CTNF 18/774,696 CTNF 82239 DETAILED ACTION This is the initial Office action based on the application filed on July 16, 2024. Claims 1-20 are pending. 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. Internet Communications Without a written authorization for Internet communications by the Applicant in place, the USPTO cannot communicate with the Applicant via email and will not respond via email to any Internet correspondence which contains information subject to the confidentiality requirement as set forth in 35 U.S.C. § 122, such as claimed subject matter in an interview agenda or proposed claim amendments for an Examiner’s Amendment. Therefore, in the interest of facilitating compact prosecution, the Examiner kindly asks the Applicant to authorize Internet communications with the USPTO by using Form PTO/SB/439 (available at https://www.uspto.gov/patents/apply/forms). The form may be submitted via the USPTO patent electronic filing system (Patent Center) using the document description “Internet Communications Authorized” to facilitate processing. The written authorization for Internet communications must be submitted on a separate paper to be entitled to acceptance in accordance with 37 CFR § 1.4(c). The separate paper will facilitate processing and avoid confusion. The written authorization for Internet communications may not be submitted via an email. See MPEP § 502.03(II). 07-30-03-h AIA Claim Interpretation During patent examination, the pending claims must be “given their broadest reasonable interpretation consistent with the specification.” See MPEP § 2111. Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time. The ordinary and customary meaning of a term may be evidenced by a variety of sources, including the words of the claims themselves, the specification, the drawings, and the prior art. See MPEP § 2111.01(I). Applicant is entitled to be their own lexicographer and may rebut the presumption that claim terms are to be given their ordinary and customary meaning by clearly setting forth a definition of the term that is different from its ordinary and customary meaning(s) in the specification at the relevant time. Where an explicit definition is provided by the Applicant for a term, that definition will control interpretation of the term as it is used in the claim. See MPEP § 2111.01(IV)(A). Any such lexicographic definition for a term will be expressly noted by the Examiner in the prior art rejections of the claims. Claim Mapping For clarity of the prosecution history record, the Examiner has provided annotations clearly in the prior art rejections of the claims to aid the Applicant in understanding the Examiner’s interpretations of the claimed invention and the prior art, such as emphasizing notable and relevant portions of the prior art citations, using item-to-item matching to the prior art citations, pairing exact claim language to particular language used in the prior art citations, and/or clearly explaining the Examiner’s interpretation as to how a prior art citation maps to the claim language, especially when there is no one-to-one matching of terms. Furthermore, the annotations are provided in the prior art rejections of the claims at the Examiner’s discretion where the Examiner deemed to be appropriate and necessary. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claim Objections 07-29-01 AIA Claim s 1, 7, and 14 are objected to because of the following informalities: Claim 1 contains a typographical error: the comma (,) after the word “method” should be deleted. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> Claims 1, 7, and 14 recite “in response to receiving the drag-and-drop operation.” It should read -- in response to receiving the drag-and-drop operation on the graphical elements --. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> Claim 7 recites “memory including instructions.” It should read -- a memory storing instructions --. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> Claims 7 and 14 recite “processing circuitry.” It should read -- a processing circuitry --. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> Claim 14 recites “[a] non-transitory machine-readable medium, including instructions.” It should read -- A non-transitory machine-readable medium, storing instructions --. Appropriate correction is required. 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 obviousness-type 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); and 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b). 08-34 AIA Claim s 1-20 are rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over Claim s 1-13 of U.S. Patent No. 12,079,600 (hereinafter “‘600”) . Although the conflicting claims are not identical, they are not patentably distinct from each other because Claims 1-20 of the instant application define an obvious variation of the invention claimed in ‘600 . Examiner respectfully submits the relevant portions of MPEP §§ 804(II)(B), 804(II)(B)(3), and 804(II)(B)(4) with emphasis added for purposes of convenience in discussion and illustration: MPEP § 804(II)(B) Nonstatutory Double Patenting 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). MPEP § 804(II)(B)(3) Obviousness Analysis Any nonstatutory double patenting rejection made under the obviousness analysis should make clear: (A) The differences between the inventions defined by the conflicting claims – a claim in the patent