DETAILED ACTION
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/08/2024, 10/31/2025 and 01/09/2026, 04/17/2026 and 07/10/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Status of the Claims
This office action is responsive to communication(s) filed on April 28th, 2026.
Claims 1-20 are currently pending.
Response to Arguments
Applicant’s arguments filed April 28th, 2026 have been fully considered but are respectfully found to be not persuasive for at least the following reason(s).
In regard to applicant’s remarks April 28th, 2026 (Pages 1-4, Sections A-C), regarding the rejection made under 35 U.S.C. 103 with regards to claims 1-20, applicant asserts that
A. Response to the Section 103 Rejections
...Independent claim 1 recites, among other limitations, "generate custom content as output of a large language model (LLM) in response to input including the subscriber data." Claim 1 further recites "retrieve, based on the unique identifier, subscriber data indicative of activity of the subscriber on the telecommunications network" and "wherein the LLM is trained based on activity data of subscribers on the telecommunications network."
The Examiner has alleged that Mycek discloses these limitations, citing paragraphs 34 and 56-57 for "content determination system, machine learning techniques." Applicant respectfully submits that Mycek does not teach or suggest these claim limitations.
First, Mycek does not disclose an LLM. Mycek discloses only generic "machine learning techniques (e.g., regression, classification)." See Mycek, paragraph [0057]. An LLM is a specific type of neural network architecture designed for natural language understanding and generation, trained on large text corpora. Regression and classification techniques are fundamentally different from LLMs. Regression and classification techniques are traditional machine learning approaches that do not involve natural language processing capabilities. The Examiner has not established that generic machine learning techniques such as regression and classification are equivalent to a large language model as recited in the claims. Neither Kostka nor any other cited reference cures this deficiency.
Second, Mycek does not teach subscriber activity data on a telecommunications network. Mycek's user profile data includes "purchase history, preferences, physical browse history, upcoming flights, automatically generated from social media, etc." See Mycek, paragraph [0057]. Mycek's user profile data is retail-focused data collected from a user's interactions within a store environment and not telecommunications carrier network activity data. In contrast, claim 1 requires "subscriber data indicative of activity of the subscriber on the telecommunications network" and further requires that the "LLM is trained based on activity data of subscribers on the telecommunications network." The claimed system 1s specifically directed to a telecommunications network operator leveraging its own subscriber activity data to personalize content, which is a context entirely absent from Mycek's retail beacon system. None of the cited references teach using telecommunications network activity data to train an LLM or generate personalized content.
Accordingly, the combination of Kostka and Mycek fails to teach or suggest all limitations of independent claim 1. Independent claim 19 similarly recites "generating custom content as output of a large language model (LLM) in response to input including the subscriber data" where the subscriber data is "indicative of activity of the subscriber on the telecommunications network." Claim 19 is therefore patentable for at least the same reasons as claim 1.
B. Response to the Section 103 Rejections
...As discussed in Section A, neither Kostka nor Mycek teaches or suggests an LLM or subscriber activity data on a telecommunications network as recited in the claims. Storm was cited to supply additional limitations such as custom controls on user interfaces, kiosk-based triggering events, image-based distance estimation, and visualization sensor scanning. However, Storm is directed to biometric authentication at kiosk devices using facial recognition. Storm's biometric authentication is a fundamentally different technical problem from personalizing subscriber experiences using telecommunications network data and LLMs. Storm does not cure the deficiencies of Kostka and Mycek regarding the LLM and telecom subscriber activity data limitations.
Independent claim 12 similarly recites "a model that is trained based on subscriber activity data to generate personalized content for the subscriber in the check-in environment, wherein the subscriber activity data indicates activity of the subscriber on the telecommunications network, including activity outside of the check-in environment." For the same reasons discussed in Section A, neither Kostka, Mycek, nor Storm teaches or suggests these limitations. Claim 12 is therefore patentable for at least the same reasons as claim 1.
C. Response to the Section 103 Rejections
...As discussed in Sections A and B, neither Kostka, Mycek, nor Storm teaches or suggests an LLM or subscriber activity data on a telecommunications network as recited in the claims. Pittman was cited to supply additional limitations such as requesting subscriber identification numbers, graphical element interactions, QR code scanning, wait time computation, and stationary device interactions. However, Pittman is directed to location and time-based advertising using geolocation data. Pittman's geolocation-based advertising does not teach an LLM or telecommunications subscriber activity data. Pittman does not cure the deficiencies identified above.
