DETAILED ACTION
1. This is a Final Office Action Correspondence in response to arguments/amendments to U.S. Application No. 18/598333 filed on September 25, 2025.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 21-22 have been added.
Claims 1-22 are now pending.
Information Disclosure Statement
3. The Information Disclosure Statement filed on December 09, 2025 was reviewed and accepted by the Examiner.
Response to Arguments
4. Applicant’s arguments have been considered but are not persuasive.
On Pg. 11 of remarks in regards to 35 U.S.C. 101, relating to Step 2A, Prong One, claim “In this regard, Applicant respectfully submits that "generating a first prompt based on the input dataset, the data schema, and a first prompt structure having one or more text strings and one or more blanks", "generating a data pipeline based on the use case, the data pipeline including one or more data processing elements", and "applying the data pipeline including the one or more data processing elements to the input dataset to generate an output dataset including at least: executing a piece of software program that includes at least one of the one or more data processing elements" (emphasis added) as recited in claim 1 include limitations that cannot be practically performed in the human mind. For example, a human mind, with or without physical aid, is not equipped to generate a prompt based on an input dataset, data schema, and a prompt structure. As an example, a human mind, with or without physical aid, is not equipped to generate a data pipeline based on the use case, where the data pipeline includes one or more data processing elements. As another example, a human mind, with or without physical aid, is not equipped to apply the data pipeline (which includes the one or more data processing elements) to the input dataset to generate an output dataset by at least executing a piece of software program that includes at least one of the one or more data processing elements. Applying the rule in MPEP § 2106.04(a)(2)(III)(A), claim 1 does not fall into the grouping of mental processes.”
Examiner replies that the claimed invention does recite abstract ideas. “Processing elements” are very board language. The processing elements can be any data that is written on paper. Examiner believes the Applicant is trying to imply that certain data elements are used with a pipeline to manipulate or change the data. This is not recited in the claims.
Executing a piece of software that includes the one or more processing elements, is a type of additional element: which is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
On Pg. 14-15 of remarks in regards to 35 U.S.C. 103, relating to claim 1, Applicant argues the amended claims.
Examiner replies that a new reference is presented below to teach the amended limitations.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-22 are rejected under 35 USC 101 as directed to an abstract idea without significantly more.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 1, specifically claim 1 recites “generating a first prompt based on the input dataset the data schema and a first prompt structure having one or more text strings and one or more blanks” in the context of this generating encompasses the ability for user to write a text sentence prompt based upon data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, and the prompt can have text such as sentences and missing words within the sentences. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 1 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example "receiving an input dataset, the input dataset including a data schema” is similar to obtaining data which is seen as MPEP 2106.05(g) iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
For example “providing the first prompt to a language model”, is similar to providing information which is seen as MPEP 2106.05(g) v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754;
For example “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset” is similar to storing the results of an analysis which is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “generating a data pipeline based on the use case the data pipeline including one or more data processing elements” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example “and applying the data pipeline including the one or more data processing elements to the input dataset to generate an output dataset, including at least executing a piece of software program that includes at least one of the one or more data processing elements” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example “wherein the input dataset is configured to be used to generate a plurality of use cases including the use case the method is performed using one or more processors” is similar to a computer processing instructions which is seen as MPEP 2106.05(f)(1) ii. A general method of screening emails on a generic computer without any limitations that addressed the issues of shrinking the protection gap and mooting the volume problem, Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1319, 120 USPQ2d 1353, 1361 (Fed. Cir. 2016).
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "receiving an input dataset, the input dataset including a data schema”, “providing the first prompt to a language model”, “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset”, “generating a data pipeline based on the use case”, “and applying the data pipeline including the one or more data processing elements to the input dataset to generate an output dataset, including at least executing a piece of software program that includes at least one of the one or more data processing elements”, “wherein the method is performed using one or more processors”.
For example, “receiving a request to manipulate the unencoded data file from the disaster recovery process”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i))
For example, “providing the first prompt to a language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “generating a data pipeline based on the use case” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent) (MPEP 2106.05(f)(2)(ii)).
For example, “and applying the data pipeline to the input dataset to generate an output dataset” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent) (MPEP 2106.05(f)(2)(ii)).
For example, “wherein the method is performed using one or more processors” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent), (MPEP 2106.05(f)(3)(i)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 2, specifically claim 2 recites “and determining an evaluation metric using the evaluation function based on the use case and the output dataset” in the context of this generating encompasses the ability for user to associate a metric with the objective of the metric and data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, wherein the prompt can have text such as sentences and missing words within the sentences and the user can determine a valuation metric for any outcome. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 2 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example “providing the second prompt to the language model”, is similar to providing information which is seen as MPEP 2106.05(g) iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).
For example “receiving an evaluation function generated by the language model” is similar to storing the results of an analysis which is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset” and “providing the second prompt to the language model”, “receiving an evaluation function generated by the language model”.
.
For example, "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
For example, “providing the second prompt to the language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of data gathering since the information is being provided to a software module (MPEP 2106.05(g)(3)(ii)).
