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 .
Information Disclosure Statement
The information disclosure statement filed 6/22/22 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the IDS does not contain the application number of the application in which the information disclosure statement is being submitted (37 CFR 1.98 (a)(1)(i)). It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Drawings
The drawings filed 6/22/22 have been reviewed and accepted.
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.
Regarding Claim 1:
Claim 1 recites
A computer-implemented method comprising:
receiving, by one or more computer processors, data and associated metadata corresponding to a machine learning task from a user;
determining, by one or more computer processors, a task context and a problem domain based on the received data and the associated metadata;
based on the task context and the problem domain, identifying, by one or more computer processors, the machine learning task;
evaluating, by one or more computer processors, a match between the problem domain and one or more pre-compiled models;
based on the match, selecting, by one or more computer processors, at least two of the one or more pre-compiled models;
generating, by one or more computer processors, one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models;
executing, by one or more computer processors, the one or more multimodal model combinations with the received data and the associated metadata;
displaying, by one or more computer processors, results of the executed one or more multimodal model combinations to the user;
determining, by one or more computer processors, whether a level of error associated with the results is acceptable to the user based on a response from the user.
Step 1: “Is the claim to a process, machine, manufacture or composition of matter?”
The limitation recites at least one step or act, including receiving data and associated metadata corresponding to a machine learning task from a user. Thus, it’s a process, which is one of the statutory categories of invention. See MPEP 2106.03.
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”
The limitations ” determining, by one or more computer processors, a task context and a problem domain based on the received data and the associated metadata”, “based on the task context and the problem domain, identifying, by one or more computer processors, the machine learning task”, “evaluating, by one or more computer processors, a match between the problem domain and one or more pre-compiled models”, “based on the match, selecting, by one or more computer processors, at least two of the one or more pre-compiled models” and “determining, by one or more computer processors, whether a level of error associated with the results is acceptable to the user based on a response from the user.” under broadest reasonable interpretation, covers performance of the limitation in the mind. A user could easily, with the use of pen and paper, determine the task context and the problem domain based on the received data and metadata, identify the machine learning task, evaluate a match between the problem domain and one or more pre-compiled models, select at least two of the one or more pre-compiled models and determine a level of error associated with the results acceptable to the user. This is considered as Mental process under Abstract ideas as they can be performed in the human mind, including concepts, observation, evaluation, judgement and opinion. See MPEP 2106.04(a)(2)(III).
Step 2A (2): “Does the claim recite additional elements that integrate the judicial exception into a practical application?”
The judicial exceptions recited are not integrated into a practical application. In general, claim 1 only recites the additional elements of “receiving, by one or more computer processors, data and associated metadata corresponding to a machine learning task from a user”, “generating, by one or more computer processors, one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models;” , “executing, by one or more computer processors, the one or more multimodal model combinations with the received data and the associated metadata;” and “displaying, by one or more computer processors, results of the executed one or more multimodal model combinations to the user;”. These are mere data gathering, and outputting recited at a high level of generality and thus are insignificant extra-solution activity. See MPEP 2106.04(d)III. Additional element “A computer-implemented method comprising:” is generically recited, thus amounts to mere instructions to apply the judicial exception on a generic computer as discussed in MPEP 2106.05(f). In addition, all uses of the recited judicial exceptions require such data gathering and training, and as such, these limitations do not impose any meaningful limits on the claim.
Step 2B: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
The claim limitations reciting the abstract idea do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receiving, by one or more computer processors, data and associated metadata corresponding to a machine learning task from a user”, “generating, by one or more computer processors, one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models;” , “executing, by one or more computer processors, the one or more multimodal model combinations with the received data and the associated metadata;” and “displaying, by one or more computer processors, results of the executed one or more multimodal model combinations to the user;” are mere data gathering, instructions and display recited at a high level of generality. Additional elements “A computer-implemented method comprising:” is generically recited, thus amounts to mere instructions to apply the judicial exception on a generic computer as discussed in MPEP 2106.05(f). Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea and insignificant extra-solution activity, which do not provide an inventive concept. See MPEP 2106.05.
Therefore, the claim limitations do not include elements that mount to significantly more.
Claim 1 is not patent eligible.
