DETAILED ACTION
Claims 1-20 are pending in the current application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Claims 1-20 are claims that are directed to a process, machine, manufacture or composition of matter.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1, 12 and 20: The limitation of “selecting, based on the description of the desired computer workflow, a set of prospective components from a plurality of components” “generating a prompt using the description of the desired computer workflow and the set of prospective components” and “generating…at least a portion of code associated with the desired computer workflow” as drafted, are functions thus under its broadest reasonable interpretation recite the abstract idea of a mental process. The limitations encompasses a human mind carrying out the function of determining from mental analysis of description of computer workflow a set of prospective components from a plurality and based on that determination with the aid of pen and paper creating a prompt from analysis of that information and from that information generating based on analysis a portion of code for the desired computer workflow through observation, evaluation, judgment and/or opinion or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Process” grouping of abstract ideas under Prong 1.
The claims have been identified to recite an abstract idea, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
generating, based on providing the prompt to a large-language-model, at least a portion of code associated with the desired computer workflow
Step 2A Prong 2:
Claims 1, 12 and 20: The abstract idea is not integrated into a practical application. In particular the claims recite the following additional element “A system comprising: one or more processors; and a memory coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors to:” “A computer program product, the computer program product being embodied in a non- transitory computer readable storage medium and comprising computer instructions for:” and “generating, based on providing the prompt to a large-language model…” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components. Additionally, the claims recite the additional element of “obtaining a description of a desired computer workflow” which is merely insignificant extra-solution activity information of receiving/transmitting data which does not integrate the judicial exception a practical application. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
After having evaluating the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims, 1, 12 and 20 not only recite an abstract idea but that the claims are directed to the abstract idea as the abstract idea has not been integrated into practical application.
Step 2B:
Claims 1, 12 and 20: The claims do not include additional elements, alone or in combination that are sufficient to amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “A system comprising: one or more processors; and a memory coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors to:” “A computer program product, the computer program product being embodied in a non- transitory computer readable storage medium and comprising computer instructions for:” and “generating, based on providing the prompt to a large-language model…” amount to no more than mere instructions, or generic computer/computer components to carry out the exception. Additionally, the additional element of “obtaining a description of a desired computer workflow” amount to no more that insignificant extra solution activity akin to receiving/transmitting data and presenting offers. Further, the insignificant extra solution data activity is also WURC, see MPEP 2106.05(d)(II), where “the courts have recognized the following computer functions as well-understood, routine and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity” i. receiving or transmitting data where the obtaining a description limitation is akin to receiving/obtaining data. The recitation of generic computer instruction and computer components to apply the judicial exception and merely receiving data, do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101.
Having concluded analysis within the provided framework, claims 1, 12 and 20 do not recite patent eligible subject matter under 35 USC 101.
As to claims 2 and 13 the limitation of “using a result of the large-language-model to automatically code the desired computer workflow” is an additional mental process element under prong 1. Moreover, claims 2 and 13 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 2 and 13 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 2 and 13 do not recite patent eligible subject matter under 35 USC 101.
As to claims 3 and 14 the limitation of “wherein the generated code associated with the desired computer workflow is a structured representation defining the desired computer workflow” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claim 3 and 14 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 3 and 14 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 3 and 14 does not recite patent eligible subject matter under 35 USC 101.
As to claims 4 and 15 the limitation of “wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes determining that each prospective component of the set of prospective components satisfies a relevance condition with respect to the desired computer workflow” is an additional mental process element under prong 1. Moreover, claims 4 and 15 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 4 and 15 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 4 and 15 do not recite patent eligible subject matter under 35 USC 101.
As to claims 5 and 16 the limitation of “wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes using a component prediction model” which amounts to no more than mere instructions, or generic computer/computer components to carry out the exception which does not integrate the judicial exception into a practical application. Moreover, claim 5 and 16 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 5 and 16 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 5 and 16 does not recite patent eligible subject matter under 35 USC 101.
As to claim 6 the limitation of “selecting the component prediction model from a plurality of component prediction models based on a workflow service configuration” is an additional mental process element under prong 1. Moreover, claim 6 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 6 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 6 does not recite patent eligible subject matter under 35 USC 101.
As to claim 7 the limitation of “wherein the workflow service configuration is associated with a set of available components accessible to a user account” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claim 7 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 7 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 7 does not recite patent eligible subject matter under 35 USC 101.
