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 .
Notice to Applicant
This communication is in response to the amendment filed 06/11/2026. Claims 1, 11 have been amended. Claims 3-4, 13-14 have been canceled. Claims 1, 5-8, 10-11, 15-18, 20 have been presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1, 5-8, 10-11, 15-18, 20 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “generate, using a projection model comprising a second machine-learning model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on the subject data and the historical visualizations, wherein the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that is optimal for viewing the information; generate a first visualization of a plurality of visualizations associated with the variance, wherein the first visualization comprises an interaction event handler associated with at least one of: a module, data structure, function, and routine for performing an action in response to a user interaction; display, using a downstream device, the first visualization through a graphical user interface; receive an interaction signal through the graphical user interface as a function of the interaction event handler, wherein the interaction signal is associated with the user interaction and comprises the variance; modify, using the interaction signal in accordance with the variance, the first visualization to produce a second visualization, wherein modifying the first visualization comprises adjusting the graphical user interface to anticipate subsequent user interactions by changing a default visualization behavior as a function of the variance, wherein changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals; and display, using the downstream device, the second visualization through the graphical user interface.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 11, claims 1, 11are hereby deemed to be allowable over prior art. Originally numbered dependent claims 5-8, 10, 15-18, 20 incorporate the allowable features of originally numbered independent claims 1, 11, through dependency, respectively.
However, the claims are still rejected under 101.
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, 5-8, 10-11, 15-18, 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to an apparatus which is within the four statutory categories (i.e., machine). Claim 11 is drawn to a method which is within the four statutory categories (i.e., method).
Independent claim 1 (which is representative of independent claim 11) recites… receive subject data associated with at least a subject; analyze, using an assessment model, the subject data wherein the assessment model is configured to: compare the subject data to predefined parameters; and determine a variance associated with a comparison between the subject data and the predefined parameters; and the assessment model comprises a first…model previously trained on assessment training data comprising historical subject data corresponding to historical predefined parameters, wherein training comprises: upsampling the assessment training data, using at least one of: a set of interpolation rules in order to predict interpolated data associated with the training data a sample expander method for adding expander data associated with the training data; and a filter for filtering the training data in accordance with a frequency downsampling, using a compressor, the training data by removing an nth entry in a sequence of the training data; processing the assessment training data by normalizing data entries using feature scaling techniques to ensure the historical predefined parameters are comparable with the subject data received; generate, using a projection model comprising a second…model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on the subject data and the historical visualizations, wherein the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that is optimal for viewing the information; generate a first visualization of a plurality of visualizations associated with the variance, wherein the first visualization comprises an interaction event handler associated with at least one of: a module, data structure, function, and routine for performing an action in response to a user interaction; [provide]…the first visualization…; receive an interaction signal…as a function of the interaction event handler, wherein the interaction signal is associated with the user interaction and comprises the variance; modify, using the interaction signal in accordance with the variance, the first visualization to produce a second visualization, wherein modifying the first visualization comprises…anticipate subsequent user interactions by changing a default visualization behavior as a function of the variance, wherein changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals; and [provide]…the second visualization...
