DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-11 are directed to an apparatus, claims 12-20 are directed to a method. Therefore, claims 1-20 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 12, recite generating or updating a map of a dining environment layout, constituting an abstract idea based on “A Mental Process” and “Certain Methods of Organizing Human Activity” related to determining the ability of an entity to continue operating over a given period of time. Specifically the independent claims recite:
(a) mental process: as drafted, the claim recites the limitations of receiving data, selecting data, determining data, and generating data which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting by a processor, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the “by at least a processor” language, the claim encompasses the user manually taking data and generating a growth approach. The mere nominal recitation of a generic computing devices does not take the claim limitation out of the mental processes grouping. This limitation is a mental process.
(c) certain methods of organizing human activity: The claim as a whole recites a method of organizing human activity. The claimed invention is a method that allows for determining the ability of an entity to continue operating over a given period of time and generating a growth strategy. “Commercial interactions” or “legal interactions” include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations.” Specifically estimating business survival and optimizing organizational growth. Thus, the claim recites an abstract idea.
The claimed limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “at least a processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “at least a processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “at least a processor” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 3-9, 14-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 2, 10-11, 13, 20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 12, do not integrate the judicial exception into a practical application. Claim 1 is a system comprising “at least a processor; a memory communicatively connected to the at least a processor, the memory containing instructions… training a life machine learning model.” Claim 12 is a method that recites limitations performed “by at least a processor…training a life machine learning model.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, generate, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 3-9, 14-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 2 introduces the additional element of “training an indicator machine learning model as a function of the indicator training data; and selecting at least one probability indicator as a function of the indicator machine learning model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 10 introduces the additional element of “create a user interface data structure, wherein the user interface data structure comprises the life probability and the growth approach; and transmit the user interface data structure; and the apparatus further comprises a display communicatively connected to the at least a processor, the display configured to: receive the user interface data structure; and display the life probability and the growth approach as a function of the user interface data structure.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 11 introduces the additional element of “wherein the life probability further comprises at least one probability deviation, wherein the display is configured to display at least one growth deviation of the growth approach as a function of a selection of the at least one probability deviation.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 13 introduces the additional element of “training an indicator machine learning model as a function of the indicator training data; and selecting at least one probability indicator as a function of the indicator machine learning model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 20 introduces the additional element of “creating, by the at least a processor, a user interface data structure, wherein the user interface data structure comprises the life probability and the growth approach; and transmitting, by the at least a processor, the user interface data structure to a display; displaying, using the display, the life probability, and the growth approach as a function of the user interface data structure.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 12, do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a system comprising “at least a processor; a memory communicatively connected to the at least a processor, the memory containing instructions… training a life machine learning model.” Claim 12 is a method that recites limitations performed “by at least a processor…training a life machine learning model.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 3-9, 14-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 2 introduces the additional element of “training an indicator machine learning model as a function of the indicator training data; and selecting at least one probability indicator as a function of the indicator machine learning model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 10 introduces the additional element of “create a user interface data structure, wherein the user interface data structure comprises the life probability and the growth approach; and transmit the user interface data structure; and the apparatus further comprises a display communicatively connected to the at least a processor, the display configured to: receive the user interface data structure; and display the life probability and the growth approach as a function of the user interface data structure.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 11 introduces the additional element of “wherein the life probability further comprises at least one probability deviation, wherein the display is configured to display at least one growth deviation of the growth approach as a function of a selection of the at least one probability deviation.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 13 introduces the additional element of “training an indicator machine learning model as a function of the indicator training data; and selecting at least one probability indicator as a function of the indicator machine learning model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea 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., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 20 introduces the additional element of “creating, by the at least a processor, a user interface data structure, wherein the user interface data structure comprises the life probability and the growth approach; and transmitting, by the at least a processor, the user interface data structure to a display; displaying, using the display, the life probability, and the growth approach as a function of the user interface data structure.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 9, 10, 12-15, 20, is/are rejected under 35 U.S.C. 103 as being unpatentable over Gembicki (US 20160034838 A1) in view of Selvadurai et al. (US 11341517 B2).
Regarding claim 1, Gembicki teaches 1. An apparatus for determining the resilience of an entity, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to (Fig. 6A-6B, discloses the structure. ¶ 39, 133, 136-139, also disclosure the claimed structure).
receive entity data from a user wherein the entity data includes function data (¶ 9-12, Some embodiments of the present technology involve a resiliency scoring system receiving, from an individual client or strategic partner, a request for one or more resiliency scores for an organization. The resiliency scoring system can obtain intelligence for the organization relating to a plurality of base resiliency indicators that are chosen by examining the request, determining an industry type described in the request by comparing the request to a resiliency index, selecting an industry specific resilience core data store, and extracting, from the industry specific resilience core data store, the plurality of resiliency indicators. ¶ 13-18, 138-142, discloses function data, ¶ 46-48, 101, 107, Fig. 2, 5A-B, also disclose receiving various forms of entity data);
select at least one probability indicator as a function of the function data (¶ 10-18, The resiliency scoring system can obtain intelligence for the organization relating to a plurality of base resiliency indicators that are chosen by examining the request, determining an industry type described in the request by comparing the request to a resiliency index, selecting an industry specific resilience core data store, and extracting, from the industry specific resilience core data store, the plurality of resiliency indicators. ¶ 42-45, The resiliency score generation engine 120 is configured to gather intelligence (e.g. raw intelligence, information quantified with a confidence score, information along with learned rules, and combinations thereof) from the intelligence engine 125 and assign a score to a plurality of resiliency key performance indicators for a strategic partner 110 or individual client 115. Approaches to scoring are explained in greater detail below. ¶ 39, 70, 106-108, 130).
