DETAILED ACTION
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 .
Status of Claims
Claims 1-20 are pending and examined herein.
Claims 2, 12, and 15 contain limitations interpreted under 35 U.S.C. 112(f).
Claims 2-6 and 12-16 are rejected under 35 U.S.C. 112(a) and 35 U.S.C. 112(b).
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 103.
Information Disclosure Statement
The attached information disclosure statement(s) (IDS) filed on 11/07/2023 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
an Al integration component (AIIC) configured to intermediate communications between the chatbot and the user interface in claim 2.
an Al integration component (AIIC) configured to manage communications between the user interface, the chatbot, and one or more technical internal components of the data platform in claim 12.
the AIIC is configured to manage the generating the graph in claim 15.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
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 2-6 and 12-16 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.
The following claim limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
an Al integration component (AIIC) configured to intermediate communications between the chatbot and the user interface in claim 2.
an Al integration component (AIIC) configured to manage communications between the user interface, the chatbot, and one or more technical internal components of the data platform in claim 12.
the AIIC is configured to manage the generating the graph in claim 15.
However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. It is unclear as to what algorithm is performing the functions, and thus no association between the structure and the functions can be found in the specification. Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Dependent claims 3-6 and 13-16 fail to resolve the issue and are rejected with the same rationale.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2-6 and 12-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
MPEP 2181 states "When a claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under 35 U.S.C. 112(a). See MPEP § 2163.03, subsection VI." Therefore, claims 2-6 and 12-16 lack written description.
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.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject
matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine,
manufacture, or composition of matter. Claims 1-10 are directed to a process, claims 11-19 are directed to a machine, and claim 20 is directed to an article of manufacture. All claims are directed to statutory categories and analysis proceeds.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Regarding claim 1, the following are abstract ideas:
formulating a problem statement based on a dialog comprising communications from a chatbot comprising the Al model and inputs received at a user interface of the data platform; and (Formulating a problem statement based on a dialog can be practically performed in the human mind. This is a mental process.)
generating a graph for a data pipeline based on the problem statement. (Generating a graph for a data pipeline can be practically performed in the human mind, with the aid of pen and paper.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A computer-implemented method comprising: (This recites a generic computer; this amounts to mere instructions to apply an exception.)
training an artificial intelligence (Al) model with training data comprising language data and data regarding a data platform; (This recites a generic machine learning process (training a model), and generic machine learning components (a machine learning model, training data). This amounts to mere instructions to apply an exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the dialog is managed by an Al integration component (AIIC) configured to intermediate communications between the chatbot and the user interface. (This recites generic software (AI integration component) that performs the existing processes of receiving and transmitting data. This amounts to mere instructions to apply an exception.)
Regarding claim 3, the rejection of claim 2 is incorporated herein. The following is an abstract idea:
generating the graph comprises translating one or more data transformation rules into graph source code. (Translating data transformation rules into code can be practically performed in the human mind. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
the AIIC manages the generating the graph; and the (This merely states that a software component is implementing the abstract idea of generating the graph. This amounts to mere instructions to apply an exception.)
Regarding claim 4, the rejection of claim 3 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
the AIIC comprises an adapter configured to connect with a connectivity framework of the data platform; and (Connecting with a framework amounts to transmitting/receiving data, which are existing processes on computers. This amounts to mere instructions to apply an exception.)
the generating the graph further comprises updating connectivity between the data platform and one or more connected systems in the connectivity framework. (Connecting systems amounts to transmitting/receiving data, which are existing processes on computers. This amounts to mere instructions to apply an exception.)
Regarding claim 5, the rejection of claim 4 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the one or more connected systems comprise at least one source system and at least one target system. (A source system is any system that data is received from. A target system is any system that data is sent to. Sending and transmitting data to systems are existing processes on computers. This amounts to mere instructions to apply an exception.)
Regarding claim 6, the rejection of claim 4 is incorporated herein. Further, the following are abstract ideas:
confirming a secure configuration of the connectivity between the data platform and the one or more connected systems; and (Given the connectivity data, confirming a secure configuration of the connectivity can be practically performed in the human mind. This is a mental process.)
updating the graph source code using audit and debug logs. (Updating the code using logs (i.e. writing the code) can be practically performed in the human mind. This is a mental process.)
Regarding claim 7, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
executing the graph in a runtime environment of the data platform to test the data pipeline; and (Executing the graph is executing code, which is an existing process on computers. This amounts to mere instructions to apply an exception.)
providing results of the test to the user interface. (Providing results is data transmission, is an existing process in computers. This amounts to mere instructions to apply an exception.)
Regarding claim 8, the rejection of claim 7 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
receiving approval of the results of the test via the user interface; and (Receiving data is a known process in computers. This amounts to mere instructions to apply an exception.)
responsive to the approval, activating the data pipeline and adding the activated data pipeline to a repository. (Activating the data pipeline is executing code and adding to a repository is storing data, both of which are known processes in computers. This amounts to mere instructions to apply an exception.)
Regarding claim 9, the rejection of claim 8 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
training the Al model using data regarding the activated data pipeline. (This recites generic machine learning processes and components; this amounts to mere instructions to apply an exception.)
Regarding claim 10, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
receiving an update to a compliance or data privacy regulation applicable to the data; and (Receiving an update is receiving data, which is a known process in computing. This amounts to mere instructions to apply an exception.)
training the Al model based on the update. (This recites generic machine learning processes and components; this amounts to mere instructions to apply an exception.)
