DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. This action is in reply to the responsive to communication(s) filed on 03/07/2024.
3. Claims 1-20 are currently pending and are rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
6. Analysis:
Step 1: Statutory Category?: (is the claim(s) directed to a process, machine, manufacture or composition of matter?) - YES: In the instant case, claims 1-11 are directed to a method (i.e., process), claims 12-20 are directed to a non-transitory computer readable medium (i.e., machine).
Regarding independent claim 1:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 1 recites the at least following limitations of “… obtaining, …, a client asset dataset (CAD) and an economic and demographic dataset (EDD), wherein the CAD comprises a plurality of assets; analyzing, …, the CAD that is received … to generate and associate an identifier for each of the plurality of assets, wherein the identifier comprises location data; associating, …, at least a portion of the EDD with each of the plurality of assets to obtain a combined dataset, wherein each asset in the combined dataset comprises the associated identifier and the associated portion of the EDD; generating, …, a predicted net operating income (NOI) and an explanation dataset for each asset in the combined dataset, wherein the combined dataset is used as an input …, and wherein the explanation dataset comprises deviations from a baseline dataset, wherein the baseline dataset is based on an aggregation of training data …; and generating, …, for at least one of the assets in the combined dataset, the visualization based on the predicted NOI and the explanation dataset, wherein the visualization comprises: a total net impact element illustrating a total difference between the predicted NOI and the baseline dataset; and deviation elements, each illustrating a corresponding one of the deviations, wherein each of the deviations is based on one type from the EDD, wherein the deviation elements are displayed in a stacked order based on a magnitude of the associated deviation, wherein deviation elements associated with a positive number are configured to extend in … and deviation elements associated with a negative number are configured to extend …, and wherein a magnitude of extension of each deviation element is based on the magnitude of the associated deviation.” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including sales activities and/or business relations for providing a visualization for a graphical user interface (GUI) of a predicted value of an asset). Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 1 further to the abstract idea includes additional elements of “a graphical user interface (GUI)”, “an orchestrator”, “an analyzer”, “an engine”, “a trained model”, “a first direction along the GUI”, and “a second direction along the GUI, opposite the first direction”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 “a graphical user interface (GUI)”, “an orchestrator”, “an analyzer”, “an engine”, “a trained model”, “a first direction along the GUI”, and “a second direction along the GUI, opposite the first direction” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Regarding independent claim 6:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 6 recites the at least following limitations of “… providing, to … and based on a user input, a client asset dataset (CAD) wherein the CAD comprises a plurality of assets, and wherein … is configured to: analyze, …, the CAD that is received … to generate and associate an identifier for each of the plurality of assets, wherein the identifier comprises location data; associate, …, at least a portion of an economic and demographic dataset (EDD) with each of the plurality of assets to obtain a combined dataset, wherein each asset in the combined dataset comprises the associated identifier and the associated portion of the EDD; generate, …, a predicted net operating income (NOI) and an explanation dataset for each asset in the combined dataset, wherein the combined dataset is used as an input …, and wherein the explanation dataset comprises deviations from a baseline dataset, wherein the baseline dataset is based on an aggregation of training data …; and generate, …, for at least one of the assets in the combined dataset, the visualization based on the predicted NOI and the explanation dataset, wherein the visualization comprises: a total net impact comprising a total difference between the predicted NOI and the baseline dataset; and the deviations, wherein each of the deviations is based on one type from the EDD, and wherein the deviations are displayed in an order based on the magnitude of the deviation; …, the visualization; and displaying the visualization ….” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including sales activities and/or business relations for providing a visualization for a graphical user interface (GUI) of a predicted value of an asset). Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 6 further to the abstract idea includes additional elements of “a graphical user interface (GUI)”, “an infrastructure node”, “an analyzer”, “the orchestrator”, “an engine”, “a trained model”, and “a display”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 “a graphical user interface (GUI)”, “an infrastructure node”, “an analyzer”, “the orchestrator”, “an engine”, “a trained model”, and “a display” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Regarding independent claim 12:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 12 recites the at least following limitations of “… generating, …, a predicted net operating income (NOI) and an explanation dataset for each asset in an asset dataset, wherein the asset dataset is used as an input …, and wherein the explanation dataset comprises a plurality of deviations from a baseline dataset; and generating, …, for at least one of the assets in the combined dataset, the visualization based on the predicted NOI and the explanation dataset, wherein the visualization comprises: a total net impact comprising a total difference between the predicted NOI and the baseline dataset; and the deviations, wherein each of the deviations is based on one type from an economic and demographic dataset (EDD), and wherein the deviations are displayed in an order based on the magnitude of the deviation.” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including sales activities and/or business relations for providing a visualization explaining an output of a predicted value of an asset). Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 12 further to the abstract idea includes additional elements of “a computer processor”, “an engine”, and “a trained model”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 “a computer processor”, “an engine”, and “a trained model” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Dependent claims 2-5, 7-11, and 13-20 have been given the full two-part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent-ineligible under 35 U.S.C. 101.