Applicant’s arguments are fully considered, however found to be not persuasive for at least the following reasons:
In regards to the limitation, “retrieve, based on the unique identifier, subscriber data indicative of activity of the subscriber on the telecommunications network”, Kostka teaches passing user data (e.g., user demographic information, user visit frequency/user loyalty indicator which may include the number and value of user purchased from the store within a time period) stored in a user profile managed by a related native retail application executing on the user's mobile computing device on Kostka, Page 4, Paragraphs 28-29, Block S140.
Mycek discloses a user device and associated user device identifier, user-associated profile, activity data and contextual information, (e.g., user -device information associated with a user profile) as well as an entity (e.g., the merchant) associated with the beacon further associated with a user profile, on Mycek, Page 5, Paragraph 44 and Page 7, Paragraphs 53 and 57.
Storm discloses a kiosk-type device receiving information regarding a user, associated with a mobile device, that is in proximity to the kiosk (e.g., a user identifier associated with a respective user, such as enrollment images) retrieved from a data store, which can be used to authenticate the identity of a user engaging with the kiosk. Services may be provided to the users based on the authentication (Storm, Pages 2-3, Paragraph 26-27 and Paragraph 30). Storm does not expressly disclose data associated with the user beyond the captured biometric information. However, the Examiner does not rely on Storm alone for teaching the limitation “subscriber data indicative of activity of the subscriber on the telecommunications network”.
Pittman discloses a mobile computing device associated with a user and related consumer profile data, which may include physical locations and historical movements of the mobile computing device, user activity and other measures of consumer interaction (e.g., consumer interaction with media content, messages and other accessible content, for example), on Col. 30, Lines 5-15, Fig. 37 and Col. 86, Lines 26-67.
The claim element, “subscriber of a telecommunications network” is considered a design choice, as the specification does not provide any evidence of a functional difference or specific technical benefit resulting from this limitation. A subscriber of a telecommunications network is a user of a telecommunications service and respective service-enabled device. Therefore, the disclosed user profile of Kostka, the user-specific data of Mycek and the consumer profile data of Pittman can be interpreted as subscriber data when associated with a subscriber.
In regards to the limitations, “generating custom content as output of a large language model (LLM) in response to input including the subscriber data” and “the LLM is trained based on activity data of subscribers on the telecommunications network”, Kostka teaches a notification generator capable of outputting generated personalized, location-aware notifications using retrieved user-specific activity data of an identified user (Kostka, Pages 2-3, Paragraphs 21, Pages 4-5, Paragraphs 28 and 31, user activity data [time-dependent estimated position/orientation of the mobile computing device], activity-based user data [e.g. user visit frequency, user demographics, predicted interests, loyalty information, shopping behavior, product engagement, movement history, shopping list, previous interactions], user-associated preferences, user input [products, pre-populated shopping list, wish list]) and current location (Kostka, Pages 3-5, Paragraphs 26, 29 and 32, notifications generated according to user location [e.g. retailer, aisle, product, orientation, store map, beacon and user device locations]) by using input data relating to the user, user mobile computing device, beacon information (Kostka, Page 4, Paragraph 29). Kostka does not disclose that the customized content is generated as output of a large language model (LLM) trained using subscriber activity data.
Mycek discloses customized contextual content which may be selected or generated using machine-learning techniques based on user-specific inputs, including natural-language sources such as social-network streams, emails, calendar information, user profiles, browsing history and purchase history (Mycek, Pages 7-8, Paragraphs 56-60, determining content functions to select or generate contextual content relevant to a user, content determination system using machine-learning techniques). Mycek further teaches that the machine-learning modules can be updated periodically; in response to and based on subsequent user actions (or non-action), user purchase history, or updated in any other manner at any suitable time (Mycek, Page 7, Paragraph 57).
