DETAILED ACTION
1.This communication is in response to the amendment filed on 01/22/2026. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1a. Status of the claims:
Claims 1 and 20 are amended.
Claims 1- 20 are pending.
Response to Arguments
2. Applicant's arguments filed 1/22/2016 have been fully considered but they are not persuasive.
A, Applicant argues that “The Office Action appears to equate "user experience" levels (e.g., a user's political view or subject matter expertise) with the recited "operations information about the network service." As shown by the disclosure above, however, Nguyen teaches sharing user context to customize a chat bot's personality or knowledge base . It does not disclose receiving "operations information" regarding the network service itself (e.g., network diagnostics, device status, or topology). In fact, nothing in Nguyen appears to teach any consideration about network service operations or any information pertaining thereto. A POSITA would not understand a user's "experience level" with a topic to teach or suggest information about the operation of the network service," as recited in claim 1. (Remarks, page 9).
In response to A, The Examiner disagrees because Nguyen’s “a list of user experience, respective problem, solutions with the shared AI chat bot running in a network system” reasonably interpreted as being “operations information about the network service. This interpretation is based on the specification that discloses in paragraph [0022] that “some embodiments can integrate a chat engine into the provider's analytic system to enhance the provider's field operations and services, generate natural and actionable chat insights, reports and surveys for the provider and/or the user, provide the user with self-help suggestions and/or promotion of operator services (e.g., via a chat interface), improve chat accuracy and relevancy for analytic applications.” Based on this the specification of the instant application it is reasonable to conclude that a list of user experience, respective problem, solutions with the shared AI chat bot running in a network system” could be reasonably interpreted as being “operations information about the network service.
B, Applicant argues that Nguyen does not disclose “The cited portions of Nguyen are reproduced above, and nothing in that text appears to teach or suggest performing any functions related to enhancing a prompt. Instead, Nguyen teaches that the model itself is updated using parameters from another model. Thus, the Office Action appears to equate updating model parameters (weights) with "generating an enhanced chat prompt." As shown by the disclosure above, however, Nguyen uses the shared data to modify the internal weights of the neural network itself . Nguyen does not generate a text-based "chat prompt" that is then transmitted to a separate engine. A POSITA would understand that the weights of a model are internal features of the model and that a chat prompt is an external stimulus provided to the model. These internal weights are then used to generate tokens in response to the prompt. Nguyen specifically teaches that the response is improved by changing weights, not that any action is performed with regard to the user's chat prompt itself. In other words, modifying the "brain" of a model is technically distinct from generating an "enhanced prompt" to be processed by that model," as recited in claims 1 and 12. (Remarks, page 11).
In response to B, The Examiner disagrees in addition that what was stated in section A, Nguyen discloses in [0015 ] generating an updating chat bot by way of interaction with AI chat bot by using a machine learning model based on a list of user experiences, respective problems, solutions with the shared AI chat bot using a software ( logic is equated to software bases on specification [0030] that discloses logic can be software or hardware circuitry),; by interaction with a user using the detailed information the chat prompt is enhanced). Based on the specification [0023] that discloses that “The term, "chat prompt," as used herein, means any text or data that can be provided, e.g., to a chat engine to prompt a response, including without limitation a user query provided by a user. Å "response" is any response received from the target of a chat prompt (e.g., a chat engine) that is, or purports to be, responsive to the chat prompt. An "enhanced" chat prompt or response is a chat prompt or response, respectively, that has been enhanced by any of the techniques described herein, e.g., by adding information or context to a user query (or any other chat prompt) and/or response that increases the probability of a relevant and/or actionable response ultimately being returned to the user. As used herein, the term "operations information" can include any information about the network (or other) service being provided, including without limitation,” . How can be seen action by a user in Nguyen ‘s reference could be equated to any action is performed with regard to the user's chat prompt itself. Therefore, the claim limitation is disclosed by Nguyen.
C, Applicant argues that Nguyen does not disclose “logic to transmit the enhanced chat prompt, through the network interface for processing general purpose conversational artificial intelligence." Nguyen, by contrast, processes the query locally using the updated specific chat bot model," as recited in claim 1. (Remarks, page 12).