For example, “receiving an evaluation function generated by the language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 3, specifically claim 3 recites "determining second text data based on the use case” in the context of this claim encompasses the user mentally mapping text based upon the usage, “and filling in the one or more second blanks using the determined second text data” in the context of this claim encompasses the user using a pen and paper to write the missing text based the identified text. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, wherein the prompt can have text such as sentences and missing words within the sentences and the user can determine a valuation metric for any outcome. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 3 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 4, specifically claim 4 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 4 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric”, “providing the second prompt to the language model”, “receiving a second use case generated by the language model for the input dataset”, “and generating a second data pipeline based on the second use case” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “providing the second prompt to the language model” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “receiving a second use case generated by the language model for the input dataset” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric”, “providing the second prompt to the language model”, “receiving a second use case generated by the language model for the input dataset”, “and generating a second data pipeline based on the second use case”.
For example, “generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “providing the second prompt to the language model”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “receiving a second use case generated by the language model for the input dataset”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “and generating a second data pipeline based on the second use case”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 5, specifically claim 5 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 5 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “receiving a second use case generated by the language model for the input dataset, the second use case including a second description of how to use the input dataset is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example “generating a second data pipeline based on the second use case” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “and applying the second data pipeline to the input dataset to generate a second output dataset” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “receiving a second use case generated by the language model for the input dataset, the second use case including a second description of how to use the input dataset”, “generating a second data pipeline based on the second use case”, “and applying the second data pipeline to the input dataset to generate a second output dataset”.
For example, “receiving a second use case generated by the language model for the input dataset, the second use case including a second description of how to use the input dataset”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
For example, “generating a second data pipeline based on the second use case”, is seen as additional elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome include: (e.g., at a high level of generality). MPEP 2106.05(f); (1), (ii).
For example, “and applying the second data pipeline to the input dataset to generate a second output dataset”, is seen as additional elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome include: (e.g., at a high level of generality). MPEP 2106.05(f); (1), (ii).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 6, specifically claim 6 recites “determining a second evaluation metric using the second evaluation function based on the second use case and the second output dataset” in the context of this generating encompasses the ability for user to associate a metric with the objective of the metric and data, “and selecting a use case from the first use case and the second use case based on the first evaluation metric and the second evaluation metric” in the context of this generating encompasses the ability for user to mentally selecting second data from a first data set based upon a metric. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, wherein the prompt can have text such as sentences and missing words within the sentences and the user can determine a valuation metric for any outcome. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 6 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example, “generating a third prompt based on the second prompt structure, the second use case, and the second output dataset” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “providing the second prompt to the language model” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(g)(3) (ii). Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “receiving a second evaluation function generated by the language model” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “generating a third prompt based on the second prompt structure, the second use case, and the second output dataset”, “providing the second prompt to the language model”, “receiving a second evaluation function generated by the language model”.
For example, “generating a third prompt based on the second prompt structure, the second use case, and the second output dataset”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(f)(2)(ii)).
For example, “providing the second prompt to the language model”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of providing second data to a software module (MPEP 2106.05(g)(3)(ii)).
For example, “receiving a second evaluation function generated by the language model”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(ii)(i)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 7, specifically claim 7 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 7 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “generating the data pipeline using a data pipeline builder including a second language model different from the first language model” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) (2) (ii). Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “generating the data pipeline using a data pipeline builder including a second language model different from the first language model”.
For example, “generating the data pipeline using a data pipeline builder including a second language model different from the first language model”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 8, specifically claim 8 recites "determining second text data based on the use case” in the context of this claim encompasses the user mentally mapping text based upon the usage; “and filling in the one or more second blanks using the determined second text data” in the context of this claim encompasses the user using a pen and paper to write the missing text based the identified text; “wherein the data schema includes one or more data field names and one or more data types” in the context of this claim encompasses the user using a pen and paper to write the names of data, the type, such as numeric or text, and the schema is the organization of the written information; “wherein the determining text data based on the input dataset comprises extracting at least one of the one or more data field names and a corresponding data type from the input dataset” in the context of this claim encompasses the user using a pen and paper to write the names of data, the type, such as numeric or text, and the schema is the organization of the written information. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, wherein the prompt can have text such as sentences and missing words within the sentences and the user can determine a valuation metric for any outcome. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 8 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 9, specifically claim 9 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 9 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “wherein the data pipeline generated based on the use case uses a subset of the input dataset” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f)(2) (ii). Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the data pipeline generated based on the use case uses a subset of the input dataset”.
For example, “wherein the data pipeline generated based on the use case uses a subset of the input dataset”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 10, specifically claim 10 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 10 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “wherein the receiving an input dataset comprises receiving a selection of the input dataset from one or more input datasets” is seen as MPEP 2106.05(g)(3) iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the receiving an input dataset comprises receiving a selection of the input dataset from one or more input datasets”.
