DETAILED ACTION
This action is in response to the RCE filed on 02/05/2026.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/05/2026.
Response to Amendment
Applicant’s amendment filed on 02/05/2026 has been entered. Claims 1, 11 and 19 have been amended. No claims have been canceled. No claims have been added. Claims 1- 20 are still pending in this application, with claim 1, 11 and 19 being independent.
Allowable Subject Matter
Claims 2 and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. After search and consideration, it has been determined that the prior art fails to teach or suggest in reasonable combination the following limitations recited in claims 2 and 12: wherein the language model includes a generative pre-trained transformer (GPT) model that is configured to interpret the user request received via the client interface, communicate with the each of the plurality of service provider platforms in connection with the user request, and generate the multi-platform response in a human language format based, at least in part, on responses received separately from the plurality of service provider platforms.
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.
Claim(s) 1, 3, 5, 6, 11, 13, 15, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gelfenbeyn et al. (US 2017/0300831) (”Gelfenbeyn”) in view of Kumar (US 10,600,105).
For claims 1, 11 and 19, Gelfenbeyn discloses a system and method (Abstract) comprising: one or more processors (Fig.1, 110 and Fig.11, 1114; [0087] [0150]) [0151]); and one or more non-transitory computer-readable storage devices (memory, Fig.1, 110 and Fig.11, 1125) storing computing instructions configured to run on the one or more processors and cause the one or more processors to execute functions ([0087] [0150] [0154] [0155]) comprising: providing a user application (automated assistant, Fig.1, 110) comprising a client interface (input processing engine, Fig.1, 112; [0047] [0054 – 0059]) and a language model (agent engine and output engine, Fig.1, 120 and 135; [0061 – 0067] [0074]); receiving, via the client interface of the user application, a user request related to a service (e.g. booking a hotel or restaurant reservation, purchasing professional services, etc., [0062 – 0064]) (Fig.4, 450; [0088]); initiating multiple communication exchanges between the language model and a plurality of separate service provider platforms (agents, Fig.1, 140A – N; [0014] [0046] [0049] [0050]) in parallel (Fig.4, 462, 464, 466; [0089] [0093]) to obtain multiple distinct sets of bids (responses) corresponding to the services from the plurality of service provider platforms (The requests component 124A generates an agent request based on the values for parameters 173. As indicated by the “AR” directed arrows of FIG. 3, the requests component 124A transmits the agent request to each of multiple agents 140A-D… ] As indicated by the “R” directed arrows of FIG. 3, the requests component 124A receives responses from each of the agents 140A-D in response to transmitting the agent request to the agents 140A-D. The responses each indicate an ability of a corresponding one of the agents 140A-D to resolve the agent request. For example, the response from a given agent can be… actual responsive content.”, [0078 – 0082] [0092 – 0094]) wherein: each service provider platform is hosted on a separate server system (A single agent/service provider is hosted on computing device, e.g. server. Therefore, each agent is hosted on a separate server., [0014] [0049] [0150 – 0157]) and each service provider platform provides a separate service provider application (agent) associated with the service offering, ([0002] [0003] [0014]); the plurality of separate service provider platforms comprise a first service provider platform that includes a first server system hosting a first service provider application related to the service offering (e.g. restaurant booking, hotel booking) ([0002] [0003] [0014] [0078 – 0082] [0092 – 0094] [0150 – 0157]) and a second service provider platform that includes a second server system hosting a second service provider application related to the service offering (e.g. restaurant booking, hotel booking), the first service provider platform and the second service provider platform are associated with separate entities that each provide the same service offering specified in the user request ([0002] [0003] [0014] [0078 – 0082] [0092 – 0094] [0150 – 0157]); the language model initiates a separate communication exchange with each of the plurality of separate service provider platforms in parallel to collect bids corresponding to the service from each of the plurality of separate service provider platforms ([0078 – 0082] [0092 – 0094]), including: a first communication exchange in which the language model is configured to generate a first request for obtaining a first set of service options from the first service provider platform ([0049] [0078 – 0082] [0092 – 0094]); and a second communication exchange in which the language model is configured to generate a second request for obtaining a second set of service options from the first service provider platform ([0049] [0078 – 0082] [0092 – 0094]); separately receiving, by the language model, the bids from each of the plurality of separate service provider platforms ([0049] [0078 – 0082] [0092 – 0094]); generating, by the language model, a multi-platform response based, at least in part, on an evaluation that jointly considers the multiple bids, obtained from the plurality of service provider platforms including the first set of service options obtained from the first service provider platform and the second set of service options obtained from the second service provider platform (“In some implementations, at block 472 the system uses the responses received at block 468 to select a subset of the agents and provides indications of the agents of the subset as output for presentation to the user.”, [0094] [0095]) and presenting, via the client interface of the user application, the multi-platform response to the end-user ([0095]).