Gembicki does not teach, however, the combination of Gembicki and Selvadurai teaches
determine a life probability of the entity as a function of the at least one probability indicator comprising (col. 8, lines 5-32, discloses data related to determining performance rate of an entity. However, the number of past years to consider may vary. Col. 17, line 65- col. 18, line 13, discloses historical entity indexes based on the entity data.);
receiving life training data comprising a plurality of the least one probability indicators correlated to a plurality of life probabilities (col. 11, lines 28-50, In various implementations, an entity index value may be determined using various machine learning models which may be generated or trained using a set of historical index data for a variety of entities. In some implementations, the models may be generated or trained using index values calculated with the equations disclosed above. For example, index values may be calculated for a training set of entities with known values and the training set may be used to generate a neural network or graph based network model. Entity information for an entity may then be provided as input and an index value for the entity may be predicted using clustering to identify similar entities with known indexes. In another implementation, a random forest classifier may be trained used training set data and may then be used to generate index values for new entities. In various implementations, such models may include one or more of various types of classifiers, deep learning networks, or other supervised or unsupervised models. Such models may, in some implementations, be updated over time through feedback on generated index values and entity health. Col. 17, line 65 – col. 18, line 25, An operation determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33);
training a life machine learning model as a function of the life training data (col. 11, lines 28-50, In various implementations, an entity index value may be determined using various machine learning models which may be generated or trained using a set of historical index data for a variety of entities. In some implementations, the models may be generated or trained using index values calculated with the equations disclosed above. For example, index values may be calculated for a training set of entities with known values and the training set may be used to generate a neural network or graph based network model. Entity information for an entity may then be provided as input and an index value for the entity may be predicted using clustering to identify similar entities with known indexes. In another implementation, a random forest classifier may be trained used training set data and may then be used to generate index values for new entities. In various implementations, such models may include one or more of various types of classifiers, deep learning networks, or other supervised or unsupervised models. Such models may, in some implementations, be updated over time through feedback on generated index values and entity health. Col. 17, line 65 – col. 18, line 25, An operation determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33);
and determining the life probability as a function of the life machine learning model (col. 11, lines 28-65, In various implementations, an entity index value may be determined using various machine learning models which may be generated or trained using a set of historical index data for a variety of entities. In some implementations, the models may be generated or trained using index values calculated with the equations disclosed above. For example, index values may be calculated for a training set of entities with known values and the training set may be used to generate a neural network or graph based network model. Entity information for an entity may then be provided as input and an index value for the entity may be predicted using clustering to identify similar entities with known indexes. In another implementation, a random forest classifier may be trained used training set data and may then be used to generate index values for new entities. In various implementations, such models may include one or more of various types of classifiers, deep learning networks, or other supervised or unsupervised models. Such models may, in some implementations, be updated over time through feedback on generated index values and entity health. Col. 17, line 65 – col. 18, line 25, An operation 374 determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33);
and generate a growth approach as a function of the life probability, wherein the growth approach identifies a growth strategy (col. 1, line 43 – col. 2, line 25, In one embodiment, a method of comparing performance of a first entity and a second entity uses performance indexing based on comparable performance metrics. A first entity performance rate and a first growth ability for the first entity are determined based on a first plurality of entity characteristics for the first entity. The first performance rate and the first growth ability are weighted based of the first plurality of entity characteristics and a first entity index value is determined based on the weighted first performance rate and first growth ability. A comparison between the first entity and the second entity is generated using the first entity index value and a second entity index value for the second entity. Col. 11, line 28 – col. 12, line 20, In several embodiments, the performance rate and/or growth ability may be weighted based on the entity information (e.g., on one or more entity characteristics, such as entity type) to determine the index. For example, one or more entity characteristics may be weighted to account for differences between diverse entities. In this example, an entity characteristic may be assigned different weights depending on the selected characteristic for the select entity. For example, one or more weighting functions (e.g., logarithmic functions) may be applied to values for one or more entity characteristics. In several embodiments, the functions applied may vary based on the entity type (e.g., different functions, values, or variables may apply depending on whether the entity is a startup or enterprise) in order to weight the entity types based on differences in their characteristics and compare them in a single ranking system. Col. 7, line 5 – co. 8, line 5, disclose a growth strategy involving an early stage of a startup company. Col. 12, lines 47-68, which discloses the use of a growth strategy. Col. 3, lines 18-46, col. 10, lines 4-40, col. 15, line 45 – col. 16, line 11, col. 17, lines 24-40).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform life training data, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such life training features into similar systems. Further, applying training a life machine learning model as a function of the life training data would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the specific data points related to lifespan the length of life of an entity to be calculated and analyzed.
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform a growth strategy, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such training features into similar systems. Further, applying a growth strategy would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow a company can adapt to the ever-changing business landscape, boost its revenue.
