CTNF 18/908,525 CTNF 80131 DETAILED ACTION Introduction This office action is in response to applicant’s claims filed 10/07/2024. Claims 1-20 are currently pending and have been examined. Applicant’s IDS have been considered. There is no claim to foreign priority. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-34 AIA Claim s 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of U.S. Patent No. 12,112,127 (hereinafter referred to as ‘127) . Although the claims at issue are not identical, they are not patentably distinct from each other because : Regarding claim 1 , ‘127 teaches a system comprising (ibid-see claim 1 corresponding and similar limitation): one or more processors (ibid); and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform (ibid): receiving one or more user utterances (ibid); using a trained NLP algorithm as one or more layers in a neural network to generate from the one or more user utterances at least one first output of at least one first output layer of the neural network and at least one second output of at least one second output layer of the neural network (ibid); using the trained NLP algorithm to combine the at least one first output of the at least one first output layer of the neural network and the at least one second output of the at least one second output layer of the neural network to create a combined output of the neural network (ibid), wherein: the at least one first output layer of the neural network is different than the at least one second output layer of the neural network (ibid); and coordinating [displaying a customized graphical user interface (GUI) using] the combined output of the neural network (ibid). ‘127 further teaches, the pre-trained NLP, as re-trained, and the combined first and second output as a “final” output, and using the “final” output in coordinating displaying a customized GUI (also seen in dependent claims 5, 6, 10, 15 and 16 “final” output). However, where an omission on a device or apparatus is no more than the “Omission of an Element and Its Function, where the Function of the Element Is Not Desired or Required” the claim is unpatentable under 35 U.S.C. 103(a). Ex parte Wu , 10 USPQ 2031 (Bd. Pat. App. & Inter. 1989). Accordingly, Applicant claims a combination that only unites old elements with no change in the respective functions of those old elements, and the omission one of those elements yields predictable results; absent evidence that the modifications necessary to effect the combination of elements is uniquely challenging or difficult for one of ordinary skill in the art, the claim is unpatentable as obvious under 35 U.S.C. 103(a). Accordingly, since the applicant[s] have submitted no persuasive evidence that the omission of the above element is uniquely challenging or difficult for one of ordinary skill in the art, the claim is unpatentable as obvious under 35 U.S.C. 103(a) because it is no more than the predictable use of prior art elements according to their established functions resulting the same resultant function. Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art, in view of the teachings of ‘127 to omit elements, such as a final output and retraining element of ‘127 as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007) , wherein the predictable result would still be coordinating displaying a customized graphical user interface using the output of the neural network (ibid-see current claim set, output, wherein the removed elements broaden the claim). Independent claims 11, and 20 set forth similar limitations as claim 1, wherein the method and computer-readable medium are deemed to embody the system, and are rejected accordingly. Dependent claims 2-10 and 12-19 , set forth limitations similar to claims 2-10 and 12-19 of ‘127, and are rejected according . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-4, 6-14 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Costello (US 202/0065384) in view of Bhagavath et al. (Bhagavath, US 2023/0086302) . As per claim 1 , Costello teaches a system comprising: one or more processors (paragraph [0027], -his processor(s), computer-readable media and corresponding instructions); and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform (ibid): receiving one or more user utterances (paragraph [0073]-his input speech utterance); using a trained NLP algorithm as one or more layers in a neural network to generate from the one or more user utterances at least one first output of at least one first output layer of the neural network and at least one second output of at least one second output layer of the neural network (paragraphs [0077-0079, 0006], Figs. 1A-1B, 3-5-his NLP-intent classification, first output layer, and the utterance also input into a second layer, which generated a second output of the neural network); using the trained NLP algorithm to combine the at least one first output of the at least one first output layer of the neural network and the at least one second output of the at least one second output layer of the neural network to create a combined output of the neural network (ibid-see also Fig. 5, [0168, 0182-0184]-his second layer ensemble, as combinations of the first-layer ensembles), wherein: the at least one first output layer of the neural network is different than the at least one second output layer of the neural network (ibid-each output layer, first and second having different pre-trained models, see paragraph [0008]-his different models discussion, and the above discussed Figs.); and coordinating [displaying a customized graphical user interface (GUI) using] the combined output of the neural network (ibid-paragraphs [0076]-his final prediction result output). Costello lacks explicitly teaching that which Bhagavath teaches, coordinating displaying a customized graphical user interface (GUI) using the combined output of the neural network (paragraphs [0023, 0053]-his configured display for presenting return item results, based on the output of the intent classification model). Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Costello and Bhagavath to combine the prior art element of a hierarchical intent classifier and determined intent using a neural network as taught by Costello with using the generated intent from a neural network to configure a display interface and present results to a user as taught by Bhagavath as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007) , wherein the predictable result would be to allow a chatbot to generate a customized display to respond to different determined intents as needed (ibid-Bhagavath, paragraph [0023]). As per claims 2 and 12 , Costello with Bhagavath make obvious the system of claim 1, Bhagavath further teaching that which Costello lacks, wherein the customized graphical user interface (GUI) comprises a reply from a chat bot generated based on the combined output of the neural network (ibid-see claim 1, Bhagavath, paragraphs [0023, 0053]-his configured display for presenting return item results, based on the output of the intent classification model, as similarly motivated in combination). As per claims 3 and 13 , Costello with Bhagavath make obvious the system of claim 1, wherein the one or more user utterances comprise messages entered into a chat bot (ibid-Costello, paragraph [0155]-his chatbot). As per claims 4 and 14 , Costello with Bhagavath make obvious the system of claim 1, wherein the trained NLP algorithm, as used in the neural network, feeds into: the at least one first output layer of the neural network and the at least one second output layer of the neural network through two different pathways (ibid-Costello, Fig. 1A, paragraphs [0077, 0066]- including his NLP trained module, see claim 1, NLP discussion, feeding into both intent model sets via different pathways). As per claims 6 and 16 , Costello further makes obvious the system of claim 1, wherein: the computing instructions are further configured to run on the one or more processors and cause the one or more processors to perform: after using the trained NLP algorithm as the one or more layers in the neural network, receiving a new user utterance from a user (Fig. 6 item S100, S200-his trained NLP model, as the used NLP algorithm in the neural network, see claim 1, NLP discussion, and the subsequent user input utterance); and the combined output of the neural network is correlated with an intent of the new user utterance (ibid, Figs. 4-6 S400, S500, the combined ensemble output of the neural network and corresponding result and output intent, as discussed in claim 1). As per claims 7 and 17 , Costello further makes obvious the system of claim 1, wherein the neural network comprises a hybrid neural network comprising at least a portion of two different types of hierarchical multi-label classification networks (ibid, Figs. 3-5, paragraphs [0071, 0072, 0108, 0121-0134]-his different models and corresponding hierarchical classification, based thereon, including CNN, RNN, etc.). As per claims 8 and 18 , Costello further makes obvious the system of claim 1, wherein the neural network comprises a hybrid neural network comprising at least one chained portion and at least one unchained portion (paragraphs [0080-0084]-his neural network, with only one trained model as the unchained portion, and plurality of models as an ensemble, as the chained portion). As per claims 9 and 19 , Costello with Bhagavath further makes obvious the system of claim 1, wherein the one or more user utterances comprise requests to return, exchange, or refund one or more items (ibid-see claim 1, paragraphs [0023, 0053]-his configured display for presenting return item results, based on the output of the intent classification model, as similarly combined and motivated). As per claim 10 , Costello with Bhagavath make obvious the system of claim 1, wherein coordinating displaying the customized GUI using the combined output of the neural network comprises: calculating a total [loss] using the at least one first output of the at least one first output layer, the at least one second output of the at least one second output layer, and the combined output of the neural network (ibid-Costello, see paragraph [0093], and Figs. 1A, 3-5, his “total” of m predictions, including a first layer, second layer, and combined layer, as discussed in claim1); and [coordinating displaying the customized GUI using the total loss] (his output, final prediction result based on the total prediction results). Costello lacks explicitly teaching that which Bhagavath teaches, calculating a total loss ], and coordinating displaying the customized GUI using the total loss (ibid-see claim 1, display discussion based on the output of the neural network, paragraph [0015]-his multiple types of loss functions, in order to display the customized GUI). Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Costello and Bhagavath to combine the prior art element of a hierarchical intent classifier and determined intent using a neural network (wherein Costello does not explicitly state any of loss functions in his regression analysis, prediction network, or any of his multiple types of neural networks for classification) as taught by Costello with using the generated intent, utilizing a loss function for each prediction analysis, from a neural network to configure a display interface and present results to a user as taught by Bhagavath as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007) , wherein the predictable result would be to allow a chatbot to generate a customized display to respond to different determined intents, based on a calculated prediction, as needed (ibid-Bhagavath, paragraph [0015, 0023]). As per claim 11 , claim 11 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the system is deemed to embody the method, such that Costello with Bhagavath make obvious a method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising (ibid-see Costello, claim 1, computer-readable media discussion, his abstract-method discussion): receiving one or more user utterances (ibid-see claim 1, corresponding and similar limitation); using a trained NLP algorithm as one or more layers in a neural network to generate from the one or more user utterances at least one first output of at least one first output layer of the neural network and at least one second output of at least one second output layer of the neural network (ibid); using the trained NLP algorithm to combine the at least one first