Regarding Claim 2:
Claim 2 recites
The computer-implemented method of claim 1, further comprising:
responsive to determining the level of error associated with the results is not acceptable to the user, iteratively repeating, by one or more computer processors, a process of generating and executing the one or more multimodal models until the level of error associated with the results is acceptable to the user.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
Claim 2 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 2 is not patent eligible.
Regarding Claim 3:
Claim 3 recites
The computer-implemented method of claim 2, further comprising:
receiving, by one or more computer processors, additional data from the user to improve the results.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 2.
Claim 3 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 3 is not patent eligible.
Regarding Claim 4:
Claim 4 recites
The computer-implemented method of claim 1, further comprising:
decomposing, by one or more computer processors, the received data into two or more data sub-types;
based on the data sub-types, selecting, by one or more computer processors, a subset of the selected at least two of the one or more pre-compiled models.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
Claim 4 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 4 is not patent eligible.
Regarding Claim 5:
Claim 5 recites
The computer-implemented method of claim 1, wherein the received data is multimodal data.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
Claim 5 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 5 is not patent eligible.
Regarding Claim 6:
Claim 6 recites
The computer-implemented method of claim 1, wherein determining the task context and the problem domain comprises:
processing, by one or more computer processors, the received data and the associated metadata using a chatbot to read textual information;
applying, by one or more computer processors, one or more natural language processing techniques to the received data and the associated metadata to extract information corresponding to the task context and the problem domain.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
Claim 6 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 6 is not patent eligible.
Regarding Claim 7:
Claim 7 recites
The computer-implemented method of claim 1, wherein the machine learning task includes at least one of a regression, a classification, and a clustering.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 1.
Claim 7 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 7 is not patent eligible.
Regarding Claim 8:
Claim 8 recites
A computer program product comprising:
one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising:
program instructions to receive data and associated metadata corresponding to a machine learning task from a user;
program instructions to determine a task context and a problem domain based on the received data and the associated metadata;
based on the task context and the problem domain, program instructions to identify the machine learning task;
program instructions to evaluate a match between the problem domain and one or more pre-compiled models;
based on the match, program instructions to select at least two of the one or more pre-compiled models;
program instructions to generate one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models;
program instructions to execute the one or more multimodal model combinations with the received data and the associated metadata;
program instructions to display results of the executed one or more multimodal model combinations to the user;
program instructions to determine whether a level of error associated with the results is acceptable to the user based on a response from the user.
Step 1: “Is the claim to a process, machine, manufacture or composition of matter?”
The limitations recite of a computer-readable storage media. Thus, it’s a manufacture, which is one of the statutory categories of invention. Also, Specification Para [0050] mentions about the computer readable storage medium not to be construed as being transitory signals per se. See MPEP 2106.03.
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”
The limitations ” program instructions to determine a task context and a problem domain based on the received data and the associated metadata; ”, “based on the task context and the problem domain, program instructions to identify the machine learning task; ”, “program instructions to evaluate a match between the problem domain and one or more pre-compiled models; ”, “based on the match, program instructions to select at least two of the one or more pre-compiled models; ” and “program instructions to determine whether a level of error associated with the results is acceptable to the user based on a response from the user.” under broadest reasonable interpretation, covers performance of the limitation in the mind. A user could easily, with the use of pen and paper, estimate the operational behavior of the vehicle like for example unusual battery behavior is due to a manufacturing defect. This is considered as Mental process under Abstract ideas as they can be performed in the human mind, including concepts, observation, evaluation, judgement and opinion. See MPEP 2106.04(a)(2)(III).
Step 2A (2): “Does the claim recite additional elements that integrate the judicial exception into a practical application?”
The judicial exceptions recited are not integrated into a practical application. In general, claim 1 only recites the additional elements of “program instructions to receive data and associated metadata corresponding to a machine learning task from a user”, “program instructions to generate one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models”, “program instructions to execute the one or more multimodal model combinations with the received data and the associated metadata” and “program instructions to display results of the executed one or more multimodal model combinations to the user”. These are mere data gathering, and outputting recited at a high level of generality and thus are insignificant extra-solution activity. See MPEP 2106.04(d)III. Additional elements “A computer program product comprising” and ”one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising” are generically recited, thus amounts to mere instructions to apply the judicial exception on a generic computer as discussed in MPEP 2106.05(f). In addition, all uses of the recited judicial exceptions require such data gathering and training, and as such, these limitations do not impose any meaningful limits on the claim.