Regarding claim 2, 13, the combination of Gembicki and Selvadurai teaches the limitations of the previous claim. Gembicki further teaches wherein selecting at least one probability indicator as a function of entity data comprises: receiving indicator training data comprising a plurality of entity data correlated to a plurality of probability indicators (¶ 39-44, The resiliency score generation engine 120 is configured to gather intelligence (e.g. raw intelligence, information quantified with a confidence score, information along with learned rules, and combinations thereof) from the intelligence engine 125 and assign a score to a plurality of resiliency key performance indicators for a strategic partner 110 or individual client 115. Approaches to scoring are explained in greater detail below. ¶ 100-102);
training an indicator machine learning model as a function of the indicator training data (¶ 39-44, The resiliency score generation engine 120 is configured to gather intelligence (e.g. raw intelligence, information quantified with a confidence score, information along with learned rules, and combinations thereof) from the intelligence engine 125 and assign a score to a plurality of resiliency key performance indicators for a strategic partner 110 or individual client 115. Approaches to scoring are explained in greater detail below. ¶ 100-102);
and selecting at least one probability indicator as a function (¶ 10, The resiliency scoring system can obtain intelligence for the organization relating to a plurality of base resiliency indicators that are chosen by examining the request, determining an industry type described in the request by comparing the request to a resiliency index, selecting an industry specific resilience core data store, and extracting, from the industry specific resilience core data store, the plurality of resiliency indicators. ¶ 104, 112, 122).
Gembicki does not specifically teach a machine learning model. However, Selvadurai teaches receiving indicator training data comprising a plurality of entity data correlated to a plurality of probability indicators (col. 18, line 13 – col. 19, line 7, An operation 376 generates a future index prediction based on historical indexes. The future index prediction may include performance indexes for each of a number of years or may include one future performance index. In some implementations, the future index prediction may be generated using time series analysis and the historical indexes. For example, a curve may be fit to the historical indexes and the future index may be predicted using the curve. Various types of linear or non-linear regression may be used in generating the future index prediction. Other implementations may use trained machine learning models receiving historical indexes or historical data to generate future index predictions.)
training an indicator machine learning model as a function of the indicator training data (col. 18, line 13 – col. 19, line 7, An operation 376 generates a future index prediction based on historical indexes. The future index prediction may include performance indexes for each of a number of years or may include one future performance index. In some implementations, the future index prediction may be generated using time series analysis and the historical indexes. For example, a curve may be fit to the historical indexes and the future index may be predicted using the curve. Various types of linear or non-linear regression may be used in generating the future index prediction. Other implementations may use trained machine learning models receiving historical indexes or historical data to generate future index predictions. Col. 11, lines 28-50);
A combination of Gembicki and Selvadurai teaches selecting at least one probability indicator as a function of the indicator machine learning model (col. 18, line 13 – col. 19, line 7, An operation 376 generates a future index prediction based on historical indexes. The future index prediction may include performance indexes for each of a number of years or may include one future performance index. In some implementations, the future index prediction may be generated using time series analysis and the historical indexes. For example, a curve may be fit to the historical indexes and the future index may be predicted using the curve. Various types of linear or non-linear regression may be used in generating the future index prediction. Other implementations may use trained machine learning models receiving historical indexes or historical data to generate future index predictions. Col. 11, lines 28-50, col. 12, lines 25-47, col. 14, lines 27-60).
Gembicki discloses training and learning, but does not specifically disclose machine learning. It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform machine learning, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such machine learning features into similar systems. Further, applying machine learning would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the system to improve the data and recognize data points.
Regarding claim 3, 14, the combination of Gembicki and Selvadurai teaches the limitations of the previous claims. Gembicki further teaches wherein selecting at least one probability indicator as a function of entity data comprises: receiving indicator training data (¶ 39-44, The resiliency score generation engine 120 is configured to gather intelligence (e.g. raw intelligence, information quantified with a confidence score, information along with learned rules, and combinations thereof) from the intelligence engine 125 and assign a score to a plurality of resiliency key performance indicators for a strategic partner 110 or individual client 115. Approaches to scoring are explained in greater detail below. ¶ 100-102).
Gembicki does not specifically teach which is taught by Selvadurai wherein the indicator training data comprises historical function data (col. 11, lines 29-50, col. 6, line 44 – col. 8, line 58, historical performance data for individuals associated with the entity may be considered even when such data predates the lifetime of the entity. For example, performance of past entities under the same management may be considered in determining performance rate of an entity. However, the number of past years to consider may vary. Col. 9, lines 50-68, discloses historical performance function data. Co. 17, line 52 – col. 18, line 42, An operation determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. An operation generates a future index prediction based on historical indexes. The future index prediction may include performance indexes for each of a number of years or may include one future performance index. In some implementations, the future index prediction may be generated using time series analysis and the historical indexes. For example, a curve may be fit to the historical indexes and the future index may be predicted using the curve. Various types of linear or non-linear regression may be used in generating the future index prediction. Other implementations may use trained machine learning models receiving historical indexes or historical data to generate future index predictions.)
Gembicki discloses training and learning, but does not specifically disclose historical indicator data. It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform wherein the indicator training data comprises historical indicator data, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such training features into similar systems. Further, applying wherein the indicator training data comprises historical indicator data would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the system to use known data to make determinations.
