CTNF 18/303,792 CTNF 87610 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. DETAILED ACTION Application 18/303,792 filed 4/20/2023 has been examined. In this Office Action, Claims 1-20 are currently pending. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites: (Step 2a, Prong One) performing actions based on mapping output and the revised mapping output. The limitation of performing actions based on mapping output and the revised mapping output, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic “computer-implemented method”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer-implemented method language, “performing” in the context of this claim encompasses the user manually determining/performing generic, unspecified “actions” using generic “mapping output” and “revised mapping output” steps. Similarly, the limitation(s) of obtaining; performing; and resolving, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer implemented method language, obtaining; performing; and resolving in the context of this claim encompasses the user manually receiving generic “structured and unstructured data” and performing generic, unspecified “mapping” and “resolving” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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, judgment, opinion)). Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) where performing generic performing steps of generic actions using generic mapping output/revised mapping outputs and generic structured/unstructured data is a method of human activity in commercial or legal interactions. Accordingly, the claim recites an abstract idea. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a computer implemented method to perform both the obtaining; performing; and resolving; and performing steps. The computer implemented method in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “performing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computer implemented method to perform both the obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 2, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 3, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the one or more mapping techniques include label mapping, identification mapping, semantic mapping and relation mapping”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the one or more mapping techniques include label mapping, identification mapping, semantic mapping and relation mapping” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the one or more mapping techniques include label mapping, identification mapping, semantic mapping and relation mapping” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 4, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the one or more mapping techniques are performed in sequential order based on one or more confidence levels of the one or more mapping techniques”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the one or more mapping techniques are performed in sequential order based on one or more confidence levels of the one or more mapping techniques” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the one or more mapping techniques are performed in sequential order based on one or more confidence levels of the one or more mapping techniques” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 5, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 6, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the at least one other mapping technique includes keyword clustering and theme clustering”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the at least one other mapping technique includes keyword clustering and theme clustering” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the at least one other mapping technique includes keyword clustering and theme clustering” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 7, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “further comprising performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “further comprising performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “further comprising performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 8, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “further comprising analyzing the revised mapping output for mapping inaccuracies to provide a final output of one or more column mappings based on the structured data and the unstructured data”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “further comprising analyzing the revised mapping output for mapping inaccuracies to provide a final output of one or more column mappings based on the structured data and the unstructured data” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “further comprising analyzing the revised mapping output for mapping inaccuracies to provide a final output of one or more column mappings based on the structured data and the unstructured data” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 9, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 10, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the performing the one or more actions includes re-training the trained artificial intelligence model based on an output of one or more column mappings generated using, at least, the data mapping engine”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the performing the one or more actions includes re-training the trained artificial intelligence model based on an output of one or more column mappings generated using, at least, the data mapping engine” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the performing the one or more actions includes re-training the trained artificial intelligence model based on an output of one or more column mappings generated using, at least, the data mapping engine” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Claim 11 recites: (Step 2a, Prong One) performing actions based on mapping output and the revised mapping output. The limitation of performing actions based on mapping output and the revised mapping output, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic “processors/memory”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processors/memory language, “performing” in the context of this claim encompasses the user manually determining/performing generic, unspecified “actions” using generic “mapping output” and “revised mapping output” steps. Similarly, the limitation(s) of obtaining; performing; and resolving, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the processors/memory language, obtaining; performing; and resolving in the context of this claim encompasses the user manually receiving generic “structured and unstructured data” and performing generic, unspecified “mapping” and “resolving” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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, judgment, opinion)). Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) where performing generic performing steps of generic actions using generic mapping output/revised mapping outputs and generic structured/unstructured data is a method of human activity in commercial or legal interactions. Accordingly, the claim recites an abstract idea. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processors/memory to perform both the obtaining; performing; and resolving; and performing steps. The processors/memory in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “performing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a processors/memory to perform both the obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 12, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 13, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 14, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “further comprising performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “further comprising performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “further comprising performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 15, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Claim 16 recites: (Step 2a, Prong One) performing actions based on mapping output and the revised mapping output. The limitation of performing actions based on mapping output and the revised mapping output, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic “processing circuit/media”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processing circuit/media language, “performing” in the context of this claim encompasses the user manually determining/performing generic, unspecified “actions” using generic “mapping output” and “revised mapping output” steps. Similarly, the limitation(s) of obtaining; performing; and resolving, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the processing circuit/media language, obtaining; performing; and resolving in the context of this claim encompasses the user manually receiving generic “structured and unstructured data” and performing generic, unspecified “mapping” and “resolving” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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, judgment, opinion)). Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) where performing generic performing steps of generic actions using generic mapping output/revised mapping outputs and generic structured/unstructured data is a method of human activity in commercial or legal interactions. Accordingly, the claim recites an abstract idea. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processing circuit/media to perform both the obtaining; performing; and resolving; and performing steps. The processing circuit/media in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “performing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a processing circuit/media to perform both the obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Additionally, Claims 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because independent claim(s) 16 does not recite statutory computer media (only generic “computer readable storage media”, as opposed to “ non-transitory computer readable storage media ”, for example) without limitation and thus the claim(s) is/are directed to a signal per se and/or mere information in the form of data, and dependent claims 17-20 do not correct this deficiency. See generally guidance on the New Form Paragraphs for Subject Matter Eligibility Rejections under the 2019 Revised Patent Subject Matter Eligibility Guidance (¶ 7.05.01 Rejection, 35 U.S.C. 101, Nonstatutory (Not One of the Four Statutory Categories); Available via: https://www.uspto.gov/sites/default/files/documents/form_para_for_2019peg_20190108.pdf Referring to claim 17, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 18, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 19, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the method further comprises performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the method further comprises performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the method further comprises performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. Referring to claim 20, (Step 2a, Prong One) this further merely performs an additional abstract mental step of “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model”. (Step 2a, Prong Two) This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Step 2b) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model” steps to perform both the aforementioned obtaining; performing; and resolving; and performing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible. 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) 1-3, 5, 7-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnan et al., US Pub. No. 2022/0036260 A1, in view of Deshpande et al., US Pub. No. 2017/0075984 A1, in view of Rout et al., US Pub. No. 2023/0153280 A1 . As to claim 1 (and substantially similar claim 11 and claim 16), Krishnan discloses a computer-implemented method of facilitating processing within a computing environment, (Krishnan abstract and [0028-0040])) the computer-implemented method comprising: obtaining structured data and unstructured data to be mapped, (Krishnan teaches receiving structured data and unstructured data to be mapped see [ 0018 ] However , input data may include a plurality of data types that exist as structured data , unstructured data , or semi - structured data that is used or operated upon by a source or input application to a business integration process.; see also [0008] FIG . 