U.S. patent application number 15/290781 was filed with the patent office on 2017-02-02 for system and method for optimizing business performance with automated social discovery.
The applicant listed for this patent is 9Lenses, Inc.. Invention is credited to Edwin Andrew Miller.
Application Number | 20170031958 15/290781 |
Document ID | / |
Family ID | 53545099 |
Filed Date | 2017-02-02 |
United States Patent
Application |
20170031958 |
Kind Code |
A1 |
Miller; Edwin Andrew |
February 2, 2017 |
SYSTEM AND METHOD FOR OPTIMIZING BUSINESS PERFORMANCE WITH
AUTOMATED SOCIAL DISCOVERY
Abstract
A system, process and method for automatically collecting,
collating and transforming data into useful formats and displaying
or otherwise outputting the transformed data into useable
information. The system provides outputs that are useful In
optimizing the enterprise performance of a business. The system,
process and method are grounded in an established logical framework
for systematically classifying areas of business concerns.
Inventors: |
Miller; Edwin Andrew;
(Leesburg, VA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
9Lenses, Inc. |
Sterling |
VA |
US |
|
|
Family ID: |
53545099 |
Appl. No.: |
15/290781 |
Filed: |
October 11, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14030815 |
Sep 18, 2013 |
9489419 |
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15290781 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/248 20190101;
G06Q 40/00 20130101; G06F 16/212 20190101; G06F 16/951 20190101;
G06Q 10/067 20130101; G06F 16/2423 20190101; G06Q 10/063
20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06Q 40/00 20060101 G06Q040/00; G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A system for presenting data according to the framework of a
selected schema, comprising: a communications system for network
communication with a plurality of data sources; a query engine
connected to the communication system for requesting data from a
plurality of data sources; a database for storing data received
from the data sources; an interpretation engine for transforming
data received from the data sources into schema data according to
the selected schema; and a system output engine and user interface
for displaying schema data and allowing a user to access the data
from the data sources upon which the schema data is based.
2. The system for presenting data according to a selected schema of
claim 1, further comprising a mapping engine for mapping data in
the database to a selected schema by mapping the selected schema to
a master scheme.
3. A system for presenting data according to a selected schema
according to claim 1, further comprising a data filter control to
allow a user to selectively remove data sources from the displayed
data.
4. A system for presenting data according to a selected schema
according to claim 1, further comprising a database containing
industry data and a comparison engine for comparing enterprise data
to industry data and displaying a comparison.
5. A system for presenting data according to a selected schema
according to claim 1, wherein the data sources include persons
responding to queries through the communication system.
6. A system for presenting data according to a selected schema
according to claim 1, wherein the data sources include internal
databases responding to queries through the communication
system.
7. A system for presenting data according to a selected schema
according to claim 1, wherein the data sources include external
databases responding to queries through the communication
system.
8. The system for presenting data according to a selected schema of
claim 1, further comprising an engine for determining the touch,
volume, and margin of a business based upon the data sources.
9. The system for presenting data according to a selected schema of
claim 1, further comprising a recommendation engine for making
recommendations for altering the financial model of a business
based upon the data sources.
10. The system for presenting data according to a selected schema
of claim 1, further comprising a publication control engine for
allowing a user automatically publish content to specified board
members.
11. The system for presenting data according to a selected schema
of claim 1, further comprising an evaluation engine for evaluating
business suggestions submitted from data sources through reference
to ratings of the suggestions and data regarding the source and
track record of the rating source.
12. The system for presenting data according to a selected schema
of claim 1, further comprising a logic engine that produces
statistical comparisons between business data.
13. A system for dynamically generating presentations of data
relevant to a selected business analytical schema comprising; a
communication system comprising hardware that provides end-to-end
connectivity according to a communications protocol to allow
transmitting and receiving data within the system and between the
system and external data sources and users; data storage for
storing data used by the system; a user interface; at least one
general purpose computer that includes a least one CPU with on
board RAM; an input/output system bus; and system memory, the
general purpose computer being capable of executing software
programs to implement software engines, the computer being in
communication with the communication system, user interface, and
data storage; the engines implemented including at least at query
engine a schematic interpretation engine and a data weighing
engine, wherein the query engine is connected to the communication
system for requesting data from a plurality of data sources, the
interpretation engine and data weighing engine are adapted to
transform data received from the data sources into schema data
according to the selected schema; and a system output engine and
user interface for displaying schema data and allowing a user to
access the data from the data sources upon which the schema data is
based.
14. The system for dynamically generating presentations of data
according to claim 13, further comprising a decision engine that
receives user selection of priorities and preferences and retrieves
stored diagnostics to generated and app composed of stored
diagnostic queries
15. The system for dynamically generating presentations of data
according to claim 13, further comprising a prediction engine for
predicting outcomes from separate business problems, the prediction
engine using actual responses around specific components and
sub-components and predicting responses to other schema queries
that have established connections to the queries for which actual
responses have been received.
16. The system for dynamically generating presentations of data
according to claim 13, further comprising a conversion system for
presenting output according to an alternative schema format based
on a master schema, the conversion system receiving a user
selection as to an alternative schema and input from an analysis
agent that defines the components and sub-components of the
alternative schema and mapping the components and subcomponents of
the alternative schema to the master schema and identification of
externalities for which the system must collect data from a source
that is relevant to one or more of the alternative schema
components, the conversion system prompting the user to approve
proposed mapping and the system displaying approved mapped
content.
17. The system for dynamically generating presentations of data
according to claim 16, wherein the agent is an automated software
agent.