Step 2B: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
The claim limitations reciting the abstract idea do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of ““program instructions to receive data and associated metadata corresponding to a machine learning task from a user”, “program instructions to generate one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models”, “program instructions to execute the one or more multimodal model combinations with the received data and the associated metadata” and “program instructions to display results of the executed one or more multimodal model combinations to the user” are mere data gathering, instructions and display recited at a high level of generality. Additional elements “A computer program product comprising” and ”one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising” are generically recited, thus amounts to mere instructions to apply the judicial exception on a generic computer as discussed in MPEP 2106.05(f). Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea and insignificant extra-solution activity, which do not provide an inventive concept. See MPEP 2106.05.
Therefore, the claim limitations do not include elements that mount to significantly more.
Claim 8 is not patent eligible.
Regarding Claim 9:
Claim 9 recites
The computer program product of claim 8, the stored program instructions further comprising:
responsive to determining the level of error associated with the results is not acceptable to the user, program instructions to iteratively repeat a process of generating and executing the one or more multimodal models until the level of error associated with the results is acceptable to the user.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 8.
Claim 9 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 9 is not patent eligible.
Regarding Claim 10:
Claim 10 recites
The computer program product of claim 9, the stored program instructions further comprising:
program instructions to receive additional data from the user to improve the results.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 9.
Claim 10 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 10 is not patent eligible.
Regarding Claim 11:
Claim 11 recites
The computer program product of claim 8, the stored program instructions further comprising:
program instructions to decompose the received data into two or more data sub-types;
based on the data sub-types, program instructions to select a subset of the selected at least two of the one or more pre-compiled models.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 8.
Claim 11 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 11 is not patent eligible.
Regarding Claim 12:
Claim 12 recites
The computer program product of claim 8, wherein the received data is multimodal data.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 8.
Claim 12 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 12 is not patent eligible.
Regarding Claim 13:
Claim 13 recites
The computer program product of claim 8, wherein the stored program instructions to determine the task context and the problem domain comprise:
program instructions to process the received data and the associated metadata using a chatbot to read textual information;
program instructions to apply one or more natural language processing techniques to the received data and the associated metadata to extract information corresponding to the task context and the problem domain.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 8.
Claim 13 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 13 is not patent eligible.
Claim 14 recites
The computer program product of claim 8, wherein the machine learning task includes at least one of a regression, a classification, and a clustering.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 8.
Claim 14 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 14 is not patent eligible.
Regarding Claim 15:
Claim 15 recites
A computer system comprising:
one or more computer processors;
one or more computer readable storage media;
program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising:
program instructions to receive data and associated metadata corresponding to a machine learning task from a user;
program instructions to determine a task context and a problem domain based on the received data and the associated metadata;
based on the task context and the problem domain, program instructions to identify the machine learning task;
program instructions to evaluate a match between the problem domain and one or more pre-compiled models;
based on the match, program instructions to select at least two of the one or more pre-compiled models;
program instructions to generate one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models;
program instructions to execute the one or more multimodal model combinations with the received data and the associated metadata;
program instructions to display results of the executed one or more multimodal model combinations to the user;
program instructions to determine whether a level of error associated with the results is acceptable to the user based on a response from the user.
Step 1: “Is the claim to a process, machine, manufacture or composition of matter?”
The limitations recite of comprising of a processor and memory to store instructions as part of a system, i.e. machine. Thus, it’s a machine, which is one of the statutory categories of invention. See MPEP 2106.03.
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”
The limitations ” program instructions to determine a task context and a problem domain based on the received data and the associated metadata; ”, “based on the task context and the problem domain, program instructions to identify the machine learning task; ”, “program instructions to evaluate a match between the problem domain and one or more pre-compiled models; ”, “based on the match, program instructions to select at least two of the one or more pre-compiled models; ” and “program instructions to determine whether a level of error associated with the results is acceptable to the user based on a response from the user.” under broadest reasonable interpretation, covers performance of the limitation in the mind. A user could easily, with the use of pen and paper, estimate the operational behavior of the vehicle like for example unusual battery behavior is due to a manufacturing defect. This is considered as Mental process under Abstract ideas as they can be performed in the human mind, including concepts, observation, evaluation, judgement and opinion. See MPEP 2106.04(a)(2)(III).
