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 .
Response to Amendment
The Amendment filed 11/11/2025 has been entered. Claims 17-20 are new. Claims 1-20 are now pending in this application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-10 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee (US 20210264311 A1) in view of Parangi et al. (US 20210232920 A1 hereinafter Parangi)
As to independent claim 1, Mukherjee teaches a computer-implemented method comprising:
receiving, from a user of a life sciences research platform comprising a plurality of computers, input data for a plurality of life sciences experiments within a research domain, the input data comprising, for each life sciences experiment: [model generation platform that receives historical data for prediction experiments ¶20 "use sets of historical data to predict future outcomes" including domains and life science ¶35, ¶41 "given problem domain (e.g., retail, financial services, video/music streaming, life science"]
(i) a collection of settings associated with the life sciences experiments, [features are selected for models associated with life science ¶36 "features may include key words and topics defining the problem (e.g., problems related to life science"]
(ii) a respective input value for each setting of the collection of settings, wherein the respective input value characterizes a respective experimental condition for the life sciences experiment, and [features and data identified ¶55, ¶60-61]
(iii) a respective empirical value for an experimental outcome metric for the research domain; [metrics/prediction output ¶3, ¶28]
automatically generating, by the life sciences research platform, a plurality of machine learning models for the research domain, wherein each machine learning model is configured to predict a value for the experimental outcome metric, comprising, for each machine learning model of the plurality of machine learning models within the research domain [automated model generation including a sequence of models and problem domain ¶50 "requesting generation of a specified sequence of machine learning models for use in solving a particular problem"]
training a candidate machine learning model from the collection of settings associated with the life sciences experiments; and [building based on training data (training from settings) ¶20 " machine learning algorithms may build a mathematical model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to perform a particular task"]
determining a performance measure for the candidate machine learning model based on a discrepancy between the respective predicted empirical value of the candidate machine learning model and a target output empirical value for the experimental outcome metric; [determines metrics and selects machine learning models based on metrics including maximum error rate, bias metric, maximum false positive rate, or maximum false negative rate ¶6, ¶3 "systems may further ensure that more accurate results may be output (e.g., both in terms of model selection and in responding to service requests (e.g., fitting data, error rates, or the like))."…"computing platform may select the one or more machine learning models and the corresponding sequence of model application based on the one or more model metrics"]
automatically selecting, by the life sciences research platform, a final machine learning model from the plurality of machine learning models within the research domain based on the performance measures of the candidate machine learning models; and [metrics like error are used for model selection ¶6, "computing platform may select the one or more machine learning models and the corresponding sequence of model application based on the one or more model metrics"]
Mukherjee does not specifically teach generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model.
However, Parangi teaches generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model. [user interface with information about important factors (explainability data) and predictive power (contribution) Fig. 1C also displays each impact on accuracy ¶41 " user may see a section identifying the “Most Important Fields,” which provides information about what factors or variables were most important, or had the most predictive power in determining outcomes for the model with this dataset"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model building platform disclosed by Mukherjee by incorporating the generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model disclosed by Parangi because both techniques address the same field of machine learning and by incorporating Parangi into Mukherjee simplifies machine learning processes requiring less user input or intervention [Parangi ¶12-13]
As to dependent claim 2, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach wherein the plurality of machine learning models for the research domain include different machine learning model types. [Mukherjee different models ¶62]
As to dependent claim 3, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach wherein the plurality of machine learning model types include one or more of: linear regression models, logistic regression models, Bayes classifier models, random classifier models, decision tree models, and neural network models. [Mukherjee regression, Bayesian, random forest (tree, random), neural ¶62]
As to dependent claim 4, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach wherein the plurality of machine learning models for the research domain are selected from a model library that is specific to the research domain specified in the input data. [Mukherjee models correspond to particular problem ¶3]
