Patent Application
20140101076
©2012-2013 BigML, Inc. A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 37 CFR §1.71(d).
This invention pertains to computer-implemented methods and apparatus for machine learning or modeling digital datasets, and utilizing data models to make predictions about new data in a secure, private or shared system.
Machine Learning uses a number of statistical methods and techniques to create predictive models for classification, regression, clustering, manifold learning, density estimation and many other tasks. A machine-learned model summarizes the statistical relationships found in raw data and is capable of generalizing them to make predictions for new data points. Machine-learned models have been and are used for an extraordinarily wide variety of problems in science, engineering, banking, finance, marketing, and many other disciplines. Uses are truly limited only by the availability and quality of datasets. Building a model on a large dataset can take a long time. Further, the time and resources necessary to build a model increases as the required quality or depth of the model increases. In view of these investments, some models are valuable to other users. Datasets themselves also may have value in view of the investment to acquire, check or scrub, and store the dataset. In fact, many interesting datasets are built after laborious processes that merge and clean multiple sources of data. Often datasets are based on proprietary or private data that owners do not want to share in their raw format.
The following is a summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not intended to identify key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented later.
In one aspect, a system in accordance with the present disclosure may enable an owner of a dataset to control permissions that would enable another party or second user to use the dataset to make a new model derived from the dataset. The owner or the system may charge the second user a fee to use the dataset. The fee may be shared with the owner.
In another aspect, a system in accordance with the present disclosure may enable an owner of a model to control permissions that would enable another party or second user to use the model to make a predictions based on a new input dataset provided by the second user. The owner or the system may charge the second user a fee to use the model. The fee may be shared with the owner of the model. The model may be, for example, a decision tree model.
According to another aspect, a dataset or a model may be displayed or advertised in a public gallery. The display may contain a summary, thumbnail or metadata describing the dataset or the model, as the case may be.
According to yet another aspect, a model may be published to a public gallery in a black-box form that enables use of the model to make predictions without disclosing its internal operation. In another aspect, a model may be published to a public gallery in a white-box form that enables use of the model to make predictions and also discloses a descriptive and actionable version of the model. The actionable version enables the user to understand operation and if desired modify the model. Different fees may be charged for black-box and white-box models. Fees may be flat rate, per prediction, or based on other criteria.
Additional aspects and advantages of this invention will be apparent from the following detailed description of preferred embodiments, which proceeds with reference to the accompanying drawings. The invention is not intended to be limited by the drawings. Rather, the drawings merely illustrate examples of some embodiments of some aspects of this disclosure.
Some portions of the detailed descriptions which follow are presented in terms of procedures, logic blocks, processing, steps, and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. A procedure, logic block, process, etc., is generally conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps require physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared and otherwise manipulated in a computer system.
In short, the invention is intended to be implemented in software; i.e., in one or more computer programs, routines, functions or the like. Thus it may best be utilized on a machine such as a computer or other device that has at least one processor and access to memory, as further described later. In a preferred embodiment, a system is hosted on a server to provide the features described herein to remote users. The server may comprise one or more processors. The server may be remotely hosted “in the cloud.” Accordingly, in this description, we will sometimes use terms like “component,” “subsystem,” “model server,” “prediction server,” or the like, each of which preferably would be implemented in software.
It should be born in mind that all of the above and similar terms are to be associated with the appropriate physical quantities they represent and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present invention, discussions utilizing terms such as ‘processing,’ computing, ‘calculating,’ ‘determining,’ displaying' or the like, refer to the action and processes of a computer system, or any other digital electronic computing device having a processor, that manipulates and transforms data represented as physical (electronic) quantities within the system's registers and memories into other data similarly represented as physical quantities within the system memories or registers or other such information storage, transmission or display devices. The term processor encompasses multiple processors acting in concert. A system may be located in one or more physical locations, e.g. distributed or housed “in the cloud” (a centralized location where plural processors and related equipment may be housed and remotely accessed). Further, we use the term “user” in a broad sense relative to a processor or other computing platform, program or service. A user may be a natural person or another processor or other computing platform, program or service. For example, an API may be used to enable another machine or processor to utilize a system of the type described herein.
Note that the invention can take the form of an entirely hardware embodiment, an entirely software/firmware embodiment or an embodiment containing both hardware and software/firmware elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
A source is usually a (large) file, and may be in a comma-separated values (CSV) format, although many other formats may be used. Each column in the data file may represent a feature or field. A column, often the last column, usually represents the class or objective field. The file might have a first row named header with a name for each field.