At the time the invention was made, it would have been obvious to one of ordinary skill in the art to modify the notification generation system of Kostka to include the machine-learning based personalized content generation techniques of Mycek because such a modification is the result of simple substitution of one known machine-learning-based content generation technique for another known technique performing the same function of generating customized user-specific notifications, producing a predictable result of personalized contextual notifications based upon available user information. Doing so would improve the generation of notifications containing personalized content for the user, based on accumulated user data and contextual information, in a beacon-based system. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Therefore, for at least the reasons set forth above, the rejection made under 35 U.S.C. 103 over the prior arts of record, Kostka in view of Mycek, with regards to claims 1-2, 5-6 and 19-20, Kostka as modified by Mycek and further in view of Storm, with regards to claims 3 and 12-16, and Kostka as modified by Mycek, in view of Storm and further in view of Pittman, with regards to claims 4, 7-11 and 17-18 are remained proper and therefore maintained.
Claim Rejections - 35 USC § 103
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 1-2, 5-6 and 19-20 are rejected under 35. U.S.C. 103 as being unpatentable over Kostka et al. US 2015 0079942 A1 hereinafter (“Kostka”) in view of Mycek et al. US 2017 0164159 A1 (hereinafter “Mycek”).
Regarding Claim 1, Kostka teaches one or more non-transitory, computer-readable storage media storing instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a system of a telecommunications network (Kostka, Page 1, Paragraphs 9-10, Fig. 1; Page 12, Paragraph 78, computer-readable medium storing computer-readable instructions), cause the system to:
receive a signal transmitted from a wireless mobile device located in a check-in environment (Kostka, Page 1, Paragraph 10-13, Block S110, Mobile computer device, retail setting, receiving a beacon signal from a wireless beacon),
wherein the wireless mobile device is associated with a subscriber of the telecommunications network, and
wherein the signal includes a unique identifier for the subscriber and an indication of the check-in environment based on a short-range signal broadcast from a beacon device located in the check-in environment (Kostka, Pages 1-2, Paragraph 15, Fig. 1, Block S120, wireless signal received subsequently of Block S110);
determine, based on the signal, physical presence of a wireless mobile device at a micro-location in the check-in environment (Kostka, Page 5, Paragraph 32, product within the store can be mapped with relation to one or more wireless beacons);
retrieve, based on the unique identifier, subscriber data indicative of activity of the subscriber on the telecommunications network (Kostka teaches passing user data [e.g., user demographic information, user visit frequency/user loyalty indicator which may include the number and value of user purchased from the store within a time period] stored in a user profile managed by a related native retail application executing on the user’s mobile computing device to the network in order to customize a notification, Page 4, Paragraphs 28-29, Fig. 1, Block S140). The claim element, “subscriber of a telecommunications network” is considered a design choice, as the specification does not provide any evidence of a functional difference or specific technical benefit resulting from this limitation. A subscriber of a telecommunications network is a user of a telecommunications service and respective service-enabled device. Therefore, the disclosed user profile can be interpreted as subscriber data when associated with a subscriber);
generate custom content as output of a large language model (LLM) in response to input including the subscriber data,
wherein the LLM is trained based on activity data of subscribers on the telecommunications network, and
wherein the custom content is customized for the subscriber and the check-in environment (Kostka teaches a notification generator capable of outputting generated personalized, location-aware notifications using retrieved user-specific activity data of an identified user [Pages 2-3, Paragraphs 21, Pages 4-5, Paragraphs 28 and 31, user activity data, e.g. time-dependent estimated position/orientation of the mobile computing device; activity-based user data, e.g. user visit frequency, user demographics, predicted interests, loyalty information, shopping behavior, product engagement, movement history, shopping list, previous interactions; user-associated preferences and user-provided input, e.g. products, pre-populated shopping list, wish list] and current location [Pages 3-5, Paragraphs 26, 29 and 32, notifications generated according to user location, e.g. retailer, aisle, product, orientation, store map, beacon and user device locations] by using input data relating to the user, user mobile computing device, beacon information [Page 4, Paragraph 29]); and
cause a display screen of the wireless mobile device to present the custom content on a user interface (Kostka, Pages 2-3, Paragraph 26, Block S140, retrieve information related to the wireless beacon based on the unique identifier, collect and present personalize micro-location-based notification to the user; Pages 4-5, Paragraph 30, Fig. 2, Block S150, present notification to the user on the mobile computer device).
Kostka fails to teach the limitations:
generate custom content as output of a large language model (LLM) in response to input including the subscriber data,
wherein the LLM is trained based on activity data of subscribers on the telecommunications network (Kostka does not disclose that the customized content is generated as output of a large language model [LLM] trained using subscriber activity data].