Dependent claims 2, 7, and 13: simply provide further definition to “the visualization” recited in independent claims 1, 6 and 12. Simply stating that wherein the visualization further comprises: an interactive element associated with each of the deviation elements, wherein interaction with the interactive element automatically causes an explanation box to be displayed that displays associated values from the combined dataset and the baseline dataset amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., an interactive element associated with each of the deviation elements, an explanation box).Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 3, 8, and 14: simply refine the abstract idea because they recite limitations (e.g., further comprising: generating, by the engine, a second visualization comprising a list view of at least a portion of the combined dataset, at least a portion of the explanation dataset, and the predicted NOI), that fall under the category of organizing human activity as described above in independent claims 1, 6, and 12. Additionally, merely stating that these process steps are performed by the engine amounts to no more than merely applying generic computer components (i.e., the engine) to implement the abstract idea on a computer. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 4 and 9: simply provide further definition to “the predicted NOI” recited in dependent claims 3 and independent claim 6. Simply stating that wherein the predicted NOI comprises a one-year forecast and a five-year forecast do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 5: simply provide further definition to “an end point of each of the deviation elements” recited in independent claim 1. Simply stating that wherein an end point of each of the deviation elements vertically aligns with a start point of a directly subsequent deviation element, and wherein the first and second directions are in a horizontal direction do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 10: simply provide further definition to “the infrastructure node” recited in independent claim 6. Simply stating that wherein the infrastructure node is further configured to: analyze, by the analyzer, a client asset dataset that is received from the orchestrator to generate and associate an identifier for each of the plurality of assets, wherein the identifier comprises location data amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., the infrastructure node, the analyzer, the orchestrator).Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., the infrastructure node, the analyzer, the orchestrator) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 11: simply provide further definition to “each asset in the combined dataset” recited in dependent claim 9. Simply stating that wherein each asset in the combined dataset comprises the associated identifier and the associated portion of the EDD do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 15: simply provide further definition to “the method” recited in independent claim 12. Simply stating that wherein the method further comprises: associate an identifier for each of a plurality of assets contained within a client asset dataset to generate the asset dataset, wherein the identifier comprises location data do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 16: simply provide further definition to “the method” recited in independent claim 12. Simply stating that wherein the method further comprises: associating, by an analyzer, at least a portion of an economic and demographic dataset (EDD) with each of a plurality of assets to obtain a combined dataset, wherein the asset dataset is the combined dataset do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 17: simply provide further definition to “each asset in the combined dataset” recited in dependent claim 16. Simply stating that wherein each asset in the combined dataset comprises the associated identifier and the associated portion of the EDD do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 18: simply provide further definition to “the method” recited in independent claim 12. Simply stating that wherein the method further comprises: associating, by an analyzer, at least a portion of an economic and demographic dataset (EDD) with each of a plurality of assets to obtain a combined dataset; and applying, by the analyzer, filter criteria received from a user to the combined dataset to obtain an augmented dataset, wherein the asset dataset is the augmented dataset amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., an analyzer).Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an analyzer) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 19: simply provide further definition to “the baseline dataset” recited in independent claim 12. Simply stating that wherein the baseline dataset is generated using a national dataset of assets having a same type as the at least one of the assets do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 20: simply provide further definition to “the baseline dataset” recited in independent claim 12. Simply stating that wherein the baseline dataset is generated using a regional asset dataset having a same type as the at least one of the assets do not add any additional element or subject matter that provides a technological improvement (i.e., an interactive element associated with each of the deviation elements, an explanation box) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
10. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Laurito et al. (U.S. Pub. No. 2011/0298805), hereinafter, “Laurito”, in view of Guo et al. (U.S. Pub. No. 12,165,228), hereinafter, “Guo”.
