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 .
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Hyperlinks are found at paragraphs [0063], [0072], [0077], [0092], [0095], [0105], and [0110] of the specification.
Claim Objections
Claim 19 objected to because of the following informality: A non-transitory computer-readable media encoded with a computer program should read “A non-transitory computer-readable medium encoded with a computer program”. Appropriate correction is required.
Claim 20 objected to because of the following informality: The non-transitory computer-readable media of claim 19 should read “The non-transitory computer-readable medium of claim 19”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1,
Claim 1 recites the limitation validating the request by processing the one or more textual requirements to determine inclusion of a minimal number of systems and actions. A “minimal number of systems and actions” appears to be relative terminology, as use of the term “minimal number” without an objective boundary for what constitutes a minimal number, and thus the scope of the claim is indefinite. For examination purposes, the limitation will be interpreted as reading “validating the request by processing the one or more textual requirements to determine inclusion of a limited number of systems and actions”.
In reference to dependent claims 2-9, claims 2-9 do not cure the deficiencies noted in the rejection of claim 1. Therefore, claims 2-9 are rejected under the same rationale as claim 1.
Regarding claim 10,
Claim 10 recites a system for performing the function of the method of claim 1. All other limitations in claim 10 are substantially the same as those in claim 1, therefore claim 10 is considered indefinite with an equivalent rationale and is interpreted for examination purposes in the same way.
In reference to dependent claims 11-18, claims 11-18 do not cure the deficiencies noted in the rejection of claim 10. Therefore, claims 11-18 are rejected under the same rationale as claim 10.
Regarding claim 19,
Claim 19 recites a medium for performing the function of the method of claim 1. All other limitations in claim 19 are substantially the same as those in claim 1, therefore claim 19 is considered indefinite with an equivalent rationale and is interpreted for examination purposes in the same way.
In reference to dependent claim 20, claim 20 does not cure the deficiencies noted in the rejection of claim 19. Therefore, claim 20 is rejected under the same rationale as claim 19.
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 No 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 abstract ideas without significantly more.
Regarding claim 1,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a process.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of validating the request by processing the one or more textual requirements to determine inclusion of a minimal number of systems and actions; recites an evaluation of requirements and a number of systems and actions to include, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of determining, using a first prediction engine, an intent and a context of the request from the one or more textual requirements, the intent comprising top-ranked systems and APIs matching the request; recites an evaluation of the intent and the context of a request, and what systems and APIs match the request, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of … the data sequence integration scenario defining an order of the actions to be performed by the top-ranked systems and APIs matching the request recites an evaluation of an order of actions to perform, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of receiving a request to generate a data sequence integration scenario, the request comprising one or more textual requirements; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of inputting the intent and the context of the request as a prompt to a second prediction engine; recites the mere extra-solution activity of data outputting, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of and receiving, from the second prediction engine, the data sequence integration scenario, responsive to the prompt, … recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of receiving a request to generate a data sequence integration scenario, the request comprising one or more textual requirements; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of inputting the intent and the context of the request as a prompt to a second prediction engine; recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of and receiving, from the second prediction engine, the data sequence integration scenario, responsive to the prompt, … recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
Therefore, claim 1 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 2,
Claim 2 adds the additional limitations to claim 1:
wherein the first prediction engine and the second prediction engine comprise a trained large language model recites the mere application of a generic large language model, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
Therefore, claim 2 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 3,
Claim 3 adds the additional limitations to claim 2:
wherein the trained large language model is trained using a plurality of requests … recites the mere application of generic training using request data, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
… requests mapped to system and API sequence settings recites an evaluation of what requests map to what system and API sequence settings, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 3 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 4,
Claim 4 adds the additional limitations to claim 3:
wherein the system and API sequence settings define workflow conditions for a plurality of system types and API types recites mere additional detail on the system and API sequence settings, without changing that … requests mapped to system and API sequence settings, recited in claim 3, is an evaluation, which is a mental process, which is an abstract idea.