However, Mycek teaches the limitations:
generate custom content as output of a large language model (LLM) in response to input including the subscriber data (Mycek, Page 3, Paragraph 34; Page 7, Paragraphs 56-57, content determination system, machine learning techniques),
wherein the LLM is trained based on activity data of subscribers on the telecommunications network (Mycek, Page 7, Paragraphs 57, machine learning modules can be updated periodically, user action)
Although Kostka addresses the remaining limitations of Claim 1, Mycek demonstrates the following limitations of one or more non-transitory, computer-readable storage media storing instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a system of a telecommunications network (Mycek, Page 1, Paragraphs 19-20, beacon system; Page 5, Paragraph 44, processing system; Page 12, Paragraph 77, memory), cause the system to:
receive a signal transmitted from a wireless mobile device located in a check-in environment (Mycek, Page 1, Paragraph 20, retail store; Page 4, Paragraph 40, radio of beacon system, transmit/receive data),
wherein the wireless mobile device is associated with a subscriber of the telecommunications network (The claim element, “subscriber of a telecommunications network” is
considered a design choice, as the specification does not provide any evidence of a functional difference or specific technical benefit resulting from this limitation. Mycek discloses a user device, user-associated profile, activity data and contextual information, [e.g., user-device information associated with a user profile] as well as an entity [e.g., the merchant] associated with the beacon further associated with a user profile, on Page 7, Paragraph 57. A subscriber of a telecommunications network is a user of a telecommunications service and respective service-enabled device. Therefore, the disclosed user-specific data can be interpreted as subscriber data when associated with a subscriber), and
wherein the signal includes a unique identifier for the subscriber (Mycek, Page 5, Paragraph 44, transmitted user device information; Page 7, Paragraph 53, user device identifier, user profile) and an indication of the check-in environment based on a short-range signal broadcast from a beacon device located in the check-in environment (Mycek, Pages 10-11, Paragraph 71, beacon);
determine, based on the signal, physical presence of a wireless mobile device at a micro-location in the check-in environment (Mycek, Pages 10-11, Paragraph 71, determination of user device proximity to the beacon);
retrieve, based on the unique identifier, subscriber data indicative of activity of the subscriber on the telecommunications network (Mycek discloses user-specific data, including user profile data such as purchase history, preferences, physical browsing history, device information, and interaction history, on Page 7, Paragraph 57);
generate custom content as output of a large language model (LLM) in response to input including the subscriber data,
wherein the LLM is trained based on activity data of subscribers on the telecommunications network (Mycek, Page 3, Paragraph 34; Page 7, Paragraphs 56-60, content determination system, machine-learning techniques), and
wherein the custom content is customized for the subscriber and the check-in environment (Mycek, Page 7, Paragraphs 56-60); and
cause a display screen of the wireless mobile device to present the custom content on a user interface (Mycek, Page 3, Paragraphs 31-33, output device, user device; Page 8, Paragraphs 58-60, content determined based on beacon system information user device information).
Mycek and Kostka are considered to be analogous to the claimed invention because they are in the same field of targeted advertisements based on user location. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the beacon-based system of Kostka, comprising a notification generator capable of outputting generated personalized and location-aware notifications using retrieved user-specific activity data of an identified user (Kostka, Pages 2-3, Paragraphs 21; Pages 4-5, Paragraphs 28 and 31) as well as current location information (Kostka, Pages 3-5, Paragraphs 26, 29 and 32) by using input data relating to the user, user mobile computing device and beacon information (Kosta, Page 4, Paragraph 29) to incorporate the teaching of the beacon system of Mycek, comprising a content determination system, wherein customized contextual content may be selected or generated using machine-learning techniques based on user-specific inputs, including natural-language sources such as social-network streams, emails, calendar information, user profiles, browsing history and purchase history (Mycek, Pages 7-8, Paragraphs 56-60, determining content functions to select or generate contextual content relevant to a user, content determination system using machine-learning techniques). Mycek further teaches that the machine-learning modules can be updated periodically; in response to and based on subsequent user actions (or non-action), user purchase history, or updated in any other manner at any suitable time (Mycek, Page 7, Paragraph 57).