output of the at least one first output layer of the neural network and the at least one second output of the at least one second output layer of the neural network to create a combined output of the neural network (ibid), wherein: the at least one first output layer of the neural network is different than the at least one second output layer of the neural network (ibid); and coordinating displaying a customized graphical user interface (GUI) using a final version of the combined output of the neural network (ibid). As per claim 20 , claim 1 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the non-transitory computer-readable medium storing instructions is deemed to embody the method, such that Costello with Bhagavath makes obvious teaches a non-transitory computer-readable medium storing instructions, wherein the instructions, upon execution by a processor, cause the processor to perform operations comprising (ibid-see claims 1 and 11, computer-readable medium and instructions discussion): receiving one or more user utterances (ibid-see claim 1, corresponding and similar limitation); using a trained NLP algorithm as one or more layers in a neural network to generate from one or more user utterances at least one first output of at least one first output layer of the neural network and at least one second output of at least one second output layer of the neural network (ibid); using the trained NLP algorithm to combine the at least one first output of the at least one first output layer of the neural network and the at least one second output of the at least one second output layer of the neural network to create a combined output of the neural network (ibid), wherein: the at least one first output layer of the neural network is different than the at least one second output layer of the neural network (ibid); and coordinating displaying a customized graphical user interface (GUI) using a version of the combined output of the neural network (ibid) . 07-21-aia AIA Claim (s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Costello (US 202/0065384) in view of Bhagavath et al. (Bhagavath, US 2023/0086302), as applied to claim 1, and further in view of Chen et al. (Chen, US 2022/0129556) . As per claims 5 and 15 , Costello with Bhagavath make obvious the system of claim 1, Costello further teaches wherein: the at least one first output of the at least one first output layer [comprises a first cross-entropy loss] (ibid-see claim 1); the at least one second output of the at least one second output layer [comprises a second cross-entropy loss] (ibid); and the combined output of the neural network [comprises mean squared error] (ibid). Costello lacks explicitly teaching that which Bhagavath teaches, cross-entropy loss (paragraph [0015]-his cross entropy loss, used for intent classification). Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Costello and Bhagavath to combine the prior art element of a hierarchical intent classifier and determined intent using a neural network as taught by Costello with using a well-known loss function to determine prediction quality of the intent as classified as taught by Bhagavath as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007) , wherein the predictable result would be allow a chatbot to generate a customized display to respond to different determined intents, using loss function for prediction accuracy analysis, as needed (ibid-Bhagavath, paragraph [0015, 0023]). The above combination lacks teaching that which Chen teaches, the combined output of the neural network comprises mean squared error (paragraphs [0201, 0267, 0401]-his mean squared error loss, used in value distribution prediction, between two output values). Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Costello and Bhagavath to combine the prior art element of a hierarchical intent classifier and determined intent using a neural network, with multiple outputs, each required classification, including a first, second, and combined, as taught by Costello with using a well-known loss function, cross-entropy, to determine prediction quality of the intent as classified as taught by Bhagavath with the mean squared error loss function, as taught by Chen as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007) , wherein the predictable result would be to allow a chatbot to generate a customized display to respond to different determined intents, using loss function for prediction accuracy analysis, as needed (ibid-Bhagavath, paragraph [0015, 0023], ibid-Chen-his value prediction using mean squared error) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892) . Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAMONT M SPOONER whose telephone number is (571)272-7613. The examiner can normally be reached 8:00 AM -5:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at (571)272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /LAMONT M SPOONER/ Primary Examiner, Art Unit 2657 5/16/2026 Application/Control Number: 18/908,525 Page 2 Art Unit: 2657 Application/Control Number: 18/908,525 Page 3 Art Unit: 2657 Application/Control Number: 18/908,525 Page 4 Art Unit: 2657 Application/Control Number: 18/908,525 Page 5 Art Unit: 2657 Application/Control Number: 18/908,525 Page 6 Art Unit: 2657 Application/Control Number: 18/908,525 Page 7 Art Unit: 2657 Application/Control Number: 18/908,525 Page 8 Art Unit: 2657 Application/Control Number: 18/908,525 Page 9 Art Unit: 2657 Application/Control Number: 18/908,525 Page 10 Art Unit: 2657 Application/Control Number: 18/908,525 Page 11 Art Unit: 2657 Application/Control Number: 18/908,525 Page 12 Art Unit: 2657 Application/Control Number: 18/908,525 Page 13 Art Unit: 2657 Application/Control Number: 18/908,525 Page 14 Art Unit: 2657 Application/Control Number: 18/908,525 Page 15 Art Unit: 2657 Application/Control Number: 18/908,525 Page 16 Art Unit: 2657 Application/Control Number: 18/908,525 Page 17 Art Unit: 2657 Application/Control Number: 18/908,525 Page 18 Art Unit: 2657 Application/Control Number: 18/908,525 Page 19 Art Unit: 2657 Application/Control Number: 18/908,525 Page 20 Art Unit: 2657 Application/Control Number: 18/908,525 Page 21 Art Unit: 2657
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