DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are pending in this Application.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, and 16 recites in part:
generating or receiving an input for an artificial intelligence (AI) model, the input comprising:
a description of a set of categories to which a parameter or component of a manufacturing system may belong, a plurality of examples each comprising
a parameter or component name and an indication of which category of the set of categories the parameter or component belongs to, and a name of a target parameter or component to be categorized according to the set of categories;
…generate an output comprising a category associated with the target parameter or component”;
Claims 9 recites in part:
obtaining, by a processing device, a plurality of training data, the training data comprising:
names of a plurality of parameters or components of one or more manufacturing systems, and
categorizations of each of the plurality of parameters or components;
…receive as input a name of a target parameter or component, and
generate output indicating classification of the component into a category corresponding to the categorizations of each of the plurality of parameters or components”.
Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation (BRI), covers steps that are easily performed in the mind and thus covers a mental process. See MPEP 210604(a)(2), III. These steps involves steps of collecting data (receive input including description of categories and data, and target data), generate an output associating a target data item to a class/category which is a step of data evaluation and judgment, which involves the classification of data based on the classes/categories and data examples. These steps were steps that were performed manually by a user according to the description. Thus, the claimed steps encompasses a mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion.
These judicial exceptions are not integrated into a practical application because the additional elements such as: “using an AI model to generate the output” as described above represents mere instructions to “apply” the abstract idea or to generally link the use of the judicial exception to a technological environment of an intended computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(h)). The claim does not provide any details about how the model/trained ANN operates or how the determination and outputting is made, and the plain meaning of “processing to generate” encompasses mental observations or evaluations. The step of receiving data including descriptions of categories, training data/examples of data association, and target data to be classified, recited at a high level of generality, represents mere data gathering to be used in the abstract idea. Claim 16, recites a non-transitory memory and a processing device to perform the method or abstract idea steps described above, and which are recited at high level of generality, which represents mere instructions to “apply” the abstract idea or to generally link the use of the judicial exception to a technological environment of an intended computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(h)). Claim 1 and 16 further recite “performing a corrective action in view of the category associated with the target parameter or component” which are recited at a high level of generality and represents an attempt to limit the judicial exception to a intended particular field of use and/or technological environment such as a parameter or component of a manufacturing system (see MPEP 2106.05(f) and 2106.05(h)). Claim 9 further recites “updating one or more parameters of a trained artificial intelligence (AI) model to generate a retrained AI model” and “using the retrained model to generate the output” recited at high level of generality represents mere instructions to “apply” the abstract idea or to generally link the use of the judicial exception to a technological environment of an intended computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims 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 “using an AI model to generate the output” as described above represents mere instructions to “apply” the abstract idea or to generally link the use of the judicial exception to a technological environment of an intended computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(h)). The claim does not provide any details about how the model/trained ANN operates or how the determination and outputting is made, and the plain meaning of “processing to generate” encompasses mental observations or evaluations. The step of receiving data including descriptions of categories, training data/examples of data association, and target data to be classified, recited at a high level of generality, represents mere data gathering to be used in the abstract idea. Claim 16, recites a non-transitory memory and a processing device to perform the method or abstract idea steps described above, and which are recited at high level of generality, which represents mere instructions to “apply” the abstract idea or to generally link the use of the judicial exception to a technological environment of an intended computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(h)). Claim 1 and 16 further recite “performing a corrective action in view of the category associated with the target parameter or component” which are recited at a high level of generality and represents an attempt to limit the judicial exception to a intended particular field of use and/or technological environment such as a parameter or component of a manufacturing system (see MPEP 2106.05(f) and 2106.05(h)). Claim 9 further recites “updating one or more parameters of a trained artificial intelligence (AI) model to generate a retrained AI model” and “using the retrained model to generate the output” recited at high level of generality represents mere instructions to “apply” the abstract idea or to generally link the use of the judicial exception to a technological environment of an intended computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are not patent eligible.
Claims 2-8, 10-15, and 17-20 depend from claims 1, 9, and 16, respectively, and thus recite the limitations and the abstract ideas of their respective parent claims.
