data may include data indicating the conscious preferences of user 110 with respect to the persona of the chatbot profile. For instance, the persona preference data may indicate how user 110 feels about the persona of the chatbot profile relative to the personas of other chatbot profiles. In some examples, the persona preference data may include self-reported emotional response values from user 110. In this example, scoring system 116 may use the self-reported emotional response values to update the score for the chatbot profile, which scoring system 116 may ultimately use to rank the chatbot profiles.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Dutta, which permit the user to set VR store chatbot behavior within a virtual reality, augmented reality or mixed reality environment, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Dutta to the teachings of Morgan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc. Please note: How the user feels about the chatbot behavior, i.e. sales clerk, may result in reinforcing the behavior established by the system or evolve to user preference behavior.
the store-side setting information concerning the behavior of the XR salesclerk is used to determine whether the XR salesclerk behaves based on artificial intelligence or behavior of a performer.
Rejection is based in part upon the teachings and rationale applied to claim 17 by Morgan-Dutta and further upon the combination of Morgan-Dutta-Leise. Although Morgan-Dutta establish avatar setting conditions by the user, Morgan-Dutta do not expressly mention determining whether the XR salesclerk behaves based on artificial intelligence or behavior of a performer. Leise on the other hand would have taught Morgan-Dutta such techniques.
In Leise see at least:
(Leise: B4: col. 1, lines 51-59) Additionally, a graphical representation of a customer service representative or call agent (a “call agent avatar”) may be generated and displayed on the user's client device such that it appears the call agent avatar is communicating the response to the user. For example, the response may be an audio response. The call agent avatar may be animated to move in synchronization with the audio response such that the call agent avatar's mouth appears to move in accordance with the words provided by the audio response.
(Leise: D13: col. 3, line 63-col. 4, line 2) As used herein, the terms “customer service representative” or “call agent,” may refer to a person who handles incoming or outgoing customer calls for an organization. The call agent may handle account inquiries, customer complaints, support issues, etc. For example, if the organization is an insurance provider, the call agent may handle insurance-related inquiries from customers. Please note: Human customer sales representative or call agent qualify as a performer.
(Leise: D20: col. 5, lines 31-46) Generally speaking, techniques for generating a call agent avatar may be implemented in a client device, one or several network servers or a system that includes a combination of these devices. However, for clarity, the examples below focus primarily on an embodiment in which an avatar generation server receives voice or text input corresponding to an insurance-related inquiry or a banking-related inquiry from a user's client device. In some embodiments, the user's client device may transcribe the voice input to text and transmit the text input to the avatar generation server. In other embodiments, the avatar generation server may transcribe the voice input to text. The avatar generation server may then generate a response to the voice or text input by retrieving data from one or several databases and/or communicating with one or several additional network servers, such as a vehicle repair facility server.
(Leise: D31: col. 8, lines 37-39) The avatar generation server 102 may include an avatar response generation module 142, a grammar module 144, and a machine learning module 146.
(Leise: D35: col. 9, lines 31-40) If the grammar module 144 cannot determine a requested action based on the text input or determines a most likely requested action having a likelihood which is less than a predetermined likelihood threshold (e.g., 75 percent, 50 percent, 30 percent, etc.), the grammar module 144 may cause the client device 10 to provide follow up questions to the user for additional input. Moreover, the grammar module 144 may call upon the machine learning module 146 to learn additional requested actions or a most likely requested action based on the text input.
(Leise: D38: col. 10, lines 3-21) In some embodiments, the machine learning module 146 may also learn frequent behavior of the user based on the user's insurance-related inquiries, based on purchases as a result of the user's insurance-related inquiries (e.g., purchases related to additional vehicle insurance coverage after asking about the types of coverage included in the user's vehicle insurance policy) and/or based on data retrieved as a result of the insurance-related inquiries, such as sensor data in a vehicle. For example, if the user asks about the status of a vehicle repair on several occasions each regarding a different type of repair to the vehicle within a predetermined amount of time (e.g., one month, three months, one year), the machine learning module 146 may learn that the user is involved in several vehicle crashes or that the user's vehicle is in poor condition. Based on this frequent behavior, the avatar response generation module 142 may generate and transmit push notifications to the client device 10 regarding auto safety recommendations, home safety recommendations, new products, etc., …
(Leise: D45: col. 10, lines 51-59) In some embodiments, the set of rules for a requested action may be pre-stored in a rules database and in other embodiments, the set of rules may automatically be learned. The avatar generation server 102 may call upon the machine learning module 146 which may use various machine learning techniques to learn the most likely insurance-related information for responding to the requested action and/or the set of rules for identifying the most likely insurance-related information.