compared to a claim in the application; and (B) The reasons why a person of ordinary skill in the art would conclude that the invention defined in the claim at issue would have been an obvious variation of the invention defined in a claim in the patent. MPEP § 804(II)(B)(4) One-Way Test for Distinctness If the patent term filing date of an application under examination is the same or later than that of a reference application or patent, only a one-way determination of distinctness is needed in resolving the issue of double patenting, i.e., whether the invention claimed in the application would have been anticipated by, or an obvious variation of, the invention claimed in the reference application or patent. See, e.g., In re Berg , 140 F.3d 1428, 1435, 46 USPQ2d 1226, 1231-32 (Fed. Cir. 1998). The court in Berg applied a one-way test where an applicant filed two separate applications even though all claims could have been filed in a single application, because the applicant’s action could have resulted in an improper timewise extension of rights if one patent expired later than the other. If a claimed invention in the application would have been obvious over a claimed invention in the patent, there would be an unjustified timewise extension of the patent and a nonstatutory double patenting rejection is proper. According to the Berg court, improperly extending the patent term “is precisely the result that the doctrine of obviousness-type double patenting was created to prevent.” Id . Similarly, even if the application under examination has the earlier patent term filing date, only a one-way determination of distinctness is needed to support a double patenting rejection in the absence of a finding: (A) that “the PTO is solely responsible for any delays” in prosecution of that application ( In re Hubbell , 709 F.3d 1140, 1150, 106 USPQ2d 1032, 1039 (Fed. Cir. 2013)); and (B) that the applicant could not have filed the conflicting claims in a single (i.e., the earlier-filed) application ( In re Kaplan , 789 F.2d 1574, 229 USPQ 678 (Fed. Cir. 1986)). It is noted that the instant application is a later-filed continuation of ‘600. It is also noted that both the instant application and ‘600 were filed by the same inventive entity and by a common assignee/owner. Claims 1-13 of ‘600 recite all the limitations of Claims 1-20 of the instant application, while also recite further limitations, and thus anticipate the claims of the instant application. The claims of the instant application therefore are not patentably distinct from the earlier patent claims and as such are unpatentable for obviousness-type double patenting. A later claim is not patentably distinct from an earlier claim if the later claim is anticipated by the earlier claim. Claims 1-6 of ‘600 as shown in the tables below recite all the limitations of Claims 1-6 of the instant application and as such anticipate Claims 1-6 of the instant application. The further limitations recited in Claims 1-6 of ‘600 are boldfaced for the Applicant’s convenience. Claims 7-13 of ‘600 are not shown with Claims 7-20 of the instant application for the purpose of brevity. U.S. Patent No. 12,079,600 Instant Application No. 18/774,696 1. A computer-implemented method, comprising: 1. A computer-implemented method, comprising: presenting a visual representation of an artificial neural network, wherein the visual representation of the artificial neural network includes graphical elements representing layers of the artificial neural network; presenting a visual representation of an artificial neural network, wherein the visual representation of the artificial neural network includes graphical elements representing layers of the artificial neural network; in response to receiving a drag-and-drop operation on the graphical elements, modifying an intermediate representation of the artificial neural network, wherein the intermediate representation of the artificial neural network is independent of a deep learning framework and the drag-and-drop operation is configured to modify connections between the graphical elements including automatically connecting a first graphical element to a second graphical element of the graphical elements; receiving a drag-and-drop operation on the graphical elements, the drag-and-drop operation configured to modify connections between the graphical elements including automatically connecting a first graphical element to a second graphical element of the graphical elements; changing, in response to receiving the drag-and-drop operation, an intermediate representation of the artificial neural network, the intermediate representation of the artificial neural network being independent of a deep learning framework; and modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network for a target deep learning framework; and modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network for a target deep learning framework. in response to receiving an instruction for changing the target deep learning framework to a further target deep learning framework, determining code of the artificial neural network for the further target deep learning framework based on the intermediate representation of the artificial neural network. U.S. Patent No. 12,079,600 Instant Application No. 18/774,696 2. The computer-implemented method of claim 1, further comprising: 2. The computer-implemented method of claim 