Patent Grant
12147647
The present disclosure relates to artificial intelligence (AI) systems, techniques, and interfaces for investigations and improved background checking.
In the field of investigation, investigators manually reviewed case files to determine connections between each clue in the case files. Using their gut and instinct, the investigators could guess at which individuals seem suspicious and further investigate those individuals. This process was very tedious and time consuming, as well as inaccurate due to the large number of documents and clues in each case file to review to determine whether an individual was suspect. Automated technologies are not to provide a satisfactory replacement and not able to provide an accurate replacement.
One technique that is employed can be to perform a criminal history background check, such as by reviewing court records. However, this provides limited information. Some states may seal or purge criminal records. Some individuals or entities engaging in illegal activity or planning to engage in illegal activity may not yet have a criminal history record. Due to such limitations, a simple criminal history background check may be a poor indicator for an individual's risk of illegal activities.
Some aspects feature a computer system configured to provide an interactive user interface for generating feedback to AI models and for efficient investigations, the computer system comprising: one or more computer readable storage devices configured to store computer readable instructions; a communications interface configured for data transmission; and one or more processors. The one or more processors are configured to execute the plurality of computer readable instructions to cause the computer system to perform operations comprising: accessing data associated with an entity; applying a plurality of models to the data to generate a plurality of assessments; and transmitting, through the communications interface, user interface data useable for rendering an interactive user interface. The interactive user interface includes: in a first user interface portion, individually selectable indications of a plurality of factors and indications of associated assessments; and a second user interface portion that is dynamically updateable to display, in response to user selections of the individually selectable factors, data associated with selected factors.
The computer system of the above paragraph can include one, any combination of, or all of the following features. The models can be artificial intelligence (AI) models. The AI models include at least one random forest model, supervised learning model, or classification model. The one or more processors are configured to execute the plurality of computer readable instructions to cause the computer system to perform further operations comprising: receiving a first analyst evaluation of a first risk factor; and updating a first AI model based at least in part on the first analyst evaluation, wherein the first AI model is used to generate a first risk assessment for determining an AI evaluation of the first risk factor. Accessing data associated with the entity includes accessing at least 10 databases storing a total of at least ten thousand data entries, the plurality of assessments includes at least 10 assessments, and different databases of the at least 10 databases are used for generating the plurality of assessments. Applying a plurality of models to the data to generate a plurality of assessments includes applying a first model to the data to generate a first assessment of a first factor, the individually selectable indications of a plurality of factors includes a textual description of the first factor, and the indications of associated assessments includes a color coded indicator of the first assessment. The one or more processors are configured to execute the plurality of computer readable instructions to cause the computer system to perform further operations comprising: generating a combined risk assessment based on a weighted combination of the plurality of assessments. The one or more processors are configured to execute the plurality of computer readable instructions to cause the computer system to perform further operations comprising: storing, in an archive, at least two of: a first AI evaluation based at least in part on one of the plurality of assessments for a first factor, an analyst evaluation of the first factor, or the data used to generate a first assessment for the first factor. The one or more processors are configured to execute the plurality of computer readable instructions to cause the computer system to perform further operations comprising: generating, using the data associated with the entity, a graphical visualization of the data, where the graphical visualization includes at least one graph, table, web, or chart. The graphical visualization includes at least one: web indicating relationships between the entity and other entities, wherein the web is dynamically configurable by the user to extend to a variable number of degrees of connections; chart or table comparing transaction data reported by the entity against transaction data compiled from the one or more internal databases of transactions by the entity; or a chart visualizing a comparison or breakdown of categories of transactions. A first model of the plurality of models is applied to a subset of the data to generate a first assessment of the plurality of assessments, and the one or more processors are configured to execute the plurality of computer readable instructions to cause the computer system to perform further operations comprising: receiving a user selection of a first risk factor that is related to the first assessment, and in response to receiving the user selection of the first risk factor, generating, using the subset of the data, a graphical visualization of the subset of the data. The one or more processors are configured to execute the plurality of computer readable instructions to cause the computer system to transmit the user interface data in response to at least one or a combination of the plurality of assessments indicating a high risk. Accessing data associated with an entity includes searching one or more databases for at least one of: asset transfer restrictions against the entity, travel restrictions against the entity, or a number of legal warrants or court orders against the entity. Accessing data associated with an entity includes searching one or more databases for at least one of: transactions or relationships between the entity and public figures or celebrities, transactions or relationships between the entity and government figures, or transactions or relationships between the entity and other entities known to or at high risk for involvement with illegal activity. Applying a plurality of models to the data to generate a plurality of assessments includes comparing at least one of: a frequency of transactions by the entity to a reference frequency of transactions, a quantity of transactions by the entity to a reference quantity of transactions, methods used by the entity to make transactions against reference methods of making transactions, or internal or private data records about the entity to public data records about or reported by the entity. Applying a plurality of models to the data to generate a plurality of assessments includes determining at least one of: a distance between a physical address associated with the entity and a physical address of a business used by that the entity, or whether addresses or contact information associated with the entity is shared by other entities. The entity and other entities are engaged in a same field of practice, and wherein applying a plurality of models to the data to generate a plurality of assessments includes at least one of: comparing registrations or licenses of the entity to registrations or licenses of the other entities, or comparing transactions of the entity to the other entities. Applying a plurality of models to the data to generate a plurality of assessments includes applying a first set of models to generate a first plurality of assessments for a first group of risks in a first context. The one or more processors are configured to execute the plurality of computer readable instructions to cause the computer system to periodically, randomly, or routinely perform further operations comprising: accessing updated data associated with the entity, and applying a second plurality of models to generate a second plurality of assessments for a second group of risks in a second context. The interactive user interface further includes a third user interface portion that is dynamically updateable to display, in response to the user selections of the individually selectable factors, AI evaluations of the selected factors.
Accordingly, in various embodiments, large amounts of data are automatically and dynamically calculated interactively in response to user inputs, and the calculated data is efficiently and compactly presented to a user by the system. Thus, in some embodiments, the user interfaces described herein are more efficient as compared to previous user interfaces in which data is not dynamically updated and compactly and efficiently presented to the user in response to interactive inputs.
Further, as described herein, the system may be configured and/or designed to generate user interface data useable for rendering the various interactive user interfaces described. The user interface data may be used by the system, and/or another computer system, device, and/or software program (for example, a browser program), to render the interactive user interfaces. The interactive user interfaces may be displayed on, for example, electronic displays (including, for example, touch-enabled displays).
Additionally, it has been noted that design of computer user interfaces “that are useable and easily learned by humans is a non-trivial problem for software developers.” (Dillon, A. (2003) User Interface Design. MacMillan Encyclopedia of Cognitive Science, Vol. 4, London: MacMillan, 453-458.) The various embodiments of interactive and dynamic user interfaces of the present disclosure are the result of significant research, development, improvement, iteration, and testing. This non-trivial development has resulted in the user interfaces described herein which may provide significant cognitive and ergonomic efficiencies and advantages over previous systems. The interactive and dynamic user interfaces include improved human-computer interactions that may provide reduced mental workloads, improved decision-making, reduced work stress, and/or the like, for a user. For example, user interaction with the interactive user interfaces described herein may provide an optimized display of time-varying and report-related information and may enable a user to more quickly access, navigate, assess, and digest such information than previous systems.
In some embodiments, data may be presented in graphical representations, such as visual representations, such as charts and graphs, where appropriate, to allow the user to comfortably review the large amount of data and to take advantage of humans' particularly strong pattern recognition abilities related to visual stimuli. In some embodiments, the system may present aggregate quantities, such as totals, counts, and averages. The system may also utilize the information to interpolate or extrapolate, e.g. forecast, future developments.
Further, the interactive and dynamic user interfaces described herein are enabled by innovations in efficient interactions between the user interfaces and underlying systems and components. For example, disclosed herein are improved methods of receiving user inputs, translation and delivery of those inputs to various system components, automatic and dynamic execution of complex processes in response to the input delivery, automatic interaction among various components and processes of the system, and automatic and dynamic updating of the user interfaces. The interactions and presentation of data via the interactive user interfaces described herein may accordingly provide cognitive and ergonomic efficiencies and advantages over previous systems.
Various embodiments of the present disclosure provide improvements to various technologies and technological fields. For example, as described above, existing system and technology for reviewing data (including, e.g., in memory databases) is limited in various ways (e.g., manual data review is slow, costly, and less detailed; data is too voluminous; etc.), and various embodiments of the disclosure provide significant improvements over such technology. Additionally, various embodiments of the present disclosure are inextricably tied to computer technology. In particular, various embodiments rely on detection of user inputs via graphical user interfaces, calculation of updates to displayed electronic data based on those user inputs, automatic processing of related electronic data, and presentation of the updates to displayed images via interactive graphical user interfaces. Such features and others (e.g., processing and analysis of large amounts of electronic data) are intimately tied to, and enabled by, computer technology, and would not exist except for computer technology. For example, the interactions with displayed data described below in reference to various embodiments cannot reasonably be performed by humans alone, without the computer technology upon which they are implemented. Further, the implementation of the various embodiments of the present disclosure via computer technology enables many of the advantages described herein, including more efficient interaction with, and presentation of, various types of electronic data.
Additional embodiments of the disclosure are described below in reference to the appended claims, which may serve as an additional summary of the disclosure.
In various embodiments, systems and/or computer systems are disclosed that comprise a computer readable storage medium having program instructions embodied therewith, and one or more processors configured to execute the program instructions to cause the one or more processors to perform operations comprising one or more aspects of the above- and/or below-described embodiments (including one or more aspects of the appended claims).
In various embodiments, computer-implemented methods are disclosed in which, by one or more processors executing program instructions, one or more aspects of the above- and/or below-described embodiments (including one or more aspects of the appended claims) are implemented and/or performed.
In various embodiments, computer program products comprising a computer readable storage medium are disclosed, wherein the computer readable storage medium has program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform operations comprising one or more aspects of the above- and/or below-described embodiments (including one or more aspects of the appended claims).
Introduction
In some cases, law-abiding individuals or organizations may want to detect or screen for client entities who are likely involved with illegal activity such as money laundering so that the law-abiding individuals or organizations can avoid working with those clients. However, manually investigating every client can be very tedious and time consuming, and many factors can be overlooked. Accordingly, disclosed herein are systems and methods for automatically analyzing and detecting clients who may be engaged in illegal activity, and doing so in a faster, cost effective, and more accurate manner. A system can, on a large scale, process records to automatically analyze those records for clues to detect possible illegal activity based on various combinations of indicators. To improve accuracy and continually adapt to changing techniques used by individuals to evade detection, the system can include an artificial intelligence component that continually improves through feedback.
Disclosed herein are embodiments that automate an investigator's tasks. Some embodiments enable a computer system to provide at least a partial substitute for the “gut” or “instinct” determination. The automation is accomplished through rules that are applied to records in a database and other information to determine weighted risk assessments. The disclosure includes many exemplary rule sets that go beyond simply performing a criminal background check. Instead, the rule sets can produce more accurate investigation results by taking into consideration combinations of indicators and new sources of data based on different contexts.