(Leise: D101: col. 20, line 62-col. 21, line 7) In any event, once a requested action is determined, the avatar response generation module 142 may identify a set of rules associated with the requested action for determining insurance-related information corresponding to the requested action. In some embodiments, the set of rules for a requested action may be pre-stored in a rules database and in other embodiments, the set of rules may automatically be learned. The avatar generation server 102 may call upon the machine learning module 146 which may use various machine learning techniques to learn the most likely insurance-related information for responding to the requested action and/or the set of rules for identifying the most likely insurance-related information.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Leise, which establish setting conditions for the use of artificial intelligence or would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of B to the teachings of A would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Regarding claim 1: Rejection is based upon the teachings and rationale applied to claim 17 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding and extended reality mall generating device.
Regarding claims 2, 3 and 5: Rejections are based upon the teachings and rationale applied to claim 1 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding consumer transaction history and/or profile, see Morgan: [0014], [0017], [0034].
Regarding claim 4: Rejection is based upon the teachings and rationale applied to claim 1 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding preference for a brand, see Morgan: [0034] preference for brand X.
Regarding claim 6: Rejection is based upon the teachings and rationale applied to claim 5 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding movement through the XR mall, see Morgan: [0008], [0029]- user navigation through the VR store (or any VR room).
Regarding claim 7: Rejections are based upon the teachings and rationale applied to claim 3 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding setting information concerning an appearance of the XR mall, see Morgan: [0014] … A model of a store is obtained, and the object images and item images are custom arranged within a dynamically rendered VR store according to the model. The items that populate the VR store and layout of the items within the VR store can be customized based on consumer-selected filters, consumer transaction history, and/or a known store (using a known planogram for the store).
Regarding claim 9: Rejection is based upon the teachings and rationale applied to claim 2 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding candidates for the plurality of XR stores.
[Morgan: 0024] Each retailer/delivery server 120 comprises at least one processor 121 and a non-transitory computer-readable storage medium 122. Medium 122 comprises executable instructions for a catalogue service 123, an order service 124, a loyalty service 125, and a delivery service. Men the executable instructions are provided to and executed by processor 121, this causes processor 121 to perform operations discussed herein and below with respect to 123-126. Medium 122 also comprises a transaction data store comprising transaction history data and transaction metrics. Please note: Multiple retailer/delivery servers offer multiple VR stores to consumers.
[Morgan: 0031] … The actual items displayed may also be negotiated with retailers, such that the retailers compete or pay to have their items presented on the shelf or presented more prominently on the shelf. Please note: Chosen retailers are candidates for the plurality of VR stores.
Claims 10 and 12: Rejections are based upon the teachings and rationale applied to claim 9 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding store-setting information concerning a commodity displayed in each of the XR stores, see [Morgan:0014] … The items that populate the VR store and layout of the items within the VR store can be customized based on consumer-selected filters, consumer transaction history, and/or a known store (using a known planogram for the store).
Regarding claim 13: Rejection is based upon the teachings and rationale applied to claim 9 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding measurement information:
[Morgan: 0040] The model for the store may be based on an actual physical store that the customer frequents. In this situation, the store's planogram is used within the model to ensure the layout and item placement within the layout comports with the physical store. This may be useful to customers that desire familiarity.