Step 2A (2): “Does the claim recite additional elements that integrate the judicial exception into a practical application?”
The judicial exceptions recited are not integrated into a practical application. In general, claim 1 only recites the additional elements of “program instructions to receive data and associated metadata corresponding to a machine learning task from a user”, “program instructions to generate one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models”, “program instructions to execute the one or more multimodal model combinations with the received data and the associated metadata” and “program instructions to display results of the executed one or more multimodal model combinations to the user”. These are mere data gathering, and outputting recited at a high level of generality and thus are insignificant extra-solution activity. See MPEP 2106.04(d)III. Additional elements “A computer system comprising”, “one or more computer processors”, “one or more computer readable storage media” and ” program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising:” are generically recited, thus amounts to mere instructions to apply the judicial exception on a generic computer as discussed in MPEP 2106.05(f). In addition, all uses of the recited judicial exceptions require such data gathering and training, and as such, these limitations do not impose any meaningful limits on the claim.
Step 2B: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
The claim limitations reciting the abstract idea do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of ““program instructions to receive data and associated metadata corresponding to a machine learning task from a user”, “program instructions to generate one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models”, “program instructions to execute the one or more multimodal model combinations with the received data and the associated metadata” and “program instructions to display results of the executed one or more multimodal model combinations to the user” are mere data gathering, instructions and display recited at a high level of generality. Additional elements “A computer system comprising”, “one or more computer processors”, “one or more computer readable storage media” and ” program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising:” are generically recited, thus amounts to mere instructions to apply the judicial exception on a generic computer as discussed in MPEP 2106.05(f). Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea and insignificant extra-solution activity, which do not provide an inventive concept. See MPEP 2106.05.
Therefore, the claim limitations do not include elements that mount to significantly more.
Claim 15 is not patent eligible.
Regarding Claim 16:
Claim 16 recites
The computer system of claim 15, the stored program instructions further comprising:
responsive to determining the level of error associated with the results is not acceptable to the user, program instructions to iteratively repeat a process of generating and executing the one or more multimodal models until the level of error associated with the results is acceptable to the user.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 16.
Claim 16 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 16 is not patent eligible.
Regarding Claim 17:
Claim 17 recites
The computer system of claim 16, the stored program instructions further comprising: program instructions to receive additional data from the user to improve the results.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 16.
Claim 17 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 17 is not patent eligible.
Regarding Claim 18:
Claim 18 recites
The computer system of claim 15, the stored program instructions further comprising:
program instructions to decompose the received data into two or more data sub-types;
based on the data sub-types, program instructions to select a subset of the selected at least two of the one or more pre-compiled models.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 15.
Claim 18 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 18 is not patent eligible.
Regarding Claim 19:
Claim 19 recites
The computer system of claim 15, wherein the received data is multimodal data.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 15.
Claim 19 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 19 is not patent eligible.
Regarding Claim 20:
Claim 20 recites
The computer system of claim 15, wherein the stored program instructions to determine the task context and the problem domain comprise:
program instructions to process the received data and the associated metadata using a chatbot to read textual information;
program instructions to apply one or more natural language processing techniques to the received data and the associated metadata to extract information corresponding to the task context and the problem domain.
This provides a further description of the data within the abstract idea, as discussed with regards to claim 15.
Claim 20 does not include any additional elements (i.e. elements other than the abstract idea) that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea, and is therefore also ineligible under 35 U.S.C 101.
Claim 20 is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 5-8, 12-15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Le United States Patent Application Publication US 2022/0094647 in view of Polleri United States Patent Application Publication US 2021/0081819.