1, further comprising: in response to an editing operation on the code of the artificial neural network for the target deep learning framework, modifying the intermediate representation of the artificial neural network; and in response to an editing operation on the code of the artificial neural network for the target deep learning framework, modifying the intermediate representation of the artificial neural network; and adjusting the visual representation of the artificial neural network based on the modified intermediate representation of the artificial neural network. adjusting the visual representation of the artificial neural network based on the intermediate representation of the artificial neural network. U.S. Patent No. 12,079,600 Instant Application No. 18/774,696 3. The computer-implemented method of claim 1, further comprising: 3. The computer-implemented method of claim 1, further comprising: in response to receiving the drag-and-drop operation on the graphical elements, validating dimensions of data associated with the layers of the artificial neural network. in response to receiving the drag-and-drop operation on the graphical elements, validating dimensions of data associated with the layers of the artificial neural network. U.S. Patent No. 12,079,600 Instant Application No. 18/774,696 4. The computer-implemented method of claim 1, further comprising: 4. The computer-implemented method of claim 1, further comprising: in response to receiving a search operation associated with a keyword, presenting graphical elements representing at least one candidate layer corresponding to the keyword; and in response to receiving a search operation associated with a keyword, presenting graphical elements representing at least one candidate layer corresponding to the keyword; and in response to receiving a selection of graphical elements of the at least one candidate layer, adding the selected graphical elements of the at least one candidate layer to the visual representation of the artificial neural network. in response to receiving a selection of graphical elements of the at least one candidate layer, adding the selected graphical elements of the at least one candidate layer to the visual representation of the artificial neural network. U.S. Patent No. 12,079,600 Instant Application No. 18/774,696 5. The computer-implemented method of claim 1, further comprising: 5. The computer-implemented method of claim 1, further comprising: presenting code stubs for customizing metrics of the artificial neural network; and in response to an editing operation on the code stubs, customizing the metrics of the artificial neural network. presenting code stubs for customizing metrics of the artificial neural network; and in response to an editing operation on the code stubs, customizing the metrics of the artificial neural network. U.S. Patent No. 12,079,600 Instant Application No. 18/774,696 6. The computer-implemented method of claim 1, further comprising: 6. The computer-implemented method of claim 1, further comprising: modifying the intermediate representation of the artificial neural network in response to at least one of: modifying the intermediate representation of the artificial neural network in response to at least one of: adding, into the visual representation of the artificial neural network, a new graphical element representing a layer of the artificial neural network; adding, into the visual representation of the artificial neural network, a new graphical element representing a layer of the artificial neural network; deleting, from the visual representation of the artificial neural network, a graphical element representing a layer of the artificial neural network; and deleting, from the visual representation of the artificial neural network, a graphical element representing a layer of the artificial neural network; and modifying parameters of a graphical element representing a layer of the artificial neural network. modifying parameters of a graphical element representing a layer of the artificial neural network . Claim Rejections - 35 U.S.C. § 103 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 1-3, 6-9, 12, 14-16, and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over US 2007/0094168 (hereinafter “Ayala”) in view of US 2019/0220253 (hereinafter “Pradhan”) . Examiner’s Remarks: In order for a reference to be proper for use in an obviousness rejection under 35 U.S.C. § 103, the reference must be analogous art to the claimed invention. In re Bigio, 381 F.3d 1320, 1325, 72 USPQ2d 1209, 1212 (Fed. Cir. 2004). A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). See MPEP § 2141.01(a)(I). Note that the claimed invention is generally directed to visual programming for deep learning (Abstract). As for the “same field of endeavor” test, Ayala is generally directed to the design and evaluation of artificial neural networks (specification, paragraph [0004]). As for the “reasonably pertinent” test, Pradhan is generally directed to improving software code quality during its development using artificial intelligence techniques (specification, paragraph [0001]). Thus, Ayala and Pradhan are both analogous art to the claimed invention (even if they address different problems or are not in the same field of endeavor as the claimed invention). As per Claim 1, Ayala discloses: A computer-implemented method (paragraph [0166], “[…] practice of the