As to dependent claim 5, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach receiving new input data for a proposed life sciences experiment within the research domain that comprises a respective new input value for each setting of the collection of settings; [Mukherjee life science and recent data ¶36-38 iterative ¶28]
using the final machine learning model and the respective new input value for each setting in the collection of settings to predict a new value for the experimental outcome metric; and [Mukherjee prediction model ¶36-37]
presenting the new value for the experimental outcome metric in the user interface presentation of the life sciences research platform. [Mukherjee interface with content from model (outcome) ¶44 "present one or more graphical user interfaces (e.g., interfaces include content generated using the one or more machine learning models and corresponding model application sequence selected by the automated model generation platform"]
As to dependent claim 6, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach wherein automatically generating, by the life sciences research platform, the plurality of machine learning models for the research domain further comprises:
obtaining a plurality of hyperparameter settings for the research domain specified in the input data; and [Mukherjee parameters ¶26]
for each machine learning model of the plurality of machine learning models within the research domain: [Mukherjee models ¶23-24]
training the candidate machine learning model from the collection of settings associated with each life sciences experiment and the plurality of hyperparameter settings to predict the value for the experimental outcome metric. [Mukherjee build (train) models ¶19-20 according to input ¶50]
As to dependent claim 7, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach wherein the input data for the plurality of life sciences experiments within the research domain represents results obtained from real-world life sciences experiments. [Mukherjee life science research ¶43]
As to dependent claim 8, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach receiving, from one or more other users of the life sciences research platform, input data for the plurality of life sciences experiments within the research domain; and [Mukherjee life science research ¶42-43]
automatically selecting the final machine learning model from the plurality of machine learning models within the research domain for each of the one or more other users. [Mukherjee indicate selection ¶42-43]
As to dependent claim 9, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach wherein the input data further comprises one or more constraints associated with the plurality of life sciences experiments, and wherein [Mukherjee life science research ¶42-43]
automatically generating the plurality of machine learning models for the research domain further comprises, for each machine learning model of the plurality of machine learning models within the research domain: [Mukherjee generates models ¶41]
training the candidate machine learning model from the collection of settings associated with each life sciences experiment and the one or more constraints. [Mukherjee build based on training ¶21-22]
As to dependent claim 10, the rejection of claim 1 is incorporated, Mukherjee and Parangi further teach wherein automatically selecting the final machine learning model from the plurality of machine learning models within the research domain based on the performance measures of the candidate machine learning models comprises: [Mukherjee error used for model selection ¶6, "computing platform may select the one or more machine learning models and the corresponding sequence of model application based on the one or more model metrics"]
automatically selecting the candidate machine learning model from the plurality of machine learning models having the highest performance measure. [Mukherjee error used for model selection ¶6, "computing platform may select the one or more machine learning models and the corresponding sequence of model application based on the one or more model metrics"]
As to dependent claim 13, the rejection of claim 11 is incorporated, Mukherjee and Parangi further teach wherein generating, by the life sciences research platform, the user interface presentation of explainability data that explains the contribution of each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model comprises: [Parangi user interface with information about important factors (explainability data) and predictive power (contribution) Fig. 1C also displays each impact on accuracy ¶41 " user may see a section identifying the “Most Important Fields,” which provides information about what factors or variables were most important, or had the most predictive power in determining outcomes for the model with this dataset"]
determining, for each setting in the collection of settings, a contribution score that characterizes a contribution of the setting to the value for the experimental outcome metric; and [Parangi accuracy score ¶40-41]
generating, in the user interface presentation, a visualization that compares the respective contribution scores of the collection of settings. [Parangi user interface Fig. 1C also displays each impact on accuracy ¶40-41]
As to dependent claim 14, the rejection of claim 13 is incorporated, Mukherjee and Parangi further teach generating, by the life sciences research platform and for each setting in the collection of settings, a visualization that compares different input values for the setting and respective contribution scores. [Parangi user interface Fig. 1C also displays each impact on accuracy as lists (visuals) ¶40-41]