Regarding claim 4, 15, the combination of Gembicki and Selvadurai teaches the limitations of the previous claims. Gembicki further teaches wherein selecting at least one probability indicator as a function of entity data comprises: receiving indicator training data (¶ 39-44, The resiliency score generation engine 120 is configured to gather intelligence (e.g. raw intelligence, information quantified with a confidence score, information along with learned rules, and combinations thereof) from the intelligence engine 125 and assign a score to a plurality of resiliency key performance indicators for a strategic partner 110 or individual client 115. Approaches to scoring are explained in greater detail below. ¶ 100-102).
Gembicki does not specifically teach which is taught by Selvadurai wherein the life training data comprises historical life data (col. 11, lines 29-50, col. 6, line 44 – col. 8, line 58, discloses historical data over a period of time. Col. 9, lines 50-68, Performance influencing factors may also include, in various examples, past performance of individuals associated with an entity (e.g., a management team or founder). Performance of individuals associated with an entity may include past performance of other entities, e.g., the CEO of a startup may analyzed based on the performance of a pervious company lead by the CEO. Further, in some implementations, performance of, for example, a management team, may be determined based on historical performance of the entity relative to other similar entities. For example, where an entity, over a historical period, outperforms similar entities with similar resources by, for example, generating more revenue, the management team may be assigned a higher performance value over the historical time period. Performance of the management team may then be quantified and provided as a performance influencing factor for determination of the index. Further, where a member of the management team moves to another entity, the performance of the management member at the previous entity may be used as a performance influencing factor for the new entity. Co. 17, line 52 – col. 18, line 42, discloses various types of linear or non-linear regression may be used in generating the future index prediction. Other implementations may use trained machine learning models receiving historical indexes or historical data to generate future index predictions.)
Gembicki discloses training and learning, but does not specifically disclose historical life cycle data. It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform wherein the life training data comprises historical life data, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such training features into similar systems. Further, applying wherein the life training data comprises historical life data would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the system to use known data to make determinations.
Regarding claim 9, the combination of Gembicki and Selvadurai teaches the limitations of the previous claims. Gembicki further teaches wherein selecting at least one probability indicator as a function of entity data comprises: receiving indicator training data (¶ 39-44, The resiliency score generation engine 120 is configured to gather intelligence (e.g. raw intelligence, information quantified with a confidence score, information along with learned rules, and combinations thereof) from the intelligence engine 125 and assign a score to a plurality of resiliency key performance indicators for a strategic partner 110 or individual client 115. Approaches to scoring are explained in greater detail below. ¶ 100-102).
Gembicki does not specifically teach which is taught by Selvadurai wherein the growth approach comprises more than one growth strategy, wherein the more than one growth strategy are configured to assist a user in completion of the growth approach (col. 3, lines 18-55, In a specific example, the system may generate a company index or benchmark for companies allowing comparison of different types of companies on the same scale. For example, the system may analyze and index companies in different fields, as well as different growth stages, and/or years in operation, as the indexing may be based on performance and/or growth metrics or indicators that can be tailored to the specific type and market of the company. Performance indicators include characteristics indicative of the performance of a company from past to present, while growth indicators include characteristics indicative of the potential of a company to perform in the future. The performance and/or growth indicators may be weighted or otherwise analyzed based on company characteristics as enterprises and startups may have varying performance and/or growth indicators due to differences in company characteristics, such as operating time, funding, revenue, number of employees, market sector, location, customers, overall business strategy, company size, and the like. For example, a large enterprise typically has more years of operation and greater revenue than a startup company, which typically has an average lifespan of 2-5 years and accordingly more limited revenue. The system accounts for such differences in company characteristics when assessing company performance and/or growth to generate an index value. The index may present a way to uniformly present companies that takes into account these differences, such that a startup can be directly compared to an enterprise company in a manner that was not previously possible. Col. 7, line 40 – col. 10, line 20, After operation 154, the method 150 proceeds to operation 156 and the entity's growth ability is estimated. Growth ability may be determined based on one or more entity characteristics indicative of the potential of a company to perform or grow in the future. For example, growth ability may be influenced by the ratio between revenue and number of employees as revenue per employee allows the company to spend in growth opportunities. This ratio between revenue and employees may be used to determine growth for various entities, such as both startups and enterprises. As another example, growth ability for a startup company may be estimated based on the type of investors funding the startup. For example, an investor that historically funds startups having aggressive growth may reflect a strong growth potential of the startup company being funded by that investor. Growth ability may also be influenced by the industry or type of technology created by the entity. Col. 14, line 27-col. 16, line 10).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform a growth strategy, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such training features into similar systems. Further, applying a growth strategy would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow a company can adapt to the ever-changing business landscape, boost its revenue.
Regarding claims 10 and 20, the combination of Gembicki and Selvadurai teaches the limitations of the claim 1. The combination of Gembicki and Selvadurai teaches life probability.
