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. This office action is in response to the claim amendment filed on August 17, 2023, in which claims 1-18 were canceled and claims 19- 35 are presented for examination. Information Disclosure Statement The information disclosure statement filed on August 17, 2023 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits. Claim Objections Claim 19 , 30, 31 and 33 are objected to because of the following informalities: claim 19 recites “a method for visual reasoning, comprising the following steps ”. The claimed feature “ the following steps ” is not a claimed language. C laim 19 should be amended to read as “a method for visual reasoning, comprising the steps ”. Claims 19, 30, 31 and 33 recite “visual reasoning”. However, the body of the claims does not mention any about visual reasoning. Appropriate correction is required. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 19- 32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more. At Step 1 : With respect to subject matter eligibility under 35 USC 101, it is determined that the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. At Step 2A, Prong One : The limitation “ determining a posterior distribution over combinations of one or more modules of the set of modules through the PGM, based on the provided sets of inputs and sets of outputs ” in claims 19, 31 and 32, as drafted this recites a mental process as a form of evaluation or judgement or opinion. One can mentally judge/evaluate the posterior distribution over combinations of one or more modules of the set of modules through the PGM , this also reflects an opinion as the mental judgement of what ' posterior distribution ' based on the provided sets of inputs and sets of outputs . If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea. At Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements : That the method is "implemented by a computing system” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. The limitation “ providing a network with sets of inputs and sets of outputs, wherein each set of inputs of the sets of inputs mapping to one of the sets of outputs corresponding to the set of inputs based on visual information on the set of inputs, and wherein the network includes a Probabilistic Generative Model (PGM) and a set of modules ” amount s to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)). The limitation “ applying domain knowledge as one or more posterior regularization constraints on the determined posterior distribution ” recites insignificant extra-solution activity such as mere outputting of the result. The mere outputting of data do es not meaningfully limit the abstract idea. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application . (See MPEP 2106.05 (g)). The limitation “ a memory; at least one processor and non-transitory computer readable medium ” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)). Note, the mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application. At Step 2B: The conclusions for the mere implementation using a computer are carried over and does not provide significantly more. With respect to the “ providing ….” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more. With respect to the " applying domain knowledge …… ” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional in displaying information as evidenced by the court cases in MPEP 2106.05(d)(II), " iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93" and "i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" . With respect to the “ a memory; at least one processor and non-transitory computer readable medium ” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPE P 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible. Accordingly, claim 1 9 is directed to an abstract idea. The remaining independent claim 31 and 3 2 fall short the 35 USC 101 requirement under the same rationale. Claim 2 0 recites “ wherein the one or more posterior regularization constraints are grouped into one or more groups of constraints according to one or more aspects of the domain knowledge ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. Claim 21 recites “ wherein the one or more aspects of the domain knowledge include one or more of: logical reasoning, and/or temporal reasoning, and/or spatial reasoning, and/or arithmetical reasoning ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. Claim 22 recites “ wherein the one or more posterior regularization constraints are one or more First-Order Logic (FOL) constraints ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. Claim 23 recites “ wherein the one or more FOL constraints are generated based on at least one of: relation types of the sets of inputs, and/or object types of the sets of inputs, and/or attribute types of the sets of inputs ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. Claim 24 recites “ wherein each of the combinations of one or more modules of the set of modules includes a modularized network, the modularized network is assembled from one or more modules of the set of modules with a structure indicating the assembled one or more modules and connections therebetween ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. Claim 25 recites “ determining a posterior distribution over structures of modularized networks through the PGM, based on the provided sets of inputs and sets of outputs ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. Claim 26 recites “ determining, through the PGM, a posterior distribution over