Regarding claim 1, Le discloses a computer-implemented method (Le, Abstract Line 1-3 Methods and systems are described for generating dynamic interface options using machine learning models; Le, para [0053], computer interpreted as device with processing circuitry that runs and stores applications) comprising:
receiving, by one or more computer processors, data and associated metadata corresponding to a machine learning task from a user (Le, para [0052], processors for sending and receiving commands; Le, para [0037], with regards to fig. 3, System 300 may receive user action data based on user actions (interpreted as data corresponding to a machine learning task from the user) with user interfaces during a device session. The user action data (e.g., data 304) may include metadata (interpreted as associated metadata));
determining, by one or more computer processors, a task context and a problem domain based on the received data and the associated metadata (Le, para [0041], transfer learning allows system 300 to deal with current scenarios (e.g., detecting user intent) by leveraging the already existing labeled data of some related task or domain. System 300 may store knowledge gained through other tasks and apply it to the current task. This is equivalent to determining a task context and a problem domain based on the received data and the associated metadata);
selecting at least two of the one or more pre-compiled models (Le, para [0020], the system may include different supervised and unsupervised machine learning models and human devised rules that may reflect accumulated domain (interpreted as problem domain) expertise. The system may include deep learning models that may include neural factorization machines, deep and wide, and multimodal models (interpreted as pre-compiled models));
generating, by one or more computer processors, one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models (Le, para [0048], First model 310, second model 312, and third model 308 may receive inputs and generate outputs that are processed by fourth model 314. Fourth model 314 may then generate a final classification 318. Fourth model 314 may include ensemble prediction. For example, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone);
executing, by one or more computer processors, the one or more multimodal model combinations with the received data and the associated metadata (Le, para [0042], with regards to fig. 3, textual image and metadata 304 serves as an input to multimodal 310. System 300 processes this information in first model 310 which is equivalent to executing the one or more multimodal model combinations with the received data and the associated metadata);
displaying, by one or more computer processors, results of the executed one or more multimodal model combinations to the user (Le, para [0075], with regards to fig. 5, At step 516, process 500 generates the dynamic conversational response during the conversational interaction. For example, the system may generate, at the user interface, the dynamic conversational response during the conversational interaction); and
determining, by one or more computer processors, whether a level of error associated with the results is acceptable to the user based on a response from the user (Le, para [0058], update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction. In further cases, uses backpropagation of error. ‘Reflective of the magnitude’ is interpreted as determining a level).
Le does not disclose:
based on the task context and the problem domain, identifying, by one or more computer processors, the machine learning task;
evaluating, by one or more computer processors, a match between the problem domain and one or more pre-compiled models;
based on the match, selecting, by one or more computer processors, at least two of the one or more pre-compiled models;
Polleri discloses:
based on the task context and the problem domain, identifying, by one or more computer processors, the machine learning task (Polleri, [0213], historical data interpreted as context and problem to be solved description interpreted as problem domain; Polleri, para [0214-215], transcribes the inputs to be text fragments. Correlates machine learning models with text fragments represents matching);
evaluating, by one or more computer processors, a match between the problem domain and one or more pre-compiled models (Polleri, para [0216], analyzes metadata of the frameworks of the machine learning models with the text fragments, where the text fragments are generated from the problem to be solved);
based on the match, selecting one or more pre-compiled models (Polleri, para [0217], performs a selection based on the match);
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the processor selection of Le to include the steps of Polleri that lead to a selection. The motivation for doing so would have been to allow for adaptation at run-time due to changes in data (Polleri, para [0008]).
Regarding claim 5, Le in view of Polleri discloses the computer implemented method of claim 1. Le additionally discloses wherein the received data is multimodal data (Le, para [0037], with regards to fig. 3, dynamic interface options using machine learning models featuring multi-modal feature inputs).
Regarding claim 6, Le in view of Polleri discloses the computer implemented method of claim 1. Le additionally discloses processing, by one or more computer processors, the received data and the associated metadata using a chatbot to read textual information (Le, para [0063-64], conversational interaction with a user interface represents a chatbot. Data and feature information received); and
applying, by one or more computer processors, one or more natural language processing techniques to the received data and the associated metadata to extract information corresponding to the task context and the problem domain (Le, para [0038, 40-41], Line 1-4 System 300 may also receive information, which may use a Bidirectional Encoder Representations from Transformers (BERT) language model for performing natural language processing),
Regarding claim 7, Le in view of Polleri discloses the computer-implemented method of claim 1. Le additionally discloses wherein the machine learning task includes at least one of a regression, a classification, and a clustering (Le, para [0020], system may include non-deep Learning classification models that may include, but are not limited to, logistic regression and Naive Bayesian).