Claim 1 –
Laurito disclose:
a method for providing a visualization for a graphical user interface (GUI) explaining an output, the method comprising (Laurito, [Abstract], [0030], “A computer-implemented method and system for producing a graphic image representative of a certain geographic region indicating certain datasets … the user is preferably provided with a Graphical User Interface (GUI) 300, preferably via a computer interface 105 (FIG. 1B)”, see also Figures 1-3):
obtaining, by an orchestrator, a client asset dataset (CAD) and an economic and demographic dataset (EDD), wherein the CAD comprises a plurality of assets (Laurito, [0027], “system and process is preferably a software and computer driven application executing on a computer 100 enabling a user to visualize demographic, economic, financial, and political and other user chosen datasets (preferably having a correlation to a financial asset) in integration with data representative of financial assets as chosen by the user (such as municipal bonds) superimposed on a graphic image preferably depicting a geographic region relating to the aforesaid data indicating the user selected financial assets in correlation with the selected user chosen datasets”, see also Figures 1-3);
analyzing, by an analyzer, the CAD that is received from the orchestrator to generate and associate an identifier for each of the plurality of assets, wherein the identifier comprises location data (Laurito, [0086], “the user can select "Search" (206) and the present invention system and process allows the user to select a specific area (State or county) or CUSIP to identify the geographic location and display data and portfolio positions associated with that area or CUSIP”, see also Figure 2);
associating, by the analyzer, at least a portion of the EDD with each of the plurality of assets to obtain a combined dataset, wherein each asset in the combined dataset comprises the associated identifier and the associated portion of the EDD (Laurito, [0087], “The search tool will return a data menu (606) that shows the data groups and datasets that can be accessed. The user can select any dataset and display the associated graph or table (608)”, see also Figure 6A);
generating, by the engine, for at least one of the assets in the combined dataset, the visualization based on the predicted NOI and the explanation dataset, wherein the visualization comprises: a total net impact element illustrating a total difference between the predicted NOI and the baseline dataset; and deviation elements, each illustrating a corresponding one of the deviations, wherein each of the deviations is based on one type from the EDD, wherein the deviation elements are displayed in a stacked order based on a magnitude of the associated deviation, wherein deviation elements associated with a positive number are configured to extend in a first direction along the GUI and deviation elements associated with a negative number are configured to extend in a second direction along the GUI, opposite the first direction, and wherein a magnitude of extension of each deviation element is based on the magnitude of the associated deviation (Laurito, [0030], “The user is preferably provided with a Graphical User Interface (GUI) 300, preferably via a computer interface 105 (FIG. 1B). A field 310 is provided to select a Data Group and a corresponding field 312 containing objective datasets are presented by system 100 and selected by the user, which are to be used to populate a generated map. A map title or legend is user selected from field 314. For instance, field 312 in FIG. 3, relates to Deficit Per Capita statistics for the United States categorized to each calendar month of a year or multiple years”, see also Figure 3)
Laurito do not explicitly disclose:
[[generating, by an engine and using a trained model, a predicted net operating income (NOI) and an explanation dataset for each asset in the combined dataset, wherein the combined dataset is used as an input to the trained model, and wherein the explanation dataset comprises deviations from a baseline dataset, wherein the baseline dataset is based on an aggregation of training data used to train the trained model]]
Guo disclose [[generating, by an engine and using a trained model, a predicted net operating income (NOI) and an explanation dataset for each asset in the combined dataset, wherein the combined dataset is used as an input to the trained model, and wherein the explanation dataset comprises deviations from a baseline dataset, wherein the baseline dataset is based on an aggregation of training data used to train the trained model]] (See at least Guo [Column 8, Lines 43-49], “the location evaluation system 108 includes an analytics engine 144 that is configured to train machine learning data models at each granularity level to generate rental income predictions and feature-level scores indicating how much each feature in the combined data sets 116 contributes to the rental income prediction and location quality score for multifamily properties”, see also Figure 1). It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the method of Laurito to include an engine and using a trained model as taught by Guo, in order to predict net operating income (NOI) and an explanation dataset for each asset in the combined dataset (see Guo [Column 8, Lines 43-49], Figure 1).