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. That is, other than reciting a “processor” (claim 1), the claim encompasses rules or instructions followed to collect user data, analyze the data, and output a conclusion about a user based on the analyzed data (i.e., to manage a user’s medical expenses). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Claim 1 recites additional elements (i.e., an apparatus comprising at least a computing device comprising a memory, a processor; a first machine-learning model previously trained; a second machine-learning model trained; a downstream device; a graphical user interface). Claim 11 recites additional elements (i.e., a processor; a first machine-learning model previously trained; a second machine-learning model trained; a downstream device; a graphical user interface). Looking to the specifications, a computing device having a memory, a processor is described at a high level of generality (¶ 0009; ¶ 0012; ¶ 0098; ¶ 0100-0104), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “a first machine-learning model previously trained” and “a second machine-learning model trained” is only used to generally apply the abstract idea without placing any limits on how the machine learning models function and only recite the outcome of the abstract idea and does not include details about how “analyze…the subject data,” “compare the subject data to predefined parameters; and determine a variance associated with a comparison between the subject data and the predefined parameters,” and “generate… prediction data based on the subject data and the historical visualizations” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). Also, “display, using a downstream device, the first visualization through a graphical user interface” and “display, using a downstream device, the second visualization through the graphical user interface” only invokes the downstream device and graphical user interface merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving and displaying data), which amounts to no more than a recitation of the words "apply it" (or an equivalent) and only generally links the claimed invention to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computing device having a memory, a processor) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, “a first machine-learning model previously trained” and “a second machine-learning model trained” is only used to generally apply the abstract idea without placing any limits on how the machine learning models function and only recite the outcome of the abstract idea and does not include details about how “analyze…the subject data,” “compare the subject data to predefined parameters; and determine a variance associated with a comparison between the subject data and the predefined parameters,” and “generate… prediction data based on the subject data and the historical visualizations” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). Also, “display, using a downstream device, the first visualization through a graphical user interface” and “display, using a downstream device, the second visualization through the graphical user interface” only invokes the downstream device and graphical user interface merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving and displaying data), which amounts to no more than a recitation of the words "apply it" (or an equivalent) and only generally links the claimed invention to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 5-8, 10, 15-18, 20 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 5-8, 15-18 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 10, 20 further recites the additional elements of “electronic health records,” which only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Response to Arguments
Applicant's arguments filed 06/11/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 06/11/2026.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejections,
“claims do not recite a method for organizing human activity involving managing personal behavior or relationships where there is no tracking of financial transaction…Claims do not recite a method of managing personal behavior where the claim is not a fundamental activity that forms the basis of our democracy…Claims do not recite a method of managing personal behavior where the claim does not provide information to a person without interfering with the person’s primary activity…claims do not disclose a claim reciting following rules or instructions…Claim 1 is not directed to managing personal behavior or relationships or interactions between people because it does not recite social activities, teaching, voting, wagering, following rules or instructions, or any other scheme for directing how a person should behave. Rather, claim 1 is directed to a specific machine-implemented process for modifying a visualization associated with subject data using particular technical components and operations, including a first machine-learning model trained using upsampling, downsampling, and normalization of assessment training data, a second machine-learning model trained on historical subject data corresponding to historical visualizations, generation of prediction data and visualizations, receipt of an interaction signal through an interaction event handler, and modification of a graphical user interface by changing default visualization behavior based on stored historical graphical user interface configurations”;
“an improvement in how a computing system generates and modifies graphical user interface visualizations associated with subject data…is not merely the use of a generic model to analyze data and display a result. Rather, claim 1 recites a particular technical workflow in which the computing device first analyzes subject data using an assessment model to determine a variance, then further generates, using a projection model comprising a second machine-learning model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on both the subject data and the historical visualizations, where the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that was most effective for viewing the information. Claim 1 further requires modifying the first visualization to produce a second visualization by changing a default visualization behavior, where changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals. Thus, claim 1 does not merely recite presenting information or organizing human activity at a high level, but instead uses historically effective interface configurations and model-generated prediction data to control how the graphical user interface is automatically reconfigured in response to the variance and interaction signal. This imposes a meaningful limit on any alleged judicial exception because the claim is tied to a specific technological manner of improving interface behavior, reducing repetitive manual reconfiguration of visualizations, and improving the operation of the computing system in generating later visualizations based on prior effective graphical configurations, rather than monopolizing any abstract idea in the abstract”; and
“multiple additional elements that do not recite any alleged abstract idea...include, without limitation, generating, using a projection model comprising a second machine-learning model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on the subject data and the historical visualizations, wherein the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that was most effective for viewing the information; and modifying the first visualization to produce a second visualization by changing a default visualization behavior, wherein changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals. Applicant respectfully submits that at least the above-described limitations of claim 1 as amended are not directed to methods of organizing human activity. Moreover, Applicant respectfully asserts that these limitations are not “well-understood, routine, [and] conventional activities”…the claim recites a particular, non-generic arrangement in which a projection model trained on historical subject data and historical visualizations generates prediction data, and that historically derived interface information is then used to alter default graphical user interface behavior when modifying the first visualization into the second visualization…the claimed combination is not taught by the relevant art.”