structures of modularized networks indicating types of the assembled one or more modules and connections therebetween, based on the provided sets of inputs and sets of outputs ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. Claim 27 recites “ optimizing the network by: updating parameters of the PGM and parameters of modules of the set of modules alternatively by maximizing evidences of the sets of inputs and the sets of outputs, to obtain an estimated posterior distribution over the combinations of one or more modules of the set of modules and optimized parameters of the modules of the set of modules; updating one or more weights of the one or more posterior regularization constraints applied to the estimated posterior distribution over the combinations of one or more modules of the set of modules, to obtain one or more optimal solutions of the one or more weights; adjusting the estimated posterior distribution over the combinations of one or more modules of the set of modules, by applying the one or more optimal solutions of the one or more weights and one or more values of the one or more constraints on the estimated posterior distribution; and updating the optimized parameters of the modules based on the adjusted estimated posterior distribution over the combinations of one or more modules of the set of modules ”. This additional element is recited at a high level of generality and would function in its ordinary capacity for updating parameters of the PGM and parameters of modules of the set of modules alternatively by maximizing evidences of the sets of inputs and the sets of outputs, to obtain an estimated posterior distribution over the combinations of one or more modules of the set of modules and optimized parameters of the modules of the set of modules; updating one or more weights of the one or more posterior regularization constraints applied to the estimated posterior distribution over the combinations of one or more modules of the set of modules, to obtain one or more optimal solutions of the one or more weights; adjusting the estimated posterior distribution over the combinations of one or more modules of the set of modules, by applying the one or more optimal solutions of the one or more weights and one or more values of the one or more constraints on the estimated posterior distribution; and updating the optimized parameters of the modules based on the adjusted estimated posterior distribution over the combinations of one or more modules of the set of modules , this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. Claim 28 recites “ wherein the one or more posterior regularization constraints are grouped into one or more groups of constraints, and each group of constraints corresponding to one weight ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. Claim 29 recites “ wherein a value of each constraint is determined based on a correlation between a set of inputs and a module in a combination of one or more modules of the set of modules generated according to the estimated posterior distribution given the set of inputs ”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement . There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception. At Step 2A, Prong One : The limitation “ generating a combination of one or more modules of the set of modules based on a posterior distribution over combinations of one or more modules of the set of modules and the set of input images, wherein the posterior distribution is formulated by the PGM trained under domain knowledge as one or more posterior regularization constraints ” in claim 30, as drafted this recites a mental process as a form of evaluation or judgement or opinion. One can mentally generat e a combination of one or more modules of the set of modules based on a posterior distribution over combinations of one or more modules of the set of modules and the set of input images , this also reflects an opinion as the mental judgement of what ' posterior distribution ' based on the formulated by the PGM trained under domain knowledge as one or more posterior regularization constraints . If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea. At Step 2A, Prong Two: This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements : That the method is "implemented by a computing system” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. The limitation “ providing the network with a set of input images and a set of candidate images ” amount s to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)). The limitation “ processing the set of input images and the set of candidate images through the generated combination of one or more modules ” amount s to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)). The limitation “ selecting a candidate image from the set of candidate images based on a score of each candidate image in the set of candidate images estimated by the processing ” amount s to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)). At Step 2B: The conclusions for the mere implementation using a computer are carried over and does not provide significantly more. With respect to the “ providing …; processing …; and selecting …” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible. Accordingly, claim 30 is directed to an abstract idea. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. Claim s 19 -35 are rejected under 35 U.S.C. 102 (a)(1) as bein g anticipated by Giuseppe et al., (hereinafter “ Giuseppe ”) article entitled “ Integrating Learning and Reasoning with Deep Logic Models ” . As to claim 33 , Giuseppe discloses a network for visual reasoning (page 1, abstract, lines 7-9 , using deep graphical models integrating deep learning and logic reasoning both for learning and inference to create an end to-end differentiable architecture ) , comprising: a set of modules, wherein each module of the set of modules being implemented as a neural network and having at least one trainable parameters for focusing the module on one or more variable image properties (abstract, page 2, par. [2]-[5]) ; and a Probabilistic Generative Model (PGM) coupled to the set of modules, wherein the PGM is configured to output a posterior distribution over combinations of one or more modules of the set of modules (page 3, fig.1) . As to claim 3 4 , Giuseppe discloses the claimed wherein each of the set of modules is configured to perform a pre-designed type of process on the one or more variable image properties, and the one or more variable image properties are resulted from processing an image feature map through the at least one trainable parameters (see pages 5-14) . As to claim 3 5, Giuseppe discloses the claimed wherein the one or more variable image properties includes one or more of: shape, and/or line, and/or size, and/or type, and/or color, and/or position, and/or number, and the pre-designed type of process includes logical AND, or logical OR, or logical XOR, or arithmetic ADD, or arithmetic SUB, or arithmetic MUL, or spatial STRUC, or temporal PROG, or temporal ID (see pages 5-14) . As to claim 19 , Giuseppe discloses a method for visual reasoning (see page 1, par. [1]-[2]) , comprising the following steps: providing a network with sets of inputs and sets of outputs, wherein each set of inputs of the sets of inputs mapping to one of the sets of outputs corresponding to the set of inputs based on visual information on the set of inputs (see abstract, page 2, par. [2]-[5]) , and wherein the network includes a Probabilistic Generative Model (PGM) and a set of modules (see abstract, page 2, par. [2]-[5] , page 11, fig.3 ) ; determining a posterior distribution over combinations of one or more modules of the set of modules through the PGM, based on the provided sets of inputs and sets of outputs ( see abstract, page 3 , par. [2]-[5]) ; and applying domain knowledge as one or more posterior regularization constraints on the determined posterior distribution (see abstract, page 1, par. [2]-[6], page 3, par. [2]-[5] and page 5, section 2.2 ) . As to claim 31, Giuseppe discloses an apparatus for visual reasoning, comprising: a memory (see fig.1) ; and at least one processor coupled to the memory and configured for visual reasoning (page 2) , the at least one processor configured to: provide a network with sets of inputs and sets of outputs, wherein each set of inputs of the sets of inputs mapping to one of the sets of outputs corresponding to the set of inputs based on visual information on the set of inputs (see abstract, page 2, par. [2]-[5]) , and wherein the network includes a Probabilistic Generative Model (PGM) and a set of modules (see abstract, page 2, par. [2]-[5]) , determine a posterior distribution over combinations of one or more modules of the set of modules through the PGM, based on the provided sets of inputs and sets of outputs (see abstract, page 3 , par. [2]-[5]) , and apply domain knowledge as one or more posterior regularization constraints on the determined posterior distribution (see abstract, page 1, par. [2]-[6], page 3, par. [2]-[5] and page 5, section 2.2) . As to claim 32, Giuseppe discloses a non-transitory computer readable medium on which is stored computer code for visual reasoning, the computer code when executed by a processor, causing the processor to perform the following steps: providing a network with sets of inputs and sets of outputs, wherein each set of inputs of the sets of inputs mapping to one of the sets of outputs corresponding to the set of inputs based on visual information on the set of inputs (see abstract, page 2, par. [2]-[5]) , and wherein the network includes a Probabilistic Generative Model (PGM) and a set of modules (see abstract, page 2, par. [2]-[5]) ; determining a posterior distribution over combinations of one or more modules of the set of modules through the PGM, based on the provided sets of inputs and sets of outputs (see abstract, page 3 , par. [2]-[5]) ; and applying domain knowledge as one or more posterior regularization constraints on the determined posterior distribution (see abstract, page 1, par. [2]-[6], page 3, par. [2]-[5] and page 5, section 2.2) . As to claim 20, Giuseppe discloses the claimed wherein the one or more posterior regularization constraints are grouped into one or more groups of constraints according to one or more aspects of the domain knowledge (see page 8, section 2.4) . As to claim 21, Giuseppe discloses the claimed wherein the one or more aspects of the domain knowledge include one or more of: logical reasoning, and/or temporal reasoning, and/or spatial reasoning, and/or arithmetical reasoning (see page 9) . As to claim 22, Giuseppe discloses the claimed wherein the one or more posterior regularization constraints are one or more First-Order Logic (FOL) constraints (see page 2, par. [2]-[5] , page 7). As to claim 23, Giuseppe discloses the claimed wherein the one or more FOL constraints are generated based on at least one of: relation types of the sets of inputs, and/or object types of the sets of inputs, and/or attribute types of the sets of inputs (see page 2, par. [2]-[5], a unified