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 currently pending in application 18/847,868.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 20240924, 20241013, 20241021, 20241029, 20241211, 20250124, 20250228, 20250326, 20250521, 20250818, 20251126, 20251211, 20251231, 20260114, 20260309 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
Claims 4 and 13 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 4 and 13 recite “the received user input” which contains insufficient antecedent basis. Correction for proper antecedent basis is requested.
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 non-statutory subject matter, specifically an abstract idea.
Claims 1-20 are directed to a judicial exception (i.e., abstract idea), without providing a practical application, and without providing significantly more.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office’s 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1,
Claims 1-9 are directed toward a process (method). Claims 10-17 are directed toward an apparatus (system). Claims 18-20 are directed toward a computer program product having computer-readable tangible storage media (article of manufacture). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1],
Claims 1-20 are directed toward the judicial exception of an abstract idea. Independent claims 1, 10 and 18 are directed specifically to the abstract idea of managing data through user intent analysis and simulation-based decision-making.
Regarding independent claims 1, 10 and 18, the underlined limitations emphasized below correspond to the abstract ideas of the claimed invention:
A method for generating a model recommendation in a computing environment, the method comprising:
determining a user intent based on a user input for executing at least one intended task by a user; [Mental process / Cognitive evaluation. Interpreting user intent is a human activity.]
converting the determined user intent to one or more machine executable instructions; [Automating a manual act. Merely using a computer to perform this conversion does not add an inventive concept.]
generating a plurality of scenarios based on the one or more machine executable instructions; [Mathematical Concepts/ Method of Organizing Human Activity: Data processing / Simulation. Generating scenarios is a mathematical or logical exercise.]
evaluating an outcome of each of the plurality of scenarios by mapping it to one or more project objectives associated with the at least one intended task; and [Method of Organizing Human Activity: Evaluating and comparing options based on objectives is a decision-making activity.]
generating at least one model recommendation associated with the user intent based on the evaluation. [Mathematical Concepts/ Mental Processes: Decision automation. The final result is a business decision or recommendation rather than a technological change.]
As the underlined claim limitations above demonstrate, independent claims 1, 10 and 18 are directed to the abstract idea of Mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations); Mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, or opinion)) - The steps of determining intent and evaluating scenarios resemble cognitive processes that can be performed in the human mind; and Certain methods of organizing human activity (fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)) - Determining user intent and generating recommendations based on project objectives is analogous to evaluating business risk or managing personal tasks.
Dependent claims 2-9, 11-18, and 19-20 provide further details to the abstract idea of claims 1, 10 and 18 regarding the received data, therefore, these claims include mathematical concepts, mental processes, and certain methods of organizing human activities for similar reasons provided above for claims 1, 10 and 18.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Regarding Step 2A [prong 2],
Claims 1-20 fail to integrate the recited judicial exception into any practical application. The claims recite additional limitations which are hardware or software elements or particular technological environment, such as a “system”, a “non-transitory computer-readable storage medium”, a “computer-executable program”, a “processor”, a “computing environment”, “machine executable instructions”, a “visual display”, and an “interface”. However, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of an abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant’s claimed invention. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Examples where the Courts have found selecting a particular data source or type of data to be manipulated to be insignificant extra-solution activity include selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); Applicant’s limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits. Dependent claims 2-9, 11-18, and 19-20 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims respectively, but these features only serve to further limit the abstract idea of independent claims. Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B,
Claims 1-20 fail to amount to “significantly more” than an abstract idea. The claims recite additional limitations which are hardware or software elements or particular technological environment, such as a “system”, a “non-transitory computer-readable storage medium”, a “computer-executable program”, a “processor”, a “computing environment”, “machine executable instructions”, a “visual display”, and an “interface”. However, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Dependent claims 2-9, 11-18, and 19-20 merely recite further additional embellishments of the abstract idea of independent claims 1, 10 and 18 respectively, but these features only serve to further limit the abstract idea of independent claims 1, 10 and 18; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits. The addition of another abstract concept to the limitations of the claims does not render the claim other than abstract. Under the Interim Guidance on Patent Subject Matter Eligibility (PEG 2019), it specifically states that narrowing an abstract idea of claims do not resolve the claims of being "significantly more" than the abstract idea. Thus, the additional elements in the dependent claims only serve to further limit the abstract idea utilizing the computer components as a tool and/or generally link the use of the abstract idea to a particular technological environment.
Therefore, since there are no limitations in the claims 1-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as a combination and as an ordered combination adds nothing that is not already present when looking at the elements taken individually, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter under 35 U.S.C. § 101.
In order to overcome the 101 rejection above, the specification must clearly describe a technical improvement (e.g., how the recommendation engine specifically improves computer efficiency, reduces memory usage, or solves a novel technical problem, rather than a business problem). Furthermore, The claims should include limitations specifying how the scenario generation or evaluation is accomplished technically (e.g., a specific, novel algorithm, not just "a simulation").
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Polleri et al. (US 20210081819 A1).