The combination of Gembicki and Selvadurai also teaches the memory further containing instructions configuring the at least a processor to: create a user interface data structure (col. 7, line 7- col. 8, line 5, discloses creating a user interface data structure), wherein the user interface data structure comprises the life probability (col. 11, lines 28-50, discloses information on the data structure Col. 17, line 65 – col. 18, line 25, An operation 374 determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33); and the growth approach (col. 17, lines 23-40, Further, as discussed, the first entity may be compatible with a stored entity when the entity index values are the same or similar. For example, entity index values may be the same when the values are identical. As another example, entity index values may be similar when they deviate a particular degree or amount (e.g., by 5%, 10%, 20%, etc.). As yet another example, entity index values may be similar when they are indicative of a similar level of performance and/or growth. For example, an index value indicative of a high performing startup may be similar to an index value indicative of a high performing enterprise, even though the index values may not be the same values or may deviate greatly from one another (e.g., a high performing startup index may be much lower than a high performing enterprise index, yet the two companies may still be considered compatible based on the weighting and scaling incorporated into the indexing system). Col. 13, line 1- col. 14, line 5).
and transmit the user interface data structure (col. 5, lines 3-20, A simplified block structure for a computing device that may be used with the system or integrated into one or more of the system components is shown in FIG. 2. Also discloses a user interface structure.);
and the apparatus further comprises a display communicatively connected to the at least a processor, the display configured to: receive the user interface data structure; and display the life probability and the growth approach as a function of the user interface data structure (col. 7, line 7- col. 8, line 5, After the user has created a user name in the entity database and/or after logging into the system, an entity information graphical user interface can be used to receive information from the user. With reference to FIG. 4B, an entity information user interface is transmitted by the server to the user device 106a-n. In this example, the entity information user interface may include one or more entity data fields configured to receive entity characteristic information directly from the user representing the entity. Examples of entity fields can include, age of the company e.g., years in operation), type of company (e.g., LLC, Ltd., Inc., enterprise, startup, etc.), location (e.g., city, state, country, or the like), team size (e.g., number of employees or persons in the entity, etc.), annual revenue 246, type of technology, number of paying customers, number of other customers (e.g., non-paying or proof of concept customers or users), average one year deal value (e.g., average values of deals that the company has previously executed or is looking for), number of current pilots, industry sector (hardware vs. software vs. tech enabled services), revenue per month, experience of founders and other key employees, money values and class raise (e.g., friends and family vs. series A vs. series B), growth rate and/or stage, board structure (e.g. private vs. public), and optionally areas where the company does not want to operate, such as excluded regions, or the like. The company fields are selected to receive information directly into the system from the user regarding the entity. In some instances the fields may be open, allowing a user to provide a textual input, but in other examples, the fields may be varied and include drop down menus, receive URL links, selectable options, or the like., col. 11, lines 28-50, In various implementations, an entity index value may be determined using various machine learning models which may be generated or trained using a set of historical index data for a variety of entities. In some implementations, the models may be generated or trained using index values calculated with the equations disclosed above. For example, index values may be calculated for a training set of entities with known values and the training set may be used to generate a neural network or graph based network model. Entity information for an entity may then be provided as input and an index value for the entity may be predicted using clustering to identify similar entities with known indexes. In another implementation, a random forest classifier may be trained used training set data and may then be used to generate index values for new entities. In various implementations, such models may include one or more of various types of classifiers, deep learning networks, or other supervised or unsupervised models. Such models may, in some implementations, be updated over time through feedback on generated index values and entity health. Col. 17, line 23 – col. 18, line 25, An operation 374 determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33); Col. 13, line 1- col. 14, line 5).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform life training data, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such life training features into similar systems. Further, applying training a life machine learning model as a function of the life training data would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the specific data points related to lifespan the length of life of an entity to be calculated and analyzed.
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform a growth strategy, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such training features into similar systems. Further, applying a growth strategy would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow a company can adapt to the ever-changing business landscape, boost its revenue.
Regarding claim 12, Gembicki teaches
receiving, by at least a processor, entity data from a user (¶ 9-12, Some embodiments of the present technology involve a resiliency scoring system receiving, from an individual client or strategic partner, a request for one or more resiliency scores for an organization. The resiliency scoring system can obtain intelligence for the organization relating to a plurality of base resiliency indicators that are chosen by examining the request, determining an industry type described in the request by comparing the request to a resiliency index, selecting an industry specific resilience core data store, and extracting, from the industry specific resilience core data store, the plurality of resiliency indicators. ¶ 13-18, 39, 46-48, 101, 107, 133, 136-139, Fig. 2, 5A-B);
selecting, by the at least a processor, at least one probability indicator as a function of the entity data (¶ 10-18, The resiliency scoring system can obtain intelligence for the organization relating to a plurality of base resiliency indicators that are chosen by examining the request, determining an industry type described in the request by comparing the request to a resiliency index, selecting an industry specific resilience core data store, and extracting, from the industry specific resilience core data store, the plurality of resiliency indicators. ¶ 42-45, The resiliency score generation engine 120 is configured to gather intelligence (e.g. raw intelligence, information quantified with a confidence score, information along with learned rules, and combinations thereof) from the intelligence engine 125 and assign a score to a plurality of resiliency key performance indicators for a strategic partner 110 or individual client 115. Approaches to scoring are explained in greater detail below. ¶ 39, 70, 106-108, 130).