framework to integrate logical reasoning and deep learning. DLMs bridge an input layer processing the sensorial input patterns, like images, video, text, from a higher level which enforces some structure to the model output. Unlike in Semantic-based Regularization [8] or Logic Tensor Networks [9], the sensorial and reasoning layers can be jointly trained, so that the high-level weights imposing the output structure are jointly learned together with the neural network weights, processing the low-level input. The bonding is very general as any (set of) deep learners can be integrated and any output structure can be expressed. This paper will mainly focus on expressing the high-level structure using logic formalism like first–order logic (FOL). In particular, a consistent and fully differentiable relaxation of FOL is used to map the knowledge into a set of potentials that can be used in training and inference ) . As to claim 24, Giuseppe discloses the claimed wherein each of the combinations of one or more modules of the set of modules includes a modularized network, the modularized network is assembled from one or more modules of the set of modules with a structure indicating the assembled one or more modules and connections therebetween (see page 3, par. [1]) . As to claim 25, Giuseppe discloses the claimed determining a posterior distribution over structures of modularized networks through the PGM, based on the provided sets of inputs and sets of outputs (see page 3, par. [1]) . As to claim 26, Giuseppe discloses the claimed wherein each module of the set of modules includes at least one trainable parameters for focusing the module on one or more variable image properties, and is configured to perform a pre-designed type of process on the one or more variable image properties, and the method further comprising: determining, through the PGM, a posterior distribution over structures of modularized networks indicating types of the assembled one or more modules and connections therebetween, based on the provided sets of inputs and sets of outputs (See Figure 1 , t he DLM graphical model assumes that the output variables y depend on the output of first stage f, processing the input X. This corresponds to the breakdown into a lower sensorial layer and a high level semantic one ) . As to claim 27, Giuseppe discloses the claimed updating parameters of the PGM and parameters of modules of the set of modules alternatively by maximizing evidences of the sets of inputs and the sets of outputs, to obtain an estimated posterior distribution over the combinations of one or more modules of the set of modules and optimized parameters of the modules of the set of modules; updating one or more weights of the one or more posterior regularization constraints applied to the estimated posterior distribution over the combinations of one or more modules of the set of modules, to obtain one or more optimal solutions of the one or more weights; adjusting the estimated posterior distribution over the combinations of one or more modules of the set of modules, by applying the one or more optimal solutions of the one or more weights and one or more values of the one or more constraints on the estimated posterior distribution; and updating the optimized parameters of the modules based on the adjusted estimated posterior distribution over the combinations of one or more modules of the set of modules (page s 5 -7 ) . As to claim 28, Giuseppe discloses the claimed wherein the one or more posterior regularization constraints are grouped into one or more groups of constraints, and each group of constraints corresponding to one weight (see page 5, section 2.2) . As to claim 29, Giuseppe discloses the claimed wherein a value of each constraint is determined based on a correlation between a set of inputs and a module in a combination of one or more modules of the set of modules generated according to the estimated posterior distribution given the set of inputs (page 3, par. [1]-[2]) . As to claim 30, Giuseppe discloses a method for visual reasoning with a network, wherein the network includes a Probabilistic Generative Model (PGM) and a set of modules, the method comprising the following steps: providing the network with a set of input images and a set of candidate images (see abstract, page 2, par. [2]-[5], section 4.1) ; generating a combination of one or more modules of the set of modules based on a posterior distribution over combinations of one or more modules of the set of modules and the set of input images, wherein the posterior distribution is formulated by the PGM trained under domain knowledge as one or more posterior regularization constraints (page 2, par. [2]-[5], section s 2.2, 2.3, 2.4 and 4.1) ; processing the set of input images and the set of candidate images through the generated combination of one or more modules (see section 4.1 and fig.1-3) ; and selecting a candidate image from the set of candidate images based on a score of each candidate image in the set of candidate images estimated by the processing (see page 12) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT JEAN M CORRIELUS whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-4032 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 6:30a-10p(Midflex) . 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, FILLIN "SPE Name?" \* MERGEFORMAT Ann J Lo can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571)272-9767 . 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. /JEAN M CORRIELUS/ Primary Examiner, Art Unit 2159 March 7, 2026