Therefore, claim 4 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 5,
Claim 5 adds the additional limitations to claim 4:
determining, using the first prediction engine, the plurality of system types and API types; recites an evaluation of system types and API types, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
ranking the plurality of system types and API types as a ranked system and API list; recites a judgement of ranking system types and API types, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
receiving a selection of system types and API types from the ranked system and API list; recites an evaluation of selecting system types and API types, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
and generating an enriched intent comprising the selection of system types and API types recites an evaluation of an enriched intent, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 5 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 6,
Claim 6 adds the additional limitations to claim 1:
invoking a migration by retrieving one or more systems and APIs in a sequence of systems and APIs from a database, the migration calling predefined templates recites the mere application of a migration and predefined templates for the migration, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
Therefore, claim 6 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 7,
Claim 7 adds the additional limitations to claim 1:
generating a graphical representation of the sequence of systems and APIs as the data sequence integration scenario recites the mere application of a graphical representation, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
Therefore, claim 7 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 8,
Claim 8 adds the additional limitations to claim 1:
wherein validating the request by processing the one or more textual requirements comprises a verification of use cases and supported features of an enterprise system recites an evaluation of requirements, use cases, and supported features, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 8 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 9,
Claim 9 adds the additional limitations to claim 1:
wherein determining, using the first prediction engine, the intent of the request comprises accessing external libraries … recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and which recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
… and providing an authorization token to the first prediction engine to access system data and API data … recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and which recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
…to discover available systems and available APIs matching the request recites an evaluation of systems and APIs that match a request, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 9 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claims 10-18,
Claims 10-18 recite a system with a computing device and a computer-readable storage device that implements the function of the method of claims 1-9, respectively, with substantially the same limitations. Therefore the same analysis and rejection applied to claims 1-9 applies to claims 10-18.
Therefore, claims 9-18 are found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 19,
Claim 19 recites a non-transitory computer-readable medium that implements the function of the method of claim 1, with substantially the same limitations. Therefore the same analysis and rejection applied to claim 1 applies to claim 19.
Therefore, claim 19 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 20,
Claim 20 recites a non-transitory computer-readable medium that implements the function of the method of claims 2 and 3, with substantially the same limitations. Therefore the same analysis and rejection applied to claim 2 and 3 applies to claim 20.
Therefore, claim 20 is found to be ineligible subject matter under 35 U.S.C. 101.
Prior Art
The following references are used for prior art claim rejections:
Fozdar et al. (U.S. Patent Application Publication No. 2025/0077538), hereinafter Fozdar
Van der Putten “Transforming Data Flow: Generative AI in ETL Pipeline Automatization”, hereinafter Van_der_Putten
Kachuee et al. (U.S. Patent No. 12,626,698), hereinafter Kachuee
Braddy et al. (U.S. Patent Application Publication No. 2018/0254989), hereinafter Braddy
Anusuri et al. (U.S. Patent Application Publication No. 2024/0427743), hereinafter Anusuri
Rashkevitch et al. (U.S. Patent Application Publication No. 2025/0173652), hereinafter Rashkevitch
Claim Rejections - 35 USC § 103
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 1-4, 10-13, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fozdar in view of Van_der_Putten.
Regarding claim 1,
Fozdar teaches A computer-implemented method comprising:
receiving a request to generate a data sequence integration scenario, ((Fozdar Abstract) “The method includes receiving, via the GUI, a natural language input representing a data integration task”) the request comprising one or more textual requirements; ((Fozdar [0026]) “The activity planning module 110 also calls the LLM described herein, as indicated by box 114, and utilizes the LLM to parse the natural language input 106 into multiple tasks, where each task corresponds to a distinct activity within a corresponding pipeline”, a required task in natural language is a textual requirement)
validating the request by processing the one or more textual requirements to determine inclusion of a minimal number of systems ((Fozdar [0023]) “the present techniques utilize an LLM to identify the specific data sources that are relevant to the natural language query…the present techniques utilize an LLM to conduct the appropriate reformulations for translating the natural language query into a set of ordered, dependent activities”, specific data sources are a number of systems) and actions; ((Fozdar [0027]) “the APIs are classified such that only certain APIs are invoked for each type of activity. As an example, the copy data repair API may only be available for a data copying activity. This API classification ensures that suitable APIs are utilized to bring in the context for each activity and also ensures that the correct sequence of actions is taken to accurately generate the full ETL/ELT pipeline, for example. Moreover, in various embodiments, functional dependencies between APIs are delineated. As an example, if data are to be extracted from an SQL source, a limited set of APIs can be called to list the files from the SQL source”, certain APIs of a limited set for activities are a number of actions)