Some organizations may provide goods or services to clients. Some of those organizations may provide goods or services that, although generally used by law-abiding citizens, can also be used for illegal activity. For example, gun stores may sell guns for hunting and self-defense, banks may provide various financial services to the general public, chemical suppliers may sell chemicals for research and industrial uses, etc. The gun stores may want to avoid selling guns to people who will use the guns for violence, banks may want to avoid working with money launderers, and chemical suppliers may want to avoid selling to terrorists. Often times, simple state or federal background checks can be insufficient. To allow for a clearer understanding of the concepts, this detailed description is discussed with respect to examples of a bank that wants to avoid taking on clients who are likely to engage in money laundering. However, it will be understood that the technology and techniques disclosed herein can be extended to any type of organization with any type of client relationship.
The law-abiding organizations may desire to document their diligence in investigating their clients. For example, the organizations may document their investigation of each client to justify engaging in a business relationship with the client. The organizations can establish business relationships with low risk clients who pass the investigation and refuse to work with clients who are deemed high risk. If the client is later discovered to engage in illegal activities, then the organization can justify the bona fide business relationship with (what was thought to be) a low risk client and avoid complicit liability.
The system and techniques disclosed herein allow for a more accurate, comprehensive, and faster analysis of a client. The amount of data used for each investigation can be increased for a more accurate result. Databases including thousands or millions of records or more can be searched. By automating certain parts, organizations can hire fewer investigators. Some or most investigations can now be completed by a computer system without human analysis and feedback, greatly reducing the number of investigations performed. Furthermore, the speed of investigations is accelerated. In addition to reducing the amount of manual review, a well-designed user interface allows for analysts to more quickly analyze data that, when presented in other formats, would be analyzed much more slowly. See Dillon, A. (2003) User Interface Design. MacMillan Encyclopedia of Cognitive Science, Vol. 4, London: MacMillan.
The system also allows for a standardized evaluation process to be performed on a plurality of clients. Available data from multiple databases is processed and presented to an analyst, even if the analyst would not have otherwise thought to examine the data. Decision making about whether to accept, reject, or terminate a relationship with a client can be speeded up.
Overview
The user input 102 can indicate a client to be analyzed to assess a risk of the client's participation in illegal activity, such as laundering. The search module 101 can search for information about the client in various databases 103 and the internet 105. The search results from the various databases 103 and/or the internet can be provided to a plurality of AI models 109. Each AI model 109 can be configured to assess a specific type of risk indicator and generate risk assessments 111. The individual risk assessments from each AI model 109 can be combined into a combined risk assessment. The combined risk assessment can be compared against a threshold amount to determine a risk. If the client's combined risk assessment indicates a low risk, then the client can be approved. Otherwise, if the client's combined risk assessment indicates a high risk, or in some embodiments, if one or a combination of individual risk assessments indicate a high risk, then a user interface 113 can be generated to facilitate an analyst's review of the data. The user interface 113 can present data 117 for analyzing one or more risk factors and AI evaluations 119 of risk factors. The analyst can review the data 117 and provide an analyst evaluation 121 of one or more risk factors. The analyst evaluation can be used as feedback to the AI model modifier 125. The AI models 109 can be modified based on the feedback. The AI and analyst risk evaluations and data for analyzing the risk factors can be stored in an archive 129. Although the user input 102 for searching is described in the context of a potential client for investigation, it will be understood that the search can be performed on any entity in any context, regardless of whether there is a potential “client” relationship.
User input 102 can indicate a client to be analyzed for a likelihood of illegal activity such as laundering. The user input can include a name of the client, government issued-identification numbers (such as a driver's license number, social security number, military ID number, national ID number, passport number, tax ID number, business entity ID number, etc.), a birthday, online usernames, addresses, and/or other personally identifying information. The type of user input provided can also affect the types of databases 103 used, the types of AI models 109 applied, the types of risk assessments 111 determined, and the types of risk factors 115 analyzed. For example, different databases 103, AI models 109, risk assessments 111, and/or risk factors 115 can be analyzed if the identifier indicates that the entity is a person (such as with a driver's license, name, passport number, or social security number) as opposed to a business (such as with a business name, tax ID number, etc.).
The user input 102 can also indicate a context for the client. The context can also affect the types of databases 103 used, the types of AI models 109 applied, the types of risk assessments 111 determined, and the types of risk factors 115 analyzed. For example, a first combination of databases 103, AI models 109, risk assessments 111, and risk factors 115 can be used to evaluate new clients. A second combination of databases 103, AI models 109, risk assessments 111, and risk factors 115 can be used to evaluate a current client. In some cases, the analysis for the “current client” context can be periodically, randomly, according to selection algorithms, or routinely run for all clients, such as daily, weekly, monthly, annually, or at other times with or without a user-provided indication of context.
A search module 101 can search multiple databases 103 for data about the client. Multiple databases 103 including databases 1 through M, where M can be any number, can be searched for information about the client. The databases 103 can include court databases, criminal history databases, and government databases. The databases 103 can also include databases of transactions involving the client, such as assignments of property, transfers of value, wires, checks, deposits, etc. including who the client transacted with. The databases 103 can also include social networks, ancestry databases, community databases, and other databases indicating relationships between individuals. The databases 103 can also include communication databases including emails, phone calls, communication records, text messages, etc. The databases 103 can also include private databases of commercial entities. For example, a group of commercial entities (such as a group of banks, a group of gun shops, or a group of chemical makers) may pool their private databases with other entities and allow access for the purpose of vetting clients. The databases 103 can also include public record databases, employment databases, news databases, and other archives. The databases 103 can also include commercial entity registration databases, securities reports and filings databases, meeting minutes databases, vote and decision databases, and other databases that may indicate an affiliation or business relationship with the client. The databases 103 can also include shipping databases, manifest databases, transportation databases, mail databases, and other databases recording the transfer of goods, product, or cargo to/from the client. Any other type of database that may have information about the client, the client's relationships with others entities, information about the other entities, and/or reference data for demographic groups and normal indicators can be searched. In the context of a current client, a database of the current client's history of activities, orders, and transactions with the organization can also be searched. In some embodiments, at least 10, 20, 30, 40, 50, 60, or any number of different databases 103 can be searched.
Additionally, the search module 101 can perform a search for the client on the internet 105, including searching databases accessible via the internet 105. Additionally, the internet can search for the client on news sites, online archives, search engines, social media sites, etc. The internet can also be used to search for and determine the context of an internet article that the client is found in, such as whether the client is found in news articles, political articles, popularly viewed posts, near keywords indicating a positive, negative, or controversial context, etc.
A data stream module 104 can be used to process the data from the databases 103. The data stream module 104 can be configured to track and cross-search the databases 103 and/or the internet 105. For example, to determine how many transactions John Doe has conducted over the past year, the data stream module can search a first database for a first company's transactions with John Doe and also search a second database for a second company's transactions with John Doe, and the data stream module 104 can generate an output including the de-duplicated union of data from the first database and from the second database. As another example, to search for the partners with equity in BigCo., the data stream module 104 can search a first database of companies and partners for data about the partners of BigCo., search a second database of subsidiaries of businesses to find subsidiaries of BigCo., search the first database again for the partners of the subsidiaries, and then generate an output including the de-duplicated union of data. The data stream module 104 can be used to provide a more comprehensive, more accurate, and smaller sized (de-duplicated) data set. In some embodiments, the data stream module can provide access to a plurality of data sets, streams of data, and/or streams of search results.
The AI evaluation module 107 can be configured to provide different combinations of the data from the databases 103 to different AI models 109. There can be a plurality of AI Models 1 to N, where N can be any number. Some models such as AI Model 3 can be used to analyze data from one database such as Database 3. Other models such as AI model 2 can be used to analyze combinations of data from a plurality of databases. Data from some databases such as Database 1 can be provided to a plurality of AI models. Each AI model can be configured to analyze a risk factor, some examples of which are provided in later sections of this disclosure. The AI models 109 can include, for example, random forest models, learning based models, classification models, nearest neighbor models, supervised or unsupervised learning models, regression models, clustering models, neural network models, reinforcement models, decision tree models, support vector machine models, association models, rule based models, or other types of AI models. Different types of AI models can be used for analyzing different risk factors.
Each AI model 109 can generate a risk assessment 111 for a risk factor 115. The risk assessment can be a Boolean variable (such as risky or not risky), a number (such as 0 to 100), etc. The risk assessments 111 can also include a combined risk assessment. The combined risk assessment can be a weighted combination of each individual risk assessment 1 through O, where O can be any number. In some embodiments, the number of risk assessments can match the number of AI models. In some embodiments, there can be a different number of risk assessments and AI models, for example, if an average output of two different AI models are used to generate a single risk assessment for a risk factor.
One or a combination of risk assessments 111 can be used to determine if the client is a low risk client. If the combined risk assessment indicates that the client is a low risk client (for example, if the combined risk assessment is compared to and determined to be less than a threshold combined risk assessment), then the client can be approved, and the organization can engage the client and provide products or services. Furthermore, the data used for analysis and the resulting risk assessments indicating a low likelihood of illegal activity such as laundering can be stored in an archive. The archive data can be preserved, such as using block chain, distributed ledger, cumulative checksums, rewrite restrictions, encryptions, or other technology to prevent later tampering.
If the combined risk assessment indicates that the client is a high risk client (for example, if the combined risk assessment is compared to and determined to be higher than a threshold combined risk assessment, or if certain one or combinations of individual risk assessments exceed thresholds), then the client can be evaluated by the AI as high risk and an interactive user interface 113 can be generated to facilitate a detailed review of data about the client.
In the user interface 113, a plurality of risk factors 115 from Risk Factor 1 to Risk Factor P, where P can be any number, can be displayed in a first portion. In the first portion, the risk factors 115 can be color coded based on risk assessments and/or AI evaluations, arranged in order of risk assessment and/or AI evaluation, or have other visual indicators that indicate a degree of risk assessed for each risk factor 115. For example, the first portion of the user interface can include a textual description of the risk factors and a color coded indicator based on the assessment(s) used for evaluating each risk factor. Additionally or alternatively, a color coded indicator based on the AI evaluations for each of the respective risk factors can be included in the first portion. A user can select one of the risk factors 115, and the user's selection can cause data 117 to be displayed in a second portion of the user interface. The data 117 can include visual data such as charts, graphs, tables, webs, etc. generated by the data visualization generator. The data 117 can include data about the client as well as contextual information (e.g., about other clients or demographics) for comparison so that the risk factor can be analyzed. An example of color coded risk factors 307 in a first portion of a user interface is shown in the menu 303 of
The data 117 can be dynamically generated for display in response to a user's selection of a risk factor (such as Risk Factor 2) and displayed in a second portion of the user interface. The data 117 can include the data from the databases used in assessing the selected risk factor. Accordingly, from among the plurality of databases 103, if a smaller subset (such as Database 1 and Database 2) of the plurality of databases is relevant to the selected risk factor (such as Risk Factor 2), then the data 117 can include the data relevant to the client from the smaller subset (such as Database 1 and Database 2) of the plurality of databases and omit information from the other databases (such as Database 3 through Database M).