For example, “wherein the receiving an input dataset comprises receiving a selection of the input dataset from one or more input datasets”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 11, specifically claim 11 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 11 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “receiving at least one input selected from a group consisting of: one or more queries, one or more input datasets, and one or more target datasets”, is seen as MPEP 2106.05(g)(3) iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
For example, “generating a model query based on the at least one input”, is seen as generating second data based upon a first set of data such as MPEP 2106.05(f)(2) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “generating a query execution plan based at least in part on the model query” is seen as generating second data based upon a first set of data such as MPEP 2106.05(f)(2) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “wherein the generating a data pipeline based on the use case comprises: generating the data pipeline based at least in part on the query execution plan” is seen as generating second data based upon a first set of data such as MPEP 2106.05(f)(2) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “receiving at least one input selected from a group consisting of: one or more queries, one or more input datasets, and one or more target datasets”, “generating a model query based on the at least one input”, “generating a query execution plan based at least in part on the model query”, “wherein the generating a data pipeline based on the use case comprises: generating the data pipeline based at least in part on the query execution plan”.
For example, “wherein the receiving an input dataset comprises receiving a selection of the input dataset from one or more input datasets”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
For example, “generating a model query based on the at least one input”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “generating a query execution plan based at least in part on the model query”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “wherein the generating a data pipeline based on the use case comprises: generating the data pipeline based at least in part on the query execution plan”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 12, specifically claim 12 recites “determining whether the confidence score is higher than a predetermined threshold” in the context of this generating encompasses the ability for user to write a text sentence prompt based upon data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, wherein the prompt can have text such as sentences and missing words within the sentences and the user can determine a valuation metric for any outcome. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 12 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example, "generating, using one or more computational models, a model result based on the model query” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “generating a confidence score associated with the model result” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “in response to determining that the confidence score is higher than the predetermined threshold, generating the query execution plan” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “in response to determining that the confidence score is lower than the predetermined threshold” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “generating one or more second queries” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “and wherein the generating a model query includes generating the model query based on the one or more second queries” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "generating, using one or more computational models, a model result based on the model query”, “generating a confidence score associated with the model result”, “in response to determining that the confidence score is higher than the predetermined threshold, generating the query execution plan”, “in response to determining that the confidence score is lower than the predetermined threshold”, “generating one or more second queries”, “and wherein the generating a model query includes generating the model query based on the one or more second queries”.
.
For example, "generating, using one or more computational models, a model result based on the model query”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
For example, “generating a confidence score associated with the model result”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
For example, “in response to determining that the confidence score is higher than the predetermined threshold, generating the query execution plan”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
For example, “in response to determining that the confidence score is lower than the predetermined threshold”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
For example, “generating one or more second queries”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
For example, “and wherein the generating a model query includes generating the model query based on the one or more second queries”.do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 13, specifically claim 13 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 13 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “wherein the language model includes a large language model” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the language model includes a large language model” is seen as additional elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome include: (e.g., at a high level of generality). MPEP 2106.05(f); (1), (ii).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one independent claim, 1, specifically claim 1 recites “generating a first prompt based on the input dataset the data schema and a first prompt structure having one or more text strings and one or more blanks” in the context of this generating encompasses the ability for user to write a text sentence prompt based upon data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, and the prompt can have text such as sentences and missing words within the sentences. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 1 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example "receiving an input dataset, the input dataset including a data schema” is similar to obtaining data which is seen as MPEP 2106.05(g) iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
For example “providing the first prompt to a language model”, is similar to providing information which is seen as MPEP 2106.05(g) v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754;
For example “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset” is similar to storing the results of an analysis which is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “generating a data pipeline based on the use case the data pipeline including one or more data processing elements” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example “and applying the data pipeline including the one or more data processing elements to the input dataset to generate an output dataset, including at least executing a piece of software program that includes at least one of the onr or more data processing elements is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example “wherein the input dataset is configured to be used to generate a plurality of use cases including the use case the method is performed using one or more processors” is similar to a computer processing instructions which is seen as MPEP 2106.05(f)(1) ii. A general method of screening emails on a generic computer without any limitations that addressed the issues of shrinking the protection gap and mooting the volume problem, Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1319, 120 USPQ2d 1353, 1361 (Fed. Cir. 2016).
For example “wherein the method is performed using one or more processors” is similar to a computer processing instructions which is seen as MPEP 2106.05(f)(1) ii. A general method of screening emails on a generic computer without any limitations that addressed the issues of shrinking the protection gap and mooting the volume problem, Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1319, 120 USPQ2d 1353, 1361 (Fed. Cir. 2016).
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "receiving an input dataset, the input dataset including a data schema”, “providing the first prompt to a language model”, “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset”, “generating a data pipeline based on the use case”, “and applying the data pipeline to the input dataset to generate an output dataset”, “wherein the method is performed using one or more processors”.
For example, “receiving a request to manipulate the unencoded data file from the disaster recovery process”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i))
For example, “providing the first prompt to a language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “generating a data pipeline based on the use case” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent) (MPEP 2106.05(f)(2)(ii)).
For example, “and applying the data pipeline to the input dataset to generate an output dataset” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent) (MPEP 2106.05(f)(2)(ii)).