Regarding claim 11, the following are abstract ideas:
formulating a problem statement based on a dialog comprising communications from a chatbot and inputs received at a user interface of a data platform, the chatbot comprising a trained artificial intelligence (Al) model; and (Formulating a problem statement based on a dialog can be practically performed in the human mind. This is a mental process.)
generating a graph for a data pipeline based on the problem statement. (Generating a graph for a data pipeline can be practically performed in the human mind, with the aid of pen and paper.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A computing system comprising: (This recites a generic computer. This amounts to mere instructions to apply an exception.)
at least one hardware processor; (This recites a generic computer component. This amounts to mere instructions to apply an exception.)
at least one memory coupled to the at least one hardware processor; and (This recites a generic computer component. This amounts to mere instructions to apply an exception.)
one or more non-transitory computer-readable media having stored therein computer-executable instructions that, when executed by the computing system, cause the computing system to perform: (This recites a generic computer component and generic computer processes. This amounts to mere instructions to apply an exception.)
Regarding claim 12, the rejection of claim 11 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
an Al integration component (AIIC) configured to manage communications between the user interface, the chatbot, and one or more technical internal components of the data platform. (This recites generic software (AI integration component) that performs the existing processes of receiving and transmitting data. This amounts to mere instructions to apply an exception.)
Regarding claim 13, the rejection of claim 12 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the technical internal components of the data platform comprise a connectivity framework, a design-time environment, and a runtime environment. (This recites generic software components (a connectivity framework, a design-time environment, and a runtime environment) without details as to how they are accomplished; this amounts to mere instructions to apply an exception.)
Regarding claim 14, the rejection of claim 13 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the AIIC comprises a first adapter configured to connect to the connectivity framework, a second adapter configured to connect to the design-time environment, and a third adapter configured to connect to the runtime environment. (Connecting with a framework/environment amounts to transmitting/receiving data, which are existing processes on computers. This amounts to mere instructions to apply an exception.)
Regarding claim 15, the rejection of claim 13 is incorporated herein. The following is an abstract idea:
wherein the generating the graph comprises translating one or more data transformation rules into graph source code (Translating data transformation rules into code can be practically performed in the human mind. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the AIIC is configured to manage the generating the graph, and (This merely states that a software component is implementing the abstract idea of generating the graph. This amounts to mere instructions to apply an exception.)
in the design-time environment. (This merely states that the abstract idea is executed in a generic software component. This amounts to mere instructions to apply an exception.)
Regarding claim 16, the rejection of claim 15 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the generating the graph further comprises updating connectivity between the data platform and one or more connected systems in the connectivity framework. (Connecting systems amounts to transmitting/receiving data, which are existing processes on computers. This amounts to mere instructions to apply an exception.)
Regarding claim 17, the rejection of claim 11 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein training data for the AI model comprises language data and data regarding the data platform. (This is the insignificant extra-solution activity of ‘selecting a particular data source or type of data to be manipulated’. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i-iv.)
Regarding claim 18, the rejection of claim 14 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the AIIC further comprises a TTS/STT engine and a parser. (This recites generic software components (TTS/STT engine and parser) at a high-level of generality without details on how they are accomplished. This amounts to mere instructions to apply an exception.)
Regarding claim 19, the rejection of claim 18 is incorporated herein. The following is an abstract idea: determine, based on the answer, whether to convey the answer to the user interface, create a connection in the connectivity framework, modify the graph for the data pipeline, or extract one or more samples from a connected system. (Determining an action based on an answer can be practically performed in the human mind. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the parser is configured to: (This recites a generic software component (parser) at a high-level of generality without details on how it is accomplished. This amounts to mere instructions to apply an exception.)
receive an answer from the chatbot; and (Receiving data is a known process in computers. This amounts to mere instructions to apply an exception.)
Regarding claim 20, the following are mental processes:
generating a graph for a data pipeline based on the problem statement, the generating the graph comprising: (Generating a graph for a data pipeline can be practically performed in the human mind, with the aid of pen and paper.)
translating one or more data transformation rules into graph source code in the design-time environment; and (Translating data transformation rules into code can be practically performed in the human mind. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
One or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by a computing system, cause the computing system to perform operations comprising: (This recites generic computer components and generic computer processes. This amounts to mere instructions to apply an exception.)
with a chatbot comprising a trained artificial intelligence (AI) model, presenting a series of prompts to a user interface of a data platform to formulate a problem statement, the data platform comprising a connectivity framework and a design-time environment; and (Presenting data is an insignificant extra-solution activity. See MPEP § 2106.05(d), list 3, ex. iv.)
updating connectivity between the data platform and one or more connected systems in the connectivity framework. (Connecting systems amounts to transmitting/receiving data, which are existing processes on computers. This amounts to mere instructions to apply an exception.)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-9 and 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polleri (US 2021/0081819 A1) and Du (US 2025/0094137 A1).
Regarding claim 1, Polleri teaches
A computer-implemented method comprising: ([0040] states "Certain embodiments of the present disclosure relate to systems, devices, computer-readable medium, and computer-implemented methods for implementing various techniques for machine learning.")
training an artificial intelligence (Al) model with training data comprising language data and data regarding a data platform; ([0112] states "Intents allow the chatbot to understand what the user wants the chatbot to do. Intents are comprised of permutations of typical user requests and statements, which are also referred to as utterances (e.g., generate a classifier application, determine most efficient employee from employment records, etc.). As used herein, an utterance or a message may refer to a set of words (e.g., one or more sentences) exchanged during a conversation with a chatbot. Intents may be created by providing a name that illustrates user action (e.g., generate a classifier) and compiling a set of real-life user statements, or utterances that are commonly associated with triggering the action. Because the chatbot's cognition is derived from these intents, each intent may be created from a data set that is robust (one to two dozen utterances) and varied, so that the chatbot may interpret ambiguous user input. A rich set of utterances enables a chatbot to understand what the user wants when it receives messages like “Use data set A” or “Identify set A as the data”—messages that mean the same thing, but are expressed differently. Collectively, the intents, and the utterances that belong to them, make up a training corpus for the chatbot. By training a model with the corpus, a user can essentially turn that model into a reference tool for resolving end user input to a single intent. A user can improve the acuity of the chatbot's cognition through rounds of intent testing and intent training." The chatbot, interpreted as the AI model, is therefore trained using language data (the utterances) and the intent. As the intents include actions that can be performed on the system (interpreted as the data platform), the intents are data regarding the data platform.)