As to claims 8 and 17 the limitation of “wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes: identifying, based on historical data, a set of common components from a plurality of existing components; selecting a set of training components, the set of training components comprising each respective component of the plurality of existing components that is not in the set of common components;” is an additional mental process element under prong 1. Additionally, claims 8 and 17 recite additional element of “training, using the set of training components, the component prediction model” which fails to meaningfully limiting the claim because it does not require a particular application of the recited “training: and is at best the equivalent or merely adding the words “apply it” to the judicial exception which does not integrate the judicial exception into a practical application. Moreover, claims 8 and 17 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 8 and 16 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 8 and 17 do not recite patent eligible subject matter under 35 USC 101.
As to claims 9 and 18 the limitation of “wherein the description of the desired computer workflow corresponds to a natural language description” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 9 and 18 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 9 and 18 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 9 and 18 do not recite patent eligible subject matter under 35 USC 101.
As to claim 10 the limitation of “wherein the description of the desired computer workflow includes an unstructured description” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claim 10 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 10 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 10 does not recite patent eligible subject matter under 35 USC 101.
As to claims 11 and 19 the limitation of “generating the prompt using the description of the desired computer workflow and the set of prospective components includes utilizing a structured format for the set of prospective components” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 11 and 19 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 11 and 19 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 11 and 19 do not recite patent eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-3, 9-14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bland et al. (Pub. No. US 2017/0364390 A1) and further in view of Sathianathan et al. (Pub. No. US 2025/0165775 A1)
As to claims 1 and 20 Bland discloses a method comprising: obtaining a description of a desired computer workflow (Bland [0029] lines 1-14 and [0047] lines 1-12; which shows receiving/obtaining a workflow definition file that includes a workflow definition that is a logical structure of the workflow represented as a series of steps, viewed as a type of descriptions of the desired workflow);
selecting, based on the description of the desired computer workflow, a set of prospective components from a plurality of components (Bland [0029] lines 1-17, [0030] lines 1-10, [0047] lines 1-28 and [0052] lines 1-9; which shows that the workflow based on the workflow definition of the workflow definition files, where the workflow definition includes all information that is specified or referenced in the file and includes information about set and variable definition in the workflow and object and actions and rules for such actions taken, where the variable definition information is able to define enabled and disabled values for elements/steps/components of the workflow and as the workflow definition include steps to cover all possible selective program component by defining though variables which are enabled and which are disabled acts a selection based on the workflow definition/description received).
Bland does not specifically disclose generating a prompt using the description of the desired computer workflow and the set of prospective components; and generating, based on providing the prompt to a large-language-model, at least a portion of code associated with the desired computer workflow.
However, Sathianathan discloses generating a prompt using the description of the desired computer workflow and the set of prospective components (Sathianathan [0185] lines 1-10, [0204] lines 1-16, [0206] lines 1-13; which shows the generation of a prompt that is based on defining/constraining the workflow generated by the large language model, where the constrains used to generate the prompt can include further constraints and rules to follow, that in light of the teachings of Bland above can be viewed as the definition and selected components to use for the workflow as the specific rules/contains to follow and thus together show generating a prompt using the description of the desired computer workflow and the set of prospective components); and
generating, based on providing the prompt to a large-language-model, at least a portion of code associated with the desired computer workflow (Sathianathan [0170] lines 1-7, [0171] lines 1-6, [0173] lines 1-14, [0174] lines 5-8; which shows the generative artificial intelligence is a large language model that is able to generate output that is executable workflow of the application, seen as a portion of the code associated with the desired computer workflow, based on provided prompt).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sathianathan showing the specifics generating a prompt based on rules for a large language model to generate an associated workflow into the workflow generation of Bland for the purpose of improving the speed of generation of workflows for a variety of devices, as taught by Sathianathan [0003] lines 1-5 and [0174] lines 5-11.
As to claim 2, Bland does not specifically disclose, however, Sathianathan discloses using a result of the large-language-model to automatically code the desired computer workflow (Sathianathan [0170] lines 1-7, [0171] lines 1-6, [0173] lines 1-14, [0174] lines 5-8; which shows the generative artificial intelligence is a large language model that is able to generate output that is executable workflow of the application, viewed as automatically code the desired workflow).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sathianathan showing the specifics generating a prompt based on rules for a large language model to generate an associated workflow into the workflow generation of Bland for the purpose of improving the speed of generation of workflows for a variety of devices, as taught by Sathianathan [0003] lines 1-5 and [0174] lines 5-11.