As to independent claim 15, Mukherjee teaches a system comprising: [computing platform ¶4]
one or more computers; and [computer processor ¶4]
one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: [memory and instructions ¶4]
receiving, from a user, input data for a plurality of life sciences experiments within a research domain, the input data comprising, for each life sciences experiment: [model generation platform that receives historical data for prediction experiments ¶20 "use sets of historical data to predict future outcomes" including domains and life science ¶35, ¶41 "given problem domain (e.g., retail, financial services, video/music streaming, life science"]
(i) a collection of settings associated with the life sciences experiments, [features are selected for models associated with life science ¶36 "features may include key words and topics defining the problem (e.g., problems related to life science"]
(ii) a respective input value for each setting of the collection of settings, wherein the respective input value characterizes a respective experimental condition for the life sciences experiment, and [features and data identified ¶55, ¶60-61]
(iii) a respective empirical value for an experimental outcome metric for the research domain; [metrics/prediction output ¶3, ¶28]
automatically generating, by the life sciences research platform, a plurality of machine learning models for the research domain, wherein each machine learning model is configured to predict a value for the experimental outcome metric, comprising, for each machine learning model of the plurality of machine learning models within the research domain [automated model generation including a sequence of models and problem domain ¶50 "requesting generation of a specified sequence of machine learning models for use in solving a particular problem"]
training a candidate machine learning model from the collection of settings associated with the life sciences experiments; and [building based on training data (training from settings) ¶20 " machine learning algorithms may build a mathematical model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to perform a particular task"]
determining a performance measure for the candidate machine learning model based on a discrepancy between the respective predicted empirical value of the candidate machine learning model and a target output empirical value for the experimental outcome metric; [determines metrics and selects machine learning models based on metrics including maximum error rate, bias metric, maximum false positive rate, or maximum false negative rate ¶6, ¶3 "systems may further ensure that more accurate results may be output (e.g., both in terms of model selection and in responding to service requests (e.g., fitting data, error rates, or the like))."…"computing platform may select the one or more machine learning models and the corresponding sequence of model application based on the one or more model metrics"]
automatically selecting, by the life sciences research platform, a final machine learning model from the plurality of machine learning models within the research domain based on the performance measures of the candidate machine learning models; and [metrics like error are used for model selection ¶6, "computing platform may select the one or more machine learning models and the corresponding sequence of model application based on the one or more model metrics"]
Mukherjee does not specifically teach generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model.
However, Parangi teaches generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model. [user interface with information about important factors (explainability data) and predictive power (contribution) Fig. 1C also displays each impact on accuracy ¶41 " user may see a section identifying the “Most Important Fields,” which provides information about what factors or variables were most important, or had the most predictive power in determining outcomes for the model with this dataset"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model building platform disclosed by Mukherjee by incorporating the generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model disclosed by Parangi because both techniques address the same field of machine learning and by incorporating Parangi into Mukherjee simplifies machine learning processes requiring less user input or intervention [Parangi ¶12-13]
As to independent claim 16, Mukherjee teaches one or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: [processor, memory and instructions ¶4]
receiving, from a user, input data for a plurality of life sciences experiments within a research domain, the input data comprising, for each life sciences experiment: [model generation platform that receives historical data for prediction experiments ¶20 "use sets of historical data to predict future outcomes" including domains and life science ¶35, ¶41 "given problem domain (e.g., retail, financial services, video/music streaming, life science"]
(i) a collection of settings associated with the life sciences experiments, [features are selected for models associated with life science ¶36 "features may include key words and topics defining the problem (e.g., problems related to life science"]
(ii) a respective input value for each setting of the collection of settings, wherein the respective input value characterizes a respective experimental condition for the life sciences experiment, and [features and data identified ¶55, ¶60-61]
(iii) a respective empirical value for an experimental outcome metric for the research domain; [metrics/prediction output ¶3, ¶28]
