DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Receipt is acknowledged of applicant amendment filed on 12/30/25. Claims 1, 2, 6, 8, 10, 11, 12, 16 and 17 amendment. Claims 1-20 are pending and an action on the merits is as follows.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. US Publication No. 2023/0403588 cited in previous action in view of Chalkley US Publication No. 20250265598.
Re Claim 1, Kumar discloses a computer-implemented method comprises: determining a first unique identifier associated with a first device in a system (P103-P104, P126; Fig. 7-9) ; receiving an installation (configuration ) request for instructions for installing the first media device; providing the first unique identifier as input to a machine-learning model(P103-104, P112, P126 Fig. 7-9 and 12); outputting, with the machine-learning model, first installation instructions; responsive to receiving one or more subsequent unique identifiers associated with one or more subsequent devices in the system, providing the first unique identifier, the one or more subsequent unique identifiers and the installation request as input to a machine-learning model (P5, P103-104, P112,P126 figs. 7-9 and 12); and outputting, with the machine-learning model, one or more subsequent installation instructions(P100, P112, P126, P13O Figs, 7-9 and 12).
Kumar fails to specifically discloses a first installation request including directions to a user on how to attach the first media device to the media system; providing the first unique identifier to the first media device as input to a machine-learning model and a second installation request as input to the machine-learning model and outputting, with the machine-learning model, one or more subsequent installation instructions that include a description of how to connect the one or more subsequent media devices to the first media device
Chalkley discloses a first installation request including directions to a user on how to attach the first media device to the media system; providing the first unique identifier to the first media device as input to a machine-learning model and a second installation request as input to the machine-learning model and outputting, with the machine-learning model, one or more subsequent installation instructions that include a description (blueprint ) of how to connect the one or more subsequent media devices to the first media device (P27-P28, P89, P118, P120, P124, P147-P149 P193, Figures 17-20 “components” intended to be installed in relation to or otherwise combined with another device, system, or apparatus, inputting data describing the component to a first machine learning model for the verifying of the component, the machine learning model 506 to determine identifiers of the component 110, such as identifier 510, that match known identifiers stored in the databases 106 (block 1704 of FIG. 17); m and a second installation request as input to the machine-learning model and outputting, with the machine-learning model, one or more subsequent installation instructions that include a description (blueprint ) of how to connect the one or more subsequent media devices to the first media device (P148-P149).
Given the teachings of Chalkley it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kumar with a first installation request including directions to a user on how to attach the first media device to the media system; providing the first unique identifier to the first media device as input to a machine-learning model and a second installation request as input to the machine-learning model and outputting, with the machine-learning model, one or more subsequent installation instructions that include a description of how to connect the one or more subsequent media devices to the first media device.
Doing so would provide a more precise installation of a component (P74) and increase a success of component installation (P32).
2. (Currently Amended) The method of claim 1, wherein the machine- learning model includes a query engine and a large language model, and, the method further comprising: wherein providing the first unique identifier, the one or more subsequent unique identifiers, and the installation request are provided as input to the query engine, and the method further comprising: combining the first unique identifier, the one or more subsequent unique identifiers, and the installation request with a template to form a query; and providing the query as input to the large language model, wherein the large language model outputs the first installation instructions that correspond to the query( P82-83, P85, P27-P28, P89, P118, P120, P124, P147-P149 P193, Figures 3, 17-20).
3. (Original) The method of claim 2, wherein combining the first unique identifier, the one or more subsequent unique identifiers, and the installation request with the template further includes specifying a prioritization of one or more data sources that are used by the large language model to output the installation instructions (see claim 16).
4. (Original) The method of claim 1, further comprising: monitoring the media system to identify information about a performance of the media system; providing the first unique identifier, the one or more subsequent unique identifiers, and information about the performance of the media system to the machine-learning model as input; and outputting, with the machine-learning model, an identification of a performance issue associated with the media system and a description of a solution to the performance issue (P27-P28, P89, P118, P120, P124, P129 , P147-P149 P193, Figures 17-20 19).
Re Claim 5, Kumar and Chalkley discloses the method of claim 4, and Chalkley discloses wherein the performance issue is associated with the first media device, the method further comprising: determining that the solution to the performance issue fails; and contacting a chatbot associated with a manufacturer of the first media device to obtain an additional solution(P129)..