formulating a problem statement based on a dialog comprising communications from a chatbot comprising the Al model and inputs received at a user interface of the data platform; and ([0065] states "At 204, the functionality includes receiving a second user input identifies a problem for which a solution can be generated by the machine learning application. In various embodiments the second user input can specify a type of problem that the user would like to implement machine learning for. In various embodiments, the problem can be identified through input of text via a user interface. In various embodiments, the problems can be entered as native language speech or text (e.g., through the use of a chatbot). The technique can decipher the native language to understand the goals of the machine learning model." Thus, the problem statement is formulated.)
Polleri does not appear to explicitly teach
generating a graph for a data pipeline based on the problem statement.
However, Du—directed to analogous art—teaches
generating a graph for a data pipeline based on the problem statement. ([0035] states "The visual programming platform can process the natural language description of the task with a machine learning coding system that includes one or more machine learned models to generate a set of pseudocode as an output of the machine learning coding system." [0037] states "Next, the platform can process the set of pseudo code with a compiler to generate a set of programming language code that defines the computational pipeline for performing the task." [0038] states "Then, the visual programming platform can generate a graphical visualization of the computational pipeline defined by the set of programming-language code. For example, the visual programming platform can map each instruction or component from the set of programming language code to corresponding graphical components that represent data flows and relationships (e.g., inputs, outputs, operations, etc.) in a visual format.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Polleri and Du, because as Du states in [0029], "More particularly, the visual programming platforms described herein can provide an interactive and userfriendly interface for the graphical development of software code. The platform allows developers, regardless of their skill level, to create software programs by using graphical representations of functionalities instead of writing traditional text-based code. The platform transforms coding to a more intuitive and visually engaging task, making it accessible to a broader range of individuals. It can also enhance the efficiency, accuracy, and speed of the software development process by providing real-time visual feedback, drag-and-drop components, and customizable templates."
Regarding claim 2, the rejection of claim 1 is incorporated herein. Polleri teaches
wherein the dialog is managed by an Al integration component (AIIC) configured to intermediate communications between the chatbot and the user interface. (Fig. 5 shows the system. The bot system, as well as the model composition engine in Fig. 1, are interpreted as the AI integration component, which receives (message-in) and transmits (message-out) data to the user interface (mobile device). Therefore, the bot system intermediates communications.)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Polleri does not appear to explicitly teach
the AIIC manages the generating the graph; and the
generating the graph comprises translating one or more data transformation rules into graph source code.
However, Du—directed to analogous art—teaches
the AIIC manages the generating the graph; and the (Fig. 1 shows the system. The visual programming platform is interpreted as the AIIC, as it receives the natural language user input.)
generating the graph comprises translating one or more data transformation rules into graph source code. ([0068] states "Referring still to FIG. 1, the pseudocode generated by the machine learning coding system 90 can describe performance of the task. Specifically, the set of pseudocode can be a structured, high-level, human-readable representation of a computer algorithm, not intended for direct execution, but to convey the logical flow and operations of the algorithm." The pseudocode is interpreted as the data transformation rules. [0069] states "Next, the platform 30 can process the set of pseudocode with a compiler 95 to generate an initial version of the set of programming-language code 80 that defines the computational pipeline for performing the task." The code that defines the computational pipeline for performing the task is interpreted as the graph source code.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Polleri and Du for the reasons given above in regards to claim 1.
Regarding claim 4, the rejection of claim 3 is incorporated herein. Polleri teaches
the AllC comprises an adapter configured to connect with a connectivity framework of the data platform; and (Fig. 1 shows the system, where the model composition engine (AIIC) is connected to the ML platform. The connection to the ML Platform is interpreted as the connectivity framework. The broadest reasonable interpretation of an adapter is a component that allows for communication. As the ML Platform and the model composition engine communicate, there must be an adapter.)
the generating the graph further comprises updating connectivity between the data platform and one or more connected systems in the connectivity framework. ([0245] states "Once a core machine learning product is specified that solves a core problem for a particular instantiation, it is matched to a machine learning model. That machine learning model is specified from library components 168 that include a pipeline 136 that specifies various microservices routines 140, software modules 144, and / or infrastructure modules 148. Functional areas of the library components 168 are customized on a per-instantiation basis that adapts to a unique client's data, QoS, KPIs, and other requirements, e.g.: automated adaption of features for library components 168, automated bias elimination in a machine learning model, automated model training to achieve QoS and KPIs, and Automated microservices routine deployment configuration." Therefore, the library components are interpreted as the connected systems. As the components are customized and added to the pipeline per-instantiation, the generating of the pipeline includes updating the connectivity between the data platform and the library components (connected systems). Note that, as generating the graph requires the generation of the pipeline, generating the graph includes this step.)
Regarding claim 5, the rejection of claim 4 is incorporated herein. Polleri teaches
wherein the one or more connected systems comprise at least one source system and at least one target system. ([0245] states "Once a core machine learning product is specified that solves a core problem for a particular instantiation, it is matched to a machine learning model. That machine learning model is specified from library components 168 that include a pipeline 136 that specifies various microservices routines 140, software modules 144, and/or infrastructure modules 148." [0392] states "During service composition, services can combined in a specific order based on their input-output dependencies to produce a desired product input-output dependencies to produce a desired product graph that besides providing a solution required by a pipe line X with Data Input Y, it is also necessary to ensure fulfillment of end-to-end QoS requirements specified by the product team ( KPIs) and the environment we are running." Therefore, as data flows from source to target in a pipeline, the first library component is the source system and the second library component is the target system.)