The dependent claims 2-8, 10-15, and 17-20 recites additional elements such as “the AI model comprises a large language model (LLM)”, recited at ahigh level of generality, and which represents mere instructions to “apply” the abstract idea or to generally link the use of the judicial exception to a technological environment of an intended computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(h)); “wherein the set of categories comprises a set of subsystems of the manufacturing system”, recited at high level of generality represents mere data gathering to be used in the abstract idea; “”wherein the set of categories comprises a degree of criticality of the parameter, wherein criticality is determined by a strength of a relationship between values of the parameter and properties of a product processed by the manufacturing system” recited at high level of generality represents mere data gathering to be used in the abstract idea; “wherein the target parameter or component comprises an equipment constant or a sensor of the manufacturing system”, recited at high level of generality represents mere data gathering to be used in the abstract idea; “wherein each of the plurality of examples further comprises an abbreviated parameter or component name, and wherein the output from the AI model further comprises a target abbreviated name in association with the target parameter or component”, recited at high level of generality represents mere data gathering to be used in the abstract idea; “wherein the set of categories comprises a data access classification, and wherein data associated with the target parameter or component is to be provided to a set of users based on the data access classification”, recited at high level of generality represents mere data gathering to be used in the abstract idea; “wherein the target parameter or component comprises an equipment constant, and wherein the method further comprises: determining that a value of the equipment constant associated with the manufacturing system is different than a reference value” recited at high level of generality represents an abstract idea of a mental process of comparing data. “wherein the corrective action comprises updating the equipment constant in further view of the reference value”, recited at high level of generality which are recited at a high level of generality and represents an attempt to limit the judicial exception to a intended particular field of use and/or technological environment such as a parameter or component of a manufacturing system (see MPEP 2106.05(f) and 2106.05(h)). The claims further recited “wherein updating one or more parameters of the trained AI model to generate the retrained AI model comprises performing parameter-efficient fine-tuning operations to update a subset of parameters of the AI model, wherein the parameter-efficient fine-tuning comprises low-rank adaptation operations” recited at high level of generality represents mere instructions to “apply” the abstract idea or to generally link the use of the judicial exception to a technological environment of an intended computer cannot provide an inventive concept as stated by the courts (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application, do not amount to significantly more than the judicial exception, and do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are 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) 1-2, 4-6, 8-9, 12-14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Baek et al (US 202302744985, cited in the IDS) in view of Kelly et al (US 20240289560, cited in the IDS).
As per claim 1, Baek teaches a method comprising:
generating or receiving an input for an artificial intelligence (AI) model (see [0046] “The preprocessing module 102 may acquire electrical measurement parameters corresponding to each of semiconductor manufacturing parameters…”), the input comprising:
processing the input using the AI model to generate an output comprising a category associated with the target parameter or component (see [0048] “The preprocessing module 102 may group the acquired electrical measurement parameters (EPMs) according to the degree of correlation. That is, the preprocessing module 102 may classify the electrical measurement parameters into a plurality of groups according to the degree of mutual correlation of the electrical measurement parameters”; also, see [0053], [0068] and fig. 3]; see [0083] and fig. 6); and
performing a corrective action in view of the category associated with the target parameter or component (see [0080] “The prediction model 104 may predict a figure of merit (FOM) of a semiconductor device by using data values of electrical measurement parameters belonging to a corresponding group for each group grouped by the preprocessing module 102 as input. Here, the figure of merit of the semiconductor device may include power delay product (PDP), frequency, ring oscillator delay (ROD), power dissipation, IR drop, etc.” [0085] “] In addition, the prediction module 104 may set a data range of an electrical measurement parameter capable of optimizing the figure of merit (FOM) of the semiconductor device among the electrical measurement parameters in each group. Specifically, the prediction module 104 may set the data range of the electrical measurement parameter capable of optimizing the figure of merit (FOM) of the semiconductor device by setting a data range within a data distribution range with the effective correlation axis of each group as a reference. That is, referring to FIG. 2, the data range of the electrical measurement parameter capable of optimizing the figure of merit (FOM) of the semiconductor device is set by limiting the data range within the data distribution range S2 by the A-axis and the B-axis, which are the correlation axes of the first electrical measurement parameter EPM.sub.1 and the second electrical measurement parameter EPM.sub.2”, thus, a corrective action is take based on/ in view of the category associated with the target parameter or component).
While Baek clearly teaches associating parameters or components to a category of a set of categories and it would be assumed that different categories exists and that in order to perform such classification, a training of the model was performed previously with training data including examples of parameters associated to a category, Baek does not explicitly teach the input comprising: a description of a set of categories to which a parameter or component of a manufacturing system may belong, a plurality of examples each comprising a parameter or component name and an indication of which category of the set of categories the parameter or component belongs to, and
a name of a target parameter or component to be categorized according to the set of categories.