Re Claim 6, Kumar and Chalkley discloses the method of claim 1, and Chalkley discloses further comprising: receiving a request for information about the first media device; providing the first unique identifier, the request for information, and information about performance of the media system to the machine-learning model; and outputting, with the machine-learning model, the information about the first media device(P27-P28, P89, P118, P120, P124, P147-P149 P193, Figures 17-20 19).
Re Claim 7, Kumar and Chalkley discloses The method of claim 1, and Chalkley discloses : receiving feedback about whether the first installation instructions were successful; and providing the feedback to the machine-learning model(P75, P57, P147 Fig. 15).
Re Claim 8, Kumar and Chalkley discloses The method of claim 7, and Chalkley discloses wherein the feedback is selected from a group of a confirmation from a user that the first installation instructions worked, an inference that the first installation instructions worked based on the first media device connecting to a network, and combinations thereof (P75, P57, P147 Fig. 15).
Re Claim 9, Kumar and Chalkley discloses The method of claim 1, and Chalkley discloses wherein outputting the first installation instructions includes generating a diagram of the first media device and the one or more subsequent media devices in the media system (P43, P74, P27-P28, P89, P118, P120, P124, P147-P149 P193, Figures 17-20 19, it is obvious that a plurality of installation instruction could comprise of diagrams, P81, p82, discloses more images of the apparatus 114 or portions of the apparatus 114, o)
Re Claim 10, Kumar and Chalkley discloses the method of claim 1, and KUMAR discloses wherein determining the first unique identifier associated with the first media device is based on one action selected from a group of: scanning a barcode or a QR code, receiving a Request For Information (RFI), receiving a Near Field Communication (NFC), receiving a manufacturer name and a model number, scanning a page that includes purchasing information, receiving the first unique identifier from a mobile device, and combinations thereof (P5, 59).
Re Claim 11, Kumar discloses a system comprising: one or more processors; and logic encoded in one or more non-transitory media for execution by the one or more processors and when executed are operable to: determine a first unique identifier associated with a first media device in a media system(P103-P104, P126; Fig. 7-9);
receive an installation request for instructions for installing the first media device; provide the first unique identifier as input to a machine-learning model(P103-104, P112, P126 Fig. 7-9 and 12); output, with the machine-learning model, first installation instructions; responsive to receiving one or more subsequent unique identifiers associated with one or more subsequent media devices in the media system, provide the first unique identifier, the one or more subsequent unique identifiers, and the installation request as input to a machine-learning model(P5, P103-104, P112,P126 figs. 7-9 and 12); and output, with the machine-learning model, one or more subsequent installation instructions (P100, P112, P126, P13O Figs, 7-9 and 12).
Kumar fails to discloses a first installation request including directions to a user on how to attach the first media device to the media system; provide the first unique identifier to the first media device as input to a machine- learning mode and the second installation request as input to the machine-learning model.
Chalkley discloses a first installation request including directions to a user on how to attach the first media device to the media system; provide the first unique identifier to the first media device as input to a machine- learning mode and the second installation request as input to the machine-learning model (P27-P28, P89, P118, P120, P124, P147-P149 P193, Figures 17-20 19 .
Given the teachings of Chalkley it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of with outputting, with the machine-learning model, one or more subsequent installation instructions that include a description of how to connect the one or more subsequent media devices to the first media device.
Doing so would provide a more precise installation of a component (P74) and increase a success of component installation (P32).
Re Claim 12, Kumar and Chalkley discloses the system of claim 11, and Chalkley discloses wherein the machine- learning model includes a query engine and a large language models and: wherein provide the first unique identifier, the one or more subsequent unique identifiers, and the installation request are provided as input to the query engine, and the method further comprising: combine the first unique identifier, the one or more subsequent unique identifiers, and the installation request with a template to form a query; and provide the query as input to the large language model, wherein the large language model outputs the first installation instructions that correspond to the query (P82-83, P85, P27-P28, P89, P118, P120, P124, P147-P149 P193, Figures 3, 17-20).
Re Claim 13, Kumar and Chalkley discloses the system of claim 12, and Chalkley discloses wherein combining the first unique identifier, the one or more subsequent unique identifiers, and the installation request with the template further includes specifying a prioritization of one or more data sources that are used by the large language model to output the installation instructions (See claim 16, P89, P118, P120, P124, P147-P149 P193) .