Data in databases can be stored in a form that is easy for computer processors to process but difficult for an analyst to quickly analyze. The data visualization generator can, for some types of data relevant to the client from the smaller subset of the plurality of databases, convert the data into a form that is more easily understood and analyzed by humans. Accordingly, the data can be reformatted for display. Furthermore, the data in the databases can be too voluminous for a person to review in a reasonable amount of time. For some types of data for some types of risk factors, the relevant data from the relevant databases can be aggregated before being displayed as data 117. For example, totals, averages, distributions, or trends over time of large amounts of data can be shown instead of the thousands, millions, or billions of underlying data entries.
The data 117 can be dynamically generated. In some embodiments, the data 117 can be generated for each risk factor for a client in response to a determination that the client is a high risk client. This way, the data can be ready for quick display when a user selects a risk factor. In some embodiments, the data 117 can be generated almost instantly or within several seconds for a selected risk factor in response to a user selection of a risk factor, even if there are very large amounts of underlying data entries. Either way, generating the visualization of data can be skipped for low risk clients (which can make up over 90% or 99% of clients), saving substantial amounts of processing time and power, and freeing up computing resources for performing detailed analysis of the data used for evaluating the high risk clients. An example of data 117 for analyzing a risk factor is shown as data 305 in
In a third portion of the user interface, an AI evaluation 119 of risk, such as high risk, medium risk, or low risk, can be generated. The AI evaluation 119 can include evaluations of whether one or more specific risk factors indicate that the client is likely to engage or have engaged in laundering. The AI evaluation 119 of a risk factor can be determined based on one or more risk assessments 111 generated by the AI models 109. In some embodiments, one AI model 109 (such as AI Model 1) can be used to generate one risk assessment 111 (such as a number from 0 to 100 indicating increasing amounts of risk) for a risk factor, and an AI evaluation 119 will indicate that the risk factor is “Risky” if the AI assessment 111 is more than a threshold (such as 50). For example, if a risk factor is a number of companies owned or managed by the client, and if the client owns or manages a large number of companies, then the AI evaluation 119 can indicate that, at least for the specific risk factor, the client is high risk because of the client's ability to use multiple companies to move assets around. In some embodiments, the AI evaluation 119 can automatically update in response to a user selection of a risk factor 115 to show the AI evaluation 119 for the selected risk factor. A summary AI evaluation 119 can include a summary and overall likelihood that the client is or will participate in laundering. The summary can include, for example, which risk factors indicate that the client is likely to engage in laundering. The summary can also include examples of the data that indicates that the client is likely to engage in laundering. For example, the summary can show comparisons of the client to other people or groups, show data values related to the client that are outside of normal ranges or otherwise suspicious. As an example of an AI evaluation, an example indicator 309 of “Risky” or “Not risky” can be selected as shown in
In a fourth portion of the user interface, an analyst evaluation 121 can be provided. The analyst evaluation can be provided for each risk factor. The analyst evaluation can additionally or alternatively be provided for the overall likelihood that the client is or will engage in laundering. An analyst can review the data 117 and the AI evaluation 119. Based on the analyst's review, the analyst can confirm or change the automatically evaluated risks. If the analyst confirms that the client is indeed a high risk client, then the organization can decline to engage with (or, in the current client context, decline to continue to engage with) the client. If the analyst changes the evaluation and indicates that the client is a low risk client, then the organization can engage with the client. As an example of an analyst evaluation 121, an analyst can select an example evaluation option 311 of “Risky” or “Not risky” as shown in
The risk evaluation including the AI evaluation 119, the analyst evaluation 121, and data for analyzing the risk factors can be stored in an archive 129. The risk evaluation can include indications of the databases 103 searched, the relevant data from the databases found as a result of a search, the AI models 109 used and the risk assessments 111, the risk factors 115, the data 117 for analyzing the risk factors, the AI evaluation 119, and the analyst evaluation 121, including whether the user confirmed or changed the AI evaluation 119. The archive data can be preserved, such as using block chain, distributed ledger, cumulative checksums, rewrite restrictions, encryption, or other technology to prevent later tampering.
The analyst evaluation 121 can be provided as feedback to the AI model modifier 125. For each risk factor 115 that the analyst evaluation 121 confirmed in AI evaluation 119, feedback can be used to update the corresponding AI model 109, such as by providing positive reinforcement feedback, changing coefficient values, adding a verified data sample and retraining the model, etc. For each risk factor 115 that the analyst evaluation 121 differed from the AI evaluation 119, feedback can be used to update the corresponding AI model 109, such as by providing negative reinforcement feedback, changing coefficient values, adding a verified data sample and retraining the model, etc.
In some cases, the analyst can review the data 117 for analyzing each risk factor in view of new training, new realizations, and/or new insights. The analyst's decision making process can change over time and adapt as clients develop new strategies for hiding their indications of engaging in illegal activity such as laundering. Accordingly, the AI models 109 can be dynamically updated over time based on feedback such that the AI evaluations 119 and approvals 127 remain accurate over time, even as tactics change.
The system described in
The navigation menu 203 can be interacted with to show different risk indicators. For example, as shown in
The display area 205 shows general information about the searched entity “SmallCo.” An area showing registration information can include alerts for the entity (such as travel restrictions, bank restrictions, warrants, sanctions, trade restrictions, evaluated risks of laundering, etc.), the entity name, area of practice or industry that the entity is engaged in, an address, date founded, and other information.
The area showing annual revenue data can include a table or other visualization of data such as revenue amounts, dates, and sources of the data, which can come from one or more different databases. In some cases, the different revenue sources can come from different databases. For example, a database of tax or securities filings can be searched to report a first amount of revenue, a database of bank transactions can be searched to report a second amount of revenue, and a third database of court filings can be searched to report a third amount of revenue. The data from the different databases can be simultaneously shown in proximity to each other so that inconsistencies can be more easily recognized by an anlyst. Otherwise, an analyst performing manual review most often will not recognize the inconsistencies when reviewing massive amounts of data. Also, when different analysts are assigned to review the different databases in an attempt to expedite review speed, none will recognize the inconsistency with other databases. The example table allows for easy recognition of significant discrepancy between transactions reported by the client for tax documents and transactions reported by a bank, indicating a likelihood of laundering.
The area showing account data can include a table or other visualization of data such as accounts of the entity with various institutions (such as checking or saving accounts), along with data about the date opened, date closed, balances, etc. Abnormal amounts of accounts and/or abnormal timings of accounts can indicate a likelihood of laundering.
The area showing owners and investors can include a table or other visualization of data such as the owners, partners, lenders, equity holders, and/or other beneficiaries of the entity. Data such as identification numbers, names, titles, amount of equity, and income for the owners and investors can also be collected from one or more databases and displayed. This can allow for recognition and familiarity of the owners and investors. Furthermore, due to the simultaneous showing and/or proximity of the data (e.g., on a same screen), suspicious patterns can be detected. For example, the area of practice is indicated as a laundromat, which is a cash-service industry. The income of the owners can be compared to models of what is typical for that type of industry. For example, it can be recognized that laundromats usually are smaller investment projects that do not require multiple owners or investors, and that the income of each owner or investor in the project can be compared to what is typical for the area of practice. The multiple investors shown can indicate a likelihood of laundering, as well as a mismatch between income and reported tax data.
In the menu 303, the individual risk factors (e.g., Risk Factor 1 through Risk Factor N, where N can be any number) can be automatically color coded based at least in part on a risk assessment and/or an AI evaluation of the risk factor. For example, a risk factor can be indicated with a color code ranging from green to red on a color spectrum based on a risk assessment used for evaluating the risk factor. As another example, a risk factor can be color coded as green if an AI evaluation for the risk factor is low risk, yellow if an AI evaluation for the risk factor is unknown or moderate risk, and red if an AI evaluation for the risk factor is high risk. Other visual indicators, such as symbols, patterns, or stylizing can also be used. Other visual indicators can include arranging the risk factors from highest risk to lowest risk. The visual indicators can allow an analyst to quickly focus on the more critical data. The analyst can, in some embodiments, skip reviewing data related to risk factors that were determined by the AI evaluation to be low risk, saving substantial amounts of time. Accordingly, an analyst can focus attention on the more critical data for a greater number of clients being investigated and make more accurate evaluations.
In the display area 305, data for analyzing each individual risk factor can be displayed. For example, Risk Factor 1 can be “Recent Corporate Changes” and data for analyzing this risk factor can include the table and timeline shown in
For each risk factor in the display area, AI evaluation indicators of the risk factor and analyst evaluation options of the risk factor can also be shown. The AI evaluation can automatically indicate a result generated based at least in part on the output of one or more AI models. For example, an indication of “Risky” or “Not Risky” can be automatically selected to indicate the AI evaluation.
An analyst can enter an analyst evaluation 313, such as by confirming or selecting the other of “Risky” or “Not Risky.” The analyst evaluation 313 can also include freely typed comments. The analyst evaluation 313 can also include one or more pre-populated reasons for confirming or changing the evaluation. The pre-populated reasons can change depending on the analyst's selected evaluation and change depending on whether or not the analyst evaluation is different from the AI evaluation. The pre-populated reasons can also be different for each risk factor and serve to remind the analyst of various reasons to consider when evaluating the presented data. In some embodiments, when the analyst evaluation is entered, the color coding of the associated risk factor can update. For example, if Risk Factor 1 in the menu 303 is green because the AI evaluation of Risk Factor 1 indicates low risk, then Risk Factor 1 in the menu 303 can change to red when the analyst changes the evaluation to high risk.
The example display area 305 also includes data for analyzing a second risk factor, such as Risk Factor 2. In some embodiments, the data for analyzing risk factors can be individually displayed in the display area 305 in response to a selection of a specific risk factor from the menu 303. In some embodiments, the data for analyzing a plurality of risk factors can be simultaneously shown in the display area 305, and selecting a specific risk factor from the menu 303 can cause the corresponding data for analysis to appear in focus in the display area 305, such as by scrolling to the corresponding data.
An example of a second risk factor is whether or not there are changes to addresses. Businesses and people usually keep their addresses for longer periods of time, and more frequent changes can indicate a higher risk of laundering. An AI model can be configured to analyze the frequency of address changes of the client in comparison to other similar entities. The display area can also show or include links to show street views of the address and/or a map of the address. This can allow the analyst to see if the addresses are in typical locations for people/businesses and/or evaluate if the street view shows an actual residence or building. If not, then the address can be suspect, and the risk factor can indicate a higher risk of laundering.
Risk factors such as “Same Addresses as Other Entities,” “Same Phone Numbers as Other Entities,” and/or same identifiers (such as tax identifiers, registration numbers, emails, etc.) as other entities can be used to determine a risk of money laundering. An AI model can be configured to analyze data to determine increased likelihoods of laundering when more addresses, phone numbers, and/or identifiers of the client are shared with other entities. This can be the case when, for example, a money launderer uses a plurality of entities to move assets around while using a single physical address, phone number, email, or other identifier for ease of coordination and management. The user interface 400 can show one or more tables or visualizations of the addresses, phone numbers, or identifiers associated with the client and, for each entity having a phone number, address, or identifier that matches the client, one or more of: notes about problems or alerts for the entity, an ID (such as a tax number, registration number, social security number, etc.) for the entity, a name of the entity, a description of the entity, a field of practice or occupation of the entity, a category of the entity, and/or other data. The visualization can also show a number of entities matching each address, phone number, and/or identifier associated with the client. The information can be shown simultaneously and/or proximately to each other.