Regarding the 103 rejections, the cited prior art reference(s) fails to teach the amended claim limitations.
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (a) regarding the 101 rejections,
“claims do not recite a method for organizing human activity involving managing personal behavior or relationships where there is no tracking of financial transaction…Claims do not recite a method of managing personal behavior where the claim is not a fundamental activity that forms the basis of our democracy…Claims do not recite a method of managing personal behavior where the claim does not provide information to a person without interfering with the person’s primary activity…claims do not disclose a claim reciting following rules or instructions…Claim 1 is not directed to managing personal behavior or relationships or interactions between people because it does not recite social activities, teaching, voting, wagering, following rules or instructions, or any other scheme for directing how a person should behave. Rather, claim 1 is directed to a specific machine-implemented process for modifying a visualization associated with subject data using particular technical components and operations, including a first machine-learning model trained using upsampling, downsampling, and normalization of assessment training data, a second machine-learning model trained on historical subject data corresponding to historical visualizations, generation of prediction data and visualizations, receipt of an interaction signal through an interaction event handler, and modification of a graphical user interface by changing default visualization behavior based on stored historical graphical user interface configurations”:
It is respectfully submitted that Applicant argues “claims do not recite a method for organizing human activity involving managing personal behavior or relationships where there is no tracking of financial transaction…Claims do not recite a method of managing personal behavior where the claim is not a fundamental activity that forms the basis of our democracy…Claims do not recite a method of managing personal behavior where the claim does not provide information to a person without interfering with the person’s primary activity…claims do not disclose a claim reciting following rules or instructions…Claim 1 is not directed to managing personal behavior or relationships or interactions between people because it does not recite social activities, teaching, voting, wagering, following rules or instructions, or any other scheme for directing how a person should behave.” However, Applicant fails to specify how the “claims do not recite a method for organizing human activity involving managing personal behavior or relationships where there is no tracking of financial transaction…do not recite a method of managing personal behavior where the claim is not a fundamental activity that forms the basis of our democracy…do not recite a method of managing personal behavior where the claim does not provide information to a person without interfering with the person’s primary activity…do not disclose a claim reciting following rules or instructions…Claim 1 is not directed to managing personal behavior or relationships or interactions between people because it does not recite social activities, teaching, voting, wagering, following rules or instructions, or any other scheme for directing how a person should behave.” Regardless, the list of examples for the enumerated sub-groupings is exemplary, and not exhaustive; Appellant’s invention need not be included in the list, as long as the claim recites an abstract idea, which it does, as explained above.
Applicant argues “claim 1 is directed to a specific machine-implemented process for modifying a visualization associated with subject data using particular technical components and operations, including a first machine-learning model trained using upsampling, downsampling, and normalization of assessment training data, a second machine-learning model trained on historical subject data corresponding to historical visualizations, generation of prediction data and visualizations, receipt of an interaction signal through an interaction event handler, and modification of a graphical user interface by changing default visualization behavior based on stored historical graphical user interface configurations.” However, the claim limitations to which Applicant seem to refer as “a first…model trained using upsampling, downsampling, and normalization of assessment training data, a second…model trained on historical subject data corresponding to historical visualizations, generation of prediction data and visualizations, receipt of an interaction signal through an interaction event handler, and modification of a graphical user interface by changing default visualization behavior based on stored historical graphical user interface configurations” are interpreted as rules or instructions followed to collect user data, analyze the data, and output a conclusion about a user based on the analyzed data (i.e., to manage a user’s medical expenses). Also, “a first machine-learning model previously trained” and “a second machine-learning model trained” is not interpreted as part of the abstract idea, but additional elements, which are only used to generally apply the abstract idea without placing any limits on how the machine learning models function and only recite the outcome of the abstract idea and does not include details about how “analyze…the subject data,” “compare the subject data to predefined parameters; and determine a variance associated with a comparison between the subject data and the predefined parameters,” and “generate… prediction data based on the subject data and the historical visualizations” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claims are directed to an abstract idea.