As to claim 3, Bland does not specifically disclose, however, Sathianathan discloses wherein the generated code associated with the desired computer workflow is a structured representation defining the desired computer workflow (Sathianathan [0170] lines 1-7, [0171] lines 1-13, [0173] lines 1-14, [0174] lines 5-8; which shows the generative artificial intelligence is a large language model that is able to generate output that is executable workflow of the application where the generated output is based on structured data thus viewed as a type of structured representation defining the workflow).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sathianathan showing the specifics generating a prompt based on rules for a large language model to generate an associated workflow into the workflow generation of Bland for the purpose of improving the speed of generation of workflows for a variety of devices, as taught by Sathianathan [0003] lines 1-5 and [0174] lines 5-11.
As to claim 9, Bland does not specifically discloses, however, Sathianathan discloses wherein the description of the desired computer workflow corresponds to a natural language description (Sathianathan [0146] lines 1-4 and [0155] lines 10-17; which shows as part of the workflow generation includes receiving a generation request in natural language associated with the generation of the workflow that include various functions, conditions, requirements or constraints, viewed as type of description of the desired workflow that can a natural language description).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sathianathan showing the specifics generating a prompt based on rules for a large language model to generate an associated workflow into the workflow generation of Bland for the purpose of improving the speed of generation of workflows for a variety of devices, as taught by Sathianathan [0003] lines 1-5 and [0174] lines 5-11.
As to claim 10, Bland does not specifically disclose, however, Sathianathan discloses wherein the description of the desired computer workflow includes an unstructured description (Sathianathan [0146] lines 1-4 and [0155] lines 10-17; which shows as part of the workflow generation includes receiving a generation request in natural language/unstructured description associated with the generation of the workflow that include various functions, conditions, requirements or constraints, viewed as type of description of the desired workflow that can a natural language description).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sathianathan showing the specifics generating a prompt based on rules for a large language model to generate an associated workflow into the workflow generation of Bland for the purpose of improving the speed of generation of workflows for a variety of devices, as taught by Sathianathan [0003] lines 1-5 and [0174] lines 5-11.
As to claim 11, Bland does not specifically disclose, however, Sathianathan discloses wherein generating the prompt using the description of the desired computer workflow and the set of prospective components includes utilizing a structured format for the set of prospective components (Sathianathan [0146] lines 1-4, [0185] lines 1-10, [0204] lines 1-16, [0206] lines 1-13 and [0227] 1-11; which shows the generation of a prompt that is based on defining/constraining the workflow generated by the large language model, where the constrains used to generate the prompt can include further constraints and rules where the request include intended function, conditions requirements and constraints viewed as description and components/functions of the workflow, where certain components/functions of the workflow may be required in a certain order or structured format that in light of the teachings of Bland above can be viewed as the definition and selected components to use for the workflow as the specific rules/contains to follow and thus together show wherein generating the prompt using the description of the desired computer workflow and the set of prospective components includes utilizing a structured format for the set of prospective components).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Sathianathan showing the specifics generating a prompt based on rules for a large language model to generate an associated workflow into the workflow generation of Bland for the purpose of improving the speed of generation of workflows for a variety of devices, as taught by Sathianathan [0003] lines 1-5 and [0174] lines 5-11.
As to claim 12, Bland discloses system, comprising: one or more processors (Bland [0087] lines 3-7); and
a memory coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors to (Bland [0087] lines 3-7 and [0099] lines 2-6)
The remaining limitation of claim 12 are comparable to claim 1 above and rejected under the same reasoning.
As to claim 13 it is comparable to claim 2 above and rejected under the same reasoning.
As to claim 14 it is comparable to claim 3 above and rejected under the same reasoning.
As to claim 18 it is comparable to claim 9 above and rejected under the same reasoning.
As to claim 19 it is comparable to claim 11 above and rejected under the same reasoning.
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bland and Sathianathan as applied to claims 1 and 13 above, and further in view of Gutierrez et al. (Pub. No. US 2022/0309037 A1).
As to claims 4 and 15 Bland as modified by Sathianathan do not specifically disclose wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes determining that each prospective component of the set of prospective components satisfies a relevance condition with respect to the desired computer workflow.
However, Gutierrez discloses wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes determining that each prospective component of the set of prospective components satisfies a relevance condition with respect to the desired computer workflow (Gutierrez [0314] lines 6-10 and [0316] lines 1-17; which shows being able to categorize workflow components by relevance as they relate to specific application ,contract, project or any other category selected by the user where the workflow thus determining relevance of each/all workflow components with respect to a desired workflow).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date to incorporate the teachings of Gutierrez showing the ability to classify workflow components as relevant to a project to the selection of workflow components for a workflow of Bland as modified by Sathianathan for the purpose of helping the user determining pertinent data associated with the generated project, as taught by Gutierrez [0313] lines 1-7.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bland and Sathianathan as applied to claims 1 and 12 above, and further in view of Chan et al. (Pub. No. US 2025/0238743 A1)
As to claims 5 and 16, Bland as modified by Sathianathan do not specifically disclose wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes using a component prediction model.