determining, using a first prediction engine, [an intent] and a context of the request ((Fozdar [0020]) “during the API selection phase of the data integration process, the LLM is utilized to call one or more activity-specific APIs for each activity identified during the activity planning phase, where such APIs are intended to provide the context for generating each corresponding activity JSON correctly”, Fozdar does not explicitly teach intent) from the one or more textual requirements, ((Fozdar [0026]) “The activity planning module 110 also calls the LLM described herein, as indicated by box 114, and utilizes the LLM to parse the natural language input 106 into multiple tasks, where each task corresponds to a distinct activity within a corresponding pipeline”, activities are used to select APIs to determine context, the activities are derived from the textual requirements)
inputting [the intent and] the context of the request as a prompt to a second prediction engine; ((Fozdar [0020]) “to build complex and realistic data pipe lines, the LLM is utilized to control, manage, and call various APIs that bring in the correct contextual information for generating each step of the data pipeline”, Fozdar does not explicitly teach intent)
and receiving, from the second prediction engine, the data sequence integration scenario, responsive to the prompt, ((Fozdar Abstract) “generating, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input”) the data sequence integration scenario defining an order of the actions to be performed by the [top-ranked] systems and APIs matching the request ((Fozdar [0038]) “a data pipeline is generated based on the set of ordered activities and the API(s) for performing each activity (in combination with the corresponding context for each activity)”, Fozdar does not explicitly teach ranking of systems and APIs)
Van_der_Putten teaches the following further limitation that Fozdar does not teach:
the intent comprising top-ranked ((Van_der_Putten Pg. 63) “if there are multiple versions of a question that could be considered correct, recall@K tells us whether the model can find them within its top K choices”, a question corresponds to an intent) systems and APIs matching the request; ((Van_der_Putten Pg. 69) “considering the user’s question: ’create a union tool in a data flow named DATA_FLOW’ and the most similar question identified in the database: ’create a union component called UNION with inside a sequence named SEQUENCE and a package named PACKAGE’”, the most similar question consists of systems and APIs)
At the time of filing, one of ordinary skill in the art would have motivation to combine Fozdar and Van_der_Putten by taking the method for generating a data processing sequence with an order of actions for systems and APIs to perform to fulfill a request for a data integration scenario, taught by Fozdar, and including determining an intent from the request comprising top-ranked systems and APIs matching the request, taught by Van_der_Putten, as Van_der_Putten teaches (Van_der_Putten Pg. 63) “For the parameter K, the value 3 was selected. This choice aims to increase the accuracy of the choice, ensuring relevant selection of the most similar questions without overloading the user. Considering that each component in the database has 3-5 related queries associated with it, K = 3 optimizes the precision of the answers provided, which is crucial for correctly identifying the Python functions of the component”. Such a combination would be obvious.
Regarding claim 2,
Fozdar and Van_der_Putten jointly teach The computer-implemented method of claim 1,
Fozdar further teaches:
wherein the first prediction engine and the second prediction engine comprise a trained large language model (Fozdar Fig. 1 shows that both Activity Planning Module (110) and API Selection Module (118) comprise a large language model (114))
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At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Fozdar and Van_der_Putten for the parent claim of claim 2, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 3,
Fozdar and Van_der_Putten jointly teach The computer-implemented method of claim 2,
Van_der_Putten further teaches:
wherein the trained large language model is trained ((Van_der_Putten Pg. 66) “the code used to train the model by fine-tuning is described in Algorithm 2”) using a plurality of requests (Van_der_Putten Pg. 61, Algorithm 2 shows that the training is done using databases of questions and answers, questions correspond to requests) mapped to system and API sequence settings (Van_der_Putten Pg. 61, Algorithm 2 shows mapping of questions to answers at lines 6-7, (Van_der_Putten Pg. 69) “considering the user’s question: ’create a union tool in a data flow named DATA_FLOW’ and the most similar question identified in the database: ’create a union component called UNION with inside a sequence named SEQUENCE and a package named PACKAGE’ with the corresponding answer: [answer]”, the answer shows settings for components in a sequence, components include systems and APIs)
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At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Fozdar and Van_der_Putten for the parent claim of claim 3, claim 2. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 4,
Fozdar and Van_der_Putten jointly teach The computer-implemented method of claim 3,
Van_der_Putten further teaches:
wherein the system and API sequence settings define workflow conditions for a plurality of system types and API types ((Van_der_Putten Pgs. 53-54) “The code 4.2 shows the generic structure of a component in SSIS. For <input></input> and <output></output> tags, specific functions have been defined with varying characteristics depending on the type of component. These functions receive the necessary parameters from an Excel file that describes the structure of the ETL flow. This file contains detailed information about the tables in the various stages of the flow, including the records in each table and their properties such as data type, length, precision, scale, and primary key indication”, component types correspond to system and API types)