For example, “wherein the method is performed using one or more processors” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent), (MPEP 2106.05(f)(3)(i)).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one dependent claim, 15, specifically claim 15 recites “and determining an evaluation metric using the evaluation function based on the use case and the output dataset” in the context of this generating encompasses the ability for user to write a text sentence prompt based upon data. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, wherein the prompt can have text such as sentences and missing words within the sentences and the user can determine a valuation metric for any outcome. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 15 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example, "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example “providing the second prompt to the language model”, is similar to providing information which is seen as MPEP 2106.05(g) iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).
For example “receiving an evaluation function generated by the language model” is similar to storing the results of an analysis which is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset” and “providing the second prompt to the language model”, “receiving an evaluation function generated by the language model”.
.
For example, "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
For example, “providing the second prompt to the language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of data gathering since the information is being provided to a software module (MPEP 2106.05(g)(3)(ii)).
For example, “receiving an evaluation function generated by the language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one dependent claim, 16, specifically claim 16 recites "determining second text data based on the use case” in the context of this claim encompasses the user mentally mapping text based upon the usage, “and filling in the one or more second blanks using the determined second text data” in the context of this claim encompasses the user using a pen and paper to write the missing text based the identified text. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, wherein the prompt can have text such as sentences and missing words within the sentences and the user can determine a valuation metric for any outcome. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 16 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one dependent claim, 17, specifically claim 17 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 17 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric”, “providing the second prompt to the language model”, “receiving a second use case generated by the language model for the input dataset”, “and generating a second data pipeline based on the second use case” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “providing the second prompt to the language model” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “receiving a second use case generated by the language model for the input dataset” is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric”, “providing the second prompt to the language model”, “receiving a second use case generated by the language model for the input dataset”, “and generating a second data pipeline based on the second use case”.
For example, “generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “providing the second prompt to the language model”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “receiving a second use case generated by the language model for the input dataset”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “and generating a second data pipeline based on the second use case”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one dependent claim, 18, specifically claim 18 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 18 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “receiving a second use case generated by the language model for the input dataset, the second use case including a second description of how to use the input dataset is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example “generating a second data pipeline based on the second use case” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “and applying the second data pipeline to the input dataset to generate a second output dataset” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “receiving a second use case generated by the language model for the input dataset, the second use case including a second description of how to use the input dataset”, “generating a second data pipeline based on the second use case”, “and applying the second data pipeline to the input dataset to generate a second output dataset”.
For example, “receiving a second use case generated by the language model for the input dataset, the second use case including a second description of how to use the input dataset”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (i).
For example, “generating a second data pipeline based on the second use case”, is seen as additional elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome include: (e.g., at a high level of generality). MPEP 2106.05(f); (1), (ii).
For example, “and applying the second data pipeline to the input dataset to generate a second output dataset”, is seen as additional elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome include: (e.g., at a high level of generality). MPEP 2106.05(f); (1), (ii).
With respect to Step 1, the claims are directed to a system.
With respect to Step 2A Prong one independent claim, 19, specifically claim 19 recites “generating a first prompt based on the input dataset the data schema and a first prompt structure having one or more text strings and one or more blanks” in the context of this generating encompasses the ability for user to write a text sentence prompt based upon data, “and determining an evaluation metric using the evaluation function based on the use case and the output dataset” in the context of this generating encompasses the ability for user to write a text sentence prompt based upon data, “wherein the data schema includes one or more data field names and one or more data types” in the context of this claim encompasses the user using a pen and paper to write the names of data, the type, such as numeric or text, and the schema is the organization of the written information, “wherein the determining text data based on the input dataset comprises extracting at least one of the one or more data field names and a corresponding data type from the input dataset” in the context of this claim encompasses the user using a pen and paper to write the names of data, the type, such as numeric or text, and the schema is the organization of the written information. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can write a prompt based upon input, and the prompt can have text such as sentences and missing words within the sentences. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 19 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example "receiving an input dataset, the input dataset including a data schema” is similar to obtaining data which is seen as MPEP 2106.05(g) iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
For example “providing the first prompt to a language model”, is similar to providing information which is seen as MPEP 2106.05(g) v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754;
For example “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset” is similar to storing the results of an analysis which is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “generating a data pipeline based on the use case the data pipeline including one or more data processing elements” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example “and applying the data pipeline including the one or more data processing elements to the input dataset to generate an output dataset, including at least executing a piece of software program that includes at least one of the one or more data processing elements”, is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example, “providing the second prompt to the language model”, is similar to providing information which is seen as MPEP 2106.05(g) iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).
For example, “receiving an evaluation function generated by the language model” is similar to storing the results of an analysis which is seen as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example, “wherein the generating a first prompt based on the input dataset and a first prompt structure comprises: determining text data based on the input dataset; “and filling in the one or more blanks using the determined text data” is similar to applying the concept of generating second data based upon first data which is seen as MPEP 2106.05(f) ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
For example “wherein the input dataset is configured to be used to generate a plurality of use cases including the use case the method is performed using one or more processors” is similar to a computer processing instructions which is seen as MPEP 2106.05(f)(1) ii. A general method of screening emails on a generic computer without any limitations that addressed the issues of shrinking the protection gap and mooting the volume problem, Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1319, 120 USPQ2d 1353, 1361 (Fed. Cir. 2016).