Yet, Gelfenbeyn fails to teach the following: the request further comprises a request for service offerings; and the bids include a distinct set of service options corresponding to the service offering.
However, Kumar discloses a service provider matching system and method (Abstract), comprising the following: receiving a user request for service offerings (Fig.2A, 1, Fig.2B, 212; column 16 lines 60 – column 17 line 11, column 19 lines 26 – 35); transmitting the request to a plurality of service provider platforms (Fig.1, 130, Fig.2A, 3 and Fig.2B, 216, 218; column 17 lines 40 – 65, column 19 lines 36 – 64, column 22 lines 25 - 37), wherein the plurality of service platforms comprises a first service provider platform and a second provider platform which are associated with separate entities that each provide the same service offering specified in the user request (Service providers systems contacted by the service provider matching system in response to a service request are separate entities. Furthermore these separate entities provide the same service offering specified in the user request., column 19 lines 35 – 65; column 22 lines 37 – column 23 line 5); and receiving bids for the plurality of service provider platforms that comprise a distinct set of service options corresponding to the service offering (Fig.2A, 4, Fig.2B, 218 and 220; column 11 lines 40 – 58, column 19 lines 35 – column 20 line 2, column 23 lines 4 - 14).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve Gelfenbeyn’s invention in the same way that Kumar’s invention has been improved to achieve the following, predictable results for the purpose of providing cost effective and extensible virtual assistant services which enable a user to access a variety of services, using a single interface (Gelfenbeyn, [0001]) (Kumar, column 1 lines 20 – 37): the request further comprises a request for service offerings; the bids include a distinct set of service options corresponding to the service offering; and the first service provider platform and the second service provider platform are associated with separate entities that each provide the same service offering specified in the user request.
For claims 3 and 13, Gelfenbeyn and Kumar further disclose, wherein: the language model is configured to analyze responses received from the plurality of service provider platforms to determine (Gelfenbeyn, “The system uses the responses received at block 468 to select a subset of the agents.”) (Kumar,” In block 220, the service provider matching system can process each service provider's response and transmit a list of service providers to the customer system. In some embodiments, the service provider matching system can combine the responses. In some embodiments, only a portion of the service providers contacted can be included in the list. For example, depending on the responses received from service providers, the list can include a portion of service providers that accepted the initial offer (as opposed to the service providers that provided a counter-offer).” Accepting an initial offer is indicative of being able to provide the requested service) or predict a service option that is optimal based on the user request (Gelfenbeyn, [0095]) (Kumar, column 19 lines 59 – column 20 line 22; column 23 lines 25 – 43) and the multi-platform response generated by the language model includes the service option determined or predicted to be optimal based on the user request (Gelfenbeyn, [0061] [0095]) (Kumar, column 23 lines 25 – 43).