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At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Fozdar and Van_der_Putten for the parent claim of claim 4, claim 3. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claims 10-13,
Claims 10-13 recite a system comprising a computing device and a storage device with instructions for performing the function of the method of claims 1-4, respectively. Specifically, claim 10 recites A system comprising: a computing device; and a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for selectively generating graphical representations with digital assistants in enterprise systems, the operations comprising: [the steps of the method of claim 1]. Fozdar recites: (Fozdar [0004]) “The service provider device includes a processor…and a computer readable storage medium operatively coupled to the processor. The computer-readable storage medium includes computer-executable instructions that, when executed by the processor, cause the processor to cause execution of the data integration application on the remote device via the network, cause surfacing of a GUI corresponding to the data integration application on a display of the remote device, and receive, via the GUI, a natural language input representing a data integration task” and (Fozdar [0033]) “the present techniques may be extended to products and/or services offering LLM-driven assistants”.
All other limitations in claims 10-13 are substantially the same as those in claims 1-4, respectively, therefore the same rationale for rejection applies.
Regarding claims 19,
Claim 19 recites a non-transitory computer-readable medium with instructions for performing the function of the method of claim 1. Specifically, claim 19 recites A non-transitory computer-readable media encoded with a computer program, the computer program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: [the steps of the method of claim 1]. Fozdar recites: (Fozdar [0005]) “In another embodiment described herein, a computer-readable storage medium is provided. The computer readable storage medium includes computer-executable instructions that, when executed by a processor, cause the processor to execute a data integration application”.
All other limitations in claims 19 are substantially the same as those in claim 1, therefore the same rationale for rejection applies.
Regarding claim 20,
Claim 20 recites a non-transitory computer-readable medium with instructions for performing the function of the method of claims 2 and 3. All other limitations in claim 20 are substantially the same as those in claims 2 and 3, therefore the same rationale for rejection applies.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Fozdar, in view of Van_der_Putten, further in view of Kachuee.
Regarding claim 5,
Fozdar and Van_der_Putten jointly teach The computer-implemented method of claim 4, further comprising:
Van_der_Putten further teaches:
determining, using the first prediction engine, the plurality of system types and API types; ((Van_der_Putten Pg. 64-65) “the use of embedding models in combination with semantic weighting shows a performance improvement…Through this approach, the model places less importance on word order or array length, focusing instead on key terms such as ‘origin’, which essentially captures the user’s desired component”, determining key terms that capture the component desired by a user corresponds to determining system and API types)
and generating an enriched intent ((Van_der_Putten Pg. 60) “Rephrase and Respond (RaR) Prompt : This approach has been devised to bridge the gap between human thought structures and those of LLMs. The method involves reformulating the original question, incorporating additional details to enhance semantic clarity and address inherent ambiguities within the question, thereby enabling the model to respond more accurately”, a reformulation of an original question to incorporate additional details and reduce ambiguity corresponds to an enriched intent) comprising the selection of system types and API types ((Van_der_Putten Pg. 69) “considering the user’s question: ’create a union tool in a data flow named DATA_FLOW’ and the most similar question identified in the database: ’create a union component called UNION with inside a sequence named SEQUENCE and a package named PACKAGE’”)
Kachuee teaches the following further limitations that neither Fozdar nor Van_der_Putten explicitly teach:
ranking the plurality of system types and API types as a ranked system and API list; ((Kachuee Abstract) “Techniques for identifying components (e.g., application program interfaces (APIs)) relevant for a large language model (LLM) prompt are described. The shortlister includes one or more proposers that each select n components for performing an instant task. The n components from each proposer may be merged and reranked to produce a ranked list of K components for inclusion in the LLM prompt”)
receiving a selection of system types and API types from the ranked system and API list; ((Kachuee Col. 16, lines 32-38) “The component shortlister 102 (and more particularly the reranking component 120 thereof) then merges all the proposed components from the different proposers 116, reranks 35 them, and outputs an indication of the ranked component(s) 112, which is returned (step 5) to the LLM orchestrator component 130. One or more system components may then use the component(s) 112 as needed”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Fozdar, Van_der_Putten, and Kachuee by taking the method of claim 4 for generating a data processing sequence, including a plurality of system and API types and generating an enriched intent comprising a selection of the system and API types, taught by Fozdar and Van_der_Putten, and including ranking the plurality of system and API types and selecting from the ranked list, taught by Kachuee, as Kachuee teaches: (Kachuee Col. 15, lines 38-43) “the reranking component 120 may implement model-based ranking, which leverages features from each proposed component as well as external metrics to optimize the ranking solution. Such models can be trained via a self-learning feedback loop to encourage up-ranking the right candidates”, that is, that ranking the components for selection allows for optimizing the ranking so that the best candidates can be highly ranked and thus selected. Such a combination would be obvious.