18. The system for dynamically generating presentations of data
according to claim 13, further comprising a data filter control to
allow a user to selectively remove data sources from the displayed
data.
19. The system for dynamically generating presentations of data
according to claim 13, wherein the data sources include external
databases responding to queries through the communication
system.
20. The system for dynamically generating presentations of data
according to claim 13, further comprising an engine for determining
the touch, volume, and margin of a business based upon the data
sources.
Description
BACKGROUND
[0001] 1. Field of Invention
[0002] The present invention relates to a system and method for
identifying and qualifying sources of data, collecting, filtering
and analyzing data and transforming the data into visual images
associated with a selected framework that is useful as tool for
managers to optimize enterprise performance.
[0003] 2. Background of the Invention
[0004] Since the mid-20th century, the field of business and
management has revolutionized business and industry. Beginning with
consultants such as Peter Drucker and W. Edwards Deming, numerous
authors and consultants have suggested frameworks for analyzing an
enterprise to give management insight and provide tools to improve
performance through, for example, enhanced clarity of roles,
responsibilities, and expectations. Numerous "frameworks" have been
suggested since that time, but the lack of systematic approach to
gaining insight into all areas of an organization diminishes the
usefulness of these frameworks.
[0005] Two distinct methodologies are commonly used to measure
organizational effectiveness and collect information on the
functional performance of business processes: outside business
consultants and internal review processes. This approach to gaining
insight into an enterprise is disconnected and disorganized.
[0006] When performance is measured based on external perspectives
from consultants, individuals or a team conduct interviews of key
executives in a company, review financials, and compare results to
their established methodology. Expertise generated by consultancies
is based on soft variables and subjective information provided by
the consultancy. The actual methodology of consulting varies widely
due to different established practices, varied strategic
differences between internal opinions provided within the business
community, and different institutional philosophies constructed on
a variety of experiences uniquely shaped by the circumstances the
individual consultancy encounters during their operation as a
functional business, Consultancies produce results based on their
varied methodologies and then provide executive recommendations
based on their private findings. Typical consulting fees are quite
expensive. This makes consultation an unattractive option for
business managers unless they are forced into unfortunate
circumstances that inhibit their operations or their strategic
position is compromised in their own space within their operational
market. In addition, the "learning" and recursive benefit that
comes from in depth analysis of different organizations inures
almost entirely to the outside consultants. Stated differently, the
more engagements a consultant takes on the wider their knowledge
base becomes. Learning form the best practices of one organization
allows a consultant to better advise another organization with
regard to benchmarking or best practices. However, the ability to
share and benefit from this increased insight is controlled by the
outside consultant. Moreover, protection of know how relating to
best practices depends on the outside consultant. Organizations
would plainly benefit from being able to capture for themselves
some of these side benefits of in depth organizational analysis.
Likewise, systemized benchmarking (as opposed to human
benchmarking) improves the ability to control the dissemination of
know how.
[0007] Business management might also choose to investigate
optimization options by establishing internal review processes.
Internal processes that businesses use to conduct performance
reviews tend to be broad and disparate. An individual business
might use performance reviews ranging from strategic off site based
internal executive team evaluations to internal employee surveys.
The variance among separate business entities is not of itself
problematic. However, it is often the case that an individual
business utilizes completely separate methods for collecting
information. Initiatives typically target separated issues based on
entirely different points of strategy. Data collection and
management can also vary greatly. These methods are all
disconnected from an overall perspective and lack organized means
of comparing the performance of each method. Since these different
methods cannot be universalized, it is difficult to examine the
strategic importance of the information.
[0008] The absence of a systemized approach to data collection
limits the ability to use the data to gain enterprise wide insight.
Most data collected through consultants and internal review,
assuming it is even translated into useful strategic insights for
the business, is eventually neglected as of little value beyond the
narrow case for collection. The disparate nature of the information
means further limits the vaue of the data gathered in conventional
internal review processes. Since there is no existing framework for
organizing all of the information, none of the respective pieces of
data have any larger meaning for the business. There is no
methodology for universalizing the information to the broader
implications of the business itself. Generated connect insights is
difficult, if not impossible, with disconnected data.
[0009] Thus, there remains a need for a system and method for
identifying, gathering and transforming useful data into a desired
framework.
SUMMARY OF THE INVENTION
[0010] The present invention provides a system and systematic
approach (method) for identifying and qualifying sources of data,
collecting, filtering and analyzing data and transforming the data
into useful output (e.g., visual images and print outs) associated
with a selected framework that is useful as tool for managers to
optimize enterprise performance. As used here, a "framework" is an
analytical structure for organized presentation of data that
encompasses the assets, processes and structures that drive
business success. Embodiments described herein refer to Edwin
Miller's 9Lenses framework, but the invention may be applied to
other frameworks as well. An embodiment of the present invention
provides a system, methods, processes, software, and standards
designed to collect and collate information pertaining to the
condition particular to the company that concern the successful
operation of the company evaluated.
[0011] A challenge encountered by business leaders seeking to
utilize a framework, (e.g., 9Lenses) is that data within and
available to the organization is not directly applicable to the
framework. Moreover, data that may be relevant or necessarily is
not being collected. The invention provides a system and systematic
approach for identifying, collecting and transforming available
data into framework data. The system takes input from a wide
variety of data sources, transforms the data by processing the
input as necessary and mapping the input to a MAIN SCHEMA using a
mapping engine. A transformation engine (analytics engine) may be
used to transform or assist in transforming the MAIN SCHEMA data
into a selected output framework (Business Context). The
presentation format may be a "preset" format related to known or
established business context or customized to meet a particular
need.