However, Kelly teaches a system comprising generating an input comprising: a description of a set of categories to which a parameter or component of a manufacturing system may belong (see 0054 “In some embodiments, a generative text request may include a request to generate a generative text document based on one or more input texts and/or other metadata associated with a topic associated with a desired controlled text document. The one or more input texts, for example, may include one or more request text fields that correspond to one or more controlled text fields of a desired controlled text document. In some examples, the other metadata may include one or more controlling rules for the desired controlled text document, such as one or more required pieces of information, a reading difficulty level, and/or the like. In some examples, the other metadata may include a reference to one or more evidentiary documents (e.g., case documentation for a healthcare appeal, etc.”) for supporting and/or generating text for a controlled text field.”, the input “generative text request” comprises the rules or set of categories such as difficulty level), a plurality of examples each comprising a parameter or component name and an indication of which category of the set of categories the parameter or component belongs to (see [0054] “…The one or more input texts, for example, may include one or more request text fields that correspond to one or more controlled text fields of a desired controlled text document. In some examples, the other metadata may include one or more controlling rules for the desired controlled text document, such as one or more required pieces of information, a reading difficulty level, and/or the like …”; also, see [0060], [0062]; also, see [0064] “In some examples, each of the historical text documents may include one or more document type classifications. In addition, or alternatively, a generative text request may include one or more request type classifications. The one or more one or more request type classifications, for example, may be manually entered by a user and/or extracted from one or more case attributes associated with the generative text request”; also, see [0104-0105] “a contextual classification is generated for the one or more request text fields. The contextual classification, for example, may be generated using a machine learning classifier model 410.…In some examples, the document data store 404 includes a plurality of historical text documents and a plurality of contextual classification labels respectively corresponding to the plurality of historical text documents…”; also, see [0110]), and a name of a target parameter or component to be categorized according to the set of categories (see [0057] “ the term “request text fields” refers to a component of a generative text request. A request text field may include a segment of text manually generated for a particular case corresponding to a desired controlled text document. Each request text field, for example, may include a natural language text that reflects a decision for a particular case that corresponds to a controlled text field of a desired controlled text document and may not satisfy one or more controlling rules for a desired controlled text document”) and machine learning classifier/AI model (see Fig. 1 classifier 410).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek’s invention to include the input comprising: a description of a set of categories to which a parameter or component of a manufacturing system may belong, a plurality of examples each comprising a parameter or component name and an indication of which category of the set of categories the parameter or component belongs to, and a name of a target parameter or component to be categorized according to the set of categories as taught by Kelly in order to use this input to perform a classification of a desired target parameter or component (see [0116] “… a contextual classification is generated for the generative text request 406 based on the one or more text request fields. For instance, the contextual classification may be generated using a machine learning classifier model 410. The same machine learning classifier model 410 may be previously used to generate one or more of a plurality of contextual classifications for the historical text documents of the document data store 404. …”; also, see [0118]).
As per claim 2, Baek-Kelly teaches the method of claim 1, Kelly further teaches wherein an AI model comprises a large language model (LLM) (see [0006] “identify, from the refined document subset, a prompt document subset based on the one or more request field embeddings; generate, using a large language model (LLM), one or more generative text fields using a generative model prompt based on the prompt document subset and the one or more request text fields; and provide a request response comprising the generative text document based on the one or more generative text fields”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek-Kelly’s combination as taught above to include wherein an AI model comprises a large language model (LLM) as taught by Kelly in order to generate text in response to a prompt in order to generate the text input used to be classified (see [0133] “ The LLM 420 may include any type of model configured, trained, and/or the like to generate natural language text in response to a textual prompt, such as the generative model prompts 416 of the present disclosure. The LLM 420 may include any type of LLM, such as a generative pre-trained transformer, and/or the like) and because LLM models are widely used to classify input text (see [0138]).
As per claim 4, Baek-Kelly teaches the method of claim 1, Baek further teaches wherein the set of categories comprises a degree of criticality of the parameter (see [0048]), wherein criticality is determined by a strength of a relationship between values of the parameter and properties of a product processed by the manufacturing system (see [0048]: "The preprocessing module 102 may group the acquired electrical measurement parameters (EPMs) according to the degree of correlation. That is, the preprocessing module 102 may classify the electrical measurement parameters into a plurality of groups according to the degree of mutual correlation of the electrical measurement parameters”; also, see [0095] “Next, the apparatus for setting the semiconductor parameter 100 may classify the acquired electrical measurement parameters into a plurality of groups according to the degree of correlation (S103). The apparatus for setting the semiconductor parameter 100 may calculate variance inflation factors between the acquired electrical measurement parameters, respectively, and classify the electrical measurement parameters into a plurality of groups based on the calculated variance inflation factor.”).
As per claim 5, Baek-Kelly teaches the method of claim 1, Baek further teaches wherein the target parameter or component comprises an equipment constant or a sensor of the manufacturing system (see [0047] “The electrical parameter measurement (EPM) is a measured parameter value capable of estimating a
semiconductor manufacturing parameter after a semiconductor manufacturing process is performed using the semiconductor manufacturing parameter as a target value"; see [0048] “The preprocessing module 102 may group the acquired electrical measurement parameters (EPMs) according to the degree of correlation. That is, the preprocessing module 102 may classify the electrical measurement parameters into a plurality of groups according to the degree of mutual correlation of the electrical measurement parameters”; also, see [0053], [0068] and fig. 3; see [0083] and fig. 6; thus, the target parameter or component comprises an equipment constant or a sensor of the manufacturing system).