Regarding claim 14,
Claim 14 recites a system for performing the function of the method of claim 5. All other limitations in claim 14 are substantially the same as those in claim 5, therefore the same rationale for rejection applies.
Claims 6, 7, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Fozdar, in view of Van_der_Putten, further in view of Braddy.
Regarding claim 6,
Fozdar and Van_der_Putten jointly teach The computer-implemented method of claim 1, further comprising:
Braddy teaches the following further limitations that neither Fozdar nor Van_der_Putten teach:
invoking a migration ((Braddy [0070]) “Flow-based data integration system 610 can be used to automate the migration, replication, transformation, and ultimately, the integration of data between sources and destinations that may have disparate or otherwise incompatible characteristics. Such processes can be referred to as ‘automated data migration flows’. In order to do provide such functionality and execute these automated data migration flows, data integration-system 610 relies upon a flow-based processing structure that is built around a series of processor blocks, wherein a given processor block can be broadly understood to be either a data connector (implements I/O operations, data retrieval or ingestion operations) or to be a data transform (implements any kind of additional processing on ingested data)”) by retrieving one or more systems and APIs ((Braddy [0073]) “In such ingestion operations, data connector processor blocks can utilize the data integration system REST APIs 632 and/or the cloud OS services REST APIs 672. In output operations, data connector processor blocks can use the cloud OS services REST APIs 672 to write data to an external cloud environment, or can use the data integration system REST APIs 632 to write data to file system 630”) in a sequence of systems and APIs from a database, ((Braddy [0092]) “the use of data integration system 610 and the Cloud NAS nodes 832, 838 permits a large and complex data migration process to be easily configured by a user to be executed automatically via the series of processing blocks stored in the processing block libraries 614-618”)
the migration calling predefined templates ((Braddy [0087]) “FIG. 8 depicts a one-to-one data migration and integration process 800, which can be user-created via flow editor 612b or can be user-customized via a template of wizard 612a (e.g. the depicted one-to-one data migration and integration process 800 can be stored as one or more templates within the data store of templates 613)”, (Braddy [0076]) “In some embodiments, these templates 613 might be pre-defined or otherwise associated with data integration system 610 and wizard 612a”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Fozdar, Van_der_Putten, and Braddy by taking the method of claim 1 for generating a data processing sequence, taught by Fozdar and Van_der_Putten, and including data migration with a sequence of systems and APIs retrieved from a database, including the use of pre-defined templates, taught by Braddy, as Braddy teaches: (Braddy [0087]) “In general, such this one-to-one data migration and integration process 800 can be employed by users wishing to move existing data that is not in the cloud, into the cloud”, that is, that migrations are helpful for moving data from legacy or physical data systems into more practical systems such as cloud-based systems. Such a combination would be obvious.
Regarding claim 7,
Fozdar, Van_der_Putten, and Braddy jointly teach The computer-implemented method of claim 6, further comprising:
Fozdar further teaches:
generating a graphical representation of the sequence of systems and APIs as the data sequence integration scenario (Fozdar Fig. 3B shows a graphical representation of a pipeline for a data integration scenario, the pipeline consists of a sequence of systems such as a source system and a destination system, and activities such as copying data, (Fozdar [0004) “select, via the LLM, one or more APIs for performing each activity within the set of ordered activities”, the activities are performed using APIs)
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At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Fozdar, Van_der_Putten, and Braddy for the parent claim of claim 7, claim 6. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claims 15 and 16,
Claims 15 and 16 recite a system for performing the function of the method of claims 6 and 7, respectively. All other limitations in claims 15 and 16 are substantially the same as those in claims 6 and 7, therefore the same rationale for rejection applies.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Fozdar, in view of Van_der_Putten, further in view of Anusuri.