[0012] The input data sources used may include both people
providing input in response to surveys or data pulled from existing
internal or external data sources. The people from whom data is
obtained can be anyone connected with the enterprise: employees,
managers, customers, vendors and any other stake holder. Existing
internal data sources could include, for example, Enterprise
resource planning (ERP) systems, human resource (HR) systems and
operational systems. External data sources could include, for
example, market intelligence and competitive rankings.
[0013] The output of the system, methods, processes, and software
can be displayed (presented) in a format tailored to address
specified problems based on criteria of assessing (1) immediate
business pains (2) specific areas of concern (3) scope of the
problem (4) potential returns for solutions to the problem.
Information is then classified according to business complexity and
immediate needs. Selections of the specific systems utilized under
the framework are based on company preference, but recommendations
are provided based on the inputs provided by the company. The raw
data is persevered in association with the transformed data and the
presentation of the data is hierarchically structured so that a
user may see all available data at the highest transformed data
level and then "drill down" into progressively lower levels so that
raw data is at the lowest level of the hierarchy.
[0014] The output of the system, methods, processes, and software
may include presentations of data transformed and applied according
to a selected schema and may include the output of one or more
software engines that provide useful business tools. For example, a
recommendation engine may be provided to make recommendations based
on the data and the selected schema. Likewise, a prediction engine
may be provided to make predications based on the data and the
selected schema. A comparison engine may be used to take system
output and compare the output to a standard for that industry using
a database that stores ideal metrics of that industry, i.e.,
compare actual to ideal. Based on signals from the comparison
engine, the system may provide a visual signal [e.g., "red"
"yellow" "green" display] to identify where the data presented lies
on the spectrum of comparable organizations. Additionally, the
comparison engine may provide recommended action-steps for using
the data in strategic plans. A valuation engine may be used to
generate a valuation of the enterprise based on the data.
[0015] The system also includes data filters that, for example,
allow a user to turn off selected segments of data from the inputs.
The segmentation of the data is based on preset organization of the
data. This functionality allows the system to display outputs based
on different combinations of segmented data from the inputs.
[0016] The present invention is applicable to a wide variety of
business problems. Utilization can theoretically apply to any
company operating with a multiplicity of employees, operations,
functions, and systems. Meaningful insight is derived from the
collection, development, and transformation of data based on the
inputs, data aggregation, and systems. Outputs regarding the
aforementioned problems functionally operate under the mechanism of
the system logic (schema) in regard to how data is transformed into
useful insight driving materials.
[0017] The tools provided by the invention may be applied to all
business problems that can be articulated in a known context for
procedural evaluation. Areas of application broadly concern market
potential, market behaviors, competitor interests, human resource
solutions, organizational design, financial resource management,
business planning strategy, marketing planning, sales strategies,
operational considerations, infrastructure planning, operational
assessment, potential returns for investments, measures for
assessment, performance assessments, stakeholder investigations,
governance practices, and legal concerns. These issues all fit into
the 9Lenses framework, called the schema. The invention develops
solutions based on this framework.
[0018] Business problems under the framework function as points of
evaluation. Points of evaluation are deployed in the system based
on the working methods established. The specific systems, methods,
processes, software, and standards utilized break down based on the
workflow of the issue classification. Aggregated data functionally
overrides strategic evaluation difficulties by automating data
collection and transforming the simple data points into meaningful
information with direct application to immediate concerns as well
as applications to future problems. Additionally, by providing
contextual understanding of comprehensive organizational structure,
the data functions as a conceptual insight engine. Data aggregation
reduces the operational and opportunity costs of strategic
assessments while maximizing the valuation and visibility of
potential solutions.
BRIEF DESCRIPTION OF DRAWINGS
[0019] FIG. 1 is a schematic diagram illustrating the architecture
of the system.
[0020] FIG. 2 is a schematic diagram illustrating the functional
overview of the system.
[0021] FIG. 3 is a process flow for how data in the system is
transformed into actionable enterprise intelligence.
[0022] FIG. 3A is an illustration of a main schema and the
accompanying output of from the system dashboard.
[0023] FIG. 3B is an illustration of an alternative output from the
system dashboard.
[0024] FIG. 3C is a illustration of the description of the main
components of a main schema.
[0025] FIG. 4A is a schematic diagram of an exemplary system for
transforming various inputs of raw data into useable information
within the schema.
[0026] FIG. 4B shows an aspect of the invention that allows the
user the ability to control data outputs.
[0027] FIG. 4C is a schematic diagram of the system used to measure
various inputs and determine comparative analysis of the entire
business operation
[0028] FIG. 5 is a schematic diagram of an exemplary system used in
a process for evaluating and confirming business data inputs based
on interpretative logic grounded in dynamic feedback loops.
[0029] FIG. 6 is a schematic diagram of an exemplary system for
collection, organization, schematization, storage and use of
queries designed to elicit data pertaining to business problems
into a universal database.
[0030] FIG. 7 is a schematic diagram of an exemplary system and
process for selection of individuals to participate in a business
initiative based on a process using a/b testing to determine
expertise on information for the purpose of planning and segmenting
participants.
[0031] FIG. 8 is a schematic diagram of the prediction engine used
in predicting outcomes from separate business problems.
[0032] FIG. 9 is a schematic diagram illustrating the system used
for aggregating business data and automatically publishing content
to specified users.
[0033] FIG. 10 is a schematic diagram of an exemplary system used
based on a decision engine for automatically generating diagnostic
queries for business problems and then refining the automatically
generated apps.