As per claim 6, Baek-Kelly teaches the method of claim 1, Kelly further teaches wherein each of the plurality of examples further comprises an abbreviated parameter or component name, and wherein the output from the AI model further comprises a target abbreviated name in association with the target parameter or component (see [0054] “…The one or more input texts, for example, may include one or more request text fields that correspond to one or more controlled text fields of a desired controlled text document. In some examples, the other metadata may include one or more controlling rules for the desired controlled text document, such as one or more required pieces of information, a reading difficulty level, and/or the like …”; also, see [0060], [0062]; also, see [0064] “In some examples, each of the historical text documents may include one or more document type classifications. In addition, or alternatively, a generative text request may include one or more request type classifications. The one or more one or more request type classifications, for example, may be manually entered by a user and/or extracted from one or more case attributes associated with the generative text request”; also, see [0104-0105] “a contextual classification is generated for the one or more request text fields. The contextual classification, for example, may be generated using a machine learning classifier model 410.…In some examples, the document data store 404 includes a plurality of historical text documents and a plurality of contextual classification labels respectively corresponding to the plurality of historical text documents…”; also, see [0070] “ a machine learning classifier model is trained to assign a contextual classification from a plurality of candidate contextual classifications to a generative text request based on one or more generative text fields of the generative text request. In some examples, the machine learning classifier model may be trained, using one or more supervisory training techniques (e.g., backpropagation of errors, etc.) to assign a contextual classification based on a plurality of historical contextual classifications respectively assigned to a plurality of historical text documents”; see [0110]).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek’s invention to include wherein each of the plurality of examples further comprises an abbreviated parameter or component name, and wherein the output from the AI model further comprises a target abbreviated name in association with the target parameter or component as taught by Kelly in order to use this input to perform a classification of a desired target parameter or component (see [0116] “… a contextual classification is generated for the generative text request 406 based on the one or more text request fields. For instance, the contextual classification may be generated using a machine learning classifier model 410. The same machine learning classifier model 410 may be previously used to generate one or more of a plurality of contextual classifications for the historical text documents of the document data store 404. …”; also, see [0118]).
As per claim 8, Baek-Kelly teaches the method of claim 1, Baek further teaches wherein the target parameter or component comprises an equipment constant (see [0047] “The electrical parameter measurement (EPM) is a measured parameter value capable of estimating a semiconductor manufacturing parameter after a semiconductor manufacturing process is performed using the semiconductor manufacturing parameter as a target value"; see [0048] “The preprocessing module 102 may group the acquired electrical measurement parameters (EPMs) according to the degree of correlation. That is, the preprocessing module 102 may classify the electrical measurement parameters into a plurality of groups according to the degree of mutual correlation of the electrical measurement parameters”; also, see [0053], [0068] and fig. 3]; see [0083] and fig. 6; thus, the target parameter or component comprises an equipment constant or a sensor of the manufacturing system), and wherein the method further comprises:
determining that a value of the equipment constant associated with the manufacturing system is different than a reference value (see [0029] “n analysis module configured to calculate sensitivity to the figure of merit of the electrical measurement parameters within the data distribution range with the correlation axis as a reference, and select an electrical measurement parameter capable of optimizing the corresponding figure of merit based on the calculated sensitivity”; see [0099-0100]), and
wherein the corrective action comprises updating the equipment constant in further view of the reference value (see [0100] “Next, the apparatus for setting the semiconductor parameter 100 may select the electrical measurement parameter capable of optimizing the figure of merit of the corresponding semiconductor device based on the calculated sensitivity (S113)”).
As per claim 9, Baek teaches a method, comprising:
obtaining, by a processing device, a plurality of training data (see [0022] “The preprocessing module may be configured to generate an artificial neural network model for each group when grouping of the electrical measurement parameters is completed, and extract a correlation axis between the electrical measurement parameters belonging to each group by training each of the generated artificial neural network models”), the training data comprising:
(see [0048] “The preprocessing module 102 may group the acquired electrical measurement parameters (EPMs) according to the degree of correlation. That is, the preprocessing module 102 may classify the electrical measurement parameters into a plurality of groups according to the degree of mutual correlation of the electrical measurement parameters”; also, see [0053], [0068] and fig. 3]; see [0083] and fig. 6).
While Baek clearly teaches associating parameters or components to a category of a set of categories and it would be assumed that different categories exists and that in order to perform such classification, a training of the model was performed previously with training data including examples of parameters associated to a category, Baek does not explicitly teach the training data comprising names of a plurality of parameters or components of one or more manufacturing systems, and
categorizations of each of the plurality of parameters or components, updating one or more parameters of a trained artificial intelligence (AI) model to generate a retrained AI model, wherein the retrained AI model is configured to receive as input a name of a target parameter or component.