disclosed […] method is not limited to any particular programming language.”) , comprising: presenting a visual representation of an artificial neural network (paragraph [0062], “In some cases, the editor interface may provide a panel or pallet for graphically establishing [presenting a visual representation] the structure or topology of the artificial neural network , while other editor interfaces may provide such configuration functionality via a number of input data panels, tables, and other graphical user interface selection items (emphasis added).”) , wherein the visual representation of the artificial neural network includes graphical elements representing layers of the artificial neural network (paragraph [0081], “With the identification of each layer, graphical representations of the neurons are depicted in a network topology panel 168 that generally forms a pallet for graphical configuration of the network topology or structure [graphical elements representing layers of the artificial neural network] .”) ; receiving a drag-and-drop operation on the graphical elements, the drag-and-drop operation configured to modify connections between the graphical elements including automatically connecting a first graphical element to a second graphical element of the graphical elements (paragraph [0081], “A user may then employ the mouse pointer to connect the neurons by clicking on one neuron and dragging the mouse pointer toward another neuron. A form of ‘drag-and-drop’ operation then forms the connection [receiving a drag-and-drop operation on the graphical elements, the drag-and-drop operation configured to modify connections between the graphical elements including automatically connecting a first graphical element to a second graphical element of the graphical elements] (emphasis added).”) ; changing, in response to receiving the drag-and-drop operation (paragraph [0081], “A user may then employ the mouse pointer to connect the neurons by clicking on one neuron and dragging the mouse pointer toward another neuron. A form of ‘drag-and-drop’ operation then forms the connection [in response to receiving the drag-and-drop operation] (emphasis added).”) , an intermediate representation of the artificial neural network (paragraph [0088], “As is known to those skilled in the art, such discriminant planes, or decision lines, provide a representation of the network by a linear or discriminant equation for classification of data points with respect to the line or plane. To that end, the selection of the hyperplanes tab in the panel 174 may provide options or settings directed to a number of discriminants for a layer of the network, as well as for identification of the reference neurons for the hyperplanes equations. The user may then specify the reference neurons for plotting the data input patterns. Once the foregoing settings are established, the data may be plotted in a customizable graph within the panel 174. In this manner, the hyperplanes provide for classification or class separation and facilitate a visualization of the regions created by the network being configured after, for instance, a training iteration [changing […] an intermediate representation of the artificial neural network] .”) ; and modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network […] (paragraph [0090], “These settings may be similar or related to the graphical settings made available to the user for customization via the panel 172 of the display interface window 160. The options pull down menu may also provide a mechanism for initiating implementation of the code insight module 116 (FIG. 1) in which case a separate display interface window may be generated to display code related to, for instance, the back propagation training algorithm associated with the feedforward network being configured [modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network {…}] .”) . Ayala does not explicitly disclose: the intermediate representation of the artificial neural network being independent of a deep learning framework; and […] a target deep learning framework. However, Pradhan discloses: an intermediate representation of the artificial neural network being independent of a deep learning framework (paragraph [0026], “[…] the training of the artificial neural network may be implemented inside a deep learning framework. In an embodiment of the present invention, the deep learning framework may be Deep Learning for Java (DL4J) platform. DL4J is an open-source, distributed deep-learning library written for Java and Scala. The deep learning framework may import the artificial neural network from any of the frameworks for the predefined training [an intermediate representation of an artificial neural network being independent of a deep learning framework] .”) ; and […] a target deep learning framework (paragraph [0026], “[…] the training of the artificial neural network may be implemented inside a deep learning framework [a target deep learning framework] . In an embodiment of the present invention, the deep learning framework may be Deep Learning for Java (DL4J) platform. DL4J is an open-source, distributed deep-learning library written for Java and Scala. The deep learning framework may import the artificial neural network from any of the frameworks for the predefined training.”) . As pointed out hereinabove, Ayala and Pradhan are both analogous art to