Regarding claim 8,
Fozdar and Van_der_Putten jointly teach The computer-implemented method of claim 1,
Anusuri teaches the following further limitations more explicitly than Fozdar and that Van_der_Putten does not teach:
wherein validating the request by processing the one or more textual requirements ((Anusuri [0003]) “The system includes…instructions that enable the creation of components, including a user interface configured to receive metadata configuration requirements, a parsing module programmed to parse the metadata configuration requirements into one or more constituent components, and a template selection module programmed to identify and select appropriate templates from a template repository used to fulfill the one or more constituent components”) comprises a verification of use cases and supported features of an enterprise system ((Anusuri [0083]) “The enterprise testing and validation controls module 134 encompasses a range of testing and validation mechanisms to verify the integrity, functionality, and performance of the data pipeline…the enterprise testing and validation controls module 134 facilitates end-to-end testing to verify the overall functionality and effectiveness of the data pipeline orchestration process, and it performs validation checks on various aspects, including data completeness, accuracy, and compliance with regulatory requirements”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Fozdar, Van_der_Putten, and Anusuri by taking the method of claim 1 for generating a data processing sequence, taught by Fozdar and Van_der_Putten, and including validating with a verification of use cases and supported features of an enterprise system, taught by Anusuri, as Anusuri teaches: (Anusuri [0083]) “The enterprise testing and validation controls module 134 contributes to maintaining data quality, identifying potential errors or discrepancies, and preventing data integrity issues, and supports organizations in meeting regulatory and compliance obligations by providing a robust mechanism for validating data accuracy, reliability, and consistency”. Such a combination would be obvious.
Regarding claim 17,
Claim 17 recites a system for performing the function of the method of claim 8. All other limitations in claim 17 are substantially the same as those in claim 8, therefore the same rationale for rejection applies.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fozdar, in view of Van_der_Putten, further in view of Rashkevitch.
Regarding claim 9,
Fozdar and Van_der_Putten jointly teach The computer-implemented method of claim 1,
Rashkevitch teaches the following further limitations that neither Fozdar, nor Van_der_Putten teach:
wherein determining, using the first prediction engine, the intent of the request comprises accessing external libraries and providing an authorization token to the first prediction engine to access system data and API data to discover available systems and available APIs matching the request ((Rashkevich [0007]) “receive a selection of a template for a predefined workflow to be implemented in an existing user's account having at least one internal resource; receive a selection of a set of resources from a plurality of resources to implement the functionality of the predefined workflow, wherein the selection of the set of includes the at least one internal resource of the existing user's account and at least one external resource accessible via an API; query a secure storage area to determine that credentials are non-existent for the at least one external resource; initiate an authentication flow with the at least one external resource to obtain a token from the at least one external resource; receive the token; store the token in the secure storage area; and initiate a configuration of the predefined workflow to the set of resources to initiate functionality of the predefined workflow”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Fozdar, Van_der_Putten, and Rashkevitch by taking the method of claim 1 for generating a data processing sequence, taught by Fozdar and Van_der_Putten, and including accessing external libraries and providing an authentication token to provide access to the external libraries, taught by Rashkevitch, as Rashkevitch teaches: (Rashkevitch [0149]) “Examples of credentials may include usernames and passwords, API keys, tokens, certificates, or key pairs. These credentials may be used to verify the identity of the user or application accessing the resource and enable secure and authorized access to the external resource. Handling and storing credentials are valuable aspects to securely protect against unauthorized access”. Such a combination would be obvious.
Regarding claim 18,
Claim 18 recites a system for performing the function of the method of claim 9. All other limitations in claim 18 are substantially the same as those in claim 9, therefore the same rationale for rejection applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Makhija et al. (U.S. Patent Application Publication No. 2025/0272652) teaches a system and method for using a large language model to generate a sequence of actions in response to a user intent.
Xiong et al. (U.S. Patent Application Publication No. 2012/0095956) teaches techniques to identify and rank relevant APIs respective to context data.
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/V.A.N./Examiner, Art Unit 2124
/MIRANDA M HUANG/ Supervisory Patent Examiner, Art Unit 2124
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