In response to C, The Examiner disagrees because Nguyen does teach an enhanced prompt from one ML engine to another because Nguyen, [0015] providing interaction with AI chat bot using a user interface by improving conversation in a AI chat bot system by providing details in a conversation specific topic of interest using a machine learning model using a software ( logic is equated to software bases on specification [0030] that discloses logic can be software or hardware circuitry); by interaction with a user using the detailed information the chat prompt is enhanced)). The interpretation of the Examiner is based on the specification [0023] that states “The term, "chat prompt," as used herein, means any text or data that can be provided, e.g., to a chat engine to prompt a response, including without limitation a user query provided by a user. Å "response" is any response received from the target of a chat prompt (e.g., a chat engine) that is, or purports to be, responsive to the chat prompt. An "enhanced" chat prompt or response is a chat prompt or response, respectively, that has been enhanced by any of the techniques described herein, e.g., by adding information or context to a user query (or any other chat prompt) and/or response that increases the probability of a relevant and/or actionable response ultimately being returned to the use. ”
D, Applicant argues that Nguyen does not disclose “Nguyen does not teach or suggest that the
updated chat bot receives any response generated by another chat bot, let alone that the updated chat bot might enhance such a received response. Nguyen teaches using a second model to generate the initial response. It does not teach receiving a generated response and then subjecting it to a post-processing enhancement by a different ML engine. Thus, while claim 1 recites a multi-stage pipeline with enhancement of both the initial prompt from the user and the initial response from a general-purpose model, Nguyen teaches a single-stage generation using an updated model," as recited in claims 1. (Remarks, page 12).
In response to D, The Examiner disagrees Nguyen does teach or suggest that the
updated chat bot receives any response generated by another chat bot, Nguyen also teaches the updated chat bot might enhance such a received response. Nguyen teaches in [0015]
(providing interaction with AI chat bot using a user interface by improving conversation in a AI chat bot system by providing details in a conversation specific topic of interest using a machine learning model using a software ( logic is equated to software bases on specification [0030] that discloses logic can be software or hardware circuitry), Nguyen, [0015]; by interaction with a user using the detailed information the chat prompt is enhanced)). Based on the specification [0023] that discloses that “The term, "chat prompt," as used herein, means any text or data that can be provided, e.g., to a chat engine to prompt a response, including without limitation a user query provided by a user. Å "response" is any response received from the target of a chat prompt (e.g., a chat engine) that is, or purports to be, responsive to the chat prompt. An "enhanced" chat prompt or response is a chat prompt or response, respectively, that has been enhanced by any of the techniques described herein, e.g., by adding information or context to a user query (or any other chat prompt) and/or response that increases the probability of a relevant and/or actionable response ultimately being returned to the user. As used herein, the term "operations information" can include any information about the network (or other) service being provided, including without limitation,” Finally Nguyen teaches in [0104] the IA model can be made of multi engines. Therefore, Nguyen teaches the claim limitations.
E, Applicant argues that “, Nguyen does not teach or suggest multiple features of claim 1 and cannot anticipate that claim. The Office Action does not allege that any of the other Cited References remedy these deficiencies of Nguyen," as recited in claim 1. (Remarks, page 13).
In response to E, The Examiner disagrees because as it was stated section above Nguyen discloses the claim limitation.
F, Applicant argues that Ingah does not teach or suggest “modifying a configuration of CPE "based at least in part on the enhanced response," as claim 2 requires “ as follows: The Office Action appears to equate restoring a backup with "modifying a configuration... to improve performance." As shown by the disclosure above, however, Ingah teaches a system for backing up and restoring static configuration settings (e.g., replacing an old modem with a new one). Ingah does not teach or suggest modifying a configuration of CPE "based at least in part on the enhanced response," as claim 2 requires. Ingah teaches modifying a configuration based on a user's direct command to restore a configuration. In fact, Ingah teaches nothing about the use of ML for any purpose," as recited in claim 2. (Remarks, page 14).
In response to F, The Examiner disagrees because Ingah discloses in [0127]; [0128] customer premises equipment is configured with a changed configuration, that is a retrieved and stored configuration that has the customer premises equipment that is computers and device associated with a user , by configuring the device with the retrieved and stored configuration, the default configuration is changed to an improved configuration that is based on the configuration settings of a customer premises equipment .
G, Applicant argues that “Nguyen does not teach or suggest modification of any CPE based on anything, let alone an enhanced response from an ML model (setting aside the fact that, as noted above, Nguyen does not teach enhancing a received response at all). Nguyen teaches a personalized chat system that has nothing to do with modifying any kind of hardware," as recited in claim 2. (Remarks, page 14).
In response to G, The Examiner disagrees Ingah discloses in [0127]; [0128] that customer premise equipment is changed based on customer by doing so a default configuration of the customer premise equipment is improved by changing the default configuration to a configuration based on the settings of the premise of the user equipments.