Claim 2 –
Laurito/Guo disclose the method of claim 1, as shown above.
Laurito further disclose:
wherein the visualization further comprises: an interactive element associated with each of the deviation elements, wherein interaction with the interactive element automatically causes an explanation box to be displayed that displays associated values from the combined dataset and the baseline dataset (Laurito, [0090], “At any time in the interactive session, the custom selected output can be captured and saved for use in the production of financial reports, newsletters and other mediums where visualization of data is desirable. The present invention system and method processes multiple datasets preferably from thirteen data groups: Budget, Commerce, Crime, Disasters, Distress, Education, Health Care, Housing, Income, Legal, Pension, Politics & Government, Population, Taxes and Unemployment/Employment”, see also Figure 4).
Claim 3 –
Laurito/Guo disclose the method of claim 1, as shown above.
Laurito further disclose:
further comprising: generating, by the engine, a second visualization comprising a list view of at least a portion of the combined dataset, at least a portion of the explanation dataset, and the predicted NOI (Laurito, [0090], “At any time in the interactive session, the custom selected output can be captured and saved for use in the production of financial reports, newsletters and other mediums where visualization of data is desirable. The present invention system and method processes multiple datasets preferably from thirteen data groups: Budget, Commerce, Crime, Disasters, Distress, Education, Health Care, Housing, Income, Legal, Pension, Politics & Government, Population, Taxes and Unemployment/Employment”, see also Figure 4).
Claim 4 –
Laurito/Guo disclose the method of claim 1, as shown above.
Laurito further disclose:
wherein the predicted NOI comprises a one-year forecast and a five-year forecast (Laurito, [0030], “The user is preferably provided with a Graphical User Interface (GUI) 300, preferably via a computer interface 105 (FIG. 1B). A field 310 is provided to select a Data Group and a corresponding field 312 containing objective datasets are presented by system 100 and selected by the user, which are to be used to populate a generated map. A map title or legend is user selected from field 314. For instance, field 312 in FIG. 3, relates to Deficit Per Capita statistics for the United States categorized to each calendar month of a year or multiple years”, see also Figure 3).
Claim 5 –
Laurito/Guo disclose the method of claim 1, as shown above.
Laurito further disclose:
wherein an end point of each of the deviation elements vertically aligns with a start point of a directly subsequent deviation element, and wherein the first and second directions are in a horizontal direction (Laurito, [0087], “The search tool will return a data menu (606) that shows the data groups and datasets that can be accessed. The user can select any dataset and display the associated graph or table (608)”, see also Figure 6A).
Claim 6 –
Laurito disclose:
a method for displaying a visualization on a graphical user interface (GUI) explaining an output, the method comprising (Laurito, [Abstract], [0030], “A computer-implemented method and system for producing a graphic image representative of a certain geographic region indicating certain datasets … the user is preferably provided with a Graphical User Interface (GUI) 300, preferably via a computer interface 105 (FIG. 1B)”, see also Figures 1-3):
providing, to an infrastructure node and based on a user input, a client asset dataset (CAD) wherein the CAD comprises a plurality of assets, and wherein the infrastructure node is configured to (Laurito, [0027], “system and process is preferably a software and computer driven application executing on a computer 100 enabling a user to visualize demographic, economic, financial, and political and other user chosen datasets (preferably having a correlation to a financial asset) in integration with data representative of financial assets as chosen by the user (such as municipal bonds) superimposed on a graphic image preferably depicting a geographic region relating to the aforesaid data indicating the user selected financial assets in correlation with the selected user chosen datasets”, see also Figures 1-3):
analyze, by an analyzer, the CAD that is received from the orchestrator to generate and associate an identifier for each of the plurality of assets, wherein the identifier comprises location data (Laurito, [0086], “the user can select "Search" (206) and the present invention system and process allows the user to select a specific area (State or county) or CUSIP to identify the geographic location and display data and portfolio positions associated with that area or CUSIP”, see also Figure 2);
associate, by the analyzer, at least a portion of an economic and demographic dataset (EDD) with each of the plurality of assets to obtain a combined dataset, wherein each asset in the combined dataset comprises the associated identifier and the associated portion of the EDD (Laurito, [0087], “The search tool will return a data menu (606) that shows the data groups and datasets that can be accessed. The user can select any dataset and display the associated graph or table (608)”, see also Figure 6A);