Claims 8 and 15 recite substantially similar limitations to claim 1 and are thus similarly rejected.
Claims 12 and 19 recite substantially similar limitations to claim 5 and are thus similarly rejected.
Claims 13 and 20 recite substantially similar limitations to claim 6 and are thus similarly rejected.
Claim 14 recites substantially similar limitations to claim 7 and is thus similarly rejected.
Claim(s) 2-3, 9-10 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Le United States Patent Application Publication US 2022/0094647 in view of Polleri United States Patent Application Publication US 2021/0081819 in further view of Orhan United States Patent US 11,748,568.
Regarding claim 2, Le in view of Polleri discloses the computer implemented method of claim 1.
Le discloses a level of error and a backpropagation process (Le, para [0058]). However, Le in view of Polleri does not disclose responsive to determining the level of error associated with the results is not acceptable to the user, iteratively repeating, by one or more computer processors, a process of generating and executing the one or more multimodal models until the level of error associated with the results is acceptable to the user.
Ohran discloses responsive to determining the level of error associated with the results is not acceptable to the user, iteratively repeating, by one or more computer processors, a process of generating and executing the one or more multimodal models until the level of error associated with the results is acceptable to the user (Col 17 Line 15-24 Clients on whose behalf the automated anomaly detection is being performed may provide client feedback 695, indicating whether the clients found the particular combinations of metrics and statistics that were selected using the model 610 useful or not (determining if the level of error is acceptable to the user or not). Such client feedback 695 may also be used to improve the model 610 over time, e.g., by labeling combinations of statistics and metrics used for re-training the model (iteratively repeating process of generating and executing the models) based on whether the client found them useful or not (re-training until results is acceptable to the user)).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify taught by Le’s dynamic interface options using machine learning models to include Orhan’s anomaly detection method. The motivation for doing so would have been to improve the user experience of application administrators and improve the user experience of application end users. (Orhan, Col 4 Line 20-35).
Regarding claim 3, Le in view of Polleri in further view of Orhan discloses the computer implemented method of claim 2.
Orhan additionally discloses further comprising: receiving, by one or more computer processors, additional data from the user to improve the results (Orhan, col 5, rows 13-41, user defined anomaly specification represents data from a user that is additional that is deemed an anomaly and therefore used to improve results).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify taught by Le’s dynamic interface options using machine learning models to include Orhan’s anomaly detection method. The motivation for doing so would have been to improve the user experience of application administrators and improve the user experience of application end users. (Orhan, Col 4 Line 20-35).
Claims 9 and 16 recite substantially similar limitations to claim 2 and are thus similarly rejected.
Claims 10 and 17 recite substantially similar limitations to claim 3 and are thus similarly rejected.
Claim(s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Le United States Patent Application Publication US 2022/0094647 in view of Polleri United States Patent Application Publication US 2021/0081819 in further view of Arici United States Patent 11,928,182.
Regarding claim 4, Le in view of Polleri discloses the computer implemented method of claim 1. Le in view of Polleri does not disclose the additional limitations of the present claim.
Arici discloses further comprising:
decomposing, by one or more computer processors, the received data into two or more data sub-types (Arici, col 8, rows 42-46, If a sufficient amount of labeled training data 201 is available, the labeled training data may be split up into two subsets 207A and 207B (interpreted as decomposing received data into data sub-types)); and
based on the data sub-types, selecting, by one or more computer processors, a subset of the selected at least two of the one or more pre-compiled models (Arici, col 8, rows 45-57, The system first selects the base models and trains them on subset 207A. It then selects the stacking model 213 to be trained on predictions from 207B where base models and stacking meta-model are a subset of pre-compiled models).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify taught by Le’s dynamic interface options using machine learning models to include Arici’s iterative stacking method. The motivation for doing so would have been to avoid overfitting, which is a known problem for some traditional types of stacking-based ensemble preparation approaches (Arici, Col 2 Line 46-50).
Claims 11 and 18 recite substantially similar limitations to claim 4 and are thus similarly rejected.
Conclusion
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/HOPE C SHEFFIELD/ Primary Examiner, Art Unit 2141
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