Regarding claim 6, the rejection of claim 4 is incorporated herein. Polleri teaches
confirming a secure configuration of the connectivity between the data platform and the one or more connected systems; and ([0249]-[0259] state "FIG. 8 illustrates a process 800 for techniques for safe serialization of the predicted pipeline (including the model). Alternative embodiments may vary in function by combining, separating, or otherwise varying the functionality described in the blocks illustrated in FIG. 8. Means for performing the functionality of one or more of the blocks illustrated in FIG. 8 may comprise hardware and/or software components of a distributed system including computing devices, storage devices, network infrastructure, and servers illustrated in FIGS. 20, 21, and 22 and described below. At 802, the functionality can include receiving a library component. At 804, the functionality can include generating a unique key pair for one or more library components. At 806, the functionality can include storing key remotely. The key storage can be separate from the one or more library components. At 808, the functionality can include signing/encrypting and storing the library components. At 810, the functionality can include authenticating the library component with a remote key once requested. At 812, the functionality can include using the library component in a machine learning model. At 814, the functionality can include generating a unique key for a machine learning model. At 816, the functionality can include storing the model key remotely. At 818, the functionality can include authenticating all interaction with the machine learning model with model key. At 820, the functionality can include rejecting any component or model that fails authenticating." Authenticating is interpreted as confirming a secure configuration between the data platform, which includes the machine learning model, and the library components (connected systems).)
updating the graph source code using audit and debug logs. ([0181] states "According to some embodiments, an analytic system may be integrated with a bot system. The analytic system may monitor events occurred during conversations between end users and the bot system, aggregate and analyze the collected events, and provide information regarding the conversations graphically on a graphic user interface at different generalization levels, such as all conversations, different categories of conversation, and individual conversations." [0210] states "Enrichment engine 660 may perform validation and enrichment on the collected events and other information and write them to database 670. For example, based on a collected IP address, enrichment engine 660 may deter mine the location of the end user associated with the IP address. As another example, enrichment engine 660 may extract certain features from the collected information, such as determining a web browser or channel used by the end user. REST server 680 may analyze the enriched events and other information and generate various reports based on certain aggregate metrics 672. The reports may be displayed to an owner, administrator, or developer of the bot system on administrator, or developer of the bot system may provide feedback 694 to the bot system for improving the bot system." One of ordinary skill in the art would realize that improving the bot system would update the graph (generated pipeline) source code. The reports are interpreted as the audit and debug logs.)
Regarding claim 7, the rejection of claim 1 is incorporated herein. Polleri teaches
executing the graph in a runtime environment of the data platform to test the data pipeline; and ([0232] states "The model composition engine 132 can be tested during development of the machine learning framework. For example, if we are creating a model to test worker productivity, the model composition engine 132 can review the employee record data, build a machine learning model to optimize productivity, apply a sample data set, and provide an answer to the user 116. This answer can be compared with user's perceptions of the most productive user to help validate the model." There must be an environment for the machine learning model to be built in, which is interpreted as the runtime environment. Testing the model is interpreted as executing the graph, as the graph includes the pipeline. [0245] states "That machine learning model is specified from library components 168 that include a pipeline 136 that specifies various microservices routines 140, software modules 144, and / or infrastructure modules 148." Therefore, testing the model is executing the pipeline.)
providing results of the test to the user interface. ([0232] states ""The model composition engine 132 can be tested during development of the machine learning framework. For example, if we are creating a model to test worker productivity, the model composition engine 132 can review the employee record data, build a machine learning model to optimize productivity, apply a sample data set, and provide an answer to the user 11.” The results must be provided through the user interface in order for the user to receive the result.)
Regarding claim 8, the rejection of claim 7 is incorporated herein. Polleri teaches
receiving approval of the results of the test via the user interface; and ([0232] states "For example, if we are creating a model to test worker productivity, the model composition engine 132 can review the employee record data, build a machine learning model to optimize productivity, apply a sample data set, and provide an answer to the user 116. This answer can be compared with user's perceptions of the most productive user to help validate the model." The answers from the user is interpreted as the approval of the results, as the validation of the model depends on it. One of ordinary skill in the art would understand that the approval would occur through the user interface.)
responsive to the approval, activating the data pipeline and adding the activated data pipeline to a repository. ([0232] states "For example, if we are creating a model to test worker productivity, the model composition engine 132 can review the employee record data, build a machine learning model to optimize productivity, apply a sample data set, and provide an answer to the user 116. This answer can be compared with user's perceptions of the most productive user to help validate the model." One of ordinary skill in the art would realize that the user answer and subsequent validation of the model would occur before the activation of the data pipeline. [0235] states "For example, a user 116 may not specify where the trained model should be stored or the source of the data used for training the model. In various embodiments, these default settings could be used by the model composition engine 132 in creating the trained model. In various embodiments, the intelligent assistant can detect that the user 116 did not specify a setting during the process of generating the model. The intelligent assistant can query the user 116 regarding the setting or make a recommendation to the user 116 for the setting." Therefore, the answers from the user regarding the storage would preempt the storing of the model (adding the activated data pipeline to repository).)