H, Applicant argues that “the combination of Nguyen and Ingah does not teach or suggest "operations information comprises performance information" or "modify a configuration... to improve performance," as required by claim 2, and the Office Action therefore does not establish that claim 2 is prima facie unpatentable under § 103," as recited in claim 2 . (Remarks, page 8).
In response to H, In response to 3G, In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(2) the claimed invention was described in a patent issued under section 151, or in an
application for patent published or deemed published under section 122(b), in which the
patent or application, as the case may be, names another inventor and was effectively
filed before the effective filing date of the claimed invention.
3. Claims 1, 3-10, and 19-20 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by Nguyen et al. (hereinafter “Nguyen”) (US 2023/0316138 A1), an IDS provided reference.
Regarding claim 1, Nguyen discloses a device, comprising:
a network interface (a network graphical user interface of a client device , Nguyen, [0036]);
one or more processors ( a processor , Nguyen, [0091]); and logic, the logic comprising instructions stored on a non-transitory computer readable medium, the instructions being executable by the one or more processors (instructions (software, program) stored in the memory executed by a processor , Nguyen, [0090]; [0091] the logic comprising:
logic to receive a user query in relation to a network service (a query is received from a user about a set of data and parameter about a chat bot running in a network system using a software ( logic is equated to software bases on specification [0030] that discloses logic can be software or hardware circuitry) , Nguyen, [0014]; [0090]; Fig 1); logic to receive operations information about the network service ( receiving a list of user experience, respective problem, solutions with the shared AI chat bot running in a network system where (shared AI chat bot running in the network system is the service) and (list of user experience, respective problem, solutions is operation information) using a software ( logic is equated to software bases on specification [0030] that discloses logic can be software or hardware circuitry), Nguyen, [0014]; [0015]; Fig.1 );
logic to generate an enhanced chat prompt, using a first machine learning engine, based at least in part on the operations information ( generating an updating chat bot by way of interaction with AI chat bot by using a machine learning model based on a list of user experiences, respective problems, solutions with the shared AI chat bot using a software ( logic is equated to software bases on specification [0030] that discloses logic can be software or hardware circuitry), Nguyen, [0015 ]; by interaction with a user using the detailed information the chat prompt is enhanced);
logic to transmit the enhanced chat prompt, through the network interface, for processing by a general purpose conversational artificial intelligence (providing interaction with AI chat bot using a user interface by improving conversation in a AI chat bot system by providing details in a conversation specific topic of interest using a machine learning model using a software ( logic is equated to software bases on specification [0030] that discloses logic can be software or hardware circuitry), Nguyen, [0015]; by interaction with a user using the detailed information the chat prompt is enhanced)); logic to receive, though the network interface, a response generated by the general purpose conversational artificial intelligence ( a user interface being used to provide a response using improved conversation in a AI chat bot system associated with a machine learning model, Nguyen, [0015]);
logic to enhance the response using a second machine learning engine ( a second machine learning model being used to provide a response to the query based on improved conversation in a AI chat bot system by providing details in a conversation specific topic of interest using a software ( logic is equated to software bases on specification [0030] that discloses logic can be software or hardware circuitry), Nguyen, [0016]); and logic to provide the enhanced response for presentation to a user (response giving to users based details in a conversation specific topic of interest using a machine learning model, response is improved (enhanced) because response is more specific and focus on topic of interest of a user , Nguyen, [0015]).
Regarding claim 3, Nguyen discloses the device of claim 1, wherein the device is customer premises equipment in a broadband network specific and focus on topic of interest ( the network that the AI chat bot system is operating is a wireless network (broadband network), Nguyen, [0023]).
Regarding claim 4, Nguyen discloses the device of claim 3, wherein: the logic to receive a user query comprises: logic to receive the user query from another device ( a user query is received from a different device, a client device in another geographic location , Nguyen, [0019]; [0028]).
Regarding claim 5, Nguyen discloses the device of claim 3, wherein the other device is customer premises equipment in a broadband network ( the network that the AI chat bot system is operating is a wireless network (broadband network), Nguyen, [0023]).
Regarding claim 6, Nguyen discloses the device of claim 1, wherein: the logic to enhance the chat prompt comprises: logic to enrich the prompt with operational information (response as an automatic interaction with a user is given to users based details in a conversation specific topic of interest using a machine learning model, response is improved because response is more specific and focus on topic of interest of a user, Nguyen, [0015]). ; and the response is an enriched response ( response being improved with specific and focus on topic of interest, by improving the response of the query with specific information the response is being improved, Nguyen, [0015]).