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "receiving an input dataset, the input dataset including a data schema”, “providing the first prompt to a language model”, “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset”, “generating a data pipeline based on the use case”, “and applying the data pipeline to the input dataset to generate an output dataset”, "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset”, “providing the second prompt to the language model”, “receiving an evaluation function generated by the language model”, "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset” and “providing the second prompt to the language model”, “receiving an evaluation function generated by the language model”, wherein the generating a first prompt based on the input dataset and a first prompt structure comprises: determining text data based on the input dataset; “and filling in the one or more blanks using the determined text data”.
For example, “receiving a request to manipulate the unencoded data file from the disaster recovery process”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i))
For example, “providing the first prompt to a language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “generating a data pipeline based on the use case” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent) (MPEP 2106.05(f)(2)(ii)).
For example, “and applying the data pipeline to the input dataset to generate an output dataset” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent) (MPEP 2106.05(f)(2)(ii)).
For example, “wherein the method is performed using one or more processors” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent), (MPEP 2106.05(f)(3)(i)).
For example, "generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of generating second data based upon first data (MPEP 2106.05(d)(II)(ii)).
For example, “providing the second prompt to the language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of data gathering since the information is being provided to a software module (MPEP 2106.05(g)(3)(ii)).
For example, “receiving an evaluation function generated by the language model” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, wherein the generating a first prompt based on the input dataset and a first prompt structure comprises: determining text data based on the input dataset; “and filling in the one or more blanks using the determined text data” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, wherein the method is performed using one or more processors” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iii)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 20, specifically claim 20 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 20 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “wherein the language model is a first language model, wherein the generating a data pipeline based on the use case comprises generating the data pipeline using a data pipeline builder including a second language model different from the first language model” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) (2) (ii). Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the language model is a first language model, wherein the generating a data pipeline based on the use case comprises generating the data pipeline using a data pipeline builder including a second language model different from the first language model”
For example, “wherein the language model is a first language model, wherein the generating a data pipeline based on the use case comprises generating the data pipeline using a data pipeline builder including a second language model different from the first language model”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 21, specifically claim 21 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 21 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “storing the data pipeline including the or more data processing elements in one or more data storage repositories” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) (2) (ii). Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “storing the data pipeline including the or more data processing elements in one or more data storage repositories”
For example, “storing the data pipeline including the or more data processing elements in one or more data storage repositories”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 22, specifically claim 22 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 22 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example “storing the data pipeline including the or more data processing elements in one or more data storage repositories” is seen as generating second data based upon first data which is similar to MPEP 2106.05(f) (2) (ii). Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “storing the data pipeline including the or more data processing elements in one or more data storage repositories”
For example, “storing the data pipeline including the or more data processing elements in one or more data storage repositories”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. Claim(s) 1-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. U.S. Patent Application Publication No. 2023/0078177 (herein as ‘Wang’) and further in view of Beller et al. U.S. 2021/0149936 (herein as ‘Beller’) and Panda et al. U. Patent Application Publication No. 2025/0014374 (herein as ‘Panda’).
As to claim 1 Wang teaches a method for data pipeline evaluations, the method comprising:
receiving an input dataset, the input dataset including a data schema (Fig. 3 and Par. 0035 Wang discloses obtain schema information associated with an item);
generating a first prompt based on the input dataset and a first prompt structure having one or more text strings and one or more blanks (Table 1, Fig. 3 and Par. 0036 Wang discloses generating the intermediate representation for execution (308) from the data sets (308). The intermediate representation is seen as the first prompt. Table 1 contains the metadata that identifies empty data. The data sets are seen as the text strings);
Wang does not teach but Beller teaches providing the first prompt to a language model (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model The first prompt is seen as the final answer and confidence);
receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset (Fig. 3 (120), (108) and Par. 0044, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. The natural language question data that includes intent is seen as the use case including a description on how to the use the data);
generating a data pipeline based on the use case, the data pipeline including one or more data processing elements ((Fig. 3 (120), (108) and Par. 0044-0045, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. The improved question generation system places the question into the QA system pipeline which is seen as creating a pipeline based on the use case, since the system will identify words based upon the input);
and applying the data pipeline to the input dataset to generate an output dataset (Fig. 3, Par. 0064 and Par. 0065 Beller discloses using the data pipeline to produce the final answer and confidence);
and applying the data pipeline including the one or more data processing elements to the input dataset to generate an output dataset including at least: executing a piece of software program that includes at least one of the one or more data processing elements (Fig. 3 (120), (108) and Par. 0044-0045, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. Receiving the natural language process is sa type of software program that includes a processing element);
Wang and Beller are analogous art because they are in the same field of endeavor, natura language processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the natural language of Wang to include the interactive generative output fed into the input of Beller, to allow for improving searching results (Par. 0004-0005 Beller).