Regarding claim 9, the rejection of claim 8 is incorporated herein. Polleri teaches
training the Al model using data regarding the activated data pipeline. ([0115] states "Accordingly, a different approach is needed to address these problems. In various embodiments, an analytic system may be integrated with a bot system. The analytic system can gather conversation logs and history, and deter mine information related to individual and / or aggregated end user conversations with a bot system as paths that include different nodes representing different stages or states of the conversations. For example, end user conversations with the bot system may be represented by paths showing the transitions from state to state, where each state may be represented by a node on the path. Statistics of the user conversation with the bot system may be generated for each node. The paths include ( i ) a number of conversations flowed through the intent-specific paths of the dialog flow for a given period, ( ii ) the number of conversations main tained between each state and the different execution paths taken because the conversation branched due to values getting set ( or not set ), or dead-ended because of some other problem like a malfunctioning custom component, and ( iii ) a final state that provides insight into the conversation's ultimate success or failure. The analytical tool may then use the information generated for each path and node to retrain the bot system or individual bot responsible for the intent/path." As the conversation includes data regarding the activated data pipeline, retraining the bot system using the conversation data paths is training the AI model using data regarding the activated data pipeline.)
Regarding claim 11, Polleri teaches
A computing system comprising: ([0433] states "FIG. 22 illustrates an exemplary computer system 2200, in which various embodiments of the present invention may be implemented. The system 2200 may be used to implement any of the computer systems described above.")
at least one hardware processor; ([0433] states "As shown in the figure, computer system 2200 includes a processing unit 2204 that communicates with a number of peripheral subsystems via a bus subsystem 2202.")
at least one memory coupled to the at least one hardware processor; and ([0433]-[0444] state "As shown in the figure, computer system 2200 includes a processing unit 2204 that communicates with a number of peripheral subsystems via a bus subsystem 2202. These peripheral subsystems may include a processing acceleration unit 2206, an I/O subsystem 2208, a storage subsystem 2218 and a communications subsystem 2224. Storage sub system 2218 includes tangible computer-readable storage media 2222 and a system memory 2210. Bus subsystem 2202 provides a mechanism for letting the various components and subsystems of computer system 2200 communicate with each other as intended.")
one or more non-transitory computer-readable media having stored therein computer-executable instructions that, when executed by the computing system, cause the computing system to perform: ([0442] states "Storage subsystem 2218 may also provide a tangible computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some embodiments. Software (programs, code modules, instructions) that when executed by a processor provide the functionality described above may be stored in storage subsystem 2218. These software modules or instructions may be executed by processing unit 2204. Storage subsystem 2218 may also provide a repository for storing data used in accordance with the present invention.")
formulating a problem statement based on a dialog comprising communications from a chatbot and inputs received at a user interface of a data platform, the chatbot comprising a trained artificial intelligence (Al) model; and ([0065] states "At 204, the functionality includes receiving a second user input identifies a problem for which a solution can be generated by the machine learning application. In various embodiments the second user input can specify a type of problem that the user would like to implement machine learning for. In various embodiments, the problem can be identified through input of text via a user interface. In various embodiments, the problems can be entered as native language speech or text (e.g., through the use of a chatbot). The technique can decipher the native language to understand the goals of the machine learning model." Thus, the problem statement is formulated. [0112] states "Intents allow the chatbot to understand what the user wants the chatbot to do. Intents are comprised of permutations of typical user requests and statements, which are also referred to as utterances (e.g., generate a classifier application, determine most efficient employee from employment records, etc.). As used herein, an utterance or a message may refer to a set of words (e.g., one or more sentences) exchanged during a conversation with a chatbot. Intents may be created by providing a name that illustrates user action (e.g., generate a classifier) and compiling a set of real-life user statements, or utterances that are commonly associated with triggering the action. Because the chatbot's cognition is derived from these intents, each intent may be created from a data set that is robust (one to two dozen utterances) and varied, so that the chatbot may interpret ambiguous user input. A rich set of utterances enables a chatbot to understand what the user wants when it receives messages like “Use data set A” or “Identify set A as the data”—messages that mean the same thing, but are expressed differently. Collectively, the intents, and the utterances that belong to them, make up a training corpus for the chatbot. By training a model with the corpus, a user can essentially turn that model into a reference tool for resolving end user input to a single intent. A user can improve the acuity of the chatbot's cognition through rounds of intent testing and intent training." The chatbot is interpreted as the AI model.)
Polleri does not appear to explicitly teach
generating a graph for a data pipeline based on the problem statement.
However, Du—directed to analogous art—teaches
generating a graph for a data pipeline based on the problem statement. ([0035] states "The visual programming platform can process the natural language description of the task with a machine learning coding system that includes one or more machine learned models to generate a set of pseudocode as an output of the machine learning coding system." [0037] states "Next, the platform can process the set of pseudo code with a compiler to generate a set of programming language code that defines the computational pipeline for performing the task." [0038] states "Then, the visual programming platform can generate a graphical visualization of the computational pipeline defined by the set of programming-language code. For example, the visual programming platform can map each instruction or component from the set of programming language code to corresponding graphical components that represent data flows and relationships (e.g., inputs, outputs, operations, etc.) in a visual format.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Polleri and Du, because as Du states in [0029], "More particularly, the visual programming platforms described herein can provide an interactive and userfriendly interface for the graphical development of software code. The platform allows developers, regardless of their skill level, to create software programs by using graphical representations of functionalities instead of writing traditional text-based code. The platform transforms coding to a more intuitive and visually engaging task, making it accessible to a broader range of individuals. It can also enhance the efficiency, accuracy, and speed of the software development process by providing real-time visual feedback, drag-and-drop components, and customizable templates."
Regarding claim 12, the rejection of claim 11 is incorporated herein. Polleri teaches
an Al integration component (AIIC) configured to manage communications between the user interface, the chatbot, and one or more technical internal components of the data platform. (Fig. 5 shows the system. The bot system, as well as the model composition engine, ML Applications, and model execution engine in Fig. 1, are interpreted as the AI integration component, which receives (message-in) and transmits (message-out) data to the user interface (mobile device). Therefore, the bot system manages communications. Fig. 1 shows the many internal technical components of the system (all components except for those in the remote system).)