[0034] FIG. 11 a schematic diagram of an exemplary system for
collecting systems data into the interpretation and scoring logic
system and aggregating the data and building it into the
schema.
[0035] FIG. 12 is a schematic diagram of the system used in a
process for collecting assorted public (external) data and
systematizing the information based on an interpretative scoring
process and sequencing it into a logical framework.
[0036] FIG. 13 is a schematic diagram of the system for generating
specific population lists to be queried based on predetermined
inputs that in turn generate an automatically selected population
for sessions based on determinant algorithms and comparisons.
[0037] FIG. 14 is a schematic diagram illustrating an exemplary
system used for creating business solutioning ideas within the
organization.
[0038] FIG. 15A is a schematic diagram of an exemplary system for
presenting output according to an alternative schema format based
on a master schema.
[0039] FIG. 15B is a schematic diagram illustrating an example of a
system from presenting translated content from alternative schemas
into a format based on a master schema.
[0040] FIG. 16 is a schematic diagram of an exemplary system for
automatically recommending business conversations based on the data
obtained from the holistic business diagnostics.
[0041] FIG. 17 is a schematic diagram of an exemplary system that
automates meetings.
[0042] FIG. 18 is a schematic diagram of an exemplary system that
monitors inputs to generate recommendations and report on
changes.
[0043] FIG. 19 is a schematic diagram illustrating an exemplary
system used for matching consultant-generated solutions concerning
specific enterprise related issues.
[0044] FIG. 20 is a schematic diagram illustrating an exemplary
system for using data inputs vetted through established protocols
to determine bid decisions for contracts.
[0045] FIG. 21 is a schematic diagram illustrating an exemplary
system for measuring the financial model of an enterprise.
[0046] FIG. 22 is a schematic diagram illustrating an exemplary
system for automatically calibrating the predictive success from
automatic interviews based on successes of previous candidates.
[0047] FIG. 23 is a schematic diagram illustrating a process of
automatically calibrating the automatic hiring determinant system
based on successes of previous candidates.
[0048] FIG. 24 is a schematic diagram the system used for process
responses from automated interviews.
DETAILED DESCRIPTION
[0049] An embodiment of the present invention provides a broad
system for business optimization by presenting data according to a
selected schema. The data presented according to the schema is
generated by transforming data received into schema data according
to a selected schema.
I. Core System Logic
[0050] FIG. 1 is an overview of the system architecture. As shown,
the system includes a communications system 100 for network
communication with a plurality of data sources. A query engine 110
is connected to the communication system for requesting data from
the data sources. The data sources include external data sources
that communicate with the system through the Global Information
Network (GIN) and internal data sources in direct communication
with the system. Examples of external sources include data feeds
122 that provide market intelligence or other news and inputs from
social media or outside sources made through communication devices
such as mobile phones 124, tablet computers 126 and other computers
126. Examples of internal sources include the CRM system 132, HR
Database 133, CRP System 134 as well as user inputs through
computers such as tablets 136 and other computers 138. A database
140 stores data received from the data sources and a schematic
interpretation engine 150 engine transforms data in the database
140 to master schema data according to a selected schema. The
system may also include an engine for transforming the master
schema data into data for alternative schemas and allows customized
schemas. Various displays 170 may be used to display data in a
format dictated by the selected schema. A user interface 160 allows
a user to control the display. The hardware used to implement the
system preferably includes at least one CPU with on board RAM; an
input/output system bus (including control bus, address bus and
data bus functionality); system memory; system storage (flash or
hard drive); communications hardware for TCP/IP (or other protocol)
based end-to-end connectivity and a wireless communication
processor for enabling Wi-Fi, Bluetooth and/or other wireless data
exchange over a local or global information network.
[0051] FIG. 2 shows a functional overview of the system. As shown,
the data from External 120 and Internal 130 sources is aggregated
210 and passed to an interpretation engine 230 for transformation
into schema data according to the selected schema 310S (in this
example the 9Lenses schema). The data is then selectively displayed
as system output 310D. The function of the invention is divided by
two categories, (1) the fundamental logic of the system that drives
the data collection, storage, transformation, and dissemination and
(2) the extended uses of the systems logic in a plurality of
subsystems.
[0052] FIG. 3 shows the process flow for data transformation
according to the invention. As shown, the process beginning at step
300 includes the step 310 of selecting a schema, which is described
in greater detail below. At step 315, the components and
sub-components of the selected schema are defined. The defined
components (and their sub-components) are the characteristics of
the enterprise that are to be evaluated according to the schema. A
challenge arises in that there is rarely (if ever) a single data
source within an enterprise that provides a complete measure of a
component used according to an established schema. Thus, it becomes
necessary to transform available data into data that provides the
desired evaluation of a component according to the selected schema.
At step 320, an available data source that is relevant to one or
more of the schema components is identified. At step 325, a
strategy for collecting the relevant data is designed and
implemented and the relevant data is collected and stored at step
330. The process is repeated (step 327) so long as there are
relevant data sources. At step 333, a determination is made as to
which of the components or sub-components each data source is
relevant to and at step 335 the importance of the data to a
component/sub-component is defined by a weighting factor assigned
to each data source. At step 340, a weighting factor is assigned to
each subcomponent to reflect the relative importance of that
sub-component to the component being measured. The weighting
factors associated with data sources are preferable dynamically
adjusted based on previous users responses from a particular
participant and past performance. For example, the input of a
particularly insightful data source (participant/respondent) may be
given more weight, while a less insightful data source may be given
less weight. A dynamic data weighing engine may be used for this
purpose. At step 350, the system displays the component level
results (as shown, for example, in FIGS. 3A and 3B) and the user is
provided with the option (though user interface 160) to display the
underlying constituent data, i.e., drill down to see the
subcomponents and data that resulted in the overall result. At step
360 users are provided the option for considering specified
sub-sets of the data (from step 333) apart from the aggregate data
provided by the system. Users can select specific data from
specified sources. At step 363, in response to the users'
selections, the system removes one or more data sources from the
calculation and reweights the remaining data sources 365. The
system also provides the user with the option of outputting data
from the system (at step 370) and allows the user to select an
output format (step 375).