Wang does not teach but Panda teaches wherein the input dataset is configured to be used to generate a plurality of use cases including the use case (Par. 0065 Panda discloses many different uses cases for the ML models to classify the data content).
Wang and Panda are analogous art because they are in the same field of endeavor, natura language processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the Panda language of Wang to include the different language models the input of Panda, to allow for different document types (Par. 0004-0005 Panda).
As to claim 2 Wang in combination with Beller and Panda teaches each and every limitation of claim 1.
In addition Beller teaches generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset; providing the second prompt to the language model (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model The first prompt is seen as the final answer and confidence);
receiving an evaluation function generated by the language model; and determining an evaluation metric using the evaluation function based on the use case and the output dataset (Fig.3, and Par. 0072 Beller discloses determining a final confidence score with the rankings of the output data set).
As to claim 3 Wang in combination with Beller and Panda teaches each and every limitation of claim 2.
In addition Beller teaches wherein the second prompt structure includes one or more second text strings and one or more second blanks, wherein the generating a second prompt based on a second prompt structure comprises:
determining second text data based on the use case; and filling in the one or more second blanks using the determined second text data (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model The first prompt is seen as the final answer and confidence).
As to claim 4 Wang in combination with Beller and Panda teaches each and every limitation of claim 1.
In addition Beller teaches further comprising: generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric; providing the second prompt to the language model; receiving a second use case generated by the language model for the input dataset; and generating a second data pipeline based on the second use case (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation based upon intent, which is seen as the language model. The intent is seen as the use case. The second prompt is seen as the final answer and confidence).
As to claim 5 Wang in combination with Beller and Panda teaches each and every limitation of claim 2.
In addition Beller teaches wherein the use case is a first use case, the output dataset is a first output dataset, and the data pipeline is a first data pipeline, wherein the method further comprises:
receiving a second use case generated by the language model for the input dataset, the second use case including a second description of how to use the input dataset and generating a second data pipeline based on the second use case; and applying the second data pipeline to the input dataset to generate a second output dataset (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation based upon intent, which is seen as the language model. The intent is seen as the use case. The second prompt is seen as the final answer and confidence. The confidence is seen as the evaluation metric).
As to claim 6 Wang in combination with Beller and Panda teaches each and every limitation of claim 5.
In addition Beller teaches wherein the evaluation function is a first evaluation function and the evaluation metric is a first evaluation metric, wherein the method further comprises: generating a third prompt based on the second prompt structure, the second use case, and the second output dataset; providing the second prompt to the language model; receiving a second evaluation function generated by the language model; determining a second evaluation metric using the second evaluation function based on the second use case and the second output dataset; and selecting a use case from the first use case and the second use case based on the first evaluation metric and the second evaluation metric (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model The third prompt is seen as the final answer and confidence. The confidence is seen as the second evaluation metric).
As to claim 7 Wang in combination with Beller and Panda teaches each and every limitation of claim 5.
In addition Beller teaches wherein the language model is a first language model, wherein the generating a data pipeline based on the use case comprises generating the data pipeline using a data pipeline builder including a second language model different from the first language model (Par. 0034 Wang discloses that multiple different machine learning models can be used in the pipeline stages).
As to claim 8 Wang in combination with Beller and Panda teaches each and every limitation of claim 1.
In addition Beller teaches wherein the generating a first prompt based on the input dataset and a first prompt structure comprises: determining text data based on the input dataset; and filling in the one or more blanks using the determined text data; wherein the data schema includes one or more data field names and one or more data types; wherein the determining text data based on the input dataset comprises extracting at least one of the one or more data field names and a corresponding data type from the input dataset (Fig. 3 and Par. 0055 Beller discloses extracting features from a input question. Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model. The first prompt is seen as the final answer and confidence. The confidence is seen as the second evaluation metric. The system creating the new improved questions is seen as filling in the blanks of the determined text).
As to claim 9 Wang in combination with Beller and Panda teaches each and every limitation of claim 1.
In addition Beller teaches wherein the data pipeline generated based on the use case uses a subset of the input dataset (Fig.3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, in a pipeline).
As to claim 10 Wang in combination with Beller and Panda teaches each and every limitation of claim 1.
In addition Beller teaches wherein the receiving an input dataset comprises receiving a selection of the input dataset from one or more input datasets (Fig.3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, in a pipeline).
As to claim 11 Wang in combination with Beller and Panda teaches each and every limitation of claim 1.
In addition Beller teaches further comprising: receiving at least one input selected from a group consisting of: one or more queries, one or more input datasets, and one or more target datasets; generating a model query based on the at least one input; generating a query execution plan based at least in part on the model query; wherein the generating a data pipeline based on the use case comprises: generating the data pipeline based at least in part on the query execution plan (Fig. 3 and Par. 0055 Beller discloses extracting features from a input question. Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model. The hypothesis generation within the QA pipeline is seen as the query execution plan. The first prompt is seen as the final answer and confidence. The confidence is seen as the second evaluation metric. The system creating the new improved questions is seen as filling in the blanks of the determined text).
As to claim 12 Wang in combination with Beller and Panda teaches each and every limitation of claim 11.