Gembicki does not teach, however, the combination of Gembicki and Selvadurai teaches determining, by the at least a processor, a life probability of the entity as a function of the at least one probability indicator comprising (col. 8, lines 5-32, discloses life probability over time. Col. 17, line 65- col. 18, line 13, An operation 374 determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year.);
receiving life training data comprising a plurality of the least one probability indicators correlated to a plurality of life probabilities (col. 11, lines 28-50, In various implementations, an entity index value may be determined using various machine learning models which may be generated or trained using a set of historical index data for a variety of entities. In some implementations, the models may be generated or trained using index values calculated with the equations disclosed above. For example, index values may be calculated for a training set of entities with known values and the training set may be used to generate a neural network or graph based network model. Entity information for an entity may then be provided as input and an index value for the entity may be predicted using clustering to identify similar entities with known indexes. In another implementation, a random forest classifier may be trained used training set data and may then be used to generate index values for new entities. In various implementations, such models may include one or more of various types of classifiers, deep learning networks, or other supervised or unsupervised models. Such models may, in some implementations, be updated over time through feedback on generated index values and entity health. Col. 17, line 65 – col. 18, line 25, An operation determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33);
training a life machine learning model as a function of the life training data (col. 11, lines 28-50, In various implementations, an entity index value may be determined using various machine learning models which may be generated or trained using a set of historical index data for a variety of entities. In some implementations, the models may be generated or trained using index values calculated with the equations disclosed above. For example, index values may be calculated for a training set of entities with known values and the training set may be used to generate a neural network or graph based network model. Entity information for an entity may then be provided as input and an index value for the entity may be predicted using clustering to identify similar entities with known indexes. In another implementation, a random forest classifier may be trained used training set data and may then be used to generate index values for new entities. In various implementations, such models may include one or more of various types of classifiers, deep learning networks, or other supervised or unsupervised models. Such models may, in some implementations, be updated over time through feedback on generated index values and entity health. Col. 17, line 65 – col. 18, line 25, An operation 374 determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33);
and determining the life probability as a function of the life machine learning model (col. 11, lines 28-65, In various implementations, an entity index value may be determined using various machine learning models which may be generated or trained using a set of historical index data for a variety of entities. In some implementations, the models may be generated or trained using index values calculated with the equations disclosed above. For example, index values may be calculated for a training set of entities with known values and the training set may be used to generate a neural network or graph based network model. Entity information for an entity may then be provided as input and an index value for the entity may be predicted using clustering to identify similar entities with known indexes. In another implementation, a random forest classifier may be trained used training set data and may then be used to generate index values for new entities. In various implementations, such models may include one or more of various types of classifiers, deep learning networks, or other supervised or unsupervised models. Such models may, in some implementations, be updated over time through feedback on generated index values and entity health. Col. 17, line 65 – col. 18, line 25, An operation 374 determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33);
and generating, by the at least a processor, a growth approach as a function of the life probability, wherein the growth approach identifies a growth strategy (col. 1, line 43 – col. 2, line 25, In one embodiment, a method of comparing performance of a first entity and a second entity uses performance indexing based on comparable performance metrics. A first entity performance rate and a first growth ability for the first entity are determined based on a first plurality of entity characteristics for the first entity. The first performance rate and the first growth ability are weighted based of the first plurality of entity characteristics and a first entity index value is determined based on the weighted first performance rate and first growth ability. A comparison between the first entity and the second entity is generated using the first entity index value and a second entity index value for the second entity. Col. 11, line 28 – col. 12, line 20, In several embodiments, the performance rate and/or growth ability may be weighted based on the entity information (e.g., on one or more entity characteristics, such as entity type) to determine the index. For example, one or more entity characteristics may be weighted to account for differences between diverse entities. In this example, an entity characteristic may be assigned different weights depending on the selected characteristic for the select entity. For example, one or more weighting functions (e.g., logarithmic functions) may be applied to values for one or more entity characteristics. In several embodiments, the functions applied may vary based on the entity type (e.g., different functions, values, or variables may apply depending on whether the entity is a startup or enterprise) in order to weight the entity types based on differences in their characteristics and compare them in a single ranking system. Col. 7, line 5 – co. 8, line 5, disclose a growth strategy involving an early stage of a startup company. Col. 12, lines 47-68, which discloses the use of a growth strategy. Col. 3, lines 18-46, col. 10, lines 4-40, col. 15, line 45 – col. 16, line 11, col. 17, lines 24-40).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform life training data, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such life training features into similar systems. Further, applying training a life machine learning model as a function of the life training data would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the specific data points related to lifespan the length of life of an entity to be calculated and analyzed.
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform a growth strategy, as taught/suggested by Selvadurai. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine various future values of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Selvadurai would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Selvadurai to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such training features into similar systems. Further, applying a growth strategy would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow a company can adapt to the ever-changing business landscape, boost its revenue.
Claim(s) 5, 6, 11, 16, 17, is/are rejected under 35 U.S.C. 103 as being unpatentable over Gembicki (US 20160034838 A1) in view of Selvadurai et al. (US 11341517 B2) in further view of Noetzold et al. (US 20040133439 A1).
Regarding claim 5, 16, the combination of Gembicki and Selvadurai teaches the limitations of the previous claims. Gembicki and Selvadurai teaches a life probability. The combination does not specifically teach wherein the life probability comprises at least one probability deviation.
However, Noetzold teaches wherein the life probability comprises at least one probability deviation (¶ 123-125, discloses a probability deviation with a life probability. ¶ 161-162, 212-214).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform wherein the life probability comprises at least one probability deviation, as taught/suggested by Noetzold. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine a value of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Noetzold would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Noetzold to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such deviation features into similar systems. Further, applying wherein the life probability comprises at least one probability deviation would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the system to use known data to make determinations.