In the illustrated example, the addresses associated with the client matches 101 other entities, and the phone numbers associated with the client matches 121 other entities. An AI model can be configured to determine, for example, that based on the large number of matching entities, the client has a higher risk of engaging in laundering.
An analyst can be configured to analyze the presented data. For example, a shopping center or mall may have a general address used by a plurality of stores located within the shopping center or mall, and this can be the case without raising suspicion. As another example, an analyst may recognize that a headquarters of a large company may have a phone number that is shared with subsidiaries of the same company, or that a dedicated call center may provide support for a number of different companies. The analyst can review the AI evaluation 411 and confirm or change the evaluation. The analyst can also provide comments, reasons, or other analysis about the analyst's evaluation 413. Although one AI evaluation 411 and one analyst evaluation 413 option is shown for the combination of phone numbers and addresses, in some embodiments, there can be separate evaluation options.
The user interface 500 includes data for analyzing a first risk factor, “Unusual Partner Data.” The data can include a table and/or other visualizations comparing demographic data about the partners, investors, or beneficiaries of the client. An AI model can be configured to analyze people investors of different business and perform a comparison of income data for the partners, investors, or beneficiaries of the client to partners, investors, or beneficiaries of similar demographics. Demographic information can include, for example, age, education, location, etc. In the example, the age of each person is shown in comparison to average ages of typical partners in the laundromat business with similar backgrounds. It can be seen that the ages are typical or within a reasonable variation, otherwise laundering may be indicated by using unusual people (such as children too young to invest in businesses) to facilitate transactions. The income of each person is also shown in comparison to average incomes of typical partners in the laundromat business with similar backgrounds. It can be seen that the incomes of Jane Doe and John Doe are within a reasonable variation of owners of laundromats in view of fractional equity, but Jordan Belfort and Al Capone are associated with incomes far in excess of typical laundromat owners, even accounting for or regardless of age and education. If an AI model determines a high risk for this risk factor, an analyst can review the data and confirm the high risk, noting the unusual incomes as risk factors.
The user interface 500 also includes data for analyzing a second risk factor, “Suspicious Extended Relationships.” The user can select a number of degrees of connections to analyze, which is currently shown as 4. Based on the number of selected degrees, connections of the client can be determined and displayed. For example, for the selected client SmallCo., the first degree can be the investors, partners, and beneficiaries of SmallCo. and/or entities that are business partners with, have a parent or subsidiary relationship with, a supply or purchase relationship with, or other type of association with SmallCo. The second degree of connections can include entities, investors, partners, and beneficiaries further associated with the entities, investors, partners, and beneficiaries from the first degree of connections. Connected entities, investors, partners, and beneficiaries may be collectively referred to as “connected entities.”
The members of each degree of connections can be shown in a table 511, nested menu 513, web 515, or other visualization, along with one or any combination of: restrictions, alerts, notes, names, identification numbers, industry, title, dates of participation or founding or birth, addresses, contact info, the type of association used to determine the connection relationship, or other information. Additionally, visual indications (such as colored circles) can be used to flag the connected entities for various situations. The flags can indicate if there are any analyst notes about or restrictions against a connected entity. The flags can also indicate if a connected entity is a previously rejected client, such as based on an AI evaluation or analyst evaluation performed in the past. The flags can also indicate if a connected entity is a public figure, such as a politician or celebrity. For example, databases and/or the internet can be searched to determine if the entity was reported in news sites, government databases, top listed search pages, popular social media sites, etc. The flags can also indicate if the connected entity is a foreign entity. An AI model can be configured to search for and analyze the connected entities of a client and determine the flags for the connected entities.
In the example shown in the user interface, a nested menu 513 shows connected entities of the client being analyzed. A connected entity in the nested layer can be selected to reveal a next degree of connection that includes people. Furthermore, in response to the selection, the web can be displayed and/or updated to show equity, association, or relationship between the selected entity and the next degree of connected entities, including indicators of the amount of equity, association, or relationship. The web 515 can use different icons to indicate people, businesses entities, etc. The web can also be used to determine if the partners of the client also have equity in other entities that have a relationship with the client. The web can also be used to easily see if the client moves assets through various connections back to the same client and/or accounts or entities owned by the same people, for example, if the web shows a plurality of links that circle back to the same entities.
By reviewing the data provided for analyzing suspicious extended relationships and the interactive web and nested menu, an analyst can make an evaluation about whether or not the data indicates a risk of laundering.
Other examples of data that can be shown in the user interface include: data about connected entities, the experience of connected entities, the age of connected entities, the income of connected entities, the addresses or citizenships of connected entities, equity of connected entities, dates of transactions or changes in equity of connected entities, corporate changes of the connected entities, etc. Furthermore, average, normal, or statistical distributions of the same types of data can be shown to provide a reference for comparison. The average, normal, or statistical distribution of data can be used to assist the analyst or AI models in determining unusual relationships.
The display area 605 includes data for a risk factor based on internal versus reported revenue consistency. This can include data from two different sources or different databases, for example, an external database of taxes reported by the client and an internal database of transactions processed for or with the client. Other examples of databases include databases from financial institutions, shipping databases, customs databases, etc. The data for analysis can be dynamically presented for a default time period. The time period can be changed by the analyst, and the displayed data can update in response. The example data shows that the reported transactions are inconsistent with and substantially less than the transactions sent and received. The data for analysis can be shown in a table and as a graph or other visualization. An AI model can be configured to determine which sources and time periods of revenues to compare for evaluating a likelihood of laundering. The sources and time period selected by the AI model can be presented as the default sources and default time period for analysis. An analyst can review the data for different sources and/or different time periods and provide an analyst evaluation. The analyst's decision may recognize situations where inconsistencies are normal, such as when figures from a less inclusive database are lower than figures from a more inclusive database, the figures from a report are calculated according to a different standard, etc. Based on the displayed data, the AI and/or analyst can recognize that the reported transactions are the net value of the transactions instead of an actual amount of transactions, and appropriate evaluations can be made.
The display area 605 can also include data for analyzing the client's transaction consistency with an industry. Transactions for various types of industries or entity types can be compiled, visually presented, and compared to the transaction data determined for the client. For example, the data shows that typical laundromats receive transactions valued at an average of 35,739, but the client's received transactions are valued at 8.6 times this amount. Data for other similar or related categories, such as cleaners, can also be shown for reference. An analyst can select to view transactions sent, received, or both, and the data can be dynamically updated based on the analyst's selection. As shown, the transactions, regardless of sending or receiving, show comparatively excessive sending and receiving of transactions by the client. This is apparent even though the client's net transaction value (25,507) is correctly reported for taxes and within a reasonable range for laundromats. The AI model can provide an evaluation, and an analyst can confirm or revise the evaluation and enter comments based on the data about the client's transaction consistency with similar industries.
The display area 605 can also include data for analyzing the client's transaction with entities in unusual regions. One or more databases can be used to analyze a distribution of transactions and geographic locations associated with the transactions. The data can be displayed in a visualization such as a map, table, graph, web, etc. An AI model can be configured to associate certain geographic locations and/or average transaction distances for certain types of clients with increased likelihoods of laundering. For example, most entities typically transact with other local people or businesses. Transactions across farther distances, foreign countries, and/or certain places such as tax havens can indicate higher likelihoods of laundering. The geographic distribution of the client's transactions can be compared to the geographic distributions of other similar entities. An analyst can review the data and provide an analyst evaluation to confirm or change an AI evaluation, as well as provide comments.
The display area 605 can also include a visualization of data for analyzing other risk factors associated with transactions (not shown). For example, one or more databases can be used to analyze a distribution of types of entities that a client transacts with. An AI model can be configured to compare the distribution for the client with distributions for other similar entities. For example, databases can be analyzed to compile, for similar laundromats with low risks of laundering, a percentage of types of entities typically transacted with. The distribution can indicate, for example, that low risk laundromats typically have 25% of their transactions (by volume or value) with landlords or real estate companies, 25% with appliance suppliers, 25% with repair services, and 25% with other categories. This can be compared to the transactions of the client, which may be, for example, 2% with a landlord, 2% with an appliance supplier, 2% with repair services, and 94% with financial institutions, trusts, and banks. An AI model can, based on the disparity, determine a high likelihood of laundering. The data can be visually displayed for an analyst who can confirm or change the evaluation (for example, if a new laundromat just opened after securing bank loans and has not opened for business).
The display area 605 can also include a visualization of data for analyzing other risk factors associated with transactions (not shown). For example, one or more databases can be used to analyze an amount of transactions. Often, anti-laundering enforcement agencies require reporting of transactions over a certain value, such as 10,000. Accordingly, AI models can be configured to assess an usually large proportion of transactions just below the reporting requirement, such as 9,500, as high risk or an even more unusually larger proportion of transactions of even smaller amounts as high risk. Additionally or alternatively, an AI model can be configured to, based on a frequency, volume, and average size of transactions by the client in comparison to other entities, assess a risk of laundering. For example, if most laundromats typically make cash daily deposits of coins in the range of a few hundred but less than one thousand, but the client typically makes multiple deposits of 1,000 to 2,000 (well below the reporting requirement), then the AI model can still flag a high risk of laundering. As another example, the method of making transactions (such as in person transactions, transactions made with automated machines, or transactions made online) by the client can be compared to the behavior of other entities, as launderers may often avoid making transactions in person to avoid showing identification.
In the user interface 700, data for analyzing the risk factor “Transactions with High Risk Entities” can be shown. An AI model can be configured to analyze data to determine entities that the client transacted with. Data from a default date range determined to be of interested by the AI model can be shown, as well as data from some or all databases determined to be of interest by the AI model. An analyst can select different date ranges and/or databases. The analyst can select transactions sent, received, or both for analysis. The analyst can also filter out certain types of transactions. The data shown in the user interface 700 can automatically update in response to the analyst's selections.
A visualization, such as a table, graph, and/or other visualizations, can display one or more of: entities that the client transacted with, identification numbers of the entities, names of the entities, direction of transactions, value of transactions, percentage of overall transactions, and other data can be shown.
A web can show certain types of transactions, such as top transactions by value, top transactions by percent of total transactions, transactions sent and/or received, transactions from certain databases, and/or transactions from entities within certain degrees of connections to the client. One or more databases can be searched to generate the web, which can represent entities with icons (such as to distinguish people and companies). The icons can be further distinguished to indicate (such as using color or different icons) people or entities flagged as high risk. Accordingly, an analyst can easily see if the client transacts with other high risk entities. The direction of net transaction flow can be indicated on the links between icons, and the links can also indicate a value or percentage of the transaction. An analyst can interact with the web to rearrange the icons, zoom in or out, and perform other manipulations. Based on the data shown in the user interface, an analyst can confirm or change an AI evaluation of risk based on transactions with high risk entities.