In addition Beller teaches further comprising: generating, using one or more computational models, a model result based on the model query; generating a confidence score associated with the model result; determining whether the confidence score is higher than a predetermined threshold; in response to determining that the confidence score is higher than the predetermined threshold, generating the query execution plan; in response to determining that the confidence score is lower than the predetermined threshold, generating one or more second queries; and wherein the generating a model query includes generating the model query based on the one or more second queries (Fig. 3 and Par. 0055 Beller discloses extracting features from a input question. Par. 0078 Beller discloses using a threshold to determine which candidate question modifiers are best to improve the question. Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model. The hypothesis generation within the QA pipeline is seen as the query execution plan. The first prompt is seen as the final answer and confidence. The confidence is seen as the second evaluation metric. The system creating the new improved questions is seen as filling in the blanks of the determined text).
As to claim 13 Wang in combination with Beller and Panda teaches each and every limitation of claim 1.
In addition Beller teaches wherein the language model includes a large language model (Par. 0049 Beller discloses machine learning models are used to train data sets).
As to claim 14 Wang teaches a system for data pipeline evaluations, the system comprising:
one or more memories having instructions stored therein (Par. 0075 Wang discloses a memory);
and one or more processors configured to execute the instructions and perform operations comprising (Par. 0073 Wang discloses a processor);
receiving an input dataset, the input dataset including a data schema (Fig. 3 and Par. 0035 Wang discloses obtain schema information associated with an item);
generating a first prompt based on the input dataset and a first prompt structure having one or more text strings and one or more blanks (Table 1, Fig. 3 and Par. 0036 Wang discloses generating the intermediate representation for execution (308) from the data sets (308). The intermediate representation is seen as the first prompt. Table 1 contains the metadata that identifies empty data. The data sets are seen as the text strings);
Wang does not teach but Beller teaches providing the first prompt to a language model (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model The first prompt is seen as the final answer and confidence);
receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset (Fig. 3 (120), (108) and Par. 0044, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. The natural language question data that includes intent is seen as the use case including a description on how to the use the data);
generating a data pipeline based on the use case, the data pipeline including one or more data processing elements (Fig. 3 (120), (108) and Par. 0044-0045, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. The improved question generation system places the question into the QA system pipeline which is seen as creating a pipeline based on the use case, since the system will identify words based upon the input);
and applying the data pipeline to the input dataset to generate an output dataset (Fig. 3, Par. 0064 and Par. 0065 Beller discloses using the data pipeline to produce the final answer and confidence).
and applying the data pipeline including the one or more data processing elements to the input dataset to generate an output dataset including at least: executing a piece of software program that includes at least one of the one or more data processing elements (Fig. 3 (120), (108) and Par. 0044-0045, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. Receiving the natural language process is sa type of software program that includes a processing element);
Wang and Beller are analogous art because they are in the same field of endeavor, natura language processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the natural language of Wang to include the interactive generative output fed into the input of Beller, to allow for improving searching results (Par. 0004-0005 Beller).
Wang does not teach but Panda teaches wherein the input dataset is configured to be used to generate a plurality of use cases including the use case (Par. 0065 Panda discloses many different uses cases for the ML models to classify the data content).
Wang and Panda are analogous art because they are in the same field of endeavor, natura language processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the Panda language of Wang to include the different language models the input of Panda, to allow for different document types (Par. 0004-0005 Panda).
As to claim 15 Wang in combination with Beller and Panda teaches each and every limitation of claim 14.
In addition Beller teaches wherein the operations further comprise:
generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset; providing the second prompt to the language model (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model The first prompt is seen as the final answer and confidence);
receiving an evaluation function generated by the language model; and determining an evaluation metric using the evaluation function based on the use case and the output dataset (Fig.3, and Par. 0072 Beller discloses determining a final confidence score with the rankings of the output data set).
As to claim 16 Wang in combination with Beller and Panda teaches each and every limitation of claim 15.
In addition Beller teaches wherein the second prompt structure includes one or more second text strings and one or more second blanks; wherein, in the operations, the generating a second prompt based on a second prompt structure comprises: determining second text data based on the use case; and filling in the one or more second blanks using the determined second text data (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation based upon intent, which is seen as the language model. The intent is seen as the use case. The second prompt is seen as the final answer and confidence).
As to claim 17 Wang in combination with Beller and Panda teaches each and every limitation of claim 14.
In addition Beller teaches wherein the operations further comprise: generating a second prompt based on the input dataset, the first prompt structure, the use case, and the evaluation metric; providing the second prompt to the language model; receiving a second use case generated by the language model for the input dataset; and generating a second data pipeline based on the second use case (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model The first prompt is seen as the final answer and confidence. The confidence is seen as the evaluation metric).
As to claim 18 Wang in combination with Beller and Panda teaches each and every limitation of claim 17.