Regarding claims 6, 17, the combination of Gembicki and Selvadurai teaches the limitations of the previous claims. Gembicki and Selvadurai teaches a life probability. The combination does not specifically teach wherein the life probability comprises at least one probability deviation.
However, Noetzold teaches wherein the at least one probability deviation is associated with the at least one probability indicator (¶ 123-125, In the next step of the integration system a consistent and coherent model of the supplied data has to be given that represents the data in a form suitable for integration. Data sets at this stage in the valuation process always are in form of probability distributions, specified at least by the quantity's average and fluctuation (i.e. in form of standard deviation, variance, or volatility). For example, predictions, estimates, or expectations of future turnover should only be quoted with its standard deviation, say, with 10% uncertainty over one year. Also, the factors for those uncertainties need also be specified and quantified. In the case of turnover, such factors could be general economic development, exchange rates for export or import oriented companies, local weather for electricity provider, etc. Many mathematical models exist to combine the effects of fluctuations and correlations into a consistent description. Those models differ in aspects that emphasize mathematical representation, characteristics, precision, etc., but are based on similar aggregation logic and lead to similar aggregation results. A popular model is the multivariate normal distribution (or process; with or without copulas). It can describe the dynamics of many quantities including their fluctuations. The model is specified by parameters, such as averages, volatilities, and correlations of the quantities. Those parameters have to be calculated for all quantities in the data set, if the multivariate normal distribution is adopted as data model. ¶ 161-162, 212-214).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform wherein the at least one probability deviation is associated with the at least one probability indicator, as taught/suggested by Noetzold. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine a value of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Noetzold would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Noetzold to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such deviation features into similar systems. Further, applying wherein the at least one probability deviation is associated with the at least one probability indicator would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the system to use known data to make determinations.
Regarding claim 11, the combination of Gembicki and Selvadurai teaches the limitations of the previous claims. Gembicki and Selvadurai teaches a life probability. Selvadurai also teaches wherein the display is configured to display at least one growth deviation of the growth approach as a function of a selection (col. 7, line 7- col. 8, line 5, After the user has created a user name in the entity database and/or after logging into the system, an entity information graphical user interface can be used to receive information from the user. With reference to FIG. 4B, an entity information user interface 236 is transmitted by the server 102 to the user device 106a-n. In this example, the entity information user interface 236 may include one or more entity data fields configured to receive entity characteristic information directly from the user representing the entity. Examples of entity fields can include, age of the company 238 (e.g., years in operation), type of company 240 (e.g., LLC, Ltd., Inc., enterprise, startup, etc.), location 242 (e.g., city, state, country, or the like), team size 244 (e.g., number of employees or persons in the entity, etc.), annual revenue 246, type of technology 248, number of paying customers 250, number of other customers 252 (e.g., non-paying or proof of concept customers or users), average one year deal value 254 (e.g., average values of deals that the company has previously executed or is looking for), number of current pilots, industry sector (hardware vs. software vs. tech enabled services), revenue per month, experience of founders and other key employees, money values and class raise (e.g., friends and family vs. series A vs. series B), growth rate and/or stage, board structure (e.g. private vs. public), and optionally areas where the company does not want to operate 256, such as excluded regions, or the like. The company fields are selected to receive information directly into the system from the user regarding the entity. In some instances the fields may be open, allowing a user to provide a textual input, but in other examples, the fields may be varied and include drop down menus, receive URL links, selectable options, or the like., col. 11, lines 28-50, In various implementations, an entity index value may be determined using various machine learning models which may be generated or trained using a set of historical index data for a variety of entities. In some implementations, the models may be generated or trained using index values calculated with the equations disclosed above. For example, index values may be calculated for a training set of entities with known values and the training set may be used to generate a neural network or graph based network model. Entity information for an entity may then be provided as input and an index value for the entity may be predicted using clustering to identify similar entities with known indexes. In another implementation, a random forest classifier may be trained used training set data and may then be used to generate index values for new entities. In various implementations, such models may include one or more of various types of classifiers, deep learning networks, or other supervised or unsupervised models. Such models may, in some implementations, be updated over time through feedback on generated index values and entity health. Col. 17, line 23 – col. 18, line 25, An operation 374 determines historical entity indexes based on the entity data. A historical entity index may generally be a performance index calculated for a particular year of operation of an entity. The historical entity indexes may include a present entity index. In some implementations, a historical entity index may be calculated for each year of an entity's operation (e.g., over the lifetime of an entity). In some situations, historical entity indexes may be calculated over a set time interval (e.g., a current index and the previous four years) instead of over the life of an entity. Such intervals may be defined based on the time interval covered by the future index prediction. For example, a future index prediction for five years in the future may use more historical data than a future index prediction for the next year. Col. 8, lines 5-33); Col. 13, line 1- col. 14, line 5).
The combination of Gembicki, Selvadurai, does not specifically teach wherein the at least one probability deviation is associated with the at least one probability indicator.