automatically generating, by the life sciences research platform, a plurality of machine learning models for the research domain, wherein each machine learning model is configured to predict a value for the experimental outcome metric, comprising, for each machine learning model of the plurality of machine learning models within the research domain [automated model generation including a sequence of models and problem domain ¶50 "requesting generation of a specified sequence of machine learning models for use in solving a particular problem"]
training a candidate machine learning model from the collection of settings associated with the life sciences experiments; and [building based on training data (training from settings) ¶20 " machine learning algorithms may build a mathematical model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to perform a particular task"]
determining a performance measure for the candidate machine learning model based on a discrepancy between the respective predicted empirical value of the candidate machine learning model and a target output empirical value for the experimental outcome metric; [determines metrics and selects machine learning models based on metrics including maximum error rate, bias metric, maximum false positive rate, or maximum false negative rate ¶6, ¶3 "systems may further ensure that more accurate results may be output (e.g., both in terms of model selection and in responding to service requests (e.g., fitting data, error rates, or the like))."…"computing platform may select the one or more machine learning models and the corresponding sequence of model application based on the one or more model metrics"]
automatically selecting, by the life sciences research platform, a final machine learning model from the plurality of machine learning models within the research domain based on the performance measures of the candidate machine learning models; and [metrics like error are used for model selection ¶6, "computing platform may select the one or more machine learning models and the corresponding sequence of model application based on the one or more model metrics"]
Mukherjee does not specifically teach generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model.
However, Parangi teaches generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model. [user interface with information about important factors (explainability data) and predictive power (contribution) Fig. 1C also displays each impact on accuracy ¶41 " user may see a section identifying the “Most Important Fields,” which provides information about what factors or variables were most important, or had the most predictive power in determining outcomes for the model with this dataset"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model building platform disclosed by Mukherjee by incorporating the generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model disclosed by Parangi because both techniques address the same field of machine learning and by incorporating Parangi into Mukherjee simplifies machine learning processes requiring less user input or intervention [Parangi ¶12-13]
As to dependent claim 17, the rejection of claim 15 is incorporated, Mukherjee and Parangi further teach generating, by the life sciences research platform, the user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model comprises: determining, for each setting in the collection of settings, a contribution score that characterizes a contribution of the setting to the value for the experimental outcome metric; and generating, in the user interface presentation, a visualization that compares the respective contribution scores of the collection of settings. [Parangi determines a accuracy score for each setting in the collection of settings related to the value for the experimental outcome metric. It involves executing a sensitivity analysis of input variables to machine learning model predictions, including removing portions of the data set, processing it again, and determining the level of impact of the removal on the outcomes. This process is used to label portions of the data as "important" based on exceeding a threshold impact level. Furthermore, it describes generating a user interface presentation, such as showing "Most Important Fields," which visualizes the relative contribution scores of various settings or factors. This aligns with the concept of determining and visualizing contribution scores to understand the importance of different settings in the collection. ¶40-41, Fig 1 C]
As to dependent claim 18, the rejection of claim 17 is incorporated, Mukherjee and Parangi further teach generating, by the life sciences research platform and for each setting in the collection of settings, a visualization that compares different input values for the setting and respective contribution scores. [Parangi discusses executing a sensitivity analysis of input variables to machine learning model predictions, identifying the "Most Important Fields" based on their impact on the model's predictions, and visualizing these contributions in the user interface. This enables the comparison of how different settings or input values contribute to the predicted outcome metric, aligning with generating visualizations that compare respective contribution scores for each input. ¶40-41, Fig 1 C]