[0053] The step 310 of selecting a schema involves selecting an
analytical structure for organized presentation of data that
encompasses the assets, processes and structures that drive
business success. By way of example, FIG. 3A shows the 9Lenses
framework 310S and one example of an output display 310D of
transformed data. In the 9Lenses schema, the components defined
(step 315) are the 9Lenses (strategy, execution, operations,
expectation, governance, entity, market, people and finance). The
sub components are the "sub lenses" of the 9Lenses schema. FIG. 3B
shows an alternative output that provides a more through overview
of the data at the component level.
[0054] As shown and explained in FIG. 3C, the 9Lenses components
provide insight into the assets, processes and structure within an
enterprise. In this regard, the market, people and finance lenses
may be grouped under the category "assets." The strategy,
operations and execution lenses may be grouped under the category
"processes." The expectation, governance and entity lenses may be
grouped under the category "structures." Other schemas typically
use different labels for the different components and sub
components used to provide insight into an enterprise. However, in
accordance with an aspect of the invention, the component data for
one schema (e.g., 9Lenses) may be transformed into and presented as
component/sub-component data for another schema using a schema
conversion process, one example of which is described in FIG. 15
below.
[0055] FIG. 4A shows the system used for transforming various
inputs from raw data into usable information within the schema.
Although FIG. 1 depicts the process at a high level as occurring in
a schematic interpretation engine 150 that is in communication with
other system components and the user interface 160, the process may
occur at various locations based on various inputs. The process
steps employed in the transformation of raw data into schema data
comprise: collection of raw data; classification of raw data;
assignment of data that has been classified; weighting of data and
application of data to the schema components/sub-components.
[0056] As shown in FIG. 4A, the raw data that has been collected is
classified (step 410) according to, for example, data type: active
412; passive 414; binary 415; scaled 416 and user generated 418. At
step 420, the data is then assigned to one or more
components/subcomponents of the schema and a weighting factor is
determined for the data with respect to each
component/subcomponent. The previous classification (412-418) is
preferably a factor in determining the weighting assigned to data
(step 420). At step 430, the transformed data is then applied to
the selected schema. Preferably, the transformed data sources are
each assigned to a subcomponent with a respective weighting factor
and the subcomponents are given a weighting factor for their
respective component. Once transformed data is applied to the
schema and appropriately weighted, the system can output schema
data in various forms according to user preference at step 440. For
example, the data may be displayed in the "dashboard" format
depicted in FIG. 3A or 3B or output to another program or
application or a printable format.
[0057] As shown at 470 in FIG. 4A, the system may also use
transformed schema data to generate and output action step guide
outputs such as recommendations 473; industry benchmark comparisons
474; red flags 475 and people analysis 476. In this way, the system
leverages the transformed data to provide additional tools in the
form of reports and indicators based on more accurate and up to
date data than would otherwise be available. For example, the
industry benchmark feature allows comparison of an enterprise's
performance to other enterprises in the industry. Importantly, the
system allows such comparisons even among companies that select
different schemas because of the ability to interpret data from
other schemas.
[0058] FIG. 4B shows an aspect of the invention which allows the
user to control, through the user interface 160, data input and
weighting to permit segmentation and analysis of the degree of
impact of departments or sectors and analysis according to one's
own view as to the significance of particular business relevant
data to business issues. As shown at 480, the system includes
control switches to allow the user to enable and disable inputs
used to generate the system output along the lines shown at 360 in
FIG. 3. As shown in FIG. 3, when data inputs are disabled, the
system reweights remaining data sources 365 and generates revised
output. The system further includes a weighting control feature 482
that allows the user to override the default weighting in defining
the weighting for a data source (step 335). The system generates
revised output based on the new weighting so that the user can see
the impact of the change in weighting.
[0059] FIG. 4C shows the system used to measure various inputs and
determine comparative analysis of the entire business operation. As
shown, the system is similar to that of FIG. 4A and system
exclusive data is depicted as distinct from public and or
enterprise data that is used for purposes other than the system per
se. System exclusive data is data that is, in the first instance,
generated or collected expressly for the purpose of inputting into
the system, e.g., responses to system queries. As shown, the system
includes an interpretation and comparison engine 478 performs
comparisons across data sets to provide additional views and
recommendations based on the transformed data. An example,
described below in connection with FIG. 8, is the predictive
analysis of predicted outcomes of business problems.
[0060] FIG. 5 shows the system used in a process for evaluating and
confirming business data inputs based on interpretative logic
grounded in dynamic feedback loops. By way of example, when data
input is based on human input (e.g., response to a system query),
the interpretation logic engine 520 evaluates the response against
previous responses 522, public data 523 and systems data 524 to
identify a possible inconsistency, incongruity or anything else
that might indicate erroneous input or enterprise inconsistency.
When a possible error is identified, the dynamic confirmation
engine 525 seeks confirmation of the data input by, for example,
sending a query to the data source. Information from the
interpretation logic engine may be viewed as a single instance
(static view) or as a dynamic view and the system generates
recommendations to remedy the detected error or inconsistency in
data input. This aspect of the invention is especially important in
detecting instances where a single input source may have relevant
information that is unknown to others and separating such instances
from mere errors in input.