4 is a block diagram illustrating of a universal data mapping pipeline managing structured and unstructured data according to an embodiment; see also fig. 7 items 726: “Determine one or more candidate mappings of input data set to target location data architecture from data integration process”) the structured data including a plurality of columns; (Krishnan [0061] the universal data mapping pipeline 400 may take sample data from various types or entries of data from an input dataset in an embodiment . The universal data mapping pipeline 400 may receive column identifying data or sample data from one or more databases 402 that store the intended input dataset) automatically performing mapping using the structured data and the unstructured data to provide a mapping output, the mapping output including at least one mapping between a selected column of the plurality of columns and another column of the plurality of columns and a mapping of selected data of the unstructured data to at least one column of the plurality of columns; (Krishnan teaches mapping from input unstructured data to candidate mappings / target columns see Fig. 7 items, 706, 712, 714, 724: “Determine type of data input”; “Unstructured text”; “Apply LTSM neural network or similar data mapping neural network algorithm”; “Apply inference module to results of matched data mapping neural network algorithms”; “Determine one or more candidate mappings of input data set to target location data architecture from data integration process”; See also [0067-0068] [0067 ] Once one or more data classifications have been determined from the input dataset and matched to a data mapping neural network algorithm according to the data classification module 404 , the inference module 414 may implement the matched data mapping neural network algorithm to generate one or more candidate mappings for the business integration process being modeled; [0068 ] Upon generation of the one or more candidate mappings to map input datasets from an input application environment to a target dataset for the target application environment by the inference module 414 , the universal data mapping pipeline 400 may provide the candidate mappings to a user.; See also [0071] Multiple models may be utilized to generate predictions for mapping vectors representing input dataset columns from column names , aliases or sampled data to input schema for a target dataset . For example , multiple columns may be assessed via a batch beam search to reduce the number of inferences . Lists may be prepared for column name and aliases as shown in FIG . 5A and for sample column data as shown in FIG . 5B . The preprocessed and tokenized inputs into the lists and a list priority queue is formed for each target column as beam search candidates as beam search candidates using a top K probably method . All queues are searched in order for each beam step to determine a certain number of candidates for the first mapping model . This is done until all candidates in the list queue are empty or all target columns of a response queue are full.) Krishnan does not disclose: automatically resolving multiple mappings of a given column, based on there being more than one mapping of the given column, to provide a revised mapping output; However, Deshpande discloses: automatically resolving multiple mappings of a given column, based on there being more than one mapping of the given column, to provide a revised mapping output; (Deshpande teaches using an entity mapping identifier for using ranked entity mappings/mapping scores for entity resolution to determine which of the entity mappings are to be used/which columns to map, i.e. “automatically resolving multiple mappings of a given column” See [0037-0038] the entity mapping identifier 110 ranks (and optionally filters) the entity mappings based on each entity mapping score. In block 306, the entity mapping identifier 110 uses the ranked entity mappings to determine which of the entity mappings are to be used to determine whether a same real-world entity is described by the first data asset and the second data asset. [0038] In certain embodiments, depending on the entity mapping score, the entity mapping identifier 110 may filter out entity mappings having entity mapping scores less than a certain threshold and may rank the remaining entity mappings using the associated entity mapping scores; see also [0105] entity mapping identifier 110 ranks the entity mappings based on each entity mapping score. In block 1206, the entity mapping identifier 110 uses the ranked entity mappings to determine which of the entity mappings are to be used to determine whether a same real-world entity is described by the first data asset and the second data asset.; see also [0034] An entity mapping may be described as a set of attributes that define a real-world object. Areal-world object may be, for example, a person, a building, a location, a car, etc. That is, an entity mapping for a data asset A with columns al, a2, ... an, with regard to data asset B with columns bl, b2 , ... bm is a set of column mappings { ai:bjlai c (al,a2, ... an), bj c (bl,b2, ... bm)} and matches at least A rows of A. With embodiments, the entity mapping is directional since the number of matching rows of A to B may be different from the number of matching rows of B to A. Entity resolution may be described as a process used to find whether two sets of attributes are describing the same real-world object; see also [0035] Entity mapping is identified between various types of data assets: structured to structured, structured to semi structured, semi-structured to structured, semi-structured to semi-structured, unstructured to structured, and unstructured to semi-structured.) It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply an entity mapping identifier with ranked entity mappings/mapping scores for entity resolution, as taught by Deshpande, to the system of Krishnan/Deshpande, since it was known in the art that database systems provide an entity mapping identifier for generating entity mappings that produce matching entities for a first data asset having attributes and a second data asset having attributes by: generating entity mappings that produce matching entities for a first data asset having attributes with attribute values and a second data asset having attributes with attribute values by: matching the attribute values of the attributes of the first data asset with the attribute values of the attributes of the second data asset, using the matching attribute values to generate matching attribute pairs, and using the matching attribute pairs to identify entity mappings where the entity mapping identifier computes an entity mapping score for each of the entity mappings based on a combination of factors and where the entity mapping identifier ranks the entity mappings based on each entity mapping score and the entity