A registered user 102 may transform her new source data 110 into a dataset 134 as follows. An illustrative system further includes a dataset server 130. The new data source 110 may provide raw data to the dataset server 130 as indicated by arrow 132. A dataset may comprise a binary and structured version of a source where each field has been processed and serialized according to its type. A field may be numeric, categorical, a date, time, or text. Other filed types may be created. In an embodiment, for each field, descriptive statistics including histograms that depict the distribution may be computed automatically, depending on the type of the field. In general the new dataset 134 is derived from the data source 110. Note the new dataset 134 is associated with user 102's private dashboard 100 so that the dataset is accessible to user 102 but not available to anyone else at this juncture.
The user 102 may transform the new dataset 134 into a model. A model is a structured representation of the dataset with predictive power. A model may be created using a subset of fields of the dataset as input fields, and another subset of the dataset fields as objective fields (the ones that the user wants the model to predict). A model can be a single model or an ensemble of models. An ensemble can be created using the same or different datasets or techniques. Details of methods for creating models are known. In the illustration of
Datasets and models may be enriched with various types of metadata, for example:
Note in the present example the new model 144 is associated with user 102's private dashboard 100 so that the model is accessible to user 102 but not available to anyone else at this juncture. The model can be used to make predictions on new input data that were not used to build the model. The model uses the new input data to predict values of the objective fields. Some examples are described later.
The user 102 may make predictions using a prediction server 150. The model 144 is accessible to the prediction server 150 as indicated by arrow 152. Further, the user provides new input data 154 to the prediction server for the purpose of predicting objective field values, based on applying the model to that new input data. The results form a new prediction 160. Note again that the model, the new input data, and the new prediction 160 are all associated exclusively to the user 102 secure dashboard 100. In an embodiment, each registered user may have a number of sources, datasets, models, and predictions that are kept private in her dashboard. In some embodiments, interactions between a user and the system can be done through a user interface or programmatically through an API.
A user 102 may make her dataset 134 public. Referring now to
Preferably, unique identifiers are assigned to each resource in the system. For example, unique identifiers may be assigned to each source, dataset, model, ensemble, prediction, and evaluation. By way of illustration, identifiers may look like this:
Any identifiers that are at least unique within the system may be used. In use, for example, a software call to create a new model may include an identifier of the specific dataset to be used to create the new model. Resource identifiers can be used by users to refer to various assets in their private dashboards or in a public gallery.
Referring again
Referring now to
Evaluations provide a convenient way to measure the performance of a predictive model. A user can evaluate a model using a dataset of her own. In some embodiments, the server may provide various performance metrics that provide an estimate of how well a given model predicts an outcome as compared to its performance faced with data similar to the dataset tested.
In an embodiment, a user also can make a model public that she has built using a system of the type illustrated herein. A model may be uploaded, for example, into a user's private dashboard from an external source. Or, the user may choose to make public a model that she created using the system, as describe above (new model 144). Once a model is made public, other users may have access to a thumbnail picture, for example, that represents the model, and or other meta information about the model. Meta data may include, for example, the number of fields and number of instances of the dataset used to build the model.
The owner of a model can control whether other users can have access to the internal structure of the model, for example using a selected one of at least two methods: black-box or white-box. If the owner so lacks a black box method, other users will not be able to see how the model works internally, but they will still be able to use the model to make predictions using their own input data. If the owner uses the white box method, other users will have access to a descriptive and actionable version of the model that explains how it works, in addition to the ability to make predictions as conferred by black-box models. For example, an actionable version of a model may be provided in various formats. These may include JSON, PMML, Microsoft Excel, a sequence of if-then rules and various programming languages such as Python, Ruby, Java, etc.
Turning now to
Once a user makes a data set or model public, the corresponding data set or model may be exposed in both an individual gallery or dashboard, and a public gallery, such as the public gallery 200. In the public gallery, data sets and models can be filtered by an interested user, according to variety of variables such as popularity, recency, white box, black-box, price and other attributes including but not limited to names, descriptions, tags, etc. In a preferred embodiment of the public gallery may be implemented on a webpage.
A person of ordinary skill in the art will recognize that they may make many changes to the details of the above-described exemplary systems and methods without departing from the underlying principles. Only the following claims, therefore, define the scope of the exemplary systems and methods.
This application is a non-provisional of U.S. Provisional Application No. 61/710,175 filed Oct. 5, 2012 which is incorporated herein by this reference.
| Number | Date | Country | |
|---|---|---|---|
| 61710175 | Oct 2012 | US |