For claim 5, Gelfenbeyn and Kumar further disclose, wherein the multi-platform response generated by the language identifies at least one service option corresponding to the service offering (Gelfenbeyn, [0095] [0096]) (Kumar, column 20 lines 18 - 35); and the end-user can communicate with the language model to schedule (Gelfenbeyn, The system utilizes a user selection in response to the output to select a single agent.) (Kumar, “In step 6, the customer associated with the customer system 140 can select a service provider from the transmitted list in step 5. Once selected, information can be transmitted to the service provider matching system 102. Also, in step 6, the customer can provide payment data, instructions, and/or preferences to the service provider matching system 102 … The service provider matching system 102 can then create a booking and indicate to the service provider system 130 in step 10 that the particular service provider has been selected by the customer and that payment has been processed. In some embodiments, the service provider matching system 102 (or other systems) can access calendar programs or services (for example, through APIs) associated with the customer or service provider and update the corresponding calendar programs or services with the confirmed booking appointment.”) or place an order for, the at least one service option (Gelfenbeyn, [0062 – 0064] [0095 - 0097) (Kumar, column 20 lines 18 – column 21 lines 18).
For claim 6, Gelfenbeyn and Kumar further discloses, wherein in response to receiving a user selection corresponding to the at least one service option, the language model communicates with at least one of the plurality of service provider platforms to schedule (Gelfenbeyn, The system utilizes a user selection in response to the output to select a single agent.) (Kumar, “In step 6, the customer associated with the customer system 140 can select a service provider from the transmitted list in step 5. Once selected, information can be transmitted to the service provider matching system 102. Also, in step 6, the customer can provide payment data, instructions, and/or preferences to the service provider matching system 102 … The service provider matching system 102 can then create a booking and indicate to the service provider system 130 in step 10 that the particular service provider has been selected by the customer and that payment has been processed. In some embodiments, the service provider matching system 102 (or other systems) can access calendar programs or services (for example, through APIs) associated with the customer or service provider and update the corresponding calendar programs or services with the confirmed booking appointment.”), or place an order for, the at least one service option corresponding to the service offering (Gelfenbeyn, [0062 – 0064] [0095 - 0097) (Kumar, column 20 lines 18 – column 21 lines 18).
For claim 15, Gelfenbeyn and Kumar further disclose, wherein the multi-platform response generated by the language identifies at least one service option corresponding to the service offering (Gelfenbeyn, [0095] [0096]) (Kumar, column 20 lines 18 - 35); the end-user can communicate with the language model to schedule (Gelfenbeyn, The system utilizes a user selection in response to the output to select a single agent.) (Kumar, “In step 6, the customer associated with the customer system 140 can select a service provider from the transmitted list in step 5. Once selected, information can be transmitted to the service provider matching system 102. Also, in step 6, the customer can provide payment data, instructions, and/or preferences to the service provider matching system 102 … The service provider matching system 102 can then create a booking and indicate to the service provider system 130 in step 10 that the particular service provider has been selected by the customer and that payment has been processed. In some embodiments, the service provider matching system 102 (or other systems) can access calendar programs or services (for example, through APIs) associated with the customer or service provider and update the corresponding calendar programs or services with the confirmed booking appointment.”) or place an order for, the at least one service option (Gelfenbeyn, [0062 – 0064] [0095 - 0097) (Kumar, column 20 lines 18 – column 21 lines 18); and in response to receiving a user selection corresponding to the at least one service option, the language model communicates with at least one of the plurality of service provider platforms to schedule (Gelfenbeyn, The system utilizes a user selection in response to the output to select a single agent.) (Kumar, “In step 6, the customer associated with the customer system 140 can select a service provider from the transmitted list in step 5. Once selected, information can be transmitted to the service provider matching system 102. Also, in step 6, the customer can provide payment data, instructions, and/or preferences to the service provider matching system 102 … The service provider matching system 102 can then create a booking and indicate to the service provider system 130 in step 10 that the particular service provider has been selected by the customer and that payment has been processed. In some embodiments, the service provider matching system 102 (or other systems) can access calendar programs or services (for example, through APIs) associated with the customer or service provider and update the corresponding calendar programs or services with the confirmed booking appointment.”), or place an order for, the at least one service option corresponding to the service offering (Gelfenbeyn, [0062 – 0064] [0095 - 0097) (Kumar, column 20 lines 18 – column 21 lines 18).