[0061] FIG. 6 shows the system used for systematic collection,
organization, schematization, storage and use of queries designed
to elicit data pertaining to business problems into a universal
database. The system includes a diagnostic input 610 for receiving
a new diagnostic query from a user or agent. The diagnostic is then
schematized 620, i.e., a record is created as to which
components/subcomponents of the schema the query is relevant to. In
addition, a record may be created as to whether the query is
enterprise (client) specific or generally applicable. If the query
is enterprise specific, it is passed to a diagnostic creation
interface where it is processed as an enterprise diagnostic for use
in an enterprise app. The query is then evaluated (at step 640) for
general applicability and if found applicable (perhaps with
modification) for general use, the query is passed to the central
repository 670. If the query is not generally applicable, it is
stored in the Enterprise Repository 650. Queries stored in the
Central Repository 670 may be displayed by the diagnostic library
display 680 and also used to create apps using the app creation
interface 690. In this way, the system permits intake of individual
diagnostics that are then transformed into queries that elicit
interrelated information based on a logical framework for
compilation into business diagnostics. The individual diagnostics
may be transformed into apps (using the app creation interface 690)
for the purpose of assessing business problems.
[0062] By virtue of the transformation and organization of data
according to a schema, stored data may be used for other purposes.
For example, FIG. 7 is a schematic diagram of an exemplary system
and process for selection of individuals to participate in a
business initiative based on a process using a/b testing to
determine expertise on information for the purpose of planning and
segmenting participants. A shown, a system query 701 initiates the
A/B test process 710. The NB test process takes into both
performance assessment 720 (based on desired resource commitment
721 and probability of success 723 given the desired resource
commitment) and influencing factors 725 regarding the proposed app.
A logic module 730 processes the inputs and outputs segmentation
750 and resource planning data 770. Segmentation 750 defines the
role, organization, tenure or other characteristics of personnel
suited for the task. Resource planning 770 outputs the availability
of personnel and the enterprise impact of assigning available
personnel.
[0063] FIG. 8 shows the prediction engine used in predicting
outcomes from separate business problems. Predictive analysis
begins with aggregated responses from schematized responses from
participants 810. Based on predetermined connections, the
prediction engine takes actual responses around specific components
and sub-components 822 and predicts responses to other schema
queries 824 that have established connections to the queries 822
for which actual responses have been received. As shown, a cross
comparison engine 830 uses the actual responses 822 together with
Historical Response Data 840 to provide inputs to a predictive
estimation engine 850 that generates a prediction of the response
to schema queries 824 that are known to have a predetermined
relationship to the actual responses 822. Once the predicted
responses to queries 824 have been generated, the system will
prompt the user at 860 to validate the predicted response, e.g.,
confirm the predicted responses or provide new input. The results
of the prediction are stored in the predictive database 870 and
used as an input to refine future predictions by the predictive
estimation engine 850. Preferably, the validation step 860 occurs
as a separate user session to allow a more comprehensive response
to specified business problems. In other words, the validation step
is more than just a data input validation, but provides an
opportunity to elicit important data used within the schema in a
systematic way that is more efficient and focused because it is
based on information already known to the system. The predictive
analysis system of FIG. 8 thus acts as an intelligent agent to
improve user input queries (at the validation step 860) though the
use of predictive estimation.
II. Functional Extension of Core Logic
[0064] FIG. 9 shows the system used for aggregating business data
and automatically publishing content to specified users, in this
case enterprise board members. At step 910 a determination is made
as to which subset of data will be provided to the user. The
selection is input to a data-filtering engine 920, which flags the
relevant data fields The automated data selection engine 930
generates an automated Relevant Data report 940 periodically or
whenever a threshold of new data in the flagged fields has been
received.
[0065] FIG. 10 shows the system used based on a decision engine for
automatically generating diagnostic queries for business problems
and then refining the automatically generated apps. As shown, the
system includes a decision engine 1010 that allows priorities to be
set according to enterprise organizational profile 1012 (industry,
size, growth, inflection points) and preferences 1014 with respect
to features such as time to completion, expertise required, source
providing resources and area of focus (e.g. operations, execution
etc.). The output of the decision engine 1010 together with the
diagnostic library 650 and/or 670 and optionally the output of the
automatic population engine of FIG. 13 are aggregated 1020 as
inputs to an automated app generation engine 1030 that generates an
automatically generated app 140 composed of diagnostic queries
selected from the repositories 650, 670 based on the output of the
decision engine 1010. The automatically generated app may then be
evaluated by the user at the diagnostic rating step 150 preferably
though a diagnostic-by-diagnostic assessment that results in a
refined app 170. The refined app 170 is then subject to active
monitoring (according to FIG. 18) to continuously refine the app
170.
[0066] FIG. 11 is a schematic diagram of an exemplary system for
collecting systems data into the interpretation and scoring logic
system and aggregating the data and building it into the schema. As
shown, internal data 130 that is not system exclusive is
transformed into schema useable data by assigning a schema useable
score to the data. The score is assigned by an interpretation
scoring engine 1110 pursuant to the selected schema (e.g., a score
of 1-9) based on predetermined conversion algorithms or tables. The
scores are then input into schema specific locations at step 1120
and applied as diagnostics input 1115 to diagnostics from the
enterprise repository 650 for use in system output 1130 such as
data interpretation, company reports and data feedback.