Regarding claim 7, Nguyen discloses the device of claim 1, wherein the logic to enhance the chat prompt comprises: logic to generate a supplementary prompt (a presentation of a response to a user has more parameters and data set providing as an automatic interaction with a user, where the presentation is based on the context of a conversation ( the presentation that is an automatic interaction is equated to a prompt) , Nguyen, [0016]; [0017]); and the device further comprises: logic to transmit the supplementary prompt for processing by a general purpose conversational artificial intelligence (the presentation of a response to a user has more parameters and data set providing as an automatic interaction, where the presentation is based on the context of a conversation using a machine learning model ( the presentation is equated to a prompt) providing is equated to transmitting , Nguyen, [0016]); logic to receive a supplementary response ( response received by a user is being improved with specific and focus on topic of interest, by improving the response of the query with specific information, a supplementary response is provided, Nguyen, [0015]; and logic to augment the response with the supplementary response ( response being improved with specific and focus on topic of interest, by improving the response of the query with specific information the response is being augmented, Nguyen, [0015]).
Regarding claim 8, Nguyen discloses the method of claim 1, wherein enhancing the response comprises enhancing the response with application-specific data ( response being improved with specific and focus on topic of interest, by improving the response of the query with specific information the response is being enhanced Nguyen, [0015]).
Regarding claim 9, Nguyen discloses the device of claim 1, wherein at least one of the machine learning engines comprises a multi-head machine learning model ( a machine learning system has a first and a second machine learning models , Nguyen, [0021]).
Regarding claim 10, Nguyen discloses the device of claim 1, wherein the multi-head machine learning model comprises a backbone network trained with common datasets and a plurality of multi-head models trained with application specific datasets ( a machine learning system has a first machine learning model and a second machine learning model where the parameters of the first machine learning model is updated to enhance the message exchanged with the second learning model, Nguyen, [0021]).
Regarding claim 19, claim 19 is substantially similar to combination of claims 1, 4, and 9 , thus the same rationale applies.
Regarding claim 20, claim 20 is substantially similar to combination of claims 1, 4, and 9 , thus the same rationale applies.
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 of this title, 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.
4. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen in view of Ingah et al. (hereinafter “Ingah”) (US 2019/0140899A1).
Regarding claim 2, Nguyen discloses the device of claim 1.
Nguyen does not disclose wherein the operations information comprises performance information about customer premises equipment associated with the user, and wherein the device further comprises logic to modify a configuration of the customer premises equipment, based at least in part on the enhanced response, to improve performance of the customer premises equipment.
Ingah discloses wherein the operations information comprises performance information about customer premises equipment associated with the user (perform processes on customer premises equipment where the customer premises equipment is computers and devices associated with a user , Ingah, [0122]; [0127]), and wherein the device further comprises logic to modify a configuration of the customer premises equipment, based at least in part on the enhanced response, to improve performance of the customer premises equipment (customer premises equipment is configured with a changed configuration, that is a retrieved and stored configuration that has the customer premises equipment that is computers and device associated with a user , by configuring the device with the retrieved and stored configuration, the default configuration is changed to an improved configuration that is based on the configuration settings of a customer premises equipment , Ingah, [0127]; [0128]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate Ingah’s teachings with Nguyen’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device, by modifying a default configuration of a device with data associated with the customer premises equipment of the device the configuration of the device is improved.
4a. Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen in view of REN et al. (hereinafter “REN”) (US 2024/0259954 A1).
Regarding claim 11, Nguyen discloses the device of claim 1.
Nguyen does not disclose wherein at least one of the machine learning engines employs adaptive multi-model switching.
REN discloses wherein at least one of the machine learning engines employs adaptive multi-model switching ( fast switching machine models, REN, [0020]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate REN’s teachings with Nguyen’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device, by applying fast switching machine learning that fastly switches data received from the network to data associated with the customer premises equipment of the device.
4b. Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen, in view of REN, and further in view of Miller et al. (hereinafter “Miller”) (US 2023/0188400 A1).
Regarding claim 12, Nguyen and REN disclose the device of claim 11.
Nguyen in view of REN do not disclose wherein the multi-model switching comprises hopping between a plurality of models.
Miller discloses wherein the multi-model switching comprises hopping between a plurality of models (frequency -hopping spread spectrum signal using sequence artificial intelligence/machine learning models machine learning model in parallel, Miller, [0009]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate Miller’s teachings with Nguyen’s teachings in view of REN’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device by using sequence artificial intelligence/machine learning models machine learning model in parallel with a frequency -hopping spread spectrum signal feature.