The numbers of high risk or low risk AI evaluations can include all of the risk factors evaluated by the AI. In some embodiments, AI evaluations may not be presented for every risk factor. For example, AI models may not analyze some types of images, and data for analyzing a risk factor based on a street view of address of or a website of the entity can be provided to an analyst for an analyst evaluation without an AI evaluation or AI assessment. If the AI evaluates all risk factors before presenting data to the analyst, then the total number of high risk and low risk evaluations can account for all risk factors evaluated by an AI model. The total number of high risk and low risk evaluations by the analyst can be lower if the analyst has not evaluated all risk factors, for example, if the analyst skipped over risk factors deemed low risk by the AI evaluation in order to save time.
For all or some (such as the high risk) risk factors, a table 811 can summarize the class of risk, the specific risk factor evaluated, the AI evaluation of the risk factor, the analyst's evaluation of the risk factor, analyst comments, and a prepopulated reason selected by the analyst. The summary can show data for all risk factors, for the risk factors evaluated by the analyst, or for the risk factors evaluated to be high risk.
Buttons at the bottom can present options to approve the client, decline the client, and to save and archive the data. The buttons to approve the client can be selectable, in various embodiments, after an analyst reviews and provides an evaluation of all risk factors or at least all risk factors initially flagged as high risk by the AI models. In some embodiments, if a certain number or combination of risk factors are flagged as high risk by the AI models and/or the analyst, then the approve client option may not be available. In some embodiments, an approval or rejection for the client can be automatically performed based on the AI evaluations and/or analyst evaluations when the “Save and Archive” button is selected. Selecting to save and archive can cause the data for evaluating the risk factors, such as shown in
AI models and analysts can evaluate additional risk factors, and data can be presented for analyzing the additional risk factors. For example, whether a client or related entity operates on certain exchanges can be flagged. As another example, the internet or registration databases can be searched to determine whether the client or related entity has a website typical for people or similar businesses. The website can be reviewed by the AI model and/or analyst to see if the website is a bona fide website or just a placeholder. The absence of a website or bona fide website can indicate a higher likelihood of laundering. The client or related entity can be checked for registrations and/or licenses for their field of practice or industry as compared to other similar entities. The absence of a license or registration can indicate a higher likelihood of laundering. Being in and/or having relationships with entities in certain fields of practice can indicate a higher likelihood of laundering. Mentions of the client and/or related entities in the press and/or political forums can indicate a higher likelihood of laundering. Accordingly, there can be more risk factors for AI model evaluation and/or analyst evaluation.
At block 901, a client is received for evaluation in an evaluation context. The client name or other identification can be received and provided to a system. In a first example context, information about a new client can be received in a new client context. In a second example, an existing client can be randomly, periodically, routinely, or algorithmically selected as part of a client checkup context.
At block 903, data associated with an entity can be accessed. The data can be data useful for analysis by one or more AI models. The data can be accessed from one or more databases and/or the internet, such as shown in
At block 905, a plurality of AI models can be applied to the data to generate a plurality of AI assessments and/or evaluations. The AI models used can depend on the context. For example, AI models that generate evaluations using data about internal transactions with the client (such as shown in
At block 907, data for displaying one or more user interfaces can be transmitted. The user interfaces can include a first portion showing risk factors, a second portion showing data for analyzing the risk factors, a third portion showing an AI evaluation of the risk factor, and/or a fourth portion allowing for an analyst to provide an evaluation of the risk factor. The portions can be displayed simultaneously and/or on the same screen. Example user interfaces and portions thereof are shown in
In some embodiments, block 907 can be performed if one or a combination of AI assessments and/or evaluations indicates high risk, otherwise block 905 can proceed to blocks 915, 917, and 919. For example, in a new client context, a client can be approved at block 919 if all or most AI assessments and/or evaluations indicate low risk. As another example, in a checkup context, no alert is generated for clients assessed by an AI to be low risk, but an analyst can be alerted using the user interface transmitted at block 909 to review data for a client indicated to be high risk in one or a combination of AI assessments or evaluations.
At block 909, data to update the user interfaces can be transmitted. The data can be generated in response, for example, to a user selection of a risk factor, a user selection of different data for analysis, etc. In response, a visualization of data for analyzing a risk factor can be updated.
At block 911, an analyst evaluation of a risk factor can be received. The analyst evaluation can also include comments and/or reasons. Analyst evaluations can be received for a plurality of risk factors and may change or override the AI evaluation for the risk factor.
At block 913, based on an analyst evaluation of a risk factor, one or more AI models used for evaluating the risk factor can be updated using the analyst's evaluation as feedback. The feedback can reinforce (positively or negatively) the AI model, act as a new verified data point for retraining the AI model, cause the AI model to update with a new path or grouping, change coefficients used in the AI model, etc. The AI model can also update based on a prepopulated reason selected by the analyst. For example, if an analyst selects a reason that the AI model did not evaluate enough data, evaluated too much data, or evaluated irrelevant data, then the AI model can be updated to evaluate different data.
At block 915, the evaluations of the analyst and/or the AI can be archived. The data for analyzing risk factors can also be archived. In some embodiments, the aggregate results (such as total transaction figures shown in
At block 917, a summary of evaluations can be generated. An example summary of evaluations is shown in
At block 919, the client can be approved if low risk or rejected if high risk. The approval or rejection can include, for example, sending a notification to an individual or computer device. A rejection can include, for example, denying a sale, transaction, service, or relationship. An approval can include, for example, performing a next step of a sale, transaction, service, or relationship. In an existing client context, a rejection can include terminating a relationship or account with a client or preventing future transactions, sales, or services with the client.
Various embodiments of the present disclosure may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or mediums) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.
For example, the functionality described herein may be performed as software instructions are executed by, and/or in response to software instructions being executed by, one or more hardware processors and/or any other suitable computing devices. The software instructions and/or other executable code may be read from a computer readable storage medium (or mediums).
The computer readable storage medium can be a tangible device that can retain and store data and/or instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device (including any volatile and/or non-volatile electronic storage devices), a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a solid state drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions (as also referred to herein as, for example, “code,” “instructions,” “module,” “application,” “software application,” and/or the like) for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. Computer readable program instructions may be callable from other instructions or from itself, and/or may be invoked in response to detected events or interrupts. Computer readable program instructions configured for execution on computing devices may be provided on a computer readable storage medium, and/or as a digital download (and may be originally stored in a compressed or installable format that requires installation, decompression or decryption prior to execution) that may then be stored on a computer readable storage medium. Such computer readable program instructions may be stored, partially or fully, on a memory device (e.g., a computer readable storage medium) of the executing computing device, for execution by the computing device. The computer readable program instructions may execute entirely on a user's computer (e.g., the executing computing device), partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart(s) and/or block diagram(s) block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer may load the instructions and/or modules into its dynamic memory and send the instructions over a telephone, cable, or optical line using a modem. A modem local to a server computing system may receive the data on the telephone/cable/optical line and use a converter device including the appropriate circuitry to place the data on a bus. The bus may carry the data to a memory, from which a processor may retrieve and execute the instructions. The instructions received by the memory may optionally be stored on a storage device (e.g., a solid state drive) either before or after execution by the computer processor.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In addition, certain blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular sequence, and the blocks or states relating thereto can be performed in other sequences that are appropriate.
It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. For example, any of the processes, methods, algorithms, elements, blocks, applications, or other functionality (or portions of functionality) described in the preceding sections may be embodied in, and/or fully or partially automated via, electronic hardware such application-specific processors (e.g., application-specific integrated circuits (ASICs)), programmable processors (e.g., field programmable gate arrays (FPGAs)), application-specific circuitry, and/or the like (any of which may also combine custom hard-wired logic, logic circuits, ASICs, FPGAs, etc. with custom programming/execution of software instructions to accomplish the techniques).
Any of the above-mentioned processors, and/or devices incorporating any of the above-mentioned processors, may be referred to herein as, for example, “computers,” “computer devices,” “computing devices,” “hardware computing devices,” “hardware processors,” “processing units,” and/or the like. Computing devices of the above-embodiments may generally (but not necessarily) be controlled and/or coordinated by operating system software, such as Mac OS, iOS, Android, Chrome OS, Windows OS (e.g., Windows XP, Windows Vista, Windows 7, Windows 10, Windows 10, Windows Server, etc.), Windows CE, Unix, Linux, SunOS, Solaris, Blackberry OS, VxWorks, or other suitable operating systems. In other embodiments, the computing devices may be controlled by a proprietary operating system. Conventional operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, I/O services, and provide a user interface functionality, such as a graphical user interface (“GUI”), among other things.
For example,
Computer system 1000 also includes a main memory 1006, such as a random access memory (RAM), cache and/or other dynamic storage devices, coupled to bus 1002 for storing information and instructions to be executed by processor 1004. Main memory 1006 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 1004. Such instructions, when stored in storage media accessible to processor 1004, render computer system 1000 into a special-purpose machine that is customized to perform the operations specified in the instructions.
Computer system 1000 further includes a read only memory (ROM) 1008 or other static storage device coupled to bus 1002 for storing static information and instructions for processor 1004. A storage device 1010, such as a magnetic disk, optical disk, or USB thumb drive (Flash drive), etc., is provided and coupled to bus 1002 for storing information and instructions.
Computer system 1000 may be coupled via bus 1002 to a display 1012, such as a cathode ray tube (CRT) or LCD display (or touch screen), for displaying information to a computer user. An input device 1014, including alphanumeric and other keys, is coupled to bus 1002 for communicating information and command selections to processor 1004. Another type of user input device is cursor control 1016, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 1004 and for controlling cursor movement on display 1012. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. In some embodiments, the same direction information and command selections as cursor control may be implemented via receiving touches on a touch screen without a cursor.
Computing system 1000 may include a user interface module to implement a GUI that may be stored in a mass storage device as computer executable program instructions that are executed by the computing device(s). Computer system 1000 may further, as described below, implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 1000 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 1000 in response to processor(s) 1004 executing one or more sequences of one or more computer readable program instructions contained in main memory 1006. Such instructions may be read into main memory 1006 from another storage medium, such as storage device 1010. Execution of the sequences of instructions contained in main memory 1006 causes processor(s) 1004 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
Various forms of computer readable storage media may be involved in carrying one or more sequences of one or more computer readable program instructions to processor 1004 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 1000 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 1002. Bus 1002 carries the data to main memory 1006, from which processor 1004 retrieves and executes the instructions. The instructions received by main memory 1006 may optionally be stored on storage device 1010 either before or after execution by processor 1004.
Computer system 1000 also includes a communication interface 1018 coupled to bus 1002. Communication interface 1018 provides a two-way data communication coupling to a network link 1020 that is connected to a local network 1022. For example, communication interface 1018 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 1018 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN (or WAN component to communicated with a WAN). Wireless links may also be implemented. In any such implementation, communication interface 1018 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
Network link 1020 typically provides data communication through one or more networks to other data devices. For example, network link 1020 may provide a connection through local network 1022 to a host computer 1024 or to data equipment operated by an Internet Service Provider (ISP) 1026. ISP 1026 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 1028. Local network 1022 and Internet 1028 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 1020 and through communication interface 1018, which carry the digital data to and from computer system 1000, are example forms of transmission media.
Computer system 1000 can send messages and receive data, including program code, through the network(s), network link 1020 and communication interface 1018. In the Internet example, a server 1030 might transmit a requested code for an application program through Internet 1028, ISP 1026, local network 1022 and communication interface 1018.