However, Kelly teaches a system comprising a training data input comprising: names of a plurality of parameters or components of one or more systems and categorizations of each of the plurality of parameters or components (see 0054 “In some embodiments, a generative text request may include a request to generate a generative text document based on one or more input texts and/or other metadata associated with a topic associated with a desired controlled text document. The one or more input texts, for example, may include one or more request text fields that correspond to one or more controlled text fields of a desired controlled text document. In some examples, the other metadata may include one or more controlling rules for the desired controlled text document, such as one or more required pieces of information, a reading difficulty level, and/or the like. In some examples, the other metadata may include a reference to one or more evidentiary documents (e.g., case documentation for a healthcare appeal, etc.”) for supporting and/or generating text for a controlled text field.”, the input “generative text request” comprises the rules or set of categories such as difficulty level; also, see [0054] “…The one or more input texts, for example, may include one or more request text fields that correspond to one or more controlled text fields of a desired controlled text document. In some examples, the other metadata may include one or more controlling rules for the desired controlled text document, such as one or more required pieces of information, a reading difficulty level, and/or the like …”; also, see [0060], [0062]; also, see [0064] “In some examples, each of the historical text documents may include one or more document type classifications. In addition, or alternatively, a generative text request may include one or more request type classifications. The one or more one or more request type classifications, for example, may be manually entered by a user and/or extracted from one or more case attributes associated with the generative text request”; also, see [0104-0105] “a contextual classification is generated for the one or more request text fields. The contextual classification, for example, may be generated using a machine learning classifier model 410.…In some examples, the document data store 404 includes a plurality of historical text documents and a plurality of contextual classification labels respectively corresponding to the plurality of historical text documents…”; also, see [0110]), updating one or more parameters of a trained artificial intelligence (AI) model to generate a retrained AI model (see [0031] “…n some examples, the feedback may be leveraged by the predictive computing entity 106 to continuously train the machine learning model over time…”; [0091] “… For example, the LLM may be trained to maximize the aggregate inferred human rating score. In some examples, the generative text fields output by the LLM may be evaluated and then stored (e.g., with an inferred human rating score) in the document data store. In such a case, the LLM may be iteratively retrained to continuously and automatically improve without a human in the loop”; also, see [0147]), wherein the retrained AI model is configured to receive as input a name of a target parameter or component (see [0057] “ the term “request text fields” refers to a component of a generative text request. A request text field may include a segment of text manually generated for a particular case corresponding to a desired controlled text document. Each request text field, for example, may include a natural language text that reflects a decision for a particular case that corresponds to a controlled text field of a desired controlled text document and may not satisfy one or more controlling rules for a desired controlled text document”) and machine learning classifier/AI model (see Fig. 1 classifier 410).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek’s invention to the training data comprising names of a plurality of parameters or components of one or more manufacturing systems, and
categorizations of each of the plurality of parameters or components, updating one or more parameters of a trained artificial intelligence (AI) model to generate a retrained AI model, wherein the retrained AI model is configured to receive as input a name of a target parameter or component as taught by Kelly in order to use this input to perform a classification of a desired target parameter or component (see [0116] “… a contextual classification is generated for the generative text request 406 based on the one or more text request fields. For instance, the contextual classification may be generated using a machine learning classifier model 410. The same machine learning classifier model 410 may be previously used to generate one or more of a plurality of contextual classifications for the historical text documents of the document data store 404. …”; also, see [0118]).
As per claim 12, Baek-Kelly teaches the method of claim 9, Baek further teaches wherein categories of the categorization comprise one or more of:
level of criticality to properties of a manufactured product of the one or more manufacturing systems (see claim 4 above same rationale applies herein)
or subsystem of the one or more manufacturing systems (defined in the alternative).
As per claim 13, Baek-Kelly teaches the method of claim 9, Baek further teaches wherein the plurality of components of the one or more manufacturing systems comprise one or more of: equipment constants (see [0047] “…may classify the electrical measurement parameters into a plurality of groups according to the degree of mutual correlation of the electrical measurement parameters);
or sensors (see [0047] “The electrical parameter measurement (EPM) is a measured parameter value capable of estimating a semiconductor manufacturing parameter after a semiconductor manufacturing process is performed using the semiconductor manufacturing parameter as a target value"; see [0048] “The preprocessing module 102 may group the acquired electrical measurement parameters (EPMs) according to the degree of correlation. That is, the preprocessing module 102 may classify the electrical measurement parameters into a plurality of groups according to the degree of mutual correlation of the electrical measurement parameters”; also, see [0053], [0068] and fig. 3; see [0083] and fig. 6; thus, the target parameter or component comprises an equipment constant or a sensor of the manufacturing system).
As per claim 14, Baek-Kelly teaches the method of claim 9, Kelly further teaches
wherein the training data further comprises abbreviated names of the plurality of parameters or components, and wherein the retrained AI model is further configured to generate output predicting an abbreviated name of the target parameter or component indicative to a user of a function of the target parameter or component (see [0054] “…The one or more input texts, for example, may include one or more request text fields that correspond to one or more controlled text fields of a desired controlled text document. In some examples, the other metadata may include one or more controlling rules for the desired controlled text document, such as one or more required pieces of information, a reading difficulty level, and/or the like …”; also, see [0060], [0062]; also, see [0064] “In some examples, each of the historical text documents may include one or more document type classifications. In addition, or alternatively, a generative text request may include one or more request type classifications. The one or more one or more request type classifications, for example, may be manually entered by a user and/or extracted from one or more case attributes associated with the generative text request”; also, see [0104-0105] “a contextual classification is generated for the one or more request text fields. The contextual classification, for example, may be generated using a machine learning classifier model 410.…In some examples, the document data store 404 includes a plurality of historical text documents and a plurality of contextual classification labels respectively corresponding to the plurality of historical text documents…”; also, see [0070] “ a machine learning classifier model is trained to assign a contextual classification from a plurality of candidate contextual classifications to a generative text request based on one or more generative text fields of the generative text request. In some examples, the machine learning classifier model may be trained, using one or more supervisory training techniques (e.g., backpropagation of errors, etc.) to assign a contextual classification based on a plurality of historical contextual classifications respectively assigned to a plurality of historical text documents”; see [0110]).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek’s invention to include wherein the training data further comprises abbreviated names of the plurality of parameters or components, and wherein the retrained AI model is further configured to generate output predicting an abbreviated name of the target parameter or component indicative to a user of a function of the target parameter or component as taught by Kelly in order to use this input to perform a classification of a desired target parameter or component (see [0116] “… a contextual classification is generated for the generative text request 406 based on the one or more text request fields. For instance, the contextual classification may be generated using a machine learning classifier model 410. The same machine learning classifier model 410 may be previously used to generate one or more of a plurality of contextual classifications for the historical text documents of the document data store 404. …”; also, see [0118]).