Noetzold teaches wherein the life probability further comprises at least one probability deviation, wherein the display is configured to display at least one growth deviation of the growth approach as a function of a selection of the at least one probability deviation (¶ 141-151, Coherent aggregation is a new aspect of at least one embodiment, compared to conventional valuation schemes. FIGS. 12 to 14 illustrate at least some of the differences between new and conventional valuation for the case of corporate rating. The Merton Model in FIG. 12 is the basis of most successful conventional rating schemes. It considers the evolution of the asset value of the company from an initial value with average growth rate into the future. The probability distribution of future asset values is characterized by the volatility, i.e. the size of fluctuations which is given by the difference between realized values and the average expected value. The so-called Distance-to-Default gives the value difference to the default point. At default value the asset value falls below the liabilities' value and default occurs. The default probability is the area under the curve below default point. In short, the Merton model considers the evolution of the asset value given by the probability distribution and calculates the default probability from asset value fluctuations below default point. This model is a one-dimensional model that considers only fluctuations of the overall asset value (or asset value minus liabilities' value)… The probabilistic nature of the quantities reflects the fact that future predictions always contain uncertainties. Fully predictable events that occur with certainty are very rare. Existing valuation procedures do not treat these uncertainties and consider only mean values. They neglect future developments that deviate from the expected outcome, such as side effects or low-probability event chains that can lead to qualitatively and quantitatively significant changes in the projected future. In many cases consideration of the full spectrum of possible effects leads to significant corrections even in average values. Existing valuation procedures neglect possible deviations around the expected values and thus also neglect the induced shifts in the expected values. ¶ 153, In the case of corporate rating, the results include a representative set of figures and ratios that quantify the fundamental units in many ways and from many viewpoints. Such figures are e.g. returns and risk-adjusted returns, interest coverage ratios, operating income per-sales, different ratios for debt per capital, income or cash flow per sales, z-scores, general figures and ratios that quantify the strengths and weaknesses of the company, internal and external factors for the company, different risk and opportunity measures, such as value-at-risk, risk and opportunity concentrations, sensitivities for all considered figures and ratios and with respect to the factors (such as interest rates, exchange rates, industry and sector figures, general global and local economic, financial and political factors, weather, etc.), results of stress testing, results of simulations and scenario analyses, and many more. Many of the mentioned figures and ratios are used in different flavors and express a different degree of detail. All figures are supplied with the full probabilistic information, e.g. in terms of probability distributions, quantifying future expectations and intrinsic uncertainties. ¶ 351, A Monte-Carlo sampling of those factors amounts to different simulated evolutions of the quality and of related quantities. A set of many simulations produces a spectrum of different outcomes. This spectrum is the probability distribution function. Since quality and related quantities are given in terms of the factors, the simulation of factors produces probability distributions for quality and related quantities. The simulation aggregates the quality fluctuations, i.e. it integrates all effects that influence total quality risk, including all interrelations between causes and all joint events that may enhance or cancel each other. ¶ 130-132, 162, 191, 249).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform wherein the at least one probability deviation is associated with the at least one probability indicator, as taught/suggested by Noetzold. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine a value of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Noetzold would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Noetzold to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such deviation features into similar systems. Further, applying wherein the at least one probability deviation is associated with the at least one probability indicator would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the system to use known data to make determinations.
Claim(s) 7, 8, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gembicki (US 20160034838 A1) in view of Selvadurai et al. (US 11341517 B2) in further view of Oehrle et al. (US 20180189691 A1).
Regarding claims 7, 18, the combination of Gembicki and Selvadurai teaches the limitations of the previous claims. Gembicki and Selvadurai teaches a life probability. The combination does not specifically teach wherein the growth strategy contains a momentum strategy.
However, Oehrle teaches wherein the growth strategy contains a momentum strategy (¶ 553, 573, discloses sequence momentum.).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform wherein the growth strategy contains a momentum strategy, as taught/suggested by Oehrle. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using various metrics to determine a value of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Oehrle would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Oehrle to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such momentum features into similar systems. Further, applying the growth strategy contains a momentum strategy would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow momentum means you’re not just buying based on future prospects, but also growth investing with the trend-following power.
Regarding claims 8, 19, the combination of Gembicki and Selvadurai teaches the limitations of the previous claims. Gembicki and Selvadurai teaches a life probability. The combination does not specifically teach wherein the growth strategy contains a momentum strategy.
However, Oehrle teaches wherein the growth strategy contains a morale strategy (¶ 6, 77, 450, 456, 647, discloses organizational morale).
It would have been obvious to one of ordinary skill in the art at the time of Applicant’s invention to modify Gembicki to include/perform wherein the growth strategy contains a morale strategy, as taught/suggested by Oehrle. This known technique is applicable to the system of Gembicki as they both share characteristics and capabilities, namely, they are directed to using metrics to determine a characteristic of an entity. One of ordinary skill in the art would have recognized that applying the known technique of Oehrle would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Oehrle to the teachings of Gembicki would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such morale features into similar systems. Further, applying wherein the growth strategy contains a morale strategy would have been recognized by those of ordinary skill in the art as resulting in an improved system that would deliver both business performance gains and a healthier, more engaged workforce.
Other pertinent pieces of prior art include Cagan (US 20060271472 A1) which discloses automated valuation model valuations and the forecast standard deviations. Torkoly (US 20190265684 A1) which discloses integration of multilevel production processes in which the end product produced by a production entity. Prieto (US 20140156323 A1) which discloses a resilience management engine that can generate a resiliency metric representative of how resilient a program is with respect to a particular event.
Conclusion
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625