However, Chan discloses wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes using a component prediction model (Chen [0012] lines 5-8, [0014] lines 1-20 and [0202] lines 1-23; which shows using a machine learning model that is able to make predictions, viewed as a type of component prediction model, about which steps/components of the workflow to include in the workflow based on the prompt defining the task to be complete, viewed as a type of description of the desired computer workflow).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Chen for the use of predictive model to determine/select specific parts of the workflow to use based on the workflow description into the used of workflow definition to select specific component for the workflow of Bland as modified by Sathianathan for the purpose of increasing the efficiency of the overall process of creating workflows, as taught by Chan [0014] lines 8-14.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Bland, Sathianathan and Chan as applied to claim 5 above, and further in view of Witt et al. (Pub. No. US 2021/0158214 A1)
As to claim 6, Bland as modified by Sathianathan and Chan do not specifically disclose selecting the component prediction model from a plurality of component prediction models based on a workflow service configuration.
However, Witt discloses selecting the component prediction model from a plurality of component prediction models based on a workflow service configuration (Witt [0048] lines 1-5, [0058] lines 7-9 and [0059] lines 1-8; which shows being able to select a specific machine learning model from a plurality of machine learning model based on associated context information, which is indicative of the situation, use case, environment and intention associated with the AI process, that in light of the specifics of the AI prediction model used in workflow generation of Chan above based on task to complete and Sathianathan teaching above for the input into a machine learning model being a type of workflow definition/description, viewed as type of workflow service configuration information can together be viewed as teaching selecting the component prediction model from a plurality of component prediction models based on a workflow service configuration ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Witt showing the specifics of selecting a specific machine learning model from a set for use into the use of a predictive machine learning model in workflow generation of Bland as modified by Sathianathan and Chan for the purpose of increasing the speed in getting AI model ready to perform processing through reuse, as taught by Witt [0059] lines 1-8.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bland, Sathianathan, Chan and Witt as applied to claim 6 above, and further in view of Shih et al. (Patent No. US 8,812,752 B1)
As to claim 7, Bland as modified by Sathianathan, Chan and Witt do not specifically disclose wherein the workflow service configuration is associated with a set of available components accessible to a user account.
However, Shih discloses wherein the workflow service configuration is associated with a set of available components accessible to a user account (Shih Col. 28 lines 34-40; which shows in defining the workflow, workflow service configuration, is able to include workflow components that are available to be selected and used by the client/user account).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Shih showing the specifics of workflow definition including available components into the generating workflow based on workflow definition of Bland as modified by Sathianathan, Chan and Witt for the purpose of increasing the accuracy of the workflow defined by determined and providing available components of the workflow for use, as taught by Shih Col. 28 lines 34-48.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bland, Sathianathan and Chan as applied to claims 5 and 16 above, and further in view of Hoover et al. (Pub. No. US 2016/0155069 A1)
As to claims 8 and 17, Bland as modified by Sathianathan and Chan do not specifically disclose wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes: identifying, based on historical data, a set of common components from a plurality of existing components; selecting a set of training components, the set of training components comprising each respective component of the plurality of existing components that is not in the set of common components; and training, using the set of training components, the component prediction model.
However, Hoover discloses wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes: identifying, based on historical data, a set of common components from a plurality of existing components; selecting a set of training components, the set of training components comprising each respective component of the plurality of existing components that is not in the set of common components; and training, using the set of training components, the component prediction model (Hoover [0005] lines 1-6, [0041] lines 1-8, [0045] lines 1-5 and [0125] lines 1-5; which shows being able to generate an training set for a machine learning classifier/model that is based on historic data where as part of determining the information in the training set general/common merchandise data object/components are removed from thus the objects left to train would be those not considered/determined to be common/general objects/components that in light of the teachings of Chen above showing the specifics of the prediction model can together be view as disclosing wherein selecting, based on the description of the desired computer workflow, the set of prospective components from the plurality of components includes: identifying, based on historical data, a set of common components from a plurality of existing components; selecting a set of training components, the set of training components comprising each respective component of the plurality of existing components that is not in the set of common components; and training, using the set of training components, the component prediction model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Hoover showing the specifics of being able to filter training set data into the use of a prediction model of Bland as modified by Sathianathan and Chan for the purpose of improving the quality of the data used training data sets used in training models, as taught by Hoover [0036] lines 1-10.
Conclusion
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