mapping identifier uses the ranked entity mappings to determine which of the entity mappings are to be used to determine whether a same real-world entity is described by the first data asset and the second data asset. (Deshpande [0105]). Krishnan/Deshpande do not disclose: and performing one or more actions based on, at least, one of the mapping output and the revised mapping output. However, Rout discloses: and performing one or more actions based on, at least, one of the mapping output and the revised mapping output (Rout teaches perform prediction-based actions based at least in part on the generated predictions/corrected prediction with anomalies removed, i.e. “performing one or more actions based on, at least, one of the mapping output and the revised mapping output” see [0053] automatically perform prediction-based actions based at least in part on the generated predictions.; See also [0051] The architecture 100 includes a predictive data analysis system 101 configured to receive predictive data analysis requests from client computing entities 102, process the predictive data analysis requests to generate predictions, provide the generated predictions to the client computing entities 102, and automatically perform prediction-based actions based at least in part on the generated predictions. An example of a prediction-based action that can be performed using the predictive data analysis system 101 is matching an incoming data column to an existing data scheme such that the incoming data will be cleared of any anomalous entries and correctly ingested into the existing data scheme; see also [0100] In some embodiments, the anomaly report may be applied to the column mapping automatically to remove anomalous values from the inbound table.) It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply a predictive data analysis system for prediction-based actions, as taught by Rout, to the system of Krishnan/Deshpande, since it was known in the art that database systems provide a predictive data analysis system which may include a predictive data analysis computing entity and a storage subsystem where the predictive data analysis computing entity may be configured to receive predictive data analysis requests from one or more client computing entities, process the predictive data analysis requests to generate predictions corresponding to the predictive data analysis requests, provide the generated predictions to the client computing entities and automatically perform prediction-based actions based at least in part on the generated predictions. (Rout [0053]). As to claim 2, Krishnan as modified discloses the computer-implemented method of claim 1, wherein the automatically performing the mapping using the structured data and the unstructured data includes performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns (Krishnan teaches using multiple models for determining predictions/candidate mappings, i.e. “performing one or more mapping techniques on the structured data to provide one or more relationships between the plurality of columns” See [0071] An inference phase may utilize a Beam search technique that may be utilized to infer top probable schema at each step of iteration for matching input dataset fields with target dataset fields . Multiple models may be utilized to generate predictions for mapping vectors representing input dataset columns from column names , aliases or sampled data to input schema for a target dataset.; see also [ 0073 ] These candidate mapping of input dataset fields to target dataset fields may be one of several candidate map pings generated by the inference module . These candidate mappings may be presented to a user customizing a mapping type visual element in modeling a business integration process via an integration application management system .) As to claim 3, Krishnan as modified discloses the computer-implemented method of claim 2, wherein the one or more mapping techniques include label mapping, (Krishnan [0056] For example , a mapping type element 312 in an embodiment may be used to convert a character to uppercase , change the format of a date or look up a value in a database . A transform map element 314 , may be another mapping type element , in an embodiment may associate a first data set field name for a data set field value being retrieved from a first application or source with a second data set field name under which that data set field value will be stored at a second application or destination .) identification mapping, (Krishnan [0062] Other input parameters to the machine learning classifier may include information identifying the input application environment of the input dataset may be used , information identifying the target application environment or columns identifiers of a target dataset may be used , or any information on data hierarchies , if any , from input application datasets or target application datasets; see also [0075] Additional inputs may also be factored in some embodiments including input application identification information , target application identification information , trading partner identification information , customer or user identification , or any known data hierarchy information for input or target datasets .) semantic mapping (Krishnan [0066] Example types of data mapping neural network algorithms for use with unstructured data 410 may include a word2vec neural network algorithm or a doc2vec neural network algorithm that manage word semantics for example . For example , unstructured data comprising text blobs , images , videos may be suited to a data mapping neural network algorithm such as encoder decoder architecture neural network.) and Deshpande, as modified, discloses: and relation mapping (Deshpande [0075] Once the data assets are joined, various operations may be performed to characterize the matching rows, e.g., defining relationship contexts between data assets.; See also [0036] FIG. 2 illustrates that entity mapping may be a directional relationship between two data assets, data asset A and data asset B. A directional relationship implies that a first data asset may be related to a second data asset, but the second data asset may not be related to the first data asset). As to claim 5, Krishnan as modified the computer-implemented method of claim 2, wherein the automatically performing the mapping using the structured data and the unstructured data includes performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column of the plurality of columns (Krishnan teaches a classification to be matched with a one or more types of data mapping neural network algorithms suited to the type of data class or classes detected in the input dataset and algorithms for managing word semantics, i.e. “performing at least one other mapping technique on one or more keywords extracted from the unstructured data to show a relationship with the at least one column” see [0066] Unstructured data classification 410 may be a classification to be matched with a one or more types of data mapping neural network algorithms suited to the type of data class or classes detected in the input dataset. For example , there may be multiple classes of unstructured data 410 in some embodiments . In such embodiments , the data classification module 404 may match each type of unstructured data 410 with the type of data mapping neural network algorithm suited to the type of data class or classes detected . Example types of data mapping neural network algorithms for use with unstructured data 410 may include a word2vec neural network algorithm or a doc2vec neural network algorithm that manage word semantics for example . For example , unstructured data comprising text blobs , images , videos may be suited to a data mapping neural network algorithm such as encoder decoder architecture neural net work . Such a data mapping neural network algorithm may be suited for assessing candidate mappings of text blobs unstructured data because they capture the meaning of the words in text blobs ( context of the words used ) for example . In another example , images unstructured data may be matched with a R - CNN based neural network algorithm . This data mapping neural network algorithm may be suited for assessing candidate mappings of images / blobs / time series unstructured data and selected due to specific suit ability of those neural network algorithms because they support object segmentations.; see also [0067] the inference module 414 may implement the matched data mapping neural network algorithm to generate one or more candidate mappings for the business integration process being modeled.; see also [0070] The encoder step 506 may convert one or more input objects , such as the column name information and column alias information as input and convert these into the feature domain as an encoded vector 504. In an example embodiment , several models may have been built for the data mapping neural network algorithm , such as for data types , column names , or samples of actual column data .). As to claim 7, Deshpande as modified discloses the computer-implemented method of claim 1, further comprising performing a weighting function to identify one or more confidence scores for the mapping output and using the one or more confidence scores in the automatically resolving the multiple mappings to provide the revised mapping output (Deshpande [0049] In certain embodiments, the entity mapping identifier 110 maintains a history of user selections and weighs the support by probability of user selections (i.e., if the user has selected a particular entity mapping compared to other entity mappings, that entity mapping is ranked higher). As to claim 8, Rout as modified discloses the computer-implemented method of claim 7, further comprising analyzing the revised mapping output for mapping inaccuracies to provide a final output of one or more column mappings based on the structured data and the unstructured data (Rout teaches anomaly detection and rejection, i.e. “analyzing the revised mapping output for mapping inaccuracies to provide a final output” See [0017] FIG. 11 provides a flowchart diagram of an example process for anomaly detection and rejection based at least in part on the value encoding output of a deep learning model for each of the values in a given column.; See also [0024] A mapping from the input table to the existing data collection scheme is then generated and applied. In addition, the output encodings for a given column can be used to generate an anomaly score and anomalous values can be purged from the inbound table. By using the noted techniques, various embodiments of the present invention use multiple machine learning models to map the input data from an inbound file to an existing data collection scheme with little to no manual intervention, while simultaneously purging the data of anomalous values. See also [0100] In some embodiments, the anomaly detection module 409 has the architecture that is depicted in FIG. 11. The anomaly detection module 409 generates an anomaly report based at least in part on intermediate values ( e.g., column value encodings) from the supervised column similarity machine learning model 407. The first step/operation in the anomaly detection module 409 is to determine an anomalous measure for each of the sampled values in a selected column from the inbound table 405, based at least in part on the intermediate values generated by the supervised column similarity machine learning model 407, using the anomaly measure module 1100. The next step/operation in the anomaly detection module 409 is to generate an anomaly report based in part on the anomalous measures for … the anomaly report generated by the anomalous value rejection module 1101 is generated based at least in part on the anomalous measure for each of the sampled values of a particular column as compared to all other sampled column values of the particular column) As to claim 9, Krishnan as modified discloses the computer-implemented method of claim 1, wherein the automatically performing the mapping using the structured data and the unstructured data to provide the mapping output is performed by a data mapping engine, the data mapping engine trained using a trained artificial intelligence model (Krishnan teaches trained mapping neural network algorithms for unstructured datasets, i.e. a “data mapping engine trained using a trained artificial intelligence model” [0097] This LTSM neural network algorithm or other suitable data mapping neural network algorithm may have been trained via test input datasets provided via crowdsourcing for datasets with unstructured text data 712 in an embodiment; see also [0019] source input data which may contain different types of data . With the classification of data types , particularized inference data mapping neural network algorithms or other particularized data models may be