As to claim 16, Baek-Kelly teaches this claim is the non-transitory machine-readable storage medium claim corresponding to the method claim 1 and is rejected for the same reasons mutatis mutandis (see Baek Fig. 8 and [0103]-[0104] “…For example, the processor 14 may execute one or more programs stored on the computer-readable storage medium 16. The one or more programs may include one or more computer-executable instructions, which, when executed by the processor 14, may be configured so that the computing apparatus 12 performs operations according to the exemplary embodiment”).
As per claim 17, Baek-Kelly teaches the non-transitory machine-readable storage medium of claim 16, Baek further teaches wherein the set of categories comprises a set of subsystems of the manufacturing system or a degree of criticality of the parameter (see claim 4 above), wherein criticality is determined by a strength of a relationship between values of the parameter and properties of a product processed by the manufacturing system (see claim 4 above same rationale applies herein).
As to claim 18, Baek-Kelly teaches this claim is the non-transitory machine-readable storage medium claim corresponding to the method claim 5 and is rejected for the same reasons mutatis mutandis.
As to claim 20, Baek-Kelly teaches this claim is the non-transitory machine-readable storage medium claim corresponding to the method claim 8 and is rejected for the same reasons mutatis mutandis.
Claim(s) 3, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Baek et al (US 202302744985, cited in the IDS) in view of Kelly et al (US 20240289560, cited in the IDS) as applied to claim 1 above, and further in view of Wang et al (CN 111753867 A as supported by the machine translation provided).
As per claim 3, Baek-Kelly teaches the method of claim 1, but it does not explicitly teach wherein the set of categories comprises a set of subsystems of the manufacturing system.
However, Wang teaches a system generating an input defining a set of categories, wherein the set of categories comprises a set of subsystems of the manufacturing system (see the Abstract “ the classifier is configured to define a category associated with the normal operation of the vehicle subsystem. The processing device is further configured to determine whether the subset of the measurement data belongs to the category…”; also, see page 2 last 2 Pars. “In addition to the one or more features described herein, the vehicle comprises a plurality of vehicle subsystems, and the processing device is configured to input the corresponding subset of the measured data to each of a plurality of one class classifier, each one class classifier is associated with a different vehicle subsystem… In addition to the one or more features described herein, a plurality of one class classifier comprises an air subsystem classifier, a fuel subsystem classifier and an ignition subsystem classifier”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek-Kelly’s combination as taught above to include generating an input defining a set of categories, wherein the set of categories comprises a set of subsystems of the manufacturing system as taught by Wang in order to classify parameters (features) according to the set of categories including subsystems (see page 2 last 1 pars.; also, see page 3 par. 5 “receiving measurement data from a plurality of sensing devices by the processing device, each sensing device
of the plurality of sensing devices is configured to measure the parameter of the vehicle system, and in response to detecting the fault in the vehicle, at least one subset of the measured data is input to the machine learning classifier associated with the vehicle subsystem, the classifier is configured to define a category associated with the normal operation of the vehicle subsystem. The method further comprises determining whether the subset of the measured data belongs to the category by the classifier, and based on at least the selected quantity of the subset of the measured data is outside the category, outputting a fault indication, the fault indication identifies the vehicle subsystem as a contribution to the fault”; also, see page 3 pars. 9-10).
As per claim 12, Baek-Kelly teaches the method of claim 9, wherein categories of the categorization comprise one or more of: level of criticality to properties of a manufactured product of the one or more manufacturing systems
or Baek-Kelly does not explicitly teach subsystem of the one or more manufacturing systems (defined in the alternative).
However, Wang teaches a system generating an input defining a set of categories, wherein the set of categories comprises a set of subsystems of the manufacturing system (see the Abstract “ the classifier is configured to define a category associated with the normal operation of the vehicle subsystem. The processing device is further configured to determine whether the subset of the measurement data belongs to the category…”; also, see page 2 last 2 Pars. “In addition to the one or more features described herein, the vehicle comprises a plurality of vehicle subsystems, and the processing device is configured to input the corresponding subset of the measured data to each of a plurality of one class classifier, each one class classifier is associated with a different vehicle subsystem… In addition to the one or more features described herein, a plurality of one class classifier comprises an air subsystem classifier, a fuel subsystem classifier and an ignition subsystem classifier”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek-Kelly’s combination as taught above to include generating an input defining a set of categories, wherein the set of categories comprises a set of subsystems of the manufacturing system as taught by Wang in order to classify parameters (features) according to the set of categories including subsystems (see page 2 last 1 pars.; also, see page 3 par. 5 “receiving measurement data from a plurality of sensing devices by the processing device, each sensing device
of the plurality of sensing devices is configured to measure the parameter of the vehicle system, and in response to detecting the fault in the vehicle, at least one subset of the measured data is input to the machine learning classifier associated with the vehicle subsystem, the classifier is configured to define a category associated with the normal operation of the vehicle subsystem. The method further comprises determining whether the subset of the measured data belongs to the category by the classifier, and based on at least the selected quantity of the subset of the measured data is outside the category, outputting a fault indication, the fault indication identifies the vehicle subsystem as a contribution to the fault”; also, see page 3 pars. 9-10).