generate, by the engine, for at least one of the assets in the combined dataset, the visualization based on the predicted NOI and the explanation dataset, wherein the visualization comprises: a total net impact comprising a total difference between the predicted NOI and the baseline dataset; and the deviations, wherein each of the deviations is based on one type from the EDD, and wherein the deviations are displayed in an order based on the magnitude of the deviation; obtaining, from the infrastructure node, the visualization; and displaying the visualization on the GUI on a display (Laurito, [0030], “The user is preferably provided with a Graphical User Interface (GUI) 300, preferably via a computer interface 105 (FIG. 1B). A field 310 is provided to select a Data Group and a corresponding field 312 containing objective datasets are presented by system 100 and selected by the user, which are to be used to populate a generated map. A map title or legend is user selected from field 314. For instance, field 312 in FIG. 3, relates to Deficit Per Capita statistics for the United States categorized to each calendar month of a year or multiple years”, see also Figure 3)
Laurito do not explicitly disclose:
[[generate, by an engine and using a trained model, a predicted net operating income (NOI) and an explanation dataset for each asset in the combined dataset, wherein the combined dataset is used as an input to the trained model, and wherein the explanation dataset comprises deviations from a baseline dataset, wherein the baseline dataset is based on an aggregation of training data used to train the trained model]]
Guo disclose [[generate, by an engine and using a trained model, a predicted net operating income (NOI) and an explanation dataset for each asset in the combined dataset, wherein the combined dataset is used as an input to the trained model, and wherein the explanation dataset comprises deviations from a baseline dataset, wherein the baseline dataset is based on an aggregation of training data used to train the trained model]] (See at least Guo [Column 8, Lines 43-49], “the location evaluation system 108 includes an analytics engine 144 that is configured to train machine learning data models at each granularity level to generate rental income predictions and feature-level scores indicating how much each feature in the combined data sets 116 contributes to the rental income prediction and location quality score for multifamily properties”, see also Figure 1). It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the method of Laurito to include an engine and using a trained model as taught by Guo, in order to predict net operating income (NOI) and an explanation dataset for each asset in the combined dataset (see Guo [Column 8, Lines 43-49], Figure 1).
Claim 7 –
Laurito/Guo disclose the method of claim 6, as shown above.
Laurito further disclose:
wherein the visualization further comprises: an interactive element associated with each of the deviations, wherein interaction with the interactive element automatically causes an explanation box to be displayed that displays associated values from the combined dataset and the baseline dataset (Laurito, [0090], “At any time in the interactive session, the custom selected output can be captured and saved for use in the production of financial reports, newsletters and other mediums where visualization of data is desirable. The present invention system and method processes multiple datasets preferably from thirteen data groups: Budget, Commerce, Crime, Disasters, Distress, Education, Health Care, Housing, Income, Legal, Pension, Politics & Government, Population, Taxes and Unemployment/Employment”, see also Figure 4).
Claim 8 –
Laurito/Guo disclose the method of claim 6, as shown above.
Laurito further disclose:
wherein the infrastructure node is further configured to: generate, by the engine, a second visualization comprising a list view of at least a portion of the combined dataset, at least a portion of the explanation dataset, and the predicted NOI (Laurito, [0090], “At any time in the interactive session, the custom selected output can be captured and saved for use in the production of financial reports, newsletters and other mediums where visualization of data is desirable. The present invention system and method processes multiple datasets preferably from thirteen data groups: Budget, Commerce, Crime, Disasters, Distress, Education, Health Care, Housing, Income, Legal, Pension, Politics & Government, Population, Taxes and Unemployment/Employment”, see also Figure 4).
Claim 9 –
Laurito/Guo disclose the method of claim 6, as shown above.
Laurito further disclose:
wherein the predicted NOI comprises a one-year forecast and a five-year forecast (Laurito, [0030], “The user is preferably provided with a Graphical User Interface (GUI) 300, preferably via a computer interface 105 (FIG. 1B). A field 310 is provided to select a Data Group and a corresponding field 312 containing objective datasets are presented by system 100 and selected by the user, which are to be used to populate a generated map. A map title or legend is user selected from field 314. For instance, field 312 in FIG. 3, relates to Deficit Per Capita statistics for the United States categorized to each calendar month of a year or multiple years”, see also Figure 3).