implemented to infer one or more sample data maps for use with a data mapping type visual element . Sample data and column identification data may be input into the machine learning classifier to identify classes of data before an inference module matches mapping between data for a first source input application and a target application for a business process integration . Structured data , unstructured data , and semi - structured data types , or other data type subclasses may then be optimally treated in the inference module with different mapping neural network algorithms or other mapping machine learning models in some embodiments . Further , unstructured data may have different subclasses that). As to claim 10, Krishnan as modified discloses the computer-implemented method of claim 9, wherein the performing the one or more actions includes re-training the trained artificial intelligence model based on an output of one or more column mappings generated using, at least, the data mapping engine (Krishnan teaches updated/further mapping feedback training, i.e. “re-training the trained artificial intelligence model” see [0068] for developing the modeled business integration processes . Selections , non - selections , modifications , or user generated mappings by the user may be recorded as mapping feedback information by the interaction module 416. This mapping feedback information may be sent to the active machine learning module 412 to further train or modify the data mapping neural network algorithms of the inference module 414 in some embodiments . The mapping feedback information may be sent to the data classification module 412 to further train or modify the machine learning classifier or other supervised learning system for detecting data classifications from input datasets in some embodiments; [0106] Proceeding to block 732 , any of these actions may be recorded by the interaction module and fed back to the data classification module at 704 in an embodiment to further train or modify the machine learning classifier to provide for better data classification determinations from input datasets . Further , any of the above actions by the user may be recorded by the interaction module at 724 and fed back via the interaction module to block 724 to update and improve operation of any of the selected data mapping neural network algorithms of the inference module used to generate candidate mappings between an input application dataset and a target application dataset to assist a user in customizing a data mapping type visual element or shape.). Referring to claim 12, this dependent claim recites similar limitations as claim 2; therefore, the arguments above regarding claim 2 are also applicable to claim 12. Referring to claim 13, this dependent claim recites similar limitations as claim 5; therefore, the arguments above regarding claim 5 are also applicable to claim 13. Referring to claim 14, this dependent claim recites similar limitations as claim 7; therefore, the arguments above regarding claim 7 are also applicable to claim 14. Referring to claim 15, this dependent claim recites similar limitations as claim 9; therefore, the arguments above regarding claim 9 are also applicable to claim 15. Referring to claim 17, this dependent claim recites similar limitations as claim 2; therefore, the arguments above regarding claim 2 are also applicable to claim 17. Referring to claim 18, this dependent claim recites similar limitations as claim 5; therefore, the arguments above regarding claim 5 are also applicable to claim 18. Referring to claim 19, this dependent claim recites similar limitations as claim 7; therefore, the arguments above regarding claim 7 are also applicable to claim 19. Referring to claim 20, this dependent claim recites similar limitations as claim 9; therefore, the arguments above regarding claim 9 are also applicable to claim 20 . 07-21-aia AIA Claim (s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnan et al., US Pub. No. 2022/0036260 A1, in view of Deshpande et al., US Pub. No. 2017/0075984 A1, in view of Rout et al., US Pub. No. 2023/0153280 A1, in view of Seetharaman et al., US Pub. No. 2018/0067732 A1 As to claim 4, Krishnan/Deshpande/Rout do not disclose: wherein the one or more mapping techniques are performed in sequential order based on one or more confidence levels of the one or more mapping techniques; However, Seetharaman discloses the computer-implemented method of claim 3, wherein the one or more mapping techniques are performed in sequential order based on one or more confidence levels of the one or more mapping techniques (Seetharaman teaches automapping/entity mapping and ranking according to the models via dataflow steps, and providing of recommendations and associated confidences, i.e. “one or more mapping techniques are performed in sequential order based on one or more confidence levels” See [0332-0338] [0332] In accordance with an embodiment, a machine learning (ML) model is used to compare pairs of sources and targets, and to score similarity of datasets or entities, based on extracted features. The feature extraction 714 includes metadata, data type and statistical profiles of randomly sampled data for each attributes.; [0333] In accordance with an embodiment, a logistic regression model 716 provides, as an output, an overall confidence of how a candidate entity is similar to an input entity. In order to find a more exact mapping, a column mapping model 718 is used to further evaluate similarity of source attribute with target attribute. [0338] In accordance with an embodiment, the result are passed to a get stats profile 734 component and the data AI system 724 provide feature extraction 735. Results are used for synthesis 736, final confidence merging and ranking 739 according to the models 723, and providing of recommendations and associated confidences 740.; See also [0179-0183] [0179] FIG. 3 illustrates the steps in a data flow, m accordance with an embodiment. [0180] As illustrated in FIG. 3, in accordance with an embodiment, the processing of a DFML data flow 260 can include a plurality of steps, including an ingest step 262, during which data is ingested from various sources, for example, Salesforce (SFDC), S3, or DBaaS. [0183] During a model step 268, one or more models are generated, together with mappings to the models. See also [0270] As illustrated in FIG. 22, in accordance with an embodiment, a pipeline 588 comprises a list