As per claim 17, Baek-Kelly teaches the non-transitory machine-readable storage medium of claim 16, wherein the set of categories comprises a set of subsystems of the manufacturing system or a degree of criticality of the parameter (defined in the alternative), wherein criticality is determined by a strength of a relationship between values of the parameter and properties of a product processed by the manufacturing system.
However, Wang teaches a system generating an input defining a set of categories, wherein the set of categories comprises a set of subsystems of the manufacturing system (see the Abstract “ the classifier is configured to define a category associated with the normal operation of the vehicle subsystem. The processing device is further configured to determine whether the subset of the measurement data belongs to the category…”; also, see page 2 last 2 Pars. “In addition to the one or more features described herein, the vehicle comprises a plurality of vehicle subsystems, and the processing device is configured to input the corresponding subset of the measured data to each of a plurality of one class classifier, each one class classifier is associated with a different vehicle subsystem… In addition to the one or more features described herein, a plurality of one class classifier comprises an air subsystem classifier, a fuel subsystem classifier and an ignition subsystem classifier”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek-Kelly’s combination as taught above to include generating an input defining a set of categories, wherein the set of categories comprises a set of subsystems of the manufacturing system as taught by Wang in order to classify parameters (features) according to the set of categories including subsystems (see page 2 last 1 pars.; also, see page 3 par. 5 “receiving measurement data from a plurality of sensing devices by the processing device, each sensing device
of the plurality of sensing devices is configured to measure the parameter of the vehicle system, and in response to detecting the fault in the vehicle, at least one subset of the measured data is input to the machine learning classifier associated with the vehicle subsystem, the classifier is configured to define a category associated with the normal operation of the vehicle subsystem. The method further comprises determining whether the subset of the measured data belongs to the category by the classifier, and based on at least the selected quantity of the subset of the measured data is outside the category, outputting a fault indication, the fault indication identifies the vehicle subsystem as a contribution to the fault”; also, see page 3 pars. 9-10).
Claim(s) 7, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Baek et al (US 202302744985, cited in the IDS) in view of Kelly et al (US 20240289560, cited in the IDS) as applied to claim 1 above, and further in view of Deshpande et al (US 20250173353).
As per claim 7, Baek-Kelly teaches the method of claim 1, it does not explicitly teach wherein the set of categories comprises a data access classification, and wherein data associated with the target parameter or component is to be provided to a set of users based on the data access classification.
Deshpande teaches a system for generating data comprising a data access classification (see [0043-0047] “the system may be able to identify what types of data the users may require over the course of the conference… the system may receive a query from one of the users for a particular piece of data or data set to be displayed from a particular perspective or view… [0047] In some embodiments, certain users may be assigned different data permissions with respect to the underlying data. Accordingly, the system may, using the AI model, determine which data and which perspectives of the data that each user is authorized to access. For instance, in some cases, the system may allow a user to access certain perspectives or final outputs based on certain types of data without allowing the user to access the underlying data itself. In this way, the system may provide a fine-tuned control over the types of data that a user is allowed to access without preventing the users from accessing the insights based on such data”), and wherein data associated with the target parameter or component is to be provided to a set of users based on the data access classification (see [0047] “….For instance, in some cases, the system may allow a user to access certain perspectives or final outputs based on certain types of data without allowing the user to access the underlying data itself. In this way, the system may provide a fine-tuned control over the types of data that a user is allowed to access without preventing the users from accessing the insights based on such data”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek-Kelly’s combination as taught above to include a set of categories comprises a data access classification, and wherein data associated with the target parameter or component is to be provided to a set of users based on the data access classification as taught by Deshpande in order to allow or deny access total access to the target data (see [0042] “Finally, certain users may not have access to certain portions of the underlying data, which may prevent the user from gaining insights into how the data was derived. Accordingly, there is a need for a more efficient way to serve the data needs of such users.”) to increase the security of the data serving process (see [0053] “…Furthermore, the architecture described herein may allow for users to receive views and insights resulting from the underlying data without the need for access to the underlying data itself, which in turn enhances the security of the data serving process.”).
As to claim 19, Baek-Kelly-Deshpande teaches this claim is the non-transitory machine-readable storage medium claim corresponding to the method claim 7 and is rejected for the same reasons mutatis mutandis.
As per claim 15, Baek-Kelly teaches the method of claim 9, but it does not explicitly teach wherein the categorization comprises a data access classification, and wherein a set of users associated with a first category of the data access classification is to be granted access to data in association with the target parameter or component, while a second set of users associated with a second category of the data access classification is not to be granted access to data in association with the target parameter or component.