At the time of the invention, it would have been obvious to one of ordinary skill in the art to modify the notification generation system of Kostka to include the machine-learning based personalized content generation techniques of Mycek because such a modification is the result of simple substitution of one known machine-learning-based content generation technique for another known technique performing the same function of generating customized user-specific notifications, producing a predictable result of personalized contextual notifications based upon available user information. Doing so would improve the generation of notifications containing personalized content for the user, based on accumulated user data and contextual information, in a beacon-based system. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding Claim 2, Kostka in view of Mycek teaches the system of claim 1, wherein the LLM is trained based on activity data including any of:
transcriptions of voice or video calls communicated over the telecommunications network, text-based messages communicated over the telecommunications network, or browsing histories of subscribers of the telecommunications network (Mycek, Page 7, Paragraph 57, user profile associated with the user device [purchase history, preferences, physical browse history, etc.], user contextual information).
Regarding Claim 5, Kostka in view of Mycek teaches the system of claim 1. Kostka does not teach the system of claim 5. Mycek teaches the system of claim 5, wherein generating the custom content comprises causing the system to:
generate a list of options selectable at the user interface based on prior interactions between the subscriber and service agents of the telecommunications network (Mycek, Page 7, Paragraph 56-57, determining content functions to go select or generate contextual content relevant to a user, content determination system, entity associated with the beacon),
wherein the list of options is presented on the user interface on the display device of the wireless mobile device (Mycek, Pages 10-11, Paragraph 71, aggregated list of tasks, controlling presentation of contextual content).
Regarding Claim 6, Kostka in view of Mycek teaches the system of claim 1, wherein determining physical presence of the wireless mobile device comprises causing the system to:
cause the beacon device to transmit beacon signals at a regular interval (Kostka, Page 7, Paragraph 47, transmission interval; Mycek, Page 5, Paragraphs 35 and 38, operation mode),
wherein the micro-location is determined based on the wireless mobile device receiving the beacon signal (Kostka, Page 1, Paragraph 15; Mycek, Pages 10-11, Paragraph 71).
Regarding Claim 19, Kostka in view of Mycek teaches a method comprising:
receiving a signal transmitted from a wireless mobile device located in a check-in environment (Kostka, Page 1, Paragraph 10-13; Mycek, Page 4, Paragraph 40),
wherein the wireless mobile device is associated with a subscriber of a telecommunications network (Mycek, Page 7, Paragraph 57), and
wherein the signal includes a unique identifier for the subscriber or wireless mobile device (Mycek, Page 7, Paragraph 53);
determining, based on the signal, physical presence of a wireless mobile device at a micro-location in the check-in environment (Kostka, Pages 2-3, Paragraph 21; Mycek, Pages 10-11, Paragraph 71);
retrieving, based on the unique identifier, subscriber data indicative of activity of the subscriber on the telecommunications network (Mycek, Page 7, Paragraph 57);
generating custom content as output of a large language model (LLM) in response to input including the subscriber data (Mycek, Page 3, Paragraph 34; Page 7, Paragraphs 56-57, content determination system, machine learning techniques); and
causing a user interface of a display device located in the check-in environment to present the custom content Kostka, Pages 2-3, Paragraph 26; Mycek, Page 3, Paragraphs 31-33; Page 8, Paragraphs 58-60).
Regarding Claim 20, Kostka in view of Mycek teaches the method of claim 19. Kostka does not teach the method of claim 20. Mycek teaches the method of claim 20:
wherein the LLM is trained based on activity data of subscribers on the telecommunications network, and
wherein the custom content is customized for the subscriber and the check-in environment Mycek, Page 3, Paragraph 34; Page 7, Paragraphs 56-57, content determination system, machine learning techniques).
Claims 3, 12-16 are rejected under 35. U.S.C. 103 as being unpatentable over Kostka as modified by Mycek and further in view of Storm et al. US 2020 0097704 A1 (hereinafter “Storm”).
Regarding Claim 3, Kostka in view of Mycek teaches the system of Claim 1. Kostka does not teach the system of claim 3. Mycek teaches the system of claim 3, wherein the custom content includes a custom control presented on the user interface, and
wherein the custom control enables an interaction at the wireless mobile device with a location-based service of the check-in environment (Mycek, Page 3, Paragraph 34, Pages 7-8, Paragraphs 56-60, content determination system).