In addition Wang teaches wherein the use case is a first use case, the output dataset is a first output dataset, and the data pipeline is a first data pipeline; wherein the operations further comprise: receiving a second use case generated by the language model for the input dataset, the second use case including a second description of how to use the input dataset; generating a second data pipeline based on the second use case; and applying the second data pipeline to the input dataset to generate a second output dataset (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the intent, which is seen as the language model. The second prompt is seen as the final answer and confidence. The confidence is seen as the evaluation metric).
As to claim 18 Wang teaches a method for data pipeline evaluations, the method comprising:
receiving an input dataset, the input dataset including a data schema (Fig. 3 and Par. 0035 Wang discloses obtain schema information associated with an item);
generating a first prompt based on the input dataset and a first prompt structure having one or more text strings and one or more blanks (Table 1, Fig. 3 and Par. 0036 Wang discloses generating the intermediate representation for execution (308) from the data sets (308). The intermediate representation is seen as the first prompt. Table 1 contains the metadata that identifies empty data. The data sets are seen as the text strings);
wherein the method is performed using one or more processors (Par. 0073 Wang discloses processors);
Wang does not teach but Beller teaches providing the first prompt to a language model (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model. The first prompt is seen as the final answer and confidence);
receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset (Fig. 3 (120), (108) and Par. 0044, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. The natural language question data that includes intent is seen as the use case including a description on how to the use the data);
generating a data pipeline based on the use case, the data pipeline including one or more data processing elements (Fig. 3 (120), (108) and Par. 0044-0045, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. The improved question generation system places the question into the QA system pipeline which is seen as creating a pipeline based on the use case, since the system will identify words based upon the input);
applying the data pipeline to the input dataset to generate an output dataset (Fig. 3, Par. 0064 and Par. 0065 Beller discloses using the data pipeline to produce the final answer and confidence which is seen as the output dataset);
and applying the data pipeline including the one or more data processing elements to the input dataset to generate an output dataset including at least: executing a piece of software program that includes at least one of the one or more data processing elements (Fig. 3 (120), (108) and Par. 0044-0045, Par. 0056 Beller discloses the improved question generation system, receiving a natural language question data which includes intent. Receiving the natural language process is sa type of software program that includes a processing element);
Wang and Beller are analogous art because they are in the same field of endeavor, natura language processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the natural language of Wang to include the interactive generative output fed into the input of Beller, to allow for improving searching results (Par. 0004-0005 Beller).
generating a second prompt based on a second prompt structure, the second prompt structure associated with the use case and the output dataset, providing the second prompt to the language model (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model. The first prompt is seen as the final answer and confidence);
receiving an evaluation function generated by the language model; and determining an evaluation metric using the evaluation function based on the use case and the output dataset (Fig.3, and Par. 0072 Beller discloses determining a final confidence score with the rankings of the output data set);
wherein the generating a first prompt based on the input dataset and a first prompt structure comprises: determining text data based on the input dataset;
and filling in the one or more blanks using the determined text data (Fig. 3 and Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model. The first prompt is seen as the final answer and confidence);
wherein the data schema includes one or more data field names and one or more data types; wherein the determining text data based on the input dataset comprises extracting at least one of the one or more data field names and a corresponding data type from the input dataset (Fig. 3 and Par. 0055 Beller discloses extracting features from a input question. Par. 0064 Beller discloses providing the final answer and confidence to the cognitive system, which provides the final answer and confidence to the improved query generations system, which provides the final answer and confidence to the QA pipeline which has a hypothesis generation, which is seen as the language model. The first prompt is seen as the final answer and confidence. The confidence is seen as the second evaluation metric. The system creating the new improved questions is seen as filling in the blanks of the determined text).
Wang does not teach but Panda teaches wherein the input dataset is configured to be used to generate a plurality of use cases including the use case (Par. 0065 Panda discloses many different uses cases for the ML models to classify the data content).
Wang and Panda are analogous art because they are in the same field of endeavor, natura language processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the Panda language of Wang to include the different language models the input of Panda, to allow for different document types (Par. 0004-0005 Panda).
As to claim 20 Wang in combination with Beller and Panda teaches each and every limitation of claim 19.
In addition Beller teaches wherein the language model is a first language model, wherein the generating a data pipeline based on the use case comprises generating the data pipeline using a data pipeline builder including a second language model different from the first language model (Par. 0034 Wang discloses that multiple different machine learning models can be used in the pipeline stages).
As to claim 21 Wang in combination with Beller and Panda teaches each and every limitation of claim 1.
In addition Wang teaches wherein the language model is a first language model, wherein the generating a data pipeline based on the use case comprises generating the data pipeline using a data pipeline builder including a second language model different from the first language model (Par. 0033-0034 Wang discloses that multiple different machine learning models can be used in the pipeline stages from different data sets).
As to claim 22 Wang in combination with Beller and Panda teaches each and every limitation of claim 14.
In addition Wang teaches wherein the language model is a first language model, wherein the generating a data pipeline based on the use case comprises generating the data pipeline using a data pipeline builder including a second language model different from the first language model (Par. 0033-0034 Wang discloses that multiple different machine learning models can be used in the pipeline stages from different data sets)
Conclusion
10. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.A.M/ December 27, 2025Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159