For claim 20, Gelfenbeyn further discloses wherein: the multi-platform response is generated in response to receiving a user request via the client interface of the user application (Gelfenbeyn, [0046] [0051 – 0053] [0087 – 0095]);the multi-platform response is generated, at least in part, by a preemptive analysis function without being prompted by the end-user.
Claim(s) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gelfenbeyn et al. (US 2017/0300831) (”Gelfenbeyn”) in view of Kumar (US 10,600,105) and further in view of Jitkoff et al. (US 2012/0036121).
For claims 4 and 14, the combination of Gelfenbeyn and Kumar fails to teach, wherein the multi-platform response generated by the language model summarizes the service options obtained from the plurality of service provider platforms.
However, Jitkoff discloses a system and method for performing computerized searches (Abstract), comprising the following: results for a search query are summarized before being output and presented to a user ([0006] [0009 – 0012] [0041 – 0047]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Gelfenbeyn and Kumar in the same way that Jitkoff’s invention has been improved to achieve the predictable results of the multi-platform response generated by the language model further summarizing the service options (results for a search query) obtained from the plurality of service provider platforms (source of search results) for the purpose of enabling a user to discern whether search results are related to a query without having to select each search result separately (Jitkoff, [0030] [0031]).
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gelfenbeyn et al. (US 2017/0300831) (”Gelfenbeyn”) in view of Kumar (US 10,600,105)
and further in view of Hakkani-Tur et al. (US 2017/0372199) (“Hak-Tur”).
For claims 7 and 16, the combination of Gelfenbeyn and Kumar fails to teach the following: wherein the language model is pre-trained on a domain-specific dataset that included textual content related to the service offering.
However, Hak-Tur discloses a system and method for performing spoken language understanding (Abstract), comprising the following: a language (spoken language understanding) model (joint multi-domain recurrent neural network) is pre-trained on a domain-specific dataset that includes textual content (utterances) related to a user query ([0004] [0017] [0018] [0024] [0087] [0099 – 0109]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Gelfenbeyn and Kumar in the same way that Hak-Tur has been to achieve the following, predictable results for the purpose of improving the process of performing spoken language understanding by using a single, multi-domain machine learning algorithm which continuously learns and adapts over time (Hak-Tur, [0001] [0002]): the language model (Gelfenbeyn, parameters module which performs spoken language understanding; [0062 – 0065]) is further pre-trained (The parameters module further comprises a machine learning algorithm such as a joint multi-domain recurrent neural network) on a domain-specific dataset that includes textual content related to a user query, e.g. service offering)
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Gelfenbeyn et al. (US 2017/0300831) (”Gelfenbeyn”) in view of Kumar (US 10,600,105) and further in view of Balzer et al. (US 2019/0044829) (“Balzer”).
For claims 8 and 17, the combination of Gelfenbeyn and Kumar fails to teach, wherein the multi-platform response is generated based, at least in part, using one or more user preferences learned by the language model from previous interactions with the end-user; and the language model include a continuous learning framework that enables the language model to learn one or more preferences.