Regarding claim 13, the rejection of claim 12 is incorporated herein. Polleri teaches
wherein the technical internal components of the data platform comprise a connectivity framework, a design-time environment, and a runtime environment. (Fig. 1 shows the system, where the model composition engine (part of the AIIC) is connected to the ML platform. The connection to the ML Platform is interpreted as the connectivity framework. [0392] states "Machine learning services and their ontologies are defined in deployable service descriptions, which are used by the model composition engine 132 to assemble a composite service to trigger search for the best architectural model for run-time. The architectural model includes a pipeline 136 specifying any microservices routines 140, software modules 144, and infrastructure modules 148 along with any customizations and interdependencies. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) as associated with a service execution based also on the type of data inputted in the pipeline (volume, velocity ), class of pipelines ( classifier, recommender system ), thereby, service composition with a large number of candidate services is a multi-objective optimization problem that we could solve to automate the run-time adaption. During service composition, multiple services can combined in a specific order based on their input-output dependencies to produce a desired product graph that besides providing a solution required by a pipe line X with Data Input Y, it is also necessary to ensure fulfillment of end-to-end QoS requirements specified by the product team ( KPIs ) and the environment we are running." The service composition environment is interpreted as the design-time environment.)
Regarding claim 14, the rejection of claim 13 is incorporated herein. Polleri teaches
wherein the AIIC comprises a first adapter configured to connect to the connectivity framework, a second adapter configured to connect to the design-time environment, and a third adapter configured to connect to the runtime environment. (Fig. 1 shows the system, where the model composition engine (part of the AIIC) is connected to the ML platform. The connection to the ML Platform is interpreted as the connectivity framework. The broadest reasonable interpretation of an adapter is a component that allows for communication. As the ML Platform and the model composition engine communicate, there must be an adapter. [0392] states "Machine learning services and their ontologies are defined in deployable service descriptions, which are used by the model composition engine 132 to assemble a composite service to trigger search for the best architectural model for run-time.” As the model composition engine (part of the AIIC) assembles the service, it must have an adapter configured to connect to the service composition environment (design time environment). [0392] states "An Execution Engine schedules and invokes machine learning service instances to be composed and served at run-time." As the execution engine (part of the AIIC) serves at run-time, it must have an adapter configured to connect to the runtime environment.)
Regarding claim 15, the rejection of claim 13 is incorporated herein. Polleri does not appear to explicitly teach
wherein the AIIC is configured to manage the generating the graph, and wherein the generating the graph comprises translating one or more data transformation rules into graph source code in the design-time environment.
However, Du—directed to analogous art—teaches
wherein the AIIC is configured to manage the generating the graph, and wherein the generating the graph comprises translating one or more data transformation rules into graph source code in the design-time environment.( (Fig. 1 shows the system. The visual programming platform is interpreted as the AIIC, as it receives the natural language user input. [0068] states "Referring still to FIG. 1, the pseudocode generated by the machine learning coding system 90 can describe performance of the task. Specifically, the set of pseudocode can be a structured, high-level, human-readable representation of a computer algorithm, not intended for direct execution, but to convey the logical flow and operations of the algorithm." The pseudocode is interpreted as the data transformation rules. [0069] states "Next, the platform 30 can process the set of pseudocode with a compiler 95 to generate an initial version of the set of programming-language code 80 that defines the computational pipeline for performing the task." The code that defines the computational pipeline for performing the task is interpreted as the graph source code. The environment that the code is generated in is interpreted as the design-time environment.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Polleri and Du for the reasons given above in regards to claim 10.
Regarding claim 16, the rejection of claim 15 is incorporated herein. Polleri teaches
wherein the generating the graph further comprises updating connectivity between the data platform and one or more connected systems in the connectivity framework. ([0245] states "Once a core machine learning product is specified that solves a core problem for a particular instantiation, it is matched to a machine learning model. That machine learning model is specified from library components 168 that include a pipeline 136 that specifies various microservices routines 140, software modules 144, and / or infrastructure modules 148. Functional areas of the library components 168 are customized on a per-instantiation basis that adapts to a unique client's data, QoS, KPIs, and other requirements, e.g.: automated adaption of features for library components 168, automated bias elimination in a machine learning model, automated model training to achieve QoS and KPIs, and Automated microservices routine deployment configuration." Therefore, the library components are interpreted as the connected systems. As the components are customized and added to the pipeline per-instantiation, the generating of the pipeline includes updating the connectivity between the data platform and the library components (connected systems) Note that, as generating the graph requires the generation of the pipeline, generating the graph includes this step.)
Regarding claim 17, the rejection of claim 11 is incorporated herein. Polleri teaches
wherein training data for the AI model comprises language data and data regarding the data platform. ([0112] states "Intents allow the chatbot to understand what the user wants the chatbot to do. Intents are comprised of permutations of typical user requests and statements, which are also referred to as utterances (e.g., generate a classifier application, determine most efficient employee from employment records, etc.). As used herein, an utterance or a message may refer to a set of words (e.g., one or more sentences) exchanged during a conversation with a chatbot. Intents may be created by providing a name that illustrates user action (e.g., generate a classifier) and compiling a set of real-life user statements, or utterances that are commonly associated with triggering the action. Because the chatbot's cognition is derived from these intents, each intent may be created from a data set that is robust (one to two dozen utterances) and varied, so that the chatbot may interpret ambiguous user input. A rich set of utterances enables a chatbot to understand what the user wants when it receives messages like “Use data set A” or “Identify set A as the data”—messages that mean the same thing, but are expressed differently. Collectively, the intents, and the utterances that belong to them, make up a training corpus for the chatbot. By training a model with the corpus, a user can essentially turn that model into a reference tool for resolving end user input to a single intent. A user can improve the acuity of the chatbot's cognition through rounds of intent testing and intent training." The chatbot, interpreted as the AI model, is therefore trained using language data (the utterances) and the intent. As the intents include actions that can be performed on the system (interpreted as the data platform), the intents are data regarding the data platform.)