Re Claim 14, Kumar and Chalkley discloses the system of claim 11, and Chalkley discloses wherein the logic is further operable to: monitor the media system to identify information about a performance of the media system; provide the first unique identifier, the one or more subsequent unique identifiers, and information about the performance of the media system to the machine-learning model as input; and output, with the machine-learning model, an identification of a performance issue associated with the media system and a description of a solution to the performance issue (P27-P28, P89, P118, P120, P124, P129 , P147-P149 P193, Figures 17-20 19).
Re Claim 15, Kumar and Chalkley discloses the system of claim 14, and Chalkley discloses wherein the performance issue is associated with the first media device and the software is further operable to: determine that the solution to the performance issue fails; and contact a chatbot associated with a manufacturer of the first media device to obtain an additional solution (P129).
Re Claim 16, Kumar discloses Software encoded in one or more non-transitory computer-readable media for execution by one or more processors and when executed is operable to: determine a first unique identifier associated with a first device in a system(P103-P104, P126; Fig. 7-9);
receive an installation request for instructions for installing the first device; provide the first unique identifier as input to a machine-learning model; output, with the machine-learning model, first installation instructions; responsive to receiving one or more subsequent unique identifiers associated with one or more subsequent media devices in the media system, provide the first unique identifier, the one or more subsequent unique identifiers, and the installation request as input to a machine-learning model (P5, P103-104, P112,P126 figs. 7-9 and 12); and output, with the machine-learning model, one or more subsequent installation instructions that include a description of how to connect the one or more subsequent devices to the first device(, P103-104, P112, P126 Fig. 7-9 and 12).
Kumar fails to specifically discloses a first installation request for instructions for installing the first media device including directions to a user on how to attach the first media device to the media system
Chalkley discloses a first installation request for instructions for installing the first media device including directions to a user on how to attach the first media device to the media system (P27-P28, P89, P118, P120, P124, P147-P149 P193, Figures 17-20 19).
Given the teachings of Chalkley it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kumar with a first installation request for instructions for installing the first media device including directions to a user on how to attach the first media device to the media system.
Doing so would provide a more precise installation of a component (P74) and increase a success of component installation (P32).
Re Claim 17, Kumar and Chalkley discloses the software of claim 16, and Chalkley discloses wherein the machine- learning model includes a query engine and a large language model: and: wherein the first unique identifier, the one or more subsequent unique identifiers, and the installation request are provided as input to the query engine, and the software is further operable to: combine the first unique identifier, the one or more subsequent unique identifiers, and the installation request with a template to form a query; and provide the query as input to the large language model, wherein the large language model outputs the first installation instructions that correspond to the query (P89, P118, P120, P124, P147-P149 P193).
Re Claim 18, Kumar and Chalkley discloses the software of claim 17, and Chalkley discloses wherein combining the first unique identifier, the one or more subsequent unique identifiers, and the installation request with the template further includes specifying a prioritization of one or more data sources that are used by the large language model to output the installation instructions (see claim 16).
Re Claim 19, Kumar and Chalkley discloses the software of claim 16, and Chalkley discloses wherein the software is further operable to: monitor the media system to identify information about a performance of the media system; provide the first unique identifier, the one or more subsequent unique identifiers, and information about the performance of the media system to the machine-learning model as input; and output, with the machine-learning model, an identification of a performance issue associated with the media system and a description of a solution to the performance issue (P27-P28, P89, P118, P120, P124, P147-P149 P193, Figures 17-20 19).
Re Claim 20, Kumar and Chalkley discloses The software of claim 19, and Chalkley discloses wherein the performance issue is associated with the first media device and the software is further operable to: determine that the solution to the performance issue fails; and contact a chatbot associated with a manufacturer of the first media device to obtain an additional solution (P129).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 11 and 16have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant amended the claims with new limitations which necessitated new search and consideration. Therefore this action is final.
Conclusion
The following reference is cited but not relied upon:
Roman et al. discloses a variety of improved methods and systems for enabling set up of a variety of disparate IoT devices coupled to the framework and/or ambient operating system of an assistant device
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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SONJI N. JOHNSON
Examiner
Art Unit 2876
/SONJI N JOHNSON/ Primary Examiner, Art Unit 2876
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