The received code may be executed by processor 1004 as it is received, and/or stored in storage device 1010, or other non-volatile storage for later execution.
As described above, in various embodiments certain functionality may be accessible by a user through a web-based viewer (such as a web browser), or other suitable software program). In such implementations, the user interface may be generated by a server computing system and transmitted to a web browser of the user (e.g., running on the user's computing system). Alternatively, data (e.g., user interface data) necessary for generating the user interface may be provided by the server computing system to the browser, where the user interface may be generated (e.g., the user interface data may be executed by a browser accessing a web service and may be configured to render the user interfaces based on the user interface data). The user may then interact with the user interface through the web-browser. User interfaces of certain implementations may be accessible through one or more dedicated software applications. In certain embodiments, one or more of the computing devices and/or systems of the disclosure may include mobile computing devices, and user interfaces may be accessible through such mobile computing devices (for example, smartphones and/or tablets).
Many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure. The foregoing description details certain embodiments. It will be appreciated, however, that no matter how detailed the foregoing appears in text, the systems and methods can be practiced in many ways. As is also stated above, it should be noted that the use of particular terminology when describing certain features or aspects of the systems and methods should not be taken to imply that the terminology is being re-defined herein to be restricted to including any specific characteristics of the features or aspects of the systems and methods with which that terminology is associated.
Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
The term “substantially” when used in conjunction with the term “real-time” forms a phrase that will be readily understood by a person of ordinary skill in the art. For example, it is readily understood that such language will include speeds in which no or little delay or waiting is discernible, or where such delay is sufficiently short so as not to be disruptive, irritating, or otherwise vexing to a user.
Conjunctive language such as the phrase “at least one of X, Y, and Z,” or “at least one of X, Y, or Z,” unless specifically stated otherwise, is to be understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z, or a combination thereof. For example, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y, and at least one of Z to each be present.
The term “a” as used herein should be given an inclusive rather than exclusive interpretation. For example, unless specifically noted, the term “a” should not be understood to mean “exactly one” or “one and only one”; instead, the term “a” means “one or more” or “at least one,” whether used in the claims or elsewhere in the specification and regardless of uses of quantifiers such as “at least one,” “one or more,” or “a plurality” elsewhere in the claims or specification.
The term “comprising” as used herein should be given an inclusive rather than exclusive interpretation. For example, a general purpose computer comprising one or more processors should not be interpreted as excluding other computer components, and may possibly include such components as memory, input/output devices, and/or network interfaces, among others.
While the above detailed description has shown, described, and pointed out novel features as applied to various embodiments, it may be understood that various omissions, substitutions, and changes in the form and details of the devices or processes illustrated may be made without departing from the spirit of the disclosure. As may be recognized, certain embodiments of the inventions described herein may be embodied within a form that does not provide all of the features and benefits set forth herein, as some features may be used or practiced separately from others. The scope of certain inventions disclosed herein is indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.
The present application is a continuation of U.S. patent application Ser. No. 16/116,629, filed Aug. 29, 2018, now U.S. Pat. No. 11,119,630, which application claims priority to U.S. Provisional Patent Application No. 62/686,994, filed Jun. 19, 2018. The entire disclosure of each of the above items is hereby made part of this specification as if set forth fully herein and incorporated by reference for all purposes, for all that it contains.
| Number | Name | Date | Kind |
|---|---|---|---|
| 5109399 | Thompson | Apr 1992 | A |
| 5329108 | Lamoure | Jul 1994 | A |
| 5670987 | Doi et al. | Sep 1997 | A |
| 5781704 | Rossmo | Jul 1998 | A |
| 5798769 | Chiu et al. | Aug 1998 | A |
| 5819226 | Gopinathan et al. | Oct 1998 | A |
| 5845300 | Comer | Dec 1998 | A |
| 5892900 | Ginter et al. | Apr 1999 | A |
| 6057757 | Arrowsmith et al. | May 2000 | A |
| 6091956 | Hollenberg | Jul 2000 | A |
| 6094643 | Anderson et al. | Jul 2000 | A |
| 6161098 | Wallman | Dec 2000 | A |
| 6219053 | Tachibana et al. | Apr 2001 | B1 |
| 6232971 | Haynes | May 2001 | B1 |
| 6247019 | Davies | Jun 2001 | B1 |
| 6279018 | Kudrolli et al. | Aug 2001 | B1 |
| 6341310 | Leshem et al. | Jan 2002 | B1 |
| 6366933 | Ball et al. | Apr 2002 | B1 |
| 6369835 | Lin | Apr 2002 | B1 |
| 6430305 | Decker | Aug 2002 | B1 |
| 6456997 | Shukla | Sep 2002 | B1 |
| 6549944 | Weinberg et al. | Apr 2003 | B1 |
| 6560620 | Ching | May 2003 | B1 |
| 6567936 | Yang et al. | May 2003 | B1 |
| 6581068 | Bensoussan et al. | Jun 2003 | B1 |
| 6594672 | Lampson et al. | Jul 2003 | B1 |
| 6631496 | Li et al. | Oct 2003 | B1 |
| 6642945 | Sharpe | Nov 2003 | B1 |
| 6714936 | Nevin, III | Mar 2004 | B1 |
| 6775675 | Nwabueze et al. | Aug 2004 | B1 |
| 6820135 | Dingman | Nov 2004 | B1 |
| 6828920 | Owen et al. | Dec 2004 | B2 |
| 6839745 | Dingari et al. | Jan 2005 | B1 |
| 6877137 | Rivette et al. | Apr 2005 | B1 |
| 6976210 | Silva et al. | Dec 2005 | B1 |
| 6978419 | Kantrowitz | Dec 2005 | B1 |
| 6980984 | Huffman et al. | Dec 2005 | B1 |
| 6985950 | Hanson et al. | Jan 2006 | B1 |
| 7036085 | Barros | Apr 2006 | B2 |
| 7043702 | Chi et al. | May 2006 | B2 |
| 7055110 | Kupka et al. | May 2006 | B2 |
| 7139800 | Bellotti et al. | Nov 2006 | B2 |
| 7158878 | Rasmussen et al. | Jan 2007 | B2 |
| 7162475 | Ackerman | Jan 2007 | B2 |
| 7168039 | Bertram | Jan 2007 | B2 |
| 7171427 | Witowski et al. | Jan 2007 | B2 |
| 7181438 | Szabo | Feb 2007 | B1 |
| 7269786 | Malloy et al. | Sep 2007 | B1 |
| 7278105 | Kitts | Oct 2007 | B1 |
| 7290698 | Poslinski et al. | Nov 2007 | B2 |
| 7333998 | Heckerman et al. | Feb 2008 | B2 |
| 7370047 | Gorman | May 2008 | B2 |
| 7373669 | Eisen | May 2008 | B2 |
| 7379811 | Rasmussen et al. | May 2008 | B2 |
| 7379903 | Caballero et al. | May 2008 | B2 |
| 7426654 | Adams et al. | Sep 2008 | B2 |
| 7451397 | Weber et al. | Nov 2008 | B2 |
| 7454466 | Bellotti et al. | Nov 2008 | B2 |
| 7467375 | Tondreau et al. | Dec 2008 | B2 |
| 7487139 | Fraleigh et al. | Feb 2009 | B2 |
| 7502786 | Liu et al. | Mar 2009 | B2 |
| 7525422 | Bishop et al. | Apr 2009 | B2 |
| 7529727 | Arning et al. | May 2009 | B2 |
| 7558677 | Jones | Jul 2009 | B2 |
| 7571109 | Fawls | Aug 2009 | B2 |
| 7574409 | Patinkin | Aug 2009 | B2 |
| 7574428 | Leiserowitz et al. | Aug 2009 | B2 |
| 7579965 | Bucholz | Aug 2009 | B2 |
| 7596285 | Brown et al. | Sep 2009 | B2 |
| 7614006 | Molander | Nov 2009 | B2 |
| 7617232 | Gabbert et al. | Nov 2009 | B2 |
| 7620628 | Kapur et al. | Nov 2009 | B2 |
| 7627812 | Chamberlain et al. | Dec 2009 | B2 |
| 7634717 | Chamberlain et al. | Dec 2009 | B2 |
| 7703021 | Flam | Apr 2010 | B1 |
| 7712049 | Williams et al. | May 2010 | B2 |
| 7716077 | Mikurak | May 2010 | B1 |
| 7725530 | Sah et al. | May 2010 | B2 |
| 7725547 | Albertson et al. | May 2010 | B2 |
| 7730082 | Sah et al. | Jun 2010 | B2 |
| 7730109 | Rohrs et al. | Jun 2010 | B2 |