[0067] FIG. 12 shows the system used in a process for collecting
assorted public (external) data 120 and systematizing the
information based on an interpretative scoring process and
sequencing it into a logical framework. As shown, external data 120
is transformed into schema useable data by assigning a schema
useable score to the data. The score is assigned by an
interpretation scoring engine 1210 pursuant to the selected schema
(e.g., a score of 1-9) based on predetermined conversion algorithms
or tables. The scores are then input into schema specific locations
at step 1220 and applied as diagnostics input 1215 to diagnostics
from the enterprise repository 650 for use in system output 1230
such as data interpretation, company reports and data feedback.
[0068] FIG. 13 shows the system for generating specific population
lists to be queried based on predetermined inputs that in turn
generate an automatically selected population for sessions based on
determinant algorithms and comparisons. As shown, a parameter
selection interface 1310 allows the user to set parameters based on
factors such as segmentation, previous participation (and
performance) and weighting of criteria. Based on the parameters set
and data drawn from a HR database 1320, an automated selection
engine 1330 generates a population selection report 1340 for user
review at step 1350. If the report 1350 is approved, it is used in
an app session at step 1360 and eventually results in a statistical
report 1370. If the report is not approved at step 1350, the user
selections participants to be removed and the process returns to
the automated selection engine 1330.
[0069] The system shown in FIG. 13 may thus be used for
automatically calculating a statistically significant population
for addressing specific business problems. Likewise, the system may
be used to invite the statistically significant population to an
application, and determine their representative perspective based
on relative calculations of the deviation of initial population
participants. The system acts as a decision engine that uses
relative A/B testing preferences to determine significant issues
and workflows for determining which populations are expert in which
topics.
[0070] FIG. 14 shows the system used for creating business
solutioning ideas within the organization. The system solicits
uncollected ideas from employees 1405 and includes a repository
1410 for storing and processing the ideas. The data is schematized
at step 1420 and at step 1430 the idea is approved or rejected
(presumably by a manager). If approved, the idea may be reformatted
and rated as an output proposition 1440 for further consideration
and rating. A logic module 1450 includes algorithms for selecting
best comments/ideas, thumbs up/down rating for manual rating,
algorithm for aggregating responses; use of the best data to
determine consistent performance. Output from the logic module 1450
may include, for example, benchmarking reports, top comment reports
and idea comparisons. The system further includes feedback loops
for identifying and relating top solvers and best ideas to
predictive solutions. As shown, ideas are associated with the
individuals submitting them in an Individual Report 1460 and
validated (or not) through future data and reports are generated on
an entire session 1470. User data is also stored in an enterprise
repository 1480 and used to identify top performers based on
submissions over time. Process steps may be performed by software
engines, agents or a combination of both.
[0071] FIG. 15A shows the system for presenting output according to
an alternative schema format based on a master schema. In the
example shown, the master schema is the 9Lenses schema. As shown,
the user selects an alternative schema at step 1510. An analysis
agent defines the components and sub-components of the alternative
schema at step 1515. The agent then maps the components and
subcomponents of the alternative schema to the master schema (step
1520). In addition, at step 1525 the agent identifies
externalities, i.e., inputs required by the alternative schema that
cannot be mapped from the master schema. To the extent
externalities exist, it becomes necessary to define and implement a
data collection strategy to satisfy the externalities. At step
1530, an available data source that is relevant to one or more of
the schema components is identified and a strategy for collecting
the relevant data is designed and implemented. The relevant data is
collected and stored at step 1540. The process is repeated (step
1550) so long as there are relevant data sources. It will be
appreciated that the agent described above maybe an automated
software agent, a human agent or a combination of both. Once
externalities are fully satisfied, the proposed mapping and
internal systems information are presented for review and approval
at step 1560. If approved, mapped content is output at step 1570.
If not approved, a reason for rejection is obtained and the system
revalidates the proposal (at step 1580) and the process resumes at
1525.
[0072] Similarly, FIG. 15B shows an example of a system for
extracting and presenting translated content from alternative
schemes into a format based on a master schema. In this example,
the analyst agent translates content from a relevant book on
business expertise 1505 according to the collection of external
research 1513 and previous information on the development of
business procedures 1515. The resultant diagnostic 610 is
translated into a master schema 1520. At step 1530, the agent
generates a refinement of the diagnostic according to
pre-established criteria on comparison to known business problems
1533, quality of the language used as it relates to traditionally
accepted terminology 1535 and investigative strength of the
diagnostic according to the likelihood of eliciting useful
responses. The refinement is presented for review and approval
1540. If approved, the diagnostic content is output at step 1560
and then stored in the diagnostic repository 670. If not approved,
a reason for rejection is obtained and the system revalidates the
diagnostic (at step 1550) and the process resumes at 1530.
[0073] FIG. 16 shows a system for automatically recommending
business conversations based on the data obtained from the holistic
business diagnostics (as shown in FIG. 4C). The system extracts
data from responses 1610 to determine the statistically significant
misalignment of scores between executives 1620 and specified needs
identified by leadership 1630. The system then compares this data
with identified areas of concern from previous data 1640. The
resulting comparisons of data are output as a proposal for which
business problems should be evaluated 1650. Ideally, the system
further includes a display with specific data within the schema
from which the proposal was generated 1660.
[0074] FIG. 17 shows the system that automates meetings. As shown,
users create draft agendas 1710 in the system. The agendas are
validated 1720 through manual confirmation from other participants
and system-generated preferences from system specific data. At step
1730, the system filters the human responses, these responses are
then schematized 1740. At step 1750, the system creates areas of
importance according to the schema components and sub-components.