As per independent Claims 1, 10, and 18, Polleri discloses a method (system; non-transitory computer-readable storage medium, having stored thereon a computer-executable program which, when executed by at least one processor, causes the at least one processor to) for generating a model recommendation in a computing environment (See at least Fig.1; Para 0011, “A chatbot can provide an intuitive interface to allow the data scientist to generate a machine learning application without considerable programming experience. A chatbot is able to translate natural language into a structured representation of a machine learning solution using a conversational interface. A chatbot can be used to indicate the location of data, select a type of machine learning solution, display optimal solutions that best meet the constraints, and recommend the best environment to deploy the solution.”; Para 0015, “… other embodiments are directed to systems, devices, and computer readable media associated with methods described herein.”; See also Para 0045, Para 0047, and Para 0105), the method comprising:
determining a user intent based on a user input for executing at least one intended task by a user (See at least Figs.1 and 4; Para 0105, “… In one aspect, techniques can be used for defining a machine learning solution, including receiving a first input (e.g., aural, textual, or GUI) describing a problem for the machine learning solution. A model composition engine 132, as shown in FIG. 1, can transcribe the first input into one or more text fragments. The model composition engine 132 can determine an intent of a user to create a machine learning architecture based at least in part on the one or more text fragments. …”; See also Para 0050, Para 0111, Para 0125, and Para 0128);
converting the determined user intent to one or more machine executable instructions (See at least Fig.1, Para 0105, “… A model composition engine 132, as shown in FIG. 1, can transcribe the first input into one or more text fragments. The model composition engine 132 can determine an intent of a user to create a machine learning architecture based at least in part on the one or more text fragments. The techniques can include correlating the one or more text fragments to one or more machine learning frameworks of a plurality of models. The techniques can include presenting (e.g., interface or audio) the one or more machine learning model to the user. The model composition engine 132 can receive a selection of one or more machine learning model (e.g., classification, recommender, reinforcement learning). The model composition engine 132 can receive several other user inputs including a second input identifying a data source for the machine learning architecture and a third input of one or more constraints (e.g., resources, location, security, or privacy) for the machine learning architecture. The model composition engine 132 can generate a plurality of code for the machine learning architecture based at least in part on the selected model, the second input identifying the data source, and the third input identifying the one or more constraints. The generated code can be stored in a memory”, Code is generated based on the intent determined from the user inputs);
generating a plurality of scenarios based on the one or more machine executable instructions; evaluating an outcome of each of the plurality of scenarios by mapping it to one or more project objectives associated with the at least one intended task (See at least Para 0069, “The performance requirements can include one or more Key Performance Indicators (KPI). Key Performance Indicators are measurable values that demonstrate how effectively the model is achieving its objectives. KPIs can be problem/solution specific and can include a measurement of the accuracy of the results of the machine learning application as compared with some ground truth test data.”; Para 0105, “Machine learning models are trained for generating predictive outcomes for code integration requests. … The model composition engine 132 can generate a plurality of code for the machine learning architecture based at least in part on the selected model, the second input identifying the data source, and the third input identifying the one or more constraints. The generated code can be stored in a memory.”; Para 0210, “An intent may include a goal that the end user would like to accomplish. An intent maps an end user input to actions that a backend system should perform for the end user. … Thus, one task of the bot system is to determining user intents from natural language utterances.”; See also Para 0264, Para 0295, and Para 0370); and
generating at least one model recommendation associated with the user intent based on the evaluation (See at least Fig.1; Para 0216, “… 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, identifies 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.”; See also Para 0011, Para 0047, and Para 0315).
As per Claims 2 and 11, Polleri discloses generating the plurality of scenarios comprises modification of one or more parameters associated with the at least one intended task, each modification associated with a corresponding outcome (See at least Para 0125, and Para 0295).
As per Claims 3, 12, and 19, Polleri discloses:
receiving the user input through a plurality of input streams, at least one of the plurality of input streams corresponding to a non-textual format (See at least Fig.4, Para 0129);
processing the plurality of input streams including converting the non-textual format of the at least one of the plurality of input streams to a textual format (See at least Fig.4, Para 0129);
generating one or more intent-based data sub-units by parsing and processing each of the plurality of input streams; generating machine executable instructions corresponding to the one or more intent-based data sub-units (See at least Fig.4, Para 0105-0107, Para 0130-0133, and Para 0155); and
combining the generated machine executable instructions corresponding to each of the plurality of processed input streams to a combined machine executable instruction (See at least Para 0107, Para 0137, and Para 0392).
As per Claims 4 and 13, Polleri discloses generating one or more intent-based data sub-units by parsing and processing the received user input (See at least Fig.4, Para 0130-0133, Para 0155); and converting the one or more intent-based data sub-units to machine executable instructions (See at least Para 0105 and Para 0107).
As per Claims 5 and 14, Polleri discloses receiving the user input as at least one of a text, image, video, gesture, and audio format (See at least Fig.1, Para 0105, and Para 0129).
As per Claims 6, Polleri discloses determining the user intent corresponding to a plurality of preferences of the user pertaining to execution of the at least one intended task (See at least Para 0100, Para 0105, and Para 0161).
As per Claims 7 and 15, Polleri discloses classifying the user intent as at least one of a temporal intent, a spatial intent, a fiscal intent, and a societal intent (See at least Para 0065, Para 0125, and Para 0210).
As per Claim 8 and 16, Polleri discloses evaluating the outcome of each of the plurality of scenarios (See at least Para 0295), the evaluating comprises:
generating a set of constraints based on the one or more project objectives (See at least Para 0105, and Para 0295);
mapping the outcome of each of the plurality of scenarios to the set of constraints (See at least Para 0105, Para 0210, Para 0231, Para 0295, and Para 0344); and
shortlisting one or more of the plurality of scenarios based on the mapping (See at least Para 0231, Para 0295, and Para 0344).
As per Claims 9, 17, and 20, Polleri discloses providing the at least one model recommendation to the user through a visual display (See at least Para 0216-0217); and enabling the user to execute a modification in real-time through an interface to the at least one model recommendation (See at least Para 0233, and Para 0352).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN P OUELLETTE whose telephone number is (571)272-6807. The examiner can normally be reached on M-F 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda C Jasmin, can be reached at telephone number (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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April 27, 2026
/JONATHAN P OUELLETTE/Primary Examiner, Art Unit 3629
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