Regarding claim 14: Rejection is based upon the teachings and rationale applied to claim 9 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding live commerce:
[Morgan: 0034] … For example, the customer may only every purchase brand X of a given item, the VR session manager 115 may populate with brand X but also include brand Y along with a promotion from a retailer to entice the customer to change to brand Y during the VR session. Please note: Enticing the customer to change to brand Y during the VR session qualifies as live commerce.
Regarding claim 15: Rejection is based upon the teachings and rationale applied to claim 1 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding XR mall available to another user:
[Morgan: 0015] Using a VR interface, the consumer traverses the VR store within a VR session and can select, discard, and inspect the items and the corresponding item information. Selected items are maintained in a cart during the VR session. The consumer can select rooms of the store for real time online interaction with other consumers that are shopping within other VR sessions. The consumers can share items from their carts with one another, audibly talk, or chat with one another as avatars customized by the consumers. A consumer can engage an automated avatar for natural language assistance during the VR session. Please note: VR store is available to other users.
[Morgan: 0021] A “VR session” is an interactive network simulation of a real-world shopping trip/journey of a given consumer within a given dynamically rendered VR store through the VR interface. The VR store may comprise the customer visiting or interacting within one or more customer selected VR rooms. Each customer may be shopping in their own independent VR session through their own customized VR store but upon entering a common VR room the customers visually and audibly see and hear the same environment associated with the common VR room and the customers become visible to one another within the common VR room. Please note: VR store is available to other users.
Regarding claim 16: Rejection is based upon the teachings and rationale applied to claim 1 by Morgan-Dutta and further upon the combination of Morgan-Dutta regarding at least one of: virtual reality, augmented reality, mixed reality, see [Morgan: 0007]- virtual reality shopping; [Dutta: 0017]- augmented/virtual/mixed reality.
Regarding claim 18: Rejection is based upon the teachings and rationale applied to claim 1 by Morgan-Dutta and further upon the combination of Morgan-Dutta. VR stores are generated and customized for each consumer [Morgan: 0014].
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 8,527,430 (Hamilton, II et al.) “Allocating Virtual Universe Customer Service,” discloses: (Abstract) Virtual universe customer service representatives are cloned and assigned as a function of observing customer behavior, retrieving historical data and creating a customer profile. Preferential subavatar assignment parameters are determined for a customer as a function of the customer profile, choosing a subavatar from a plurality of subavatars as a function of a correlation of a subavatar performance characteristic with the preferential subavatar assignment parameter and a store objective, and the clone is populated with the chosen subavatar. Choosing a subavatar may comprise preferentially rating subavatars and determining an appropriateness threshold as a function of the subavatar assignment parameter, the performance characteristics and the store objective. Some embodiments reset a threshold in response to time-in-queue or to repetitively observing customer behavior, retrieving customer data, determining a subavatar assignment parameter and choosing a highest-rated available subavatar meeting a revised threshold. Subavatars may comprise automated, customer service representative-controlled and jointly-controlled subavatars.
Lee et al., “A web DSS approach to building an intelligent internet shopping mall by integrating virtual reality and avatar,’ discloses: [Abstract] This paper is concerned with designing and implementing the Internet shopping mall by using a virtual reality-driven avatar and web decision support system (Web DSS). Traditionally, the Internet shopping mall has been designed based on the combination of several hyperlinks, images, and texts. However, this sort of approach results in a lower performance because possible customers cannot make accurate shopping decisions. To overcome these kinds of pitfalls facing the current Internet shopping malls, we propose using a combination of virtual reality and Web DSS. The main virtues of our proposed approach to designing the Internet shopping mall are as follows: First, the virtual reality technique is emerging as one of the alternatives that guarantee a sense of reality for the customers' part and facilitating the complex process of shopping decision makings. Especially, the avatar, which is an artificially designed man working on the Internet, can make the Internet shopping-related decision making processes easier. Second, the Web DSS approach can provide an effective decision support mechanism for customers. Especially, we design a set of intelligent agents for the proposed Web DSS. Experimental results with an illustrative example showed that our proposed approach can yield a new Internet shopping mall paradigm with which customers can benefit from a high level of decision support functions.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 June 6, 2026
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