Claim 10 –
Laurito/Guo disclose the method of claim 6, as shown above.
Laurito further disclose:
wherein the infrastructure node is further configured to: analyze, by the analyzer, a client asset dataset that is received from the orchestrator to generate and associate an identifier for each of the plurality of assets, wherein the identifier comprises location data (Laurito, [0086], “the user can select "Search" (206) and the present invention system and process allows the user to select a specific area (State or county) or CUSIP to identify the geographic location and display data and portfolio positions associated with that area or CUSIP”, see also Figure 2).
Claim 11 –
Laurito/Guo disclose the method of claim 9, as shown above.
Laurito further disclose:
wherein each asset in the combined dataset comprises the associated identifier and the associated portion of the EDD (Laurito, [0086], “the user can select "Search" (206) and the present invention system and process allows the user to select a specific area (State or county) or CUSIP to identify the geographic location and display data and portfolio positions associated with that area or CUSIP”, see also Figure 2).
Claim 12 –
Laurito disclose:
a non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for providing a visualization explaining an output, the method comprising (Laurito, [Abstract], [0030], “A computer-implemented method and system for producing a graphic image representative of a certain geographic region indicating certain datasets … the user is preferably provided with a Graphical User Interface (GUI) 300, preferably via a computer interface 105 (FIG. 1B)”, see also Figures 1-3):
generating, by the engine, for at least one of the assets in the combined dataset, the visualization based on the predicted NOI and the explanation dataset, wherein the visualization comprises: a total net impact comprising a total difference between the predicted NOI and the baseline dataset; and the deviations, wherein each of the deviations is based on one type from an economic and demographic dataset (EDD), and wherein the deviations are displayed in an order based on the magnitude of the deviation (Laurito, [0030], “The user is preferably provided with a Graphical User Interface (GUI) 300, preferably via a computer interface 105 (FIG. 1B). A field 310 is provided to select a Data Group and a corresponding field 312 containing objective datasets are presented by system 100 and selected by the user, which are to be used to populate a generated map. A map title or legend is user selected from field 314. For instance, field 312 in FIG. 3, relates to Deficit Per Capita statistics for the United States categorized to each calendar month of a year or multiple years”, see also Figure 3)
Laurito do not explicitly disclose:
[[generating, by an engine and using a trained model, a predicted net operating income (NOI) and an explanation dataset for each asset in an asset dataset, wherein the asset dataset is used as an input to the trained model, and wherein the explanation dataset comprises a plurality of deviations from a baseline dataset]]
Guo disclose [[generating, by an engine and using a trained model, a predicted net operating income (NOI) and an explanation dataset for each asset in an asset dataset, wherein the asset dataset is used as an input to the trained model, and wherein the explanation dataset comprises a plurality of deviations from a baseline dataset]] (See at least Guo [Column 8, Lines 43-49], “the location evaluation system 108 includes an analytics engine 144 that is configured to train machine learning data models at each granularity level to generate rental income predictions and feature-level scores indicating how much each feature in the combined data sets 116 contributes to the rental income prediction and location quality score for multifamily properties”, see also Figure 1). It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the method of Laurito to include an engine and using a trained model as taught by Guo, in order to predict net operating income (NOI) and an explanation dataset for each asset in the combined dataset (see Guo [Column 8, Lines 43-49], Figure 1).
Claim 13 –
Laurito/Guo disclose the non-transitory computer readable medium of claim 12, as shown above.
Laurito further disclose:
wherein the visualization further comprises: an interactive element associated with each of the deviations, wherein interaction with the interactive element automatically causes an explanation box to be displayed that displays associated values from the combined dataset and the baseline dataset (Laurito, [0090], “At any time in the interactive session, the custom selected output can be captured and saved for use in the production of financial reports, newsletters and other mediums where visualization of data is desirable. The present invention system and method processes multiple datasets preferably from thirteen data groups: Budget, Commerce, Crime, Disasters, Distress, Education, Health Care, Housing, Income, Legal, Pension, Politics & Government, Population, Taxes and Unemployment/Employment”, see also Figure 4).
Claim 14 –
Laurito/Guo disclose the non-transitory computer readable medium of claim 12, as shown above.