Regarding claim 18, the rejection of claim 14 is incorporated herein. Polleri teaches
wherein the AIIC further comprises a TTS/STT engine and a parser. ([0129] states "A user utterance can be in text form ( e.g., when the user types something as input to digital assistant 406 ) or in audio input or speech form ( e.g., when the user says something as input to digital assistant 406 ). The utterances are typically in a language spoken by the user 408. When a user input 410 is in speech form, the speech input is converted to text form utterances in that particular language and the text utterances are then processed by digital assistant 406. Various speech-to-text processing techniques may be used to convert a speech or audio input to a text utterance, which is then processed by digital assistant 406." This is interpreted as the STT engine. [0184] states "In some embodiments, intent modeler 314 for determining an intent of an end user based on one or more messages received by the bot system from the end user may use a natural language processor to tag the parts of speech (verb, noun, adjective), find lemmas/stems ( runs/running/ran- > run), and tag entities (Texas- > LOCATION). In some embodiments, intent modeler 314 may normalize the message. For example, "Mary ran to Texas” may become “PERSON run to LOCATION.” Intent modeler may also include logic to detect words which have the same meaning within an end user message" This is interpreted as the parser. [0137] states "These responses may be in the form of text or audio messages to the user and/or using simple user interface elements (e.g., select lists) that are presented to the user for the user to make selections" As the responses may be in audio or text form, one of ordinary skill in the art would realize that a TTS engine would be used.)
Regarding claim 19, the rejection of claim 18 is incorporated herein. Polleri teaches
wherein the parser is configured to: receive an answer from the chatbot; and ([0213] states "At 702, the technique can include receiving a first input describing a problem to be solved by generating a machine learning solution via interface 104, shown in FIG. 1."
determine, based on the answer, whether to convey the answer to the user interface, create a connection in the connectivity framework, modify the graph for the data pipeline, or extract one or more samples from a connected system. ([0216] states "At 708, the technique can include correlating the one or more text fragments to one or more machine learning models of the plurality of machine learning models stored in the library components 168, shown in FIG. 1. Each of the one or more machine learning models can have associated metadata. The associated metadata can be compared with the one more text fragments. In this way, model composition engine 132 maps the first input or query of a user 116 to certain phrases to determine the intent of the user 116. If the correlation of the one or more text fragments with the associated metadata exceeds a predetermined percentage, the model composition engine 132, shown in FIG. 1, iden tifies the machine learning model as being correlated to the one or more text fragments. The model composition engine 132 can recommend the correlated machine learning model to the user. The model composition engine 132 can present the correlated model via a user interface, chatbot, or display." [0245] states "Once a core machine learning product is specified that solves a core problem for a particular instantiation, it is matched to a machine learning model. That machine learning model is specified from library components 168 that include a pipeline 136 that specifies various microservices routines 140, software modules 144, and / or infrastructure modules 148. Functional areas of the library components 168 are customized on a per-instantiation basis that adapts to a unique client's data, QoS, KPIs, and other requirements, e.g.: automated adaption of features for library components 168, automated bias elimination in a machine learning model, automated model training to achieve QoS and KPIs, and Automated microservices routine deployment configuration." Therefore, the library components are interpreted as the connected systems. As the components are customized and added to the pipeline per-instantiation, the generating of the pipeline includes updating the connectivity between the data platform and the library components (connected systems).)
Regarding claim 20, Polleri teaches
One or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by a computing system, cause the computing system to perform operations comprising: ([0433]-[0444] state "As shown in the figure, computer system 2200 includes a processing unit 2204 that communicates with a number of peripheral subsystems via a bus subsystem 2202. These peripheral subsystems may include a processing acceleration unit 2206, an I/O subsystem 2208, a storage subsystem 2218 and a communications subsystem 2224. Storage sub system 2218 includes tangible computer-readable storage media 2222 and a system memory 2210. Bus subsystem 2202 provides a mechanism for letting the various components and subsystems of computer system 2200 communicate with each other as intended." [0442] states "Storage subsystem 2218 may also provide a tangible computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some embodiments. Software (programs, code modules, instructions) that when executed by a processor provide the functionality described above may be stored in storage subsystem 2218. These software modules or instructions may be executed by processing unit 2204. Storage subsystem 2218 may also provide a repository for storing data used in accordance with the present invention.")