Kostka in view of Mycek fails to fully teach the limitation:
The custom content includes a custom control presented on the user interface
However, Storm further teaches the limitation:
The custom content includes a custom control presented on the user interface (Storm, Pages 2-3, Paragraphs 26-27 and Paragraphs 30-32, display 110 displays information to the user, may be used by user to interact with the kiosk 140; Page 5, Paragraph 54, Fig. 3, system 300, display device 315; Paragraph 73, mobile computing device).
Storm, Mycek and Kostka are considered to be analogous to the claimed invention because they are in the same field of authentication in wireless communication networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kostka in view of Mycek to incorporate the teaching of Storm in which the custom content includes a custom control presented on the user interface. Doing so would increase functionality of a system through the ability to collect user input, allowing for a more dynamic interactive retail environment.
Regarding Claim 12, Kostka as modified by Mycek and further in view of Storm teaches a system configured to generate personalized content for a subscriber of a telecommunications network in a check-in environment, the system comprising:
at least one hardware processor; and
at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor (Kostka, Page 1, Paragraphs 9-10, Fig. 1; Page 12, Paragraph 78, computer-readable medium storing computer-readable instruction; Mycek, Page 1, Paragraphs 19-20, beacon system; Page 5, Paragraph 44, processing system; Page 12, Paragraph 77, memory; Storm, Page 5, Paragraph 54, Fig. 3, system 300) cause the system to:
detect, at a kiosk device, a triggering event that indicates physical presence of a wireless mobile device in the check-in environment (Storm, Page 3, Paragraph 32-34, camera 105, transceiver 120, mobile devices within vicinity of kiosk; Page 4, Paragraph 39-40, mobile devices may provide the user identifier along with the signal characteristics to the kiosk),
wherein the wireless mobile device (Kostka, Page 1, Paragraph 10, mobile computer device) is associated with a subscriber of the telecommunications network (Mycek, Page 7, Paragraph 57, user device associated with a user profile),
wherein the physical presence includes a micro-location in the check-in environment (Kostka, Page 1, Paragraph 15, strength of the wireless signal; Mycek, Pages 10-11, Paragraph 71, determination of user device proximity to the beacon, signal strength exceeding a threshold strength; Storm, Page 4, Paragraph 39-40), and
wherein the triggering event includes an indication of a unique identifier of the subscriber (Mycek, Page 7, Paragraph 53, user device identifier);
input the unique identifier to a model that is trained based on subscriber activity data to generate personalized content for the subscriber in the check-in environment (Mycek, Page 3, Paragraph 34; Page 7, Paragraphs 56-57, content determination system, machine learning techniques),
wherein the subscriber activity data indicates activity of the subscriber on the telecommunications network, including activity outside of the check-in environment (Mycek, Page 7, Paragraphs 57, machine learning modules can be updated periodically, user action);
receive, as output from the model, personalized content for the subscriber in the check-in environment (Mycek, Page 3, Paragraph 34; Page 7, Paragraphs 56-57);
transmit the personalized content to the wireless mobile device associated with the subscriber; and
cause a display of the wireless mobile device associated to present the personalized content while the wireless mobile device is located in the check-in environment (Mycek, Page 3, Paragraphs 31-33, output device, user device; Page 8, Paragraphs 58-60, content determined based on beacon system information user device information).
Regarding Claim 13, Kostka as modified by Mycek and further in view of Storm teaches the system of claim 12, wherein to detect the triggering event comprises causing the system to:
detect, at the kiosk device, a signal from the wireless mobile device, wherein the signal is generated without user intervention; and
determine the triggering event by identifying the signal from the wireless mobile device and the micro-location of the wireless mobile device (Kostka, Page 1, Paragraph 15; Mycek, Pages 10-11, Paragraph 71; Storm, Page 4, Paragraph 39-40).
Regarding Claim 14, Kostka as modified by Mycek and further in view of Storm teaches the system of claim 13:
wherein the signal from the wireless mobile device is transmitted using a short-range radio frequency (RF) protocol (Kostka, Page 1, Paragraph 12-13; Mycek, Page 4, Paragraph 40; Storm, Page 3, Paragraph 35, Page 7, Paragraph 71), and
wherein the triggering event is determined when a value for a received signal strength indicator (RSSI) of the signal meets or exceeds a threshold value (Kostka, Page 1, Paragraph 15, strength of the wireless signal; Mycek, Pages 10-11, Paragraph 71, determination of user device proximity to the beacon, signal strength exceeding a threshold strength; Storm, Page 4, Paragraph 39-40).