“an improvement in how a computing system generates and modifies graphical user interface visualizations associated with subject data…is not merely the use of a generic model to analyze data and display a result. Rather, claim 1 recites a particular technical workflow in which the computing device first analyzes subject data using an assessment model to determine a variance, then further generates, using a projection model comprising a second machine-learning model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on both the subject data and the historical visualizations, where the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that was most effective for viewing the information. Claim 1 further requires modifying the first visualization to produce a second visualization by changing a default visualization behavior, where changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals. Thus, claim 1 does not merely recite presenting information or organizing human activity at a high level, but instead uses historically effective interface configurations and model-generated prediction data to control how the graphical user interface is automatically reconfigured in response to the variance and interaction signal. This imposes a meaningful limit on any alleged judicial exception because the claim is tied to a specific technological manner of improving interface behavior, reducing repetitive manual reconfiguration of visualizations, and improving the operation of the computing system in generating later visualizations based on prior effective graphical configurations, rather than monopolizing any abstract idea in the abstract”:
Applicant argues “an improvement in how a computing system generates and modifies graphical user interface visualizations associated with subject data…is not merely the use of a generic model to analyze data and display a result. Rather, claim 1 recites a particular technical workflow in which the computing device first analyzes subject data using an assessment model to determine a variance, then further generates, using a projection model comprising a second machine-learning model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on both the subject data and the historical visualizations, where the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that was most effective for viewing the information. Claim 1 further requires modifying the first visualization to produce a second visualization by changing a default visualization behavior, where changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals. Thus, claim 1 does not merely recite presenting information or organizing human activity at a high level, but instead uses historically effective interface configurations and model-generated prediction data to control how the graphical user interface is automatically reconfigured in response to the variance and interaction signal. This imposes a meaningful limit on any alleged judicial exception because the claim is tied to a specific technological manner of improving interface behavior, reducing repetitive manual reconfiguration of visualizations, and improving the operation of the computing system in generating later visualizations based on prior effective graphical configurations, rather than monopolizing any abstract idea in the abstract.” However, the alleged improvements of “how the graphical user interface is automatically reconfigured” and “reducing repetitive manual reconfiguration of visualizations” do not seem to address a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. For example, how is “the operation of the computing system” itself improved? Furthermore, the courts have indicated that “Mere automation of manual processes” may not be sufficient to show an improvement in computer functionality.
Furthermore, the claim limitations to which Applicant seem to refer as “a particular technical workflow in which…first analyzes subject data using an assessment model to determine a variance, then further generates, using a projection model comprising a second…model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on both the subject data and the historical visualizations, where the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that was most effective for viewing the information…modifying the first visualization to produce a second visualization by changing a default visualization behavior, where changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals…uses historically effective interface configurations and model-generated prediction data to control how the graphical user interface is automatically reconfigured in response to the variance and interaction signal” are interpreted as rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect user data, analyze the data, and output a conclusion about a user based on the analyzed data (i.e., to manage a user’s medical expenses), which is the abstract idea and not additional elements to be interpreted in Step 2A, Prong Two. Also, “a first machine-learning model previously trained” and “a second machine-learning model trained” is not interpreted as part of the abstract idea, but additional elements, which are only used to generally apply the abstract idea without placing any limits on how the machine learning models function and only recite the outcome of the abstract idea and does not include details about how “analyze…the subject data,” “compare the subject data to predefined parameters; and determine a variance associated with a comparison between the subject data and the predefined parameters,” and “generate… prediction data based on the subject data and the historical visualizations” is accomplished, respectively, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Even if the claims provide the alleged improvements, any alleged benefits of the invention are at best, an improvement to rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect user data, analyze the data, and output a conclusion about a user based on the analyzed data (i.e., to manage a user’s medical expenses), which is the abstract idea. However, an improved abstract idea is still an abstract idea and the claims do not provide a technical improvement.