Laurito further disclose:
wherein the method further comprises: generating, by the engine, a second visualization comprising a list view of at least a portion of the asset dataset, at least a portion of the explanation dataset, and the predicted NOI (Laurito, [0090], “At any time in the interactive session, the custom selected output can be captured and saved for use in the production of financial reports, newsletters and other mediums where visualization of data is desirable. The present invention system and method processes multiple datasets preferably from thirteen data groups: Budget, Commerce, Crime, Disasters, Distress, Education, Health Care, Housing, Income, Legal, Pension, Politics & Government, Population, Taxes and Unemployment/Employment”, see also Figure 4).
Claim 15 –
Laurito/Guo disclose the non-transitory computer readable medium of claim 12, as shown above.
Laurito further disclose:
wherein the method further comprises: associate an identifier for each of a plurality of assets contained within a client asset dataset to generate the asset dataset, wherein the identifier comprises location data (Laurito, [0086], “the user can select "Search" (206) and the present invention system and process allows the user to select a specific area (State or county) or CUSIP to identify the geographic location and display data and portfolio positions associated with that area or CUSIP”, see also Figure 2).
Claim 16 –
Laurito/Guo disclose the non-transitory computer readable medium of claim 12, as shown above.
Laurito further disclose:
wherein the method further comprises: associating, by an analyzer, at least a portion of an economic and demographic dataset (EDD) with each of a plurality of assets to obtain a combined dataset, wherein the asset dataset is the combined dataset (Laurito, [0086], “the user can select "Search" (206) and the present invention system and process allows the user to select a specific area (State or county) or CUSIP to identify the geographic location and display data and portfolio positions associated with that area or CUSIP”, see also Figure 2).
Claim 17 –
Laurito/Guo disclose the non-transitory computer readable medium of claim 12, as shown above.
Laurito further disclose:
wherein each asset in the combined dataset comprises the associated identifier and the associated portion of the EDD (Laurito, [0086], “the user can select "Search" (206) and the present invention system and process allows the user to select a specific area (State or county) or CUSIP to identify the geographic location and display data and portfolio positions associated with that area or CUSIP”, see also Figure 2).
Claim 18 –
Laurito/Guo disclose the non-transitory computer readable medium of claim 12, as shown above.
Laurito further disclose:
wherein the method further comprises: associating, by an analyzer, at least a portion of an economic and demographic dataset (EDD) with each of a plurality of assets to obtain a combined dataset; and applying, by the analyzer, filter criteria received from a user to the combined dataset to obtain an augmented dataset, wherein the asset dataset is the augmented dataset (Laurito, [0086], “the user can select "Search" (206) and the present invention system and process allows the user to select a specific area (State or county) or CUSIP to identify the geographic location and display data and portfolio positions associated with that area or CUSIP”, see also Figure 2).
Claim 19 –
Laurito/Guo disclose the non-transitory computer readable medium of claim 12, as shown above.
Laurito further disclose:
wherein the baseline dataset is generated using a national dataset of assets having a same type as the at least one of the assets (Laurito, [0087], “The search tool will return a data menu (606) that shows the data groups and datasets that can be accessed. The user can select any dataset and display the associated graph or table (608)”, see also Figure 6A).
Claim 20 –
Laurito/Guo disclose the non-transitory computer readable medium of claim 12, as shown above.
Laurito further disclose:
wherein the baseline dataset is generated using a regional asset dataset having a same type as the at least one of the assets (Laurito, [0087], “The search tool will return a data menu (606) that shows the data groups and datasets that can be accessed. The user can select any dataset and display the associated graph or table (608)”, see also Figure 6A).
Relevant Prior Art
11. The prior art made of record and not relied upon are considered pertinent to applicant's disclosure:
Darden (U.S. Pub. No. 2018/0150926) teach systems and methods for automated assessment for remediation and/or redevelopment of brownfield real estate.
Keifer, III (U.S. Patent No. 11,223,873) teach methods and systems for remote streaming of a user-customized user interface.
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Liz Nguyen whose telephone number is (571) 272-5414. The examiner can normally be reached on Monday to Friday 8:00 A.M to 5:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Gart, can be reached on (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Center system (visit: https://patentcenter.uspto.gov). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800) 786-9199 (USA or CANADA) or (571) 272-1000.
/LIZ P NGUYEN/
Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696