Regarding Claim 15, Kostka as modified by Mycek and further in view of Storm teaches the system of claim 12. Kostka as modified by Mycek does not teach the system of claim 15. Storm teaches the system of claim 15, wherein detecting the triggering event comprises causing the system to:
estimate a distance to the wireless mobile device from the kiosk device based on an image of the wireless mobile device captured by a camera of the kiosk device (Storm, Page 5, Paragraph 54, display device 315, image sensor 305 can be component of the camera),
wherein the estimate is based on a size of the wireless mobile device in the image (Storm, Page 6, Paragraphs 58-59, image comparison); and
determine that the estimate meets or exceeds a threshold distance,
wherein the triggering event is detected when the estimated distance meets or exceeds a threshold distance (Storm, Page 6, Paragraphs 58-59, captured image of the user, score value meeting a threshold, determined proximity of a respective mobile device for each of the users).
Regarding Claim 16, Kostka as modified by Mycek and further in view of Storm teaches the system of claim 12. Kostka as modified by Mycek does not teach the system of claim 16. Storm teaches the system of claim 16, wherein detecting the triggering event comprises causing the system to:
scan a spatial area proximate to the kiosk device at regular intervals using a visualization sensor,
wherein the visualization sensor is integrated in the kiosk device (Storm, Pages 4-5, Paragraph 47, Paragraph 54, kiosk, image sensor); and
detect presence of the wireless mobile device in the scan of the spatial area,
wherein the triggering event is detected based on the detected presence of the wireless mobile device in the scan of the spatial area (Storm, Page 3, Paragraph 32-34, camera 105, transceiver 120, mobile devices within vicinity of kiosk).
Claims 4, 7-11 and 17-18 are rejected under 35. U.S.C. 103 as being unpatentable over Kostka as modified by Mycek and in view of Storm and further in view of Pittman, U.S. Patent No 11,790,401 B2 (hereinafter “Pittman”.)
Regarding Claim 4, Kostka in view of Mycek teaches the system of claim 1. Kostka in view of Mycek does not teach the system of claim 4.
However, Pittman teaches the system of claim 4 further caused to, prior to the subscriber data being received:
request a subscriber identification number from the wireless mobile device (Pittman discloses a mobile computing device associated with a user and related consumer profile data, which may include physical locations and historical movements of the mobile computing device, user activity and other measures of consumer interaction [e.g., consumer interaction with media content, messages and other accessible content, for example], on Col. 30, Lines 5-15, Fig. 37. The claim element, “subscriber of a telecommunications network” is considered a design choice, as the specification does not provide any evidence of a functional difference or specific technical benefit resulting from this limitation. A subscriber of a telecommunications network is a user of a telecommunications service and respective service-enabled device. Therefore, the consumer profile data of Pittman can be interpreted as subscriber data when associated with a subscriber. See also Col. 32, Lines 59-67, Col. 33, Lines 1-30, Fig. 1, geolocation and time-based advertisements, mobile communications device, mobile device, client device),
wherein the request is generated in response to an interaction at a stationary device located in the check-in environment (Pittman, Col. 24, Lines 12-49, promotional content, QR code, tracking and interacting with users, advertiser and connect point information; Col. 34, Lines 22-64, advertisement trigger parameters, space-time); and
in response to the request, receive the subscriber identification number, wherein the unique identifier is based on the subscriber identification number (Pittman, Col. 68, Lines 27-67, consumer analytics, unique identifier).
Pittman, Storm, Mycek and Kostka are considered to be analogous to the claimed invention because they are in the same field of authentication in wireless communication networks. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kostka in view of Mycek to incorporate the teaching of Storm in which prior to the subscriber data being received request a subscriber identification number from the wireless mobile device, wherein the request is generated in response to an interaction at a stationary device located in the check-in; and in response to the request, receive the subscriber identification number, wherein the unique identifier is based on the subscriber identification number. Doing so would allow a user more interactive control over the presentation customized content by incorporating active trigger events.