Examiner cannot find any problem caused by the technological environment to which the claims are confined, which per broadest reasonable interpretation of the claim in light of the specification, is a well-known, general purpose computer. The computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a).
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
“multiple additional elements that do not recite any alleged abstract idea...include, without limitation, generating, using a projection model comprising a second machine-learning model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on the subject data and the historical visualizations, wherein the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that was most effective for viewing the information; and modifying the first visualization to produce a second visualization by changing a default visualization behavior, wherein changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals. Applicant respectfully submits that at least the above-described limitations of claim 1 as amended are not directed to methods of organizing human activity. Moreover, Applicant respectfully asserts that these limitations are not “well-understood, routine, [and] conventional activities”…the claim recites a particular, non-generic arrangement in which a projection model trained on historical subject data and historical visualizations generates prediction data, and that historically derived interface information is then used to alter default graphical user interface behavior when modifying the first visualization into the second visualization…the claimed combination is not taught by the relevant art”:
Applicant argues “multiple additional elements that do not recite any alleged abstract idea...include, without limitation, generating, using a projection model comprising a second machine-learning model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on the subject data and the historical visualizations, wherein the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that was most effective for viewing the information; and modifying the first visualization to produce a second visualization by changing a default visualization behavior, wherein changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals. Applicant respectfully submits that at least the above-described limitations of claim 1 as amended are not directed to methods of organizing human activity.” However, the claim limitations to which Applicant seem to refer as “generating, using a projection model comprising a second…model trained on prediction training data comprising historical subject data corresponding to historical visualizations, prediction data based on the subject data and the historical visualizations, wherein the historical visualizations comprise modified graphical user interfaces previously used effectively based on interaction signals and stored with a graphical user interface configuration that was most effective for viewing the information; and modifying the first visualization to produce a second visualization by changing a default visualization behavior, wherein changing the default visualization behavior comprises applying a graphical user interface configuration stored in historical visualizations previously used effectively based on interaction signals” are interpreted as rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect user data, analyze the data, and output a conclusion about a user based on the analyzed data (i.e., to manage a user’s medical expenses), which is the abstract idea and not additional elements to be interpreted in Step 2B. Also, “a second machine-learning model trained” is not interpreted as part of the abstract idea, but additional elements, which are only used to generally apply the abstract idea without placing any limits on how the machine learning models function and only recite the outcome of the abstract idea and does not include details about how “generate… prediction data based on the subject data and the historical visualizations” is accomplished, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “these limitations are not “well-understood, routine, [and] conventional activities”…the claim recites a particular, non-generic arrangement in which a projection model trained on historical subject data and historical visualizations generates prediction data, and that historically derived interface information is then used to alter default graphical user interface behavior when modifying the first visualization into the second visualization.” However, whether the elements define only well-understood, routine, conventional activity is not a standalone test for determining eligibility, but an exemplary consideration in a non-limiting list of considerations.
Applicant argues “the claimed combination is not taught by the relevant art.” However, per MPEP § 2106.05(I): “the search for an inventive concept should not be confused with a novelty or non-obviousness determination…As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter…a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty…Because [novelty and obviousness] are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.”
Thus, Examiner maintains the 101 rejections of claims 1, 5-8, 10-11, 15-18, 20, which have been updated to address Applicant’s remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
In response to Applicant’s argument that (b) regarding the 103 rejections, the cited prior art reference(s) fails to teach the amended claim limitations:
It is respectfully submitted that Examiner withdraws the aforementioned 103 rejections of Office Action dated 03/12/2026 because the amendments have rendered the rejections moot.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily Huynh whose telephone number is (571)272-8317. The examiner can normally be reached on M-Th 8-5 PM.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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