However, Balzer discloses a system and method for orchestrating a processing of service requests (Abstract), comprising the following: generating a multi-platform response based on, at least in part using one or more preferences learned by an orchestrator (Fig.1, 118; [0020 – 0022]) from interactions with an end-user (The outputs produced from the bots represent the services available that match the list of inputs included within the instructions. The outputs may be provided as responses to the orchestrator 118 … The orchestrator 118 may integrate the received responses into a service response, and provide the service response to a requestor of the service request … In some embodiments, during the integration process, the orchestrator 118 may be configured to employ machine learning techniques to determine service preferences for the user 104 based on past experience … Once responses from the bots have been received, the orchestration service 308 may integrate the received responses into a service response 328. During the integration process, machine learning techniques may be employed for determining service preferences of the user based on past experience. These determined service preferences may be in addition to those service preferences provided in the service request 304. For example, if it is learned that the user favors certain options based on past experience (e.g., the user favors bed and breakfasts over hotels), the orchestration service 308 may automatically prioritize the favored option in the service response 328, [0024 – 0030] [0044] [0045] [0048] [0052] [0053]); and the orchestrator includes a continuous learning framework (machine framework learning) that enables the orchestrator to learn the one or more user preferences ([0030] [0033] [0051] [0053] [0070] [0105]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Gelfenbeyn and Kumar in the same way that Balzer’s invention has been improved to achieve the following predictable results for the purpose of providing enriched responses to service requests (Balzer, Abstract): the language model (orchestrator) further learns user preferences based on previous interactions with the end; the multi-platform response is generated based, at least in part, using one or more user preferences learned by the language model from previous interactions with the end-user; and the language model further includes a continuous learning framework that enables the language model to learn one or more preferences.
Claim(s) 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gelfenbeyn et al. (US 2017/0300831) (”Gelfenbeyn”) in view of Kumar (US 10,600,105) and further in view of Maeda et al. (US 2022/0108693) (“Maeda”).
For claims 9 and 18, the combination of Gelfenbeyn and Kumar fails to teach, wherein the language model is configured to utilize learned activity patterns of the end-user to preemptively communicate with the end-user via the client interface.
However, Maeda discloses a response processing device and method (Abstract), where learned activity patterns of an end-user are used to preemptively communicate (Fig.3B, A33) with an end-user via a client interface ([0095 – 0102]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Gelfenbeyn and Kumar in the same way that Maeda’s invention has been improved to achieve the following predictable results for the purpose of increasing user satisfaction by enabling a user to interact with a digital assistant system to access and receive service information provided by variety of third party service providers in a more user friendly format: the language model is further configured to learn activity patterns of the end-user; and the language model is configured to utilize the learned activity patterns of the end-user to preemptively communicate with the end-user via the client interface.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Gelfenbeyn et al. (US 2017/0300831) (”Gelfenbeyn”) in view of Kumar (US 10,600,105) and further in view of Acosta et al. (US 2021/0019853) (“Acosta”).
For claim 10, the combination of Gelfenbeyn and Kumar fails to teach the following: the service offering identified in the user request is related to a ride hailing service offering; the plurality of service provider platforms offer the ride hailing service offering; the service options correspond to ride hailing service options; and the multi-platform response identifies one or more of the ride hailing service options based on the user request.
However, Acosta discloses a system and method for soliciting at least one bid from mobility as a service provider system (Abstract), comprising the following: a service offering identified in a user request is related to a ride hailing service offering ([0017] [0047] [0048]); a plurality of service provider platforms offer the ride hailing service offering (MAAS provider systems which provide ridesharing or ride-hailing services, Fig.1, 20b and 20C; [0003] [0017] [0049] [0050]); service options correspond to ride hailing service options ([0003] [0027]); and a multi-platform response identifies one or more of the ride hailing service options based on a user request (Fig.3B; [0003] [0027]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Gelfenbeyn and Kumar in the same way that Acosta’s invention has been improved to achieve the following, predictable results for the purpose of efficiently matching customers to ride sharing service providers (Kumar, column 1 lines 20 – 55 and column 2 lines 25 – 51)(Acosta, [0002] [0003]): the service offering identified in the user request is further related to a ride hailing service offering; the plurality of service provider platforms further offer the ride hailing service offering; the service options further correspond to ride hailing service options; and the multi-platform response further identifies one or more of the ride hailing service options based on the user request.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1- 20 have been fully considered but are moot in view of the new ground(s) of rejection.
Conclusion
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/SONIA L GAY/Primary Examiner, Art Unit 2657