The system may use active monitoring 1760, further described in
FIG. 18, as a feedback loop to confirm the accuracy of the
system-generated preferences. The system generates an actions and
recommendation report 1770, which users and participants may then
validate according to their own preferences. The system may then
provide outputs on the validated actions 1780.
[0075] As the system collects data from a plurality of sources, the
user may monitor the general trends in the usefulness of
information that the system collects from different systems. As
shown in FIG. 18, the system monitors individual inputs using
decision logic modules to generate recommendations on how sources
should be weighted. Data from a plurality of sources, 120, 410, and
130 for example, is aggregated 1810, similar to the system in FIG.
2, and passed to an interpretation engine 1820 for transformation
into schema data according to a master schema 1830. At step 1840,
the user may select criteria for preferences regarding data sources
according to the decision logic engine. The system consistently
tracks the inputs from the schematized data and the selections made
in the engine. The engine outputs recommendations for data
weighting 1850 according to the resultant information from the
output feedback and the decision logic engine.
[0076] FIG. 19 shows the system used for matching
consultant-generated solutions concerning specific enterprise
related issues. As shown in step 1910, users input solution data
based on established criteria (preferably solution implemented,
relevant characteristics of consultant, and experience in field).
Solutions data may be stored in a database 1920. The system then
schematizes the data pursuant the main schema 1930. Step 1940 shows
the users generate data on specific enterprise problems. The data
is input into a problem-matching engine that associated the
specific problem with the main schema 1930 and generates matching
recommendations for the solutioning the enterprise problems 1950.
The recommendations may then be evaluated by the respective
executives managing the enterprise issue 1960. The system uses the
feedback generated by the executives to further improve the problem
matching engine suggestions 1970. The system then generates a
proposed solution report 1980.
[0077] The present embodiment of the invention has multiple systems
for automating enterprise processes. For example, FIG. 20 shows the
system for automating bid/no bid decisions on contracts. Data from
existing workflows, ratings from participants, and corporate
resource management 2010 is aggregated 2015 and passed into an
interpretation engine 2020 for transformation into the main schema
2030. A logic engine 2040 processes the schematized data. The logic
engine may then output a bid/no-bid report detailing predictive
success from the data. Step 2060 validates the actual decision.
Users indicate the wins and losses on specific bid and input
reasons for the outcome. The system generates comparative reviews
of these reasons for improving the accuracy of future
predictions.
[0078] FIG. 21 shows a system for automatically determining the
financial model of an enterprise. The system pulls data generated
from automated interviews 2110, further illustrated in FIG. 24, and
system data 2120 targeted around financial information. The system
displays an output model of the aggregated information 2130
according to three criteria (touch, volume, and margin). The system
compares data from the output model to available industry data
within the system and publically available data 2140. The system
generates an automated value estimate 2150 that, preferably,
provides a "best in class" comparison financial models The system
also displays a benchmarking report 2160 that provides information
for strategic improvement of the financial model. The system
generates a KPI report 2177 and displays particular action steps
2173 for recommended actions for altering an enterprise financial
model.
[0079] The system may provide a system that automatically
interviews candidates for employment. As shown in FIG. 22, the
system queries the user based on pre-determined characteristics
2210. The system then feeds those inputs into the automatic
interview engine 2220, further illustrated in FIG. 24. The system
is further comprised of a ranking integration for classified data
410, as illustrated in FIG. 4C. The system pulls the resultant data
from automated interviews to generate a predictive probability for
successful performance of the candidate within the enterprise role
according to the predictive success indicator engine 2240 and
outputs a display of the results accordingly 2250. The system is
further comprised of a self-correcting feedback loop that pulls
information from the auto-tuned feature 2260, further illustrated
in FIG. 23. The system creates a corrective formulation for
comparisons 2270 that feeds into the calculations provided by the
predictive success indicator engine.
[0080] FIG. 23 shows the system for automatically calibrating the
predictive success of job applicants from automatic interviews
based on successes of previous candidates. The system pulls user
inputs from the pre-determined characteristics 2210. The outcomes
measuring engine 2310 provides an approximated value for current
user responses by assigning a numerical value to their responses.
The system generates a deviation score for estimating how much the
respondent differs from the predictive model for a successful
candidate 2320. The system compares the deviation score with the
results of the success measurement engine 2330, which uses
standardized measurements from a plurality of inputs (for example,
performance review, training costs, established enterprise
performance metrics, and employee engagement). The system outputs
the data of successful candidates and stores them in a repository
2340. The data from the repository is further used to adjust 2350
the estimated weighting of scores provided by the outcomes
measuring engine.
[0081] FIG. 24 shows the system used for process responses from
automated interviews. The system displays the M13 criteria 2210 in
a standardized user interface 2410. Through the interface, users
input responses 2420 to targeted queries The responses are
preferably input into a response database 2430. The system
generates a success criteria 2440 preference that marks the data
according to the pre-established success measurement categories, as
described in FIG. 23. The raw responses and annotated responses and
compared at step 2450 and the system outputs the resultant
responses for use within the system 2460.
[0082] As described above, the system uses various engines and
agents to perform specified functions. The engines are preferably
implemented as general purpose computing devices controlled by
software to perform as special purpose engines. The computing
device(s) on which the system is implemented communicate with other
system components and external system systems and users through
conventional communications protocols and interfaces. The agents
used or interacting with the system may be automated agents or
human agents or combinations of both.
[0083] The embodiments described herein are exemplary and not
intended to be exhaustive of the applications of the systems and
methods of the invention.
* * * * *