with a chatbot comprising a trained artificial intelligence (AI) model, presenting a series of prompts to a user interface of a data platform to formulate a problem statement, the data platform comprising a connectivity framework and a design-time environment; and ([0112] states "Intents allow the chatbot to understand what the user wants the chatbot to do. Intents are comprised of permutations of typical user requests and statements, which are also referred to as utterances (e.g., generate a classifier application, determine most efficient employee from employment records, etc.). As used herein, an utterance or a message may refer to a set of words (e.g., one or more sentences) exchanged during a conversation with a chatbot. Intents may be created by providing a name that illustrates user action (e.g., generate a classifier) and compiling a set of real-life user statements, or utterances that are commonly associated with triggering the action. Because the chatbot's cognition is derived from these intents, each intent may be created from a data set that is robust (one to two dozen utterances) and varied, so that the chatbot may interpret ambiguous user input. A rich set of utterances enables a chatbot to understand what the user wants when it receives messages like “Use data set A” or “Identify set A as the data”—messages that mean the same thing, but are expressed differently. Collectively, the intents, and the utterances that belong to them, make up a training corpus for the chatbot. By training a model with the corpus, a user can essentially turn that model into a reference tool for resolving end user input to a single intent. A user can improve the acuity of the chatbot's cognition through rounds of intent testing and intent training." The chatbot, interpreted as the AI model, is therefore trained using language data (the utterances) and the intent. As the intents include actions that can be performed on the system (interpreted as the data platform), the intents are data regarding the data platform. [0065] states "At 204, the functionality includes receiving a second user input identifies a problem for which a solution can be generated by the machine learning application. In various embodiments the second user input can specify a type of problem that the user would like to implement machine learning for. In various embodiments, the problem can be identified through input of text via a user interface. In various embodiments, the problems can be entered as native language speech or text (e.g., through the use of a chatbot). The technique can decipher the native language to understand the goals of the machine learning model." Thus, the problem statement is formulated. Fig. 1 shows the system, where the model composition engine (part of the AIIC) is connected to the ML platform. The connection to the ML Platform is interpreted as the connectivity framework. [0392] states "Machine learning services and their ontologies are defined in deployable service descriptions, which are used by the model composition engine 132 to assemble a composite service to trigger search for the best architectural model for run-time. The architectural model includes a pipeline 136 specifying any microservices routines 140, software modules 144, and infrastructure modules 148 along with any customizations and interdependencies. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) as associated with a service execution based also on the type of data inputted in the pipeline (volume, velocity ), class of pipelines ( classifier, recommender system ), thereby, service composition with a large number of candidate services is a multi-objective optimization problem that we could solve to automate the run-time adaption. During service composition, multiple services can combined in a specific order based on their input-output dependencies to produce a desired product graph that besides providing a solution required by a pipe line X with Data Input Y, it is also necessary to ensure fulfillment of end-to-end QoS requirements specified by the product team (KPIs) and the environment we are running." The service composition environment is interpreted as the design-time environment.)
updating connectivity between the data platform and one or more connected systems in the connectivity framework.
Polleri does not appear to explicitly teach
generating a graph for a data pipeline based on the problem statement, the generating the graph comprising:
translating one or more data transformation rules into graph source code in the design-time environment; and
However, Du—directed to analogous art—teaches
generating a graph for a data pipeline based on the problem statement, the generating the graph comprising: ([0035] states "The visual programming platform can process the natural language description of the task with a machine learning coding system that includes one or more machine learned models to generate a set of pseudocode as an output of the machine learning coding system." [0037] states "Next, the platform can process the set of pseudo code with a compiler to generate a set of programming language code that defines the computational pipeline for performing the task." [0038] states "Then, the visual programming platform can generate a graphical visualization of the computational pipeline defined by the set of programming-language code. For example, the visual programming platform can map each instruction or component from the set of programming language code to corresponding graphical components that represent data flows and relationships (e.g., inputs, outputs, operations, etc.) in a visual format.")
translating one or more data transformation rules into graph source code in the design-time environment; and ([0068] states "Referring still to FIG. 1, the pseudocode generated by the machine learning coding system 90 can describe performance of the task. Specifically, the set of pseudocode can be a structured, high-level, human-readable representation of a computer algorithm, not intended for direct execution, but to convey the logical flow and operations of the algorithm." The pseudocode is interpreted as the data transformation rules. [0069] states "Next, the platform 30 can process the set of pseudocode with a compiler 95 to generate an initial version of the set of programming-language code 80 that defines the computational pipeline for performing the task." The code that defines the computational pipeline for performing the task is interpreted as the graph source code.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Polleri and Du, because as Du states in [0029], "More particularly, the visual programming platforms described herein can provide an interactive and userfriendly interface for the graphical development of software code. The platform allows developers, regardless of their skill level, to create software programs by using graphical representations of functionalities instead of writing traditional text-based code. The platform transforms coding to a more intuitive and visually engaging task, making it accessible to a broader range of individuals. It can also enhance the efficiency, accuracy, and speed of the software development process by providing real-time visual feedback, drag-and-drop components, and customizable templates."
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Polleri (US 2021/0081819 A1) and Du (US 2025/0094137 A1) as applied to claim 1 above, and further in view of Veale (“Algorithms that remember: model inversion attacks and data protection law”, 2018).
Regarding claim 10, the rejection of claim 1 is incorporated herein. The combination of Polleri and Du does not appear to explicitly teach
receiving an update to a compliance or data privacy regulation applicable to the data; and training the Al model based on the update.
However, Veale—directed to analogous art—teaches
receiving an update to a compliance or data privacy regulation applicable to the data; and training the Al model based on the update. (Page 9 states "The famous ‘right to be forgotten’ is a qualified right of a data subject to ‘the erasure of personal data concerning him or her’ (Article 17). Core reasons a data subject might want to erase herself from a model overlap with the general reasons for model control presented earlier—to erase insights about her she might dislike; to erase unwanted insights about a group she identifies as being part of; or to erase insights which might lead to data breaches. There are two main ways to erase data from a trained model. First, a model can be trained based upon an amended training dataset." Therefore, the update received is the amended dataset without the personal data, and the retraining trains the AI model based on the update.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Polleri and Du with the teachings of Veale because, as stated by Veale on page 9, "The famous ‘right to be forgotten’ is a qualified right of a data subject to ‘the erasure of personal data concerning him or her’ (Article 17). Core reasons a data subject might want to erase herself from a model overlap with the general reasons for model control presented earlier—to erase insights about her she might dislike; to erase unwanted insights about a group she identifies as being part of; or to erase insights which might lead to data breaches." Additionally, Veale teaches that there are two main ways to erase data from a trained model, one being the retraining of the model and the other amending the model after training. Veale states that amending the model after training is "not easy, and rarely currently possible in modern systems. Approaches for quick and easy ‘machine unlearning’ are only beginning to be proposed and are still largely unexplored, let alone at a stage ready for deployment [38,39]. Methods currently on the table cannot be retrofitted onto existing systems, and would require entire model pipelines to be re-conceived, with unclear effects." Therefore, one of ordinary skill in the art would be instead motivated to retrain the model.
Conclusion
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/J.T.P./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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