Deshpande teaches a system for generating data comprising a categorization comprises a data access classification (see [0043-0047] “the system may be able to identify what types of data the users may require over the course of the conference… the system may receive a query from one of the users for a particular piece of data or data set to be displayed from a particular perspective or view… [0047] In some embodiments, certain users may be assigned different data permissions with respect to the underlying data. Accordingly, the system may, using the AI model, determine which data and which perspectives of the data that each user is authorized to access. For instance, in some cases, the system may allow a user to access certain perspectives or final outputs based on certain types of data without allowing the user to access the underlying data itself. In this way, the system may provide a fine-tuned control over the types of data that a user is allowed to access without preventing the users from accessing the insights based on such data”), and wherein a set of users associated with a first category of the data access classification is to be granted access to data in association with the target parameter or component (see [0047] “….For instance, in some cases, the system may allow a user to access certain perspectives or final outputs based on certain types of data without allowing the user to access the underlying data itself. In this way, the system may provide a fine-tuned control over the types of data that a user is allowed to access without preventing the users from accessing the insights based on such data”), while a second set of users associated with a second category of the data access classification is not to be granted access to data in association with the target parameter or component (see [0047] “In some embodiments, certain users may be assigned different data permissions with respect to the underlying data. Accordingly, the system may, using the AI model, determine which data and which perspectives of the data that each user is authorized to access. For instance, in some cases, the system may allow a user to access certain perspectives or final outputs based on certain types of data without allowing the user to access the underlying data itself. In this way, the system may provide a fine-tuned control over the types of data that a user is allowed to access without preventing the users from accessing the insights based on such data” ).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek-Kelly’s combination as taught above to include a categorization comprises a data access classification, and wherein a set of users associated with a first category of the data access classification is to be granted access to data in association with the target parameter or component, while a second set of users associated with a second category of the data access classification is not to be granted access to data in association with the target parameter or component as taught by Deshpande in order to allow or deny access total access to the target data (see [0042] “Finally, certain users may not have access to certain portions of the underlying data, which may prevent the user from gaining insights into how the data was derived. Accordingly, there is a need for a more efficient way to serve the data needs of such users.”) to increase the security of the data serving process (see [0053] “…Furthermore, the architecture described herein may allow for users to receive views and insights resulting from the underlying data without the need for access to the underlying data itself, which in turn enhances the security of the data serving process.”).
Claim(s) 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Baek et al (US 202302744985, cited in the IDS) in view of Kelly et al (US 20240289560, cited in the IDS) as applied to claim 1 above, and further in view of Kaplan et al (WO 2024/182819 A2, cited in the IDS).
As per claim 10-11, Baek-Kelly teaches the method of claim 9, but it does not explicitly teach wherein updating one or more parameters of the trained AI model to generate the retrained AI model comprises performing parameter-efficient fine-tuning operations to update a subset of parameters of the AI model, wherein the parameter-efficient fine-tuning comprises low-rank adaptation operations.
However Kaplan teaches a system comprising updating one or more parameters of the trained AI model to generate the retrained AI model comprises performing parameter-efficient fine-tuning operations to update a subset of parameters of the AI model (see [00162] “fine tune the trained based model…” [0469] “ note the weights used to fine tune LLMs (even if the weights for the foundational model are frozen) - e.g., using Low-Rank Adaptation of Large Language Models (“LoRA adaptors”) - can also be averaged using linear combinations or any of the other mathematical techniques described in this and previously cited PPAs”; also, see [003336].), wherein the parameter-efficient fine-tuning comprises low-rank adaptation operations (see [0469]).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Baek-Kelly’s combination to include updating one or more parameters of the trained AI model to generate the retrained AI model comprises performing parameter-efficient fine-tuning operations to update a subset of parameters of the AI model, wherein the parameter-efficient fine-tuning comprises low-rank adaptation operations as taught by Kaplan in order to increase the performance of the AI model (see [00162]).
Conclusion
The prior art made of record and not relied upon, as cited in PTO form 892, is considered pertinent to applicant's disclosure.0
Luzin (US 20250077882) teaches a LLM model for classification of manufacturing system data (see 0039), wherein the model is fined tuned.
Liu et al (US 8473785) teaches a system using a classification system for classifying components to subsystems (see page 14 claim13).
Rosswog et al (US 9514414) teaches a system for categorizing data, using input or training data of seed sets or parameters associated to a category (see Col 2 lines 40-60 “The generated seed sets may be categorized by a user and fed into the applied machine learning system in order to retrain or further train the electronic document categorization systems and methods. Once retrained on a new seed set of categorizations, the retrained electronic document categorization systems and methods may again categorize the remaining documents in the corpus and track the performance of the categorization.…”)
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. Applicant must also show how the amendments avoid or differentiate from such references or objections. See 37 CFR 1.111 (c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLVIN LOPEZ ALVAREZ whose telephone number is (571) 270-7686 and fax (571) 270-8686. The examiner can normally be reached Monday thru Friday from 9:00 A.M. to 6:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Robert Fennema, can be reached at (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/O. L./
Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