Regarding Claim 7, Kostka as modified by Mycek in view of Storm and further in view of Pittman teaches the system of claim 1. Kostka does not teach the system of claim 7. Mycek as modified by Storm in view of Pittman teaches the system of claim 7, wherein determining physical presence of the wireless mobile device comprises causes the system to:
cause a stationary device located at the check-in environment to display a graphical element (Pittman, Col. 41, Lines 19-63, mobile device receives wireless signals transmitted by the beacon; Storm, Pages 3-4, Paragraph 37, kiosk, entry point deployment),
wherein the stationary device is subscribed to the telecommunications network (Mycek, Page 3, auxiliary devices, merchant devices); and
detect an interaction of the wireless mobile device with the graphical element (Mycek, Pages 10-11, Paragraph 71, facilitating user actions, prompting the user with contextual content to perform an action [e.g. with the user device], detecting performed action; Pittman, Col. 24, Lines 12-49, promotional content, QR code).
Regarding Claim 8, Mycek as modified by Storm in view of Pittman teaches the system of claim 7. Mycek in view of Storm does not teach the system of claim 8. Pittman teaches the system of claim 8:
wherein the graphical element is a 2-dimensional code presented on the display of the stationary device, and
wherein the subscriber and indoor location are detected based on a scan by the wireless mobile device of the 2-dimensional code (Pittman, Col. 12, Lines 61-67, geolocation and time-based advertising, indoor advertising; Col. 24, Lines 11-35, promotional content, QR code, tracking user data.)
Regarding Claim 9, Mycek as modified by Storm in view of Pittman teaches the system of claim 7, wherein to generate custom content comprises causing the system to:
input interactions to the stationary device by a user of the wireless mobile device (Mycek, Pages 10-11, Paragraph 71, user actions; Pittman, Col. 30, Lines 5-15, individual consumer profile data, consumer interactions) as input to the LLM (Mycek, Pages 7-8, Paragraphs 56-60, content determination system).
Regarding Claim 10, Kostka as modified by Mycek in view of Storm teaches the system of claim 1. Kostka does not teach the system of claim 10. Mycek in view of Storm and further in view of Pittman teaches the system of claim 1, wherein generating the custom content comprises causing the system to:
compute a threshold time of the wireless mobile device relative to a reference time of subscribers at the check-in environment,
wherein the threshold time is calculated based on user traffic data and customer experience data of subscribers to the telecommunications network (Mycek, Page 11, Paragraph 72, tracking user device progression through the space; Page 7, Paragraph 57, general contextual information; Storm, Page 5, Paragraph 48, gathered user data, behavior patterns; Pittman, Col. 30, Lines 5-15, consumer profile data, historical movements, locations, interactivity, and other measures of consumer interaction; Col. 52, Lines 20-50, data associated with geofence).
Regarding Claim 11, Mycek in view of Pittman teaches the system of claim 10, wherein to compute the threshold time comprises causing the system to:
determine an entry time when the wireless mobile device entered the check-in environment; or
determine a wait time based on a difference between the entry time and a current time of the wireless mobile device in the check-in environment (Mycek, Pages 1-2, Paragraph 21, user device entry into the beacon system's communication range; Pittman, Col. 30, Lines 5-15, user location and other measures of consumer interaction).
Regarding Claim 17, Kostka as modified by Mycek in view of Storm and further in view of Pittman teaches the system of claim 12. Kostka as modified by Mycek does not teach the system of claim 17. Storm in view of Pittman teaches the system of claim 17, wherein detecting the triggering event comprises causing the system to:
cause display of a graphical element on the kiosk device (Storm, Page 3, Paragraph 32, kiosk, display 110 displays information to the user, graphical user interface),
wherein the triggering event is detected based on an interaction of the wireless mobile device with the graphical element (Pittman, Col. 24, Lines 12-49, promotional content, QR code).
Regarding Claim 18, Storm in view of Pittman teaches the system of claim 17. Storm does not teach the system of claim 18. Pittman teaches the system of claim 18:
wherein the graphical element is a 2-dimensional code, and
wherein the triggering event is determined in response to the wireless mobile device scanning the 2-dimensional code (Pittman, Col. 2, Lines 20-26, digital signage, kiosk; Col. 24, Lines 12-49; Col. 32, Fig. 1, Lines 59-67, Col. 33, Lines 1-30; Col. 34, Lines 22-64, advertisement trigger parameters, space-time).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIELLE N DAI whose telephone number is (571)272-6693. The examiner can normally be reached Mon - Thu. 8:30am - 5:30pm.
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/GABRIELLE N DAI/Examiner, Art Unit 2681
/AKWASI M SARPONG/SPE, Art Unit 2681 7/22/2026
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