the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Pradhan into the teaching of Ayala to include “the intermediate representation of the artificial neural network being independent of a deep learning framework; and […] a target deep learning framework.” The modification would be obvious because one of ordinary skill in the art would be motivated to import an artificial neural network from any frameworks for a predefined training (Pradhan, paragraph [0026]). As per Claim 2, the rejection of Claim 1 is incorporated; and Ayala further discloses: in response to an editing operation on the code of the artificial neural network for the target deep learning framework, modifying the intermediate representation of the artificial neural network (paragraph [0088], “As is known to those skilled in the art, such discriminant planes, or decision lines, provide a representation of the network by a linear or discriminant equation for classification of data points with respect to the line or plane. To that end, the selection of the hyperplanes tab in the panel 174 may provide options or settings directed to a number of discriminants for a layer of the network, as well as for identification of the reference neurons for the hyperplanes equations. The user may then specify the reference neurons for plotting the data input patterns. Once the foregoing settings are established, the data may be plotted in a customizable graph within the panel 174. In this manner, the hyperplanes provide for classification or class separation and facilitate a visualization of the regions created by the network being configured after, for instance, a training iteration.”) ; and adjusting the visual representation of the artificial neural network based on the intermediate representation of the artificial neural network (paragraph [0090], “For instance, in this embodiment, the options available for customization by the user include a number formatting option, a background height option, and a series or set of graphs settings options to specify background colors or styles, as well as graphical item sizes. These settings may be similar or related to the graphical settings made available to the user for customization via the panel 172 of the display interface window 160. The options pull down menu may also provide a mechanism for initiating implementation of the code insight module 116 (FIG. 1) in which case a separate display interface window may be generated to display code related to, for instance, the back propagation training algorithm associated with the feedforward network being configured. Lastly, the options pull down menu may provide a mechanism for saving one or more of the settings specified by the user, such as saving the graph itself set forth in the network topology panel 168.”) . As per Claim 3, the rejection of Claim 1 is incorporated; and Ayala further discloses: in response to receiving the drag-and-drop operation on the graphical elements (paragraph [0081], “A user may then employ the mouse pointer to connect the neurons by clicking on one neuron and dragging the mouse pointer toward another neuron. A form of ‘drag-and-drop’ operation then forms the connection.”) , validating dimensions of data associated with the layers of the artificial neural network (paragraph [0117], “[…] the learning pattern module 108 may support the customization and setup of pattern data sets for cross-validation techniques, as well as perform analysis of the pattern data sets to determine their suitability for use in training and/or testing the neural network.”) . As per Claim 6, the rejection of Claim 1 is incorporated; and Ayala further discloses: modifying the intermediate representation of the artificial neural network in response to at least one of: adding, into the visual representation of the artificial neural network, a new graphical element representing a layer of the artificial neural network; deleting, from the visual representation of the artificial neural network, a graphical element representing a layer of the artificial neural network (paragraph [0096], “The commands made available via the menu bar 202 and the command bar 204 generally enable a user to manage the pattern data set(s) by adding one or more patterns, deleting one or more patterns and saving the pattern data set. In the exemplary case shown in FIG. 9, an icon 206 is included the command bar 204 to delete a number of rows (i.e., patterns) specified in an input data field 207, with the location of the rows to be deleted specified via additional input data fields 208 and 209. Alternatively, or additionally, patterns or rows of the table may be deleted via command key strokes or any other desired interface mechanism.”) ; and modifying parameters of a graphical element representing a layer of the artificial neural network. Claims 7-9 and 12 are device claims corresponding to the computer-implemented method claims hereinabove (Claims 1-3 and 6, respectively). Therefore, Claims 7-9 and 12 are rejected for the same reasons set forth in the rejections of Claims 1-3 and 6, respectively. Claims 14-16 and 19 are non-transitory machine-readable medium claims corresponding to the computer-implemented method claims hereinabove (Claims 1-3 and 6, respectively). Therefore, Claims 14-16 and 19 are rejected for the same reasons set forth in the rejections of Claims 1-3 and 6, respectively. Allowable Subject Matter Claims 4, 5, 10, 11, 13, 17, 18, and 20 are objected to as being dependent upon a rejected base claim under 35 U.S.C. § 103, but would be allowable over the cited prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and overcome any corresponding objections and/or rejections set forth hereinabove. 