As to dependent claim 19, the rejection of claim 16 is incorporated, Mukherjee and Parangi further teach wherein generating, by the life sciences research platform, the user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model comprises: determining, for each setting in the collection of settings, a contribution score that characterizes a contribution of the setting to the value for the experimental outcome metric; and generating, in the user interface presentation, a visualization that compares the respective contribution scores of the collection of settings. [Parangi determines a accuracy score for each setting in the collection of settings related to the value for the experimental outcome metric. It involves executing a sensitivity analysis of input variables to machine learning model predictions, including removing portions of the data set, processing it again, and determining the level of impact of the removal on the outcomes. This process is used to label portions of the data as "important" based on exceeding a threshold impact level. Furthermore, it describes generating a user interface presentation, such as showing "Most Important Fields," which visualizes the relative contribution scores of various settings or factors. This aligns with the concept of determining and visualizing contribution scores to understand the importance of different settings in the collection. ¶40-41, Fig 1 C]
As to dependent claim 20, the rejection of claim 19 is incorporated, Mukherjee and Parangi further teach generating, by the life sciences research platform and for each setting in the collection of settings, a visualization that compares different input values for the setting and respective contribution scores. [Parangi discusses executing a sensitivity analysis of input variables to machine learning model predictions, identifying the "Most Important Fields" based on their impact on the model's predictions, and visualizing these contributions in the user interface. This enables the comparison of how different settings or input values contribute to the predicted outcome metric, aligning with generating visualizations that compare respective contribution scores for each input. ¶40-41, Fig 1 C]
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mukherjee and Parangi as applied in the rejection of claim 1 above, and further in view of Convertino et al. (US 20200097847 A1 hereinafter Convertino)
As to dependent claim 11, Mukherjee and Parangi teach the method of claim 1 above that is incorporated,
Mukherjee and Parangi do not specifically teach generating, in the user interface presentation of the life sciences research platform, a table for each life sciences experiment, wherein each table specifies: (i) an identification number of the life sciences experiment, and
(ii) the empirical value of the experimental outcome metric for the life sciences experiment.
However, Convertino teaches generating, in the user interface presentation of the life sciences research platform, a table for each life sciences experiment, wherein each table specifies: [Fig. 26A-B 2604a illustrate a results table or experiments ¶158-159 "After executing one or more batches of experiments, the user can switch to a hyperparameter analytics dashboard to perform further analysis of the results"]
(i) an identification number of the life sciences experiment, and [run id corresponds with an experiment number Fig. 26A-B ¶161 " line 2606b indicates to the user that experiment 4 (i.e., associated with “runid” 4)"]
(ii) the empirical value of the experimental outcome metric for the life sciences experiment. [acc and loss are also outcomes of experiments ¶160-161"performance metrics for training accuracy (“train_acc”) and overall accuracy (“acc”), as indicated by the boxes 2608 and 2610 (respectively). The user's brushing input may filter the displayed results"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model building disclosed by Mukherjee and Parangi by incorporating the generating, in the user interface presentation of the life sciences research platform, a table for each life sciences experiment, wherein each table specifies: (i) an identification number of the life sciences experiment, and (ii) the empirical value of the experimental outcome metric for the life sciences experiment. disclosed by Convertino because all techniques address the same field of machine learning and by incorporating Convertino into Mukherjee and Parangi better optimizes the tuning process of models with iterative processes [Convertino ¶41]
As to dependent claim 12, the rejection of claim 11 is incorporated, Mukherjee, Parangi and Convertino further teach presenting, in the user interface presentation of the life sciences research platform, a column user interface control; and [Convertino interface allows selection of columns/experiments accordingly ¶159-160]
receiving, from the user of the life sciences research platform, a column selection through the column user interface control. [Convertino interface user clicks on column/row ¶159-160]
Response to Arguments
Applicant's arguments filed 11/11/2025, with respect to the 101 rejections. These rejections are withdrawn.
Applicant's arguments filed 11/11/2025. In the remark, applicant argues that:
(1) Mukherjee and Ardel fail to teach “generating, by the life sciences research platform, a user interface presentation of explainability data comprising a contribution of respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model.” as recited by amended claim 1, 15 and 16.
As to point (1), Applicant’s arguments with respect to claim 1 have been considered but are moot in view of a new ground of rejection as set forth above of Mukherjee in view of Parangi.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
SOLOMON et al. (US 20210057107 A1) teaches predictions of patient treatment outcomes (life science) using parameters (settings) (see ¶110).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143