of pipeline steps. Different types of pipeline steps represent different kinds of operations that can be performed in the pipeline. Each pipeline step can have a number of input data sets and a number of output data sets, generally described by pipeline step parameters.). It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply automapping, as taught by Seetharaman, to the system of Krishnan/Deshpande/Rout, since it was known in the art that database systems provide a dataflow, pipeline, or Lambda application, the user may desire to choose data to be mapped from a source or input dataset or entity, within an input HUB, to a target or output dataset or entity, within an output HUB where since generating a map of data from an input HUB to an output HUB for very large set of HUBs and dataset or entities by hand can be an extremely time consuming and inefficient task, automapping can enable a user to focus on simplification of a dataflow application, e.g., pipeline, Lambda application, by providing a user with recommendations for mapping data. (Seetharaman [0303-0304]) . 07-21-aia AIA Claim (s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnan et al., US Pub. No. 2022/0036260 A1, in view of Deshpande et al., US Pub. No. 2017/0075984 A1, in view of Rout et al., US Pub. No. 2023/0153280 A1, in view of Jing et al., US Pub. No.: US 2025/0013886 A1 . As to claim 6, Krishnan/Deshpande/Rout do not disclose: the computer-implemented method of claim 5, wherein the at least one other mapping technique includes keyword clustering and theme clustering; However, Jing discloses the computer-implemented method of claim 5, wherein the at least one other mapping technique includes keyword clustering and theme clustering (Jing teaches keyword/label clustering and theme clustering/evaluation See [0093] The unstructured sentences are fed to a neural network to thereby extract unstructured raw triples, while apriori data is used with a rule-based object mapping to generate a relationship between the extracted triples and the apriori data which constitutes the pre-processing block.; see also [0105] With the predicate representations, a minibatch K-Means is adopted for clustering of the embeddings. Clustering refers to the task of grouping a set of objects in such a way that objects in the same group, which is called a cluster, are more similar to each other than to those in other groups. And [0130-0134] [0130] Within the clusters that were labeled to have the same sentiment feature, different semantic meanings across these clusters were observed… [0134] The labels of each cluster were generated from the vocabulary with the top entropy scores. A heuristic was applied to generate the cluster labels as shown in Algorithm 5, below.; see also [0143] If prompting can help with the Pre make more consistent predictions, the clustering results should exhibit high coherence within each cluster and high separation between different clusters. Second, whether a label generated fits the theme of the cluster and whether the data points in the cluster are appropriate require some human intervention to evaluate. However, due to the various number of experiment groups and the large size of the dataset, only the group with the best feedback from the first part is considered for human coder to evaluate. The human coder evaluates on two aspects, first, whether the data points in the same cluster fit well with each other. Second, whether the computed labels is a good representation of the main theme of the cluster.). It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply clustering, as taught by Jing, to the system of Krishnan/Deshpande/Rout, since it was known in the art that database systems provide clustering for the task of grouping a set of objects in such a way that objects in the same group, which is called a cluster, are more similar to each other than to those in other groups where two well-known types of clustering methods are "hard clustering" and "soft clustering" where a representative "hard clustering" algorithm is the K-means algorithm, which is performed by partitioning documents based on the nearest mean with each document belonging to only one cluster and while "soft clustering", such as fuzzy c-means algorithm that proposed on top of k-means, allows each document to belong to multiple clusters using a variable measures the degree of belongingness. (Jing [0105-0106]) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Bhide et al., US Pub. No. 2013/0073515 A1, teaches Executing a plurality of transform stages in an extract, transform and load (ETL) job including, for each of the transform stages, receiving a plurality of input row identifiers (RIDs) corresponding to a first plurality of source database table rows in a source database table. Data is retrieved directly from a subset of the source database table columns in the first plurality of source database table rows based on the input RIDs and transform logic. Partial row data including data from the subset of the source database table columns is generated for each of the first plurality of source database table rows. Transformed data is generated based on the partial row data and to the transform logic. Output RIDs corresponding to a second plurality of rows in the source database table that include a least a subset of the transformed data are output to a downstream stage; Pisipati et al., US Pub. No. 2020/0097451, teaches a system and method of recognizing data in a table area from unstructured data includes a computer network, one or more processors communicatively coupled with the computer network, a storage location, and a graph-theoretic engine that receives an input stream of unstructured data associated. A table area is recognized from unstructured data, through one or more computer processors, from an input stream of unstructured data received over a computer network. One or more table headers associated with the detected one or more table areas are recognized. Further, one or more column delimiters associated with each column of the detected one or more table areas are determined. One or more tabular data associated with the detected one or more table areas are extracted. The extracted tabular data is mapped to one or more target schema to store onto a relational database; McPherson et al., US Pub. No. 2021/0342316 A1, teaches a method and/or system of extracting a table having data in a plurality of rows from a Not Only Structured