| 7756843 | Palmer | Jul 2010 | B1 |
| 7770100 | Chamberlain et al. | Aug 2010 | B2 |
| 7783658 | Bayliss | Aug 2010 | B1 |
| 7805457 | Viola et al. | Sep 2010 | B1 |
| 7809703 | Balabhadrapatruni et al. | Oct 2010 | B2 |
| 7814102 | Miller et al. | Oct 2010 | B2 |
| 7818658 | Chen | Oct 2010 | B2 |
| 7870493 | Pall et al. | Jan 2011 | B2 |
| 7894984 | Rasmussen et al. | Feb 2011 | B2 |
| 7899611 | Downs et al. | Mar 2011 | B2 |
| 7899796 | Borthwick et al. | Mar 2011 | B1 |
| 7917376 | Bellin et al. | Mar 2011 | B2 |
| 7920963 | Jouline et al. | Apr 2011 | B2 |
| 7933862 | Chamberlain et al. | Apr 2011 | B2 |
| 7941321 | Greenstein et al. | May 2011 | B2 |
| 7962281 | Rasmussen et al. | Jun 2011 | B2 |
| 7962495 | Jain et al. | Jun 2011 | B2 |
| 7962848 | Bertram | Jun 2011 | B2 |
| 7970240 | Chao et al. | Jun 2011 | B1 |
| 7971150 | Raskutti et al. | Jun 2011 | B2 |
| 7984374 | Caro et al. | Jul 2011 | B2 |
| 8001465 | Kudrolli et al. | Aug 2011 | B2 |
| 8001482 | Bhattiprolu et al. | Aug 2011 | B2 |
| 8010545 | Stefik et al. | Aug 2011 | B2 |
| 8015487 | Roy et al. | Sep 2011 | B2 |
| 8024778 | Cash et al. | Sep 2011 | B2 |
| 8036632 | Cona et al. | Oct 2011 | B1 |
| 8036971 | Aymeloglu et al. | Oct 2011 | B2 |
| 8046283 | Burns | Oct 2011 | B2 |
| 8046362 | Bayliss | Oct 2011 | B2 |
| 8054756 | Chand et al. | Nov 2011 | B2 |
| 8103543 | Zwicky | Jan 2012 | B1 |
| 8134457 | Velipasalar et al. | Mar 2012 | B2 |
| 8135679 | Bayliss | Mar 2012 | B2 |
| 8135719 | Bayliss | Mar 2012 | B2 |
| 8145703 | Frishert et al. | Mar 2012 | B2 |
| 8185819 | Sah et al. | May 2012 | B2 |
| 8214361 | Sandler et al. | Jul 2012 | B1 |
| 8214490 | Vos et al. | Jul 2012 | B1 |
| 8214764 | Gemmell et al. | Jul 2012 | B2 |
| 8225201 | Michael | Jul 2012 | B2 |
| 8229902 | Vishniac et al. | Jul 2012 | B2 |
| 8229947 | Fujinaga | Jul 2012 | B2 |
| 8230333 | Decherd et al. | Jul 2012 | B2 |
| 8266168 | Bayliss | Sep 2012 | B2 |
| 8271461 | Pike et al. | Sep 2012 | B2 |
| 8280880 | Aymeloglu et al. | Oct 2012 | B1 |
| 8290838 | Thakur et al. | Oct 2012 | B1 |
| 8290942 | Jones et al. | Oct 2012 | B2 |
| 8301464 | Cave et al. | Oct 2012 | B1 |
| 8301904 | Gryaznov | Oct 2012 | B1 |
| 8302855 | Ma et al. | Nov 2012 | B2 |
| 8312367 | Foster | Nov 2012 | B2 |
| 8312546 | Alme | Nov 2012 | B2 |
| 8321943 | Walters et al. | Nov 2012 | B1 |
| 8347398 | Weber | Jan 2013 | B1 |
| 8352881 | Champion et al. | Jan 2013 | B2 |
| 8368695 | Howell et al. | Feb 2013 | B2 |
| 8397171 | Klassen et al. | Mar 2013 | B2 |
| 8412707 | Mianji | Apr 2013 | B1 |
| 8447674 | Choudhuri et al. | May 2013 | B2 |
| 8447722 | Ahuja et al. | May 2013 | B1 |
| 8452790 | Mianji | May 2013 | B1 |
| 8463036 | Ramesh et al. | Jun 2013 | B1 |
| 8473454 | Evanitsky et al. | Jun 2013 | B2 |
| 8484115 | Aymeloglu et al. | Jul 2013 | B2 |
| 8484168 | Bayliss | Jul 2013 | B2 |
| 8489331 | Kopf et al. | Jul 2013 | B2 |
| 8489641 | Seefeld et al. | Jul 2013 | B1 |
| 8495077 | Bayliss | Jul 2013 | B2 |
| 8498969 | Bayliss | Jul 2013 | B2 |
| 8498984 | Hwang et al. | Jul 2013 | B1 |
| 8514082 | Cova et al. | Aug 2013 | B2 |
| 8515207 | Chau | Aug 2013 | B2 |
| 8554579 | Tribble et al. | Oct 2013 | B2 |
| 8554653 | Falkenborg et al. | Oct 2013 | B2 |
| 8554709 | Goodson et al. | Oct 2013 | B2 |
| 8560413 | Quarterman | Oct 2013 | B1 |
| 8577911 | Stepinski et al. | Nov 2013 | B1 |
| 8589273 | Creeden et al. | Nov 2013 | B2 |
| 8600872 | Yan | Dec 2013 | B1 |
| 8620641 | Farnsworth et al. | Dec 2013 | B2 |
| 8639757 | Zang et al. | Jan 2014 | B1 |
| 8646080 | Williamson et al. | Feb 2014 | B2 |
| 8666861 | Li et al. | Mar 2014 | B2 |
| 8676857 | Adams et al. | Mar 2014 | B1 |
| 8682764 | Love | Mar 2014 | B2 |
| 8688573 | Ruknoic et al. | Apr 2014 | B1 |
| 8689108 | Duffield et al. | Apr 2014 | B1 |
| 8713467 | Goldenberg et al. | Apr 2014 | B1 |
| 8726379 | Stiansen et al. | May 2014 | B1 |
| 8739278 | Varghese | May 2014 | B2 |
| 8742934 | Sarpy et al. | Jun 2014 | B1 |
| 8744890 | Bernier | Jun 2014 | B1 |
| 8745516 | Mason et al. | Jun 2014 | B2 |
| 8781169 | Jackson et al. | Jul 2014 | B2 |
| 8787939 | Papakipos et al. | Jul 2014 | B2 |
| 8788407 | Singh et al. | Jul 2014 | B1 |
| 8798354 | Bunzel et al. | Aug 2014 | B1 |
| 8799799 | Cervelli et al. | Aug 2014 | B1 |
| 8812960 | Sun et al. | Aug 2014 | B1 |
| 8818892 | Sprague et al. | Aug 2014 | B1 |
| 8830322 | Nerayoff et al. | Sep 2014 | B2 |
| 8832594 | Thompson et al. | Sep 2014 | B1 |
| 8868537 | Colgrove et al. | Oct 2014 | B1 |
| 8917274 | Ma et al. | Dec 2014 | B2 |
| 8924388 | Elliot et al. | Dec 2014 | B2 |
| 8924389 | Elliot et al. | Dec 2014 | B2 |
| 8924872 | Bogomolov et al. | Dec 2014 | B1 |
| 8937619 | Sharma et al. | Jan 2015 | B2 |
| 8938686 | Erenrich et al. | Jan 2015 | B1 |
| 8949164 | Mohler | Feb 2015 | B1 |
| 9009171 | Grossman et al. | Apr 2015 | B1 |
| 9009827 | Albertson et al. | Apr 2015 | B1 |
| 9021260 | Falk et al. | Apr 2015 | B1 |
| 9021384 | Beard et al. | Apr 2015 | B1 |
| 9032531 | Scorvo et al. | May 2015 | B1 |
| 9043696 | Meiklejohn et al. | May 2015 | B1 |
| 9043894 | Dennison et al. | May 2015 | B1 |
| 9100428 | Visbal | Aug 2015 | B1 |
| 9129219 | Robertson et al. | Sep 2015 | B1 |
| 9609011 | Muddu et al. | Mar 2017 | B2 |
| 9721296 | Chrapko | Aug 2017 | B1 |
| 9898509 | Saperstein et al. | Feb 2018 | B2 |
| 11119630 | Marchetti et al. | Sep 2021 | B1 |
| 11842354 | Chamberlain | Dec 2023 | B1 |
| 20010027424 | Torigoe | Oct 2001 | A1 |
| 20020033848 | Sciammarella et al. | Mar 2002 | A1 |
| 20020065708 | Senay et al. | May 2002 | A1 |
| 20020091707 | Keller | Jul 2002 | A1 |
| 20020095360 | Joao | Jul 2002 | A1 |
| 20020095658 | Shulman | Jul 2002 | A1 |
| 20020103705 | Brady | Aug 2002 | A1 |
| 20020103747 | Lawrence | Aug 2002 | A1 |
| 20020116120 | Ruiz et al. | Aug 2002 | A1 |
| 20020138407 | Lawrence | Sep 2002 | A1 |
| 20020147805 | Leshem et al. | Oct 2002 | A1 |
| 20020174201 | Ramer et al. | Nov 2002 | A1 |
| 20020194119 | Wright et al. | Dec 2002 | A1 |
| 20030028560 | Kudrolli et al. | Feb 2003 | A1 |
| 20030033228 | Bosworth-Davies et al. | Feb 2003 | A1 |
| 20030039948 | Donahue | Feb 2003 | A1 |
| 20030097330 | Hillmer et al. | May 2003 | A1 |
| 20030126102 | Borthwick | Jul 2003 | A1 |
| 20030144868 | MacIntyre et al. | Jul 2003 | A1 |
| 20030163352 | Surpin et al. | Aug 2003 | A1 |
| 20030225687 | Lawrence | Dec 2003 | A1 |
| 20030225755 | Iwayama et al. | Dec 2003 | A1 |
| 20030229848 | Arend et al. | Dec 2003 | A1 |
| 20040006533 | Lawrence | Jan 2004 | A1 |
| 20040032432 | Baynger | Feb 2004 | A1 |
| 20040034570 | Davis | Feb 2004 | A1 |
| 20040064256 | Barinek et al. | Apr 2004 | A1 |
| 20040085318 | Hassler et al. | May 2004 | A1 |
| 20040095349 | Bito et al. | May 2004 | A1 |
| 20040111410 | Burgoon et al. | Jun 2004 | A1 |
| 20040111480 | Yue | Jun 2004 | A1 |
| 20040126840 | Cheng et al. | Jul 2004 | A1 |
| 20040133508 | Lawrence | Jul 2004 | A1 |
| 20040143602 | Ruiz et al. | Jul 2004 | A1 |
| 20040143796 | Lerner et al. | Jul 2004 | A1 |
| 20040153418 | Hanweck | Aug 2004 | A1 |
| 20040163039 | Gorman | Aug 2004 | A1 |
| 20040193600 | Kaasten et al. | Sep 2004 | A1 |
| 20040205524 | Richter et al. | Oct 2004 | A1 |
| 20040221223 | Yu et al. | Nov 2004 | A1 |
| 20040236688 | Bozeman | Nov 2004 | A1 |
| 20040260702 | Cragun et al. | Dec 2004 | A1 |
| 20050010472 | Quatse et al. | Jan 2005 | A1 |
| 20050027705 | Sadri et al. | Feb 2005 | A1 |
| 20050028094 | Allyn | Feb 2005 | A1 |
| 20050039119 | Parks et al. | Feb 2005 | A1 |
| 20050044037 | Lawrence | Feb 2005 | A1 |
| 20050080769 | Gemmell | Apr 2005 | A1 |
| 20050086207 | Heuer et al. | Apr 2005 | A1 |
| 20050108063 | Madill et al. | May 2005 | A1 |
| 20050125715 | Di Franco et al. | Jun 2005 | A1 |
| 20050133588 | Williams | Jun 2005 | A1 |
| 20050149455 | Bruesewitz et al. | Jul 2005 | A1 |
| 20050154628 | Eckart et al. | Jul 2005 | A1 |
| 20050154769 | Eckart et al. | Jul 2005 | A1 |
| 20050162523 | Darrell et al. | Jul 2005 | A1 |
| 20050180330 | Shapiro | Aug 2005 | A1 |
| 20050182793 | Keenan et al. | Aug 2005 | A1 |
| 20050183005 | Denoue et al. | Aug 2005 | A1 |
| 20050222928 | Steier et al. | Oct 2005 | A1 |
| 20050246327 | Yeung et al. | Nov 2005 | A1 |