4c. Claims 13 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen, in view of REN, and further in view of OKUNO et al. (hereinafter “OKUNO”) (US 2023/0141483 A1).
Regarding claim 13, Nguyen and REN disclose the device of claim 11.
Nguyen in view of REN do not disclose wherein the multi-model switching comprises running a plurality of models in parallel.
OKUNO discloses wherein the multi-model switching comprises running a plurality of models in parallel (machine learning model in parallel, OKUNO, [0120]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate OKUNO’s teachings with Nguyen’s teachings in view of REN’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device by using sequence artificial intelligence/machine learning models machine learning model in parallel feature.
4d. Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen, in view of REN, in view of OKUNO, and further in view of XU (US 2020/0244744 A1).
Regarding claim 14, Nguyen, REN, OKUNO disclose the device of claim 13.
Nguyen in view of REN and in view of OKUNO do not disclose wherein running a plurality of models in parallel comprises employing an optimal output selection strategy.
XU discloses wherein running a plurality of models in parallel comprises employing an optimal output selection strategy (selection model strategy for determining the optimal service type of a machine learning model, XU, [0057]) .
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate XU’s teachings with Nguyen’s teachings in view of REN’s teachings and in view of OKUNO’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device by using sequence artificial intelligence/machine learning models machine learning model with selection model strategy for determining the optimal service type of a machine learning mode.
4e. Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen, in view of REN, in view of OKUNO, and further in view of HALL et al. (hereinafter “HALL”) (US 2022/0198657 A1).
Regarding claim 15, Nguyen, REN, OKUNO disclose the device of claim 13.
Nguyen in view of REN and in view of OKUNO do not disclose wherein running a plurality of models in parallel comprises employing an output majority voting strategy.
HALL discloses wherein running a plurality of models in parallel comprises employing an output majority voting strategy (majority mean voting strategy for AI model, HALL, [0187]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate HALL’s teachings with Nguyen’s teachings in view of REN’s teachings and in view of OKUNO’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device by using sequence artificial intelligence/machine learning models machine learning model with majority mean voting strategy.
4f. Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen, in view of REN, in view of OKUNO, and further in view of Di Pietro et al. (hereinafter “Di Pietro”) (US 2015/0195146 A1).
Regarding claim 16, Nguyen, REN, OKUNO disclose the device of claim 13.
Nguyen in view of REN and in view of OKUNO do not disclose wherein running a plurality of models in parallel comprises employing an output aggregation strategy.
Di Pietro discloses wherein running a plurality of models in parallel comprises employing an output aggregation strategy ( aggregation strategy , Di Pietro, [0103]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate Di Pietro’s teachings with Nguyen’s teachings in view of REN’s teachings and in view of OKUNO’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device by using sequence artificial intelligence/machine learning models machine learning model with aggregation strategy feature.
4g. Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen, in view of REN, in view of OKUNO, and further in view of Degroat et al. (hereinafter “Degroat”) (US 2017/0104829 A1).
Regarding claim 17, Nguyen, REN, OKUNO disclose the device of claim 13.
Nguyen in view of REN and in view of OKUNO do not disclose wherein running a plurality of models in parallel comprises employing a model delegation strategy.
Degroat discloses wherein running a plurality of models in parallel comprises employing a model delegation strategy ( delegation strategy , Degroat, [0053]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate Di Pietro’s teachings with Nguyen’s teachings in view of REN’s teachings and in view of OKUNO’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device by using sequence artificial intelligence/machine learning models machine learning model with delegation strategy feature.
4h. Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable Nguyen, in view of REN, and further in view of Polleri et al. (hereinafter “Polleri”) (US 2021/0081819 A1).
Regarding claim 18, Nguyen discloses the device of claim 11.
Nguyen does not disclose wherein the multi-model switching comprises running a plurality of models serially.
Polleri discloses wherein the multi-model switching comprises running a plurality of models serially ( serialized machine learning model, Polleri, [0248]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate Polleri’s teachings with Nguyen’s teachings in view of REN’s teachings. One skilled in the art would be motivated to combine them in order to efficiently improve a device configuration with data about the customer premises equipment of the device by using sequence serialized learning models machine.
Conclusion
5. 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 extension fee 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 MARIEGEORGES A HENRY whose telephone number is (571)270-3226. The examiner can normally be reached on 11:00am -8:00pm East M-F.
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/MARIEGEORGES A HENRY/Examiner, Art Unit 2455
/DAVID R LAZARO/Primary Examiner, Art Unit 2455
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