Query Language (NoSQL) database to a different type of database that includes: scanning all the rows in a desired table in the NoSQL database and producing a list of column families and associated column names; creating a schema for a new table having a table catalog of new column names using a Java Script Object Notation (JSON) structure to extract the columns names from the list of column families; reading and extracting at least a portion of the data from the desired table in the NoSQL database into the new table having the table catalog of new columns names; associating a creation timestamp with the new table; and saving the new table having the table catalog of new column names to the different database; Harrison et al., US Pub. No. 2018/0096000 A1, teaches a method and software tool for identifying relationships between columns of one or more data tables are disclosed. In the disclosed method, a relationship indicator is computed for each of a plurality of column pairs, each column pair comprising respective first and second columns selected from the one or more data tables. The relationship indicator comprises a measure of a relationship (e.g. indicating a strength or likelihood of a relationship) between data of the first column and data of the second column. Relationships between columns of the data tables are then identified in dependence on the computed relationship indicators. The identified relationships may be used to create and execute data queries; Dettman et al., US Pub. No. 2016/0117293, teaches techniques for transforming input documents having disparate formats into a normalized format (e.g., Atom, RSS, HTML, customized XML, etc.). According to one embodiment, a plurality of fields is identified in an input document that has a given format. Each field includes a descriptor and text content associated with the descriptor. For each field, semantic properties are evaluated for the descriptor and text content against a plurality of mapping rules to determine whether the field is consistent with one of a plurality of fields of a target format. Each mapping rule specifies characteristics associated with one of the fields in the target format. Once so determined, a mapping from the first field to the second field is defined; Cerino et al., US Pub. No. 2021/0089589 A1, teaches computer-implemented systems and methods for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, and images and voice. Embodiments of the systems and the methods further provide for generating responses to specific questions based on the standardized input data; Brown et al., US Pub. No. 2022/0292092, teaches a system for querying multiple data sources and a method therefor is provided. The computing system may comprise one or more nodes in communication with at least one data source of the multiple data sources to access data therefrom. The computing system may further comprise a second node in communication with the one or more nodes. The second node may be configured to receive a query instance and process the query instance to generate one or more relational query instances. The one or more relational query instances may be distributed among the one or more nodes to extract data from the at least one data source in communication therewith corresponding to the respective one or more relational query instances. The second node may be further configured to receive extracted data from each of the one or more nodes queried. The second node may be further configured to aggregate the extracted data. CONTACT INFORMATION Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVAN S ASPINWALL whose telephone number is (571)270-7723. The examiner can normally be reached Monday-Friday 8am-5pm. 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, Ajay Bhatia can be reached at 571-272-3906. 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. /Evan Aspinwall/Primary Examiner, Art Unit 2156 5/21/2026 Application/Control Number: 18/303,792 Page 2 Art Unit: 2156 Application/Control Number: 18/303,792 Page 3 Art Unit: 2156 Application/Control Number: 18/303,792 Page 4 Art Unit: 2156 Application/Control Number: 18/303,792 Page 5 Art Unit: 2156 Application/Control Number: 18/303,792 Page 6 Art Unit: 2156 Application/Control Number: 18/303,792 Page 7 Art Unit: 2156 Application/Control Number: 18/303,792 Page 8 Art Unit: 2156 Application/Control Number: 18/303,792 Page 9 Art Unit: 2156 Application/Control Number: 18/303,792 Page 10 Art Unit: 2156 Application/Control Number: 18/303,792 Page 11 Art Unit: 2156 Application/Control Number: 18/303,792 Page 12 Art Unit: 2156 Application/Control Number: 18/303,792 Page 13 Art Unit: 2156 Application/Control Number: 18/303,792 Page 14 Art Unit: 2156 Application/Control Number: 18/303,792 Page 15 Art Unit: 2156 Application/Control Number: 18/303,792 Page 16 Art Unit: 2156 Application/Control Number: 18/303,792 Page 17 Art Unit: 2156 Application/Control Number: 18/303,792 Page 18 Art Unit: 2156 Application/Control Number: 18/303,792 Page 19 Art Unit: 2156 Application/Control Number: 18/303,792 Page 20 Art Unit: 2156 Application/Control Number: 18/303,792 Page 21 Art Unit: 2156 Application/Control Number: 18/303,792 Page 22 Art Unit: 2156 Application/Control Number: 18/303,792 Page 23 Art Unit: 2156 Application/Control Number: 18/303,792 Page 24 Art Unit: 2156 Application/Control Number: 18/303,792 Page 25 Art Unit: 2156 Application/Control Number: 18/303,792 Page 26 Art Unit: 2156 Application/Control Number: 18/303,792 Page 27 Art Unit: 2156 Application/Control Number: 18/303,792 Page 28 Art Unit: 2156 Application/Control Number: 18/303,792 Page 29 Art Unit: 2156 Application/Control Number: 18/303,792 Page 30 Art Unit: 2156 Application/Control Number: 18/303,792 Page 31 Art Unit: 2156 Application/Control Number: 18/303,792 Page 32 Art Unit: 2156 Application/Control Number: 18/303,792 Page 33 Art Unit: 2156 Application/Control Number: 18/303,792 Page 34 Art Unit: 2156 Application/Control Number: 18/303,792 Page 35 Art Unit: 2156 Application/Control Number: 18/303,792 Page 36 Art Unit: 2156 Application/Control Number: 18/303,792 Page 37 Art Unit: 2156 Application/Control Number: 18/303,792 Page 38 Art Unit: 2156 Application/Control Number: 18/303,792 Page 39 Art Unit: 2156 Application/Control Number: 18/303,792 Page 40 Art Unit: 2156 Application/Control Number: 18/303,792 Page 41 Art Unit: 2156 Application/Control Number: 18/303,792 Page 42 Art Unit: 2156
Prosecution Insights