13-03-01 The following is an Examiner’s statement of reasons for the indication of allowable subject matter: As per Claims 4, 10, and 17, the closest cited prior art, the combination of Ayala and Pradhan fails to teach or suggest, among the other claimed limitations, “in response to receiving a search operation associated with a keyword, presenting graphical elements representing at least one candidate layer corresponding to the keyword; and in response to receiving a selection of graphical elements of the at least one candidate layer, adding the selected graphical elements of the at least one candidate layer to the visual representation of the artificial neural network.” These claimed limitations, in combination with the other claimed limitations, are neither taught nor suggested by the combination of Ayala and Pradhan. As per Claims 5, 11, and 18, the closest cited prior art, the combination of Ayala and Pradhan fails to teach or suggest, among the other claimed limitations, “presenting code stubs for customizing metrics of the artificial neural network; and in response to an editing operation on the code stubs, customizing the metrics of the artificial neural network.” These claimed limitations, in combination with the other claimed limitations, are neither taught nor suggested by the combination of Ayala and Pradhan. As per Claims 13 and 20, the closest cited prior art, the combination of Ayala and Pradhan fails to teach or suggest, among the other claimed limitations, “in response to receiving an instruction for changing the target deep learning framework to a further target deep learning framework, determining code of the artificial neural network for the further target deep learning framework based on the intermediate representation of the artificial neural network.” These claimed limitations, in combination with the other claimed limitations, are neither taught nor suggested by the combination of Ayala and Pradhan. Pertinent Prior Art 07-96 The prior art made of record and not relied upon is considered pertinent to the Applicant’s disclosure. They are as follows: US 2003/0110464 (hereinafter “Davidson”) discloses generating optimized code and configuration settings for a target device, such as a programmable circuit or chip. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> US 2007/0236509 (hereinafter “Eldridge”) discloses creating/configuring control programs for continuous and/or discrete processes. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> US 2017/0351401 (hereinafter “Pascale”) discloses visualizing neural networks. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> US 2018/0307978 (hereinafter “AR”) discloses multi-modal construction of deep learning networks. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> US 2019/0205728 (hereinafter “Feng”) discloses visualizing neural network models. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> US 5,222,210 (hereinafter “Leivian”) discloses displaying the state of an artificial neural network on a graphic display terminal. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> US 8,627,276 (hereinafter “Lin”) discloses a graphical modeling environment to allow a user to generate a graphical model that maps to multiple entities. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> US 9,846,836 (hereinafter “Gao”) discloses constructing and using deep neural networks to learn a DSM of interestingness. <<>> + <<>> + <<>> • × • <<>> + <<>> + <<>> US 9,934,462 (hereinafter “Healey”) discloses graphical user interfaces for visualizing deep neural networks. Conclusion Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Qing Chen whose telephone number is 571-270-1071. The Examiner can normally be reached on Monday through Friday from 9:00 AM to 5:00 PM ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, the Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at https://www.uspto.gov/ interviewpractice. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Wei Mui, can be reached at 571-272-3708. 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 more 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. /Qing Chen/ Primary Examiner, Art Unit 2191 Application/Control Number: 18/774,696 Page 2 Art Unit: 2191 Application/Control Number: 18/774,696 Page 3 Art Unit: 2191 Application/Control Number: 18/774,696 Page 4 Art Unit: 2191 Application/Control Number: 18/774,696 Page 5 Art Unit: 2191 Application/Control Number: 18/774,696 Page 6 Art Unit: 2191 Application/Control Number: 18/774,696 Page 7 Art Unit: 2191 Application/Control Number: 18/774,696 Page 8 Art Unit: 2191 Application/Control Number: 18/774,696 Page 9 Art Unit: 2191 Application/Control Number: 18/774,696 Page 10 Art Unit: 2191 Application/Control Number: 18/774,696 Page 11 Art Unit: 2191 Application/Control Number: 18/774,696 Page 12 Art Unit: 2191 Application/Control Number: 18/774,696 Page 13 Art Unit: 2191 Application/Control Number: 18/774,696 Page 14 Art Unit: 2191 Application/Control Number: 18/774,696 Page 15 Art Unit: 2191 Application/Control Number: 18/774,696 Page 16 Art Unit: 2191
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