| 20050251786 | Citron et al. | Nov 2005 | A1 |
| 20060002387 | Lawrence | Jan 2006 | A1 |
| 20060004719 | Lawrence | Jan 2006 | A1 |
| 20060004814 | Lawrence | Jan 2006 | A1 |
| 20060004866 | Lawrence | Jan 2006 | A1 |
| 20060026120 | Carolan et al. | Feb 2006 | A1 |
| 20060026170 | Kreitler et al. | Feb 2006 | A1 |
| 20060036560 | Fogel | Feb 2006 | A1 |
| 20060059139 | Robinson | Mar 2006 | A1 |
| 20060074881 | Vembu et al. | Apr 2006 | A1 |
| 20060080619 | Carlson et al. | Apr 2006 | A1 |
| 20060129746 | Porter | Jun 2006 | A1 |
| 20060139375 | Rasmussen et al. | Jun 2006 | A1 |
| 20060142949 | Helt | Jun 2006 | A1 |
| 20060143034 | Rothermel | Jun 2006 | A1 |
| 20060143075 | Carr et al. | Jun 2006 | A1 |
| 20060143079 | Basak et al. | Jun 2006 | A1 |
| 20060149596 | Surpin et al. | Jul 2006 | A1 |
| 20060203337 | White | Sep 2006 | A1 |
| 20060218637 | Thomas et al. | Sep 2006 | A1 |
| 20060241974 | Chao et al. | Oct 2006 | A1 |
| 20060242040 | Rader et al. | Oct 2006 | A1 |
| 20060242630 | Koike et al. | Oct 2006 | A1 |
| 20060271277 | Hu et al. | Nov 2006 | A1 |
| 20060279630 | Aggarwal et al. | Dec 2006 | A1 |
| 20070000999 | Kubo et al. | Jan 2007 | A1 |
| 20070011150 | Frank | Jan 2007 | A1 |
| 20070011304 | Error | Jan 2007 | A1 |
| 20070016363 | Huang et al. | Jan 2007 | A1 |
| 20070038646 | Thota | Feb 2007 | A1 |
| 20070038962 | Fuchs et al. | Feb 2007 | A1 |
| 20070057966 | Ohno et al. | Mar 2007 | A1 |
| 20070061259 | Zoldi et al. | Mar 2007 | A1 |
| 20070078832 | Ott et al. | Apr 2007 | A1 |
| 20070083541 | Fraleigh et al. | Apr 2007 | A1 |
| 20070106582 | Baker et al. | May 2007 | A1 |
| 20070143851 | Nicodemus | Jun 2007 | A1 |
| 20070150369 | Zivin | Jun 2007 | A1 |
| 20070150801 | Chidlovskii et al. | Jun 2007 | A1 |
| 20070156673 | Maga | Jul 2007 | A1 |
| 20070174760 | Chamberlain et al. | Jul 2007 | A1 |
| 20070185867 | Maga | Aug 2007 | A1 |
| 20070192265 | Chopin et al. | Aug 2007 | A1 |
| 20070198571 | Ferguson et al. | Aug 2007 | A1 |
| 20070208497 | Downs et al. | Sep 2007 | A1 |
| 20070208498 | Barker et al. | Sep 2007 | A1 |
| 20070208736 | Tanigawa et al. | Sep 2007 | A1 |
| 20070239606 | Eisen | Oct 2007 | A1 |
| 20070240062 | Christena et al. | Oct 2007 | A1 |
| 20070266336 | Nojima et al. | Nov 2007 | A1 |
| 20070284433 | Domenica et al. | Dec 2007 | A1 |
| 20070294200 | Au | Dec 2007 | A1 |
| 20070294643 | Kyle | Dec 2007 | A1 |
| 20080040684 | Crump | Feb 2008 | A1 |
| 20080046481 | Gould et al. | Feb 2008 | A1 |
| 20080051989 | Welsh | Feb 2008 | A1 |
| 20080052142 | Bailey et al. | Feb 2008 | A1 |
| 20080069081 | Chand et al. | Mar 2008 | A1 |
| 20080077597 | Butler | Mar 2008 | A1 |
| 20080077642 | Carbone et al. | Mar 2008 | A1 |
| 20080082374 | Kennis et al. | Apr 2008 | A1 |
| 20080103798 | Domenikos et al. | May 2008 | A1 |
| 20080103996 | Forman et al. | May 2008 | A1 |
| 20080104019 | Nath | May 2008 | A1 |
| 20080126951 | Sood et al. | May 2008 | A1 |
| 20080133567 | Ames et al. | Jun 2008 | A1 |
| 20080140576 | Lewis et al. | Jun 2008 | A1 |
| 20080148398 | Mezack et al. | Jun 2008 | A1 |
| 20080155440 | Trevor et al. | Jun 2008 | A1 |
| 20080195417 | Surpin et al. | Aug 2008 | A1 |
| 20080195608 | Clover | Aug 2008 | A1 |
| 20080222038 | Eden et al. | Sep 2008 | A1 |
| 20080222295 | Robinson et al. | Sep 2008 | A1 |
| 20080243711 | Aymeloglu et al. | Oct 2008 | A1 |
| 20080255973 | El Wade et al. | Oct 2008 | A1 |
| 20080263468 | Cappione et al. | Oct 2008 | A1 |
| 20080267107 | Rosenberg | Oct 2008 | A1 |
| 20080276167 | Michael | Nov 2008 | A1 |
| 20080278311 | Grange et al. | Nov 2008 | A1 |
| 20080288306 | MacIntyre et al. | Nov 2008 | A1 |
| 20080288425 | Posse et al. | Nov 2008 | A1 |
| 20080301042 | Patzer | Dec 2008 | A1 |
| 20080301643 | Appleton et al. | Dec 2008 | A1 |
| 20080313132 | Hao et al. | Dec 2008 | A1 |
| 20090018940 | Wang et al. | Jan 2009 | A1 |
| 20090018996 | Hunt et al. | Jan 2009 | A1 |
| 20090024505 | Patel et al. | Jan 2009 | A1 |
| 20090027418 | Maru et al. | Jan 2009 | A1 |
| 20090030915 | Winter et al. | Jan 2009 | A1 |
| 20090044279 | Crawford et al. | Feb 2009 | A1 |
| 20090055251 | Shah et al. | Feb 2009 | A1 |
| 20090076845 | Bellin et al. | Mar 2009 | A1 |
| 20090082997 | Tokman et al. | Mar 2009 | A1 |
| 20090083184 | Eisen | Mar 2009 | A1 |
| 20090088964 | Schaaf et al. | Apr 2009 | A1 |
| 20090094166 | Aymeloglu et al. | Apr 2009 | A1 |
| 20090106178 | Chu | Apr 2009 | A1 |
| 20090112745 | Stefanescu | Apr 2009 | A1 |
| 20090119309 | Gibson et al. | May 2009 | A1 |
| 20090125359 | Knapic | May 2009 | A1 |
| 20090125369 | Kloosstra et al. | May 2009 | A1 |
| 20090125459 | Norton et al. | May 2009 | A1 |
| 20090132921 | Hwangbo et al. | May 2009 | A1 |
| 20090132953 | Reed et al. | May 2009 | A1 |
| 20090144262 | White et al. | Jun 2009 | A1 |
| 20090144274 | Fraleigh et al. | Jun 2009 | A1 |
| 20090164934 | Bhattiprolu et al. | Jun 2009 | A1 |
| 20090171939 | Athsani et al. | Jul 2009 | A1 |
| 20090172511 | Decherd et al. | Jul 2009 | A1 |
| 20090172821 | Daira et al. | Jul 2009 | A1 |
| 20090177962 | Gusmorino et al. | Jul 2009 | A1 |
| 20090179892 | Tsuda et al. | Jul 2009 | A1 |
| 20090187546 | Whyte et al. | Jul 2009 | A1 |
| 20090187548 | Ji et al. | Jul 2009 | A1 |
| 20090192957 | Subramanian et al. | Jul 2009 | A1 |
| 20090222400 | Kupershmidt et al. | Sep 2009 | A1 |
| 20090222760 | Halverson et al. | Sep 2009 | A1 |
| 20090228365 | Tomchek et al. | Sep 2009 | A1 |
| 20090234720 | George et al. | Sep 2009 | A1 |
| 20090249244 | Robinson et al. | Oct 2009 | A1 |
| 20090254970 | Agarwal et al. | Oct 2009 | A1 |
| 20090271343 | Vaiciulis et al. | Oct 2009 | A1 |
| 20090281839 | Lynn et al. | Nov 2009 | A1 |
| 20090287470 | Farnsworth et al. | Nov 2009 | A1 |
| 20090287628 | Indeck et al. | Nov 2009 | A1 |
| 20090292626 | Oxford | Nov 2009 | A1 |
| 20090300589 | Watters et al. | Dec 2009 | A1 |
| 20090307049 | Elliott et al. | Dec 2009 | A1 |
| 20090313463 | Pang et al. | Dec 2009 | A1 |
| 20090319418 | Herz | Dec 2009 | A1 |
| 20090319891 | MacKinlay | Dec 2009 | A1 |
| 20100011282 | Dollard et al. | Jan 2010 | A1 |
| 20100030722 | Goodson et al. | Feb 2010 | A1 |
| 20100031141 | Summers et al. | Feb 2010 | A1 |
| 20100042922 | Bradateanu et al. | Feb 2010 | A1 |
| 20100057622 | Faith et al. | Mar 2010 | A1 |
| 20100057716 | Stefik et al. | Mar 2010 | A1 |
| 20100070523 | Delgo et al. | Mar 2010 | A1 |
| 20100070842 | Aymeloglu et al. | Mar 2010 | A1 |
| 20100070845 | Facemire et al. | Mar 2010 | A1 |
| 20100070897 | Aymeloglu et al. | Mar 2010 | A1 |
| 20100077483 | Stolfo et al. | Mar 2010 | A1 |
| 20100094765 | Nandy | Apr 2010 | A1 |
| 20100098318 | Anderson | Apr 2010 | A1 |
| 20100100963 | Mahaffey | Apr 2010 | A1 |
| 20100103124 | Kruzeniski et al. | Apr 2010 | A1 |
| 20100106611 | Paulsen et al. | Apr 2010 | A1 |
| 20100114887 | Conway et al. | May 2010 | A1 |
| 20100122152 | Chamberlain et al. | May 2010 | A1 |
| 20100125546 | Barrett et al. | May 2010 | A1 |
| 20100131457 | Heimendinger | May 2010 | A1 |
| 20100131502 | Fordham | May 2010 | A1 |
| 20100138354 | Lawrence | Jun 2010 | A1 |
| 20100161735 | Sharma | Jun 2010 | A1 |
| 20100162176 | Dunton | Jun 2010 | A1 |
| 20100169192 | Zoldi et al. | Jul 2010 | A1 |
| 20100169237 | Howard et al. | Jul 2010 | A1 |
| 20100185691 | Irmak et al. | Jul 2010 | A1 |
| 20100191563 | Schlaifer et al. | Jul 2010 | A1 |
| 20100198684 | Eraker et al. | Aug 2010 | A1 |
| 20100199225 | Coleman et al. | Aug 2010 | A1 |
| 20100228812 | Uomini | Sep 2010 | A1 |
| 20100235915 | Memon et al. | Sep 2010 | A1 |
| 20100250412 | Wagner | Sep 2010 | A1 |
| 20100262688 | Hussain et al. | Oct 2010 | A1 |
| 20100280857 | Liu et al. | Nov 2010 | A1 |
| 20100293174 | Bennett et al. | Nov 2010 | A1 |
| 20100306029 | Jolley | Dec 2010 | A1 |
| 20100306713 | Geisner et al. | Dec 2010 | A1 |
| 20100312837 | Bodapati et al. | Dec 2010 | A1 |
| 20100313119 | Baldwin et al. | Dec 2010 | A1 |
| 20100318924 | Frankel et al. | Dec 2010 | A1 |
| 20100321399 | Ellren et al. | Dec 2010 | A1 |
| 20100325526 | Ellis et al. | Dec 2010 | A1 |
| 20100325581 | Finkelstein et al. | Dec 2010 | A1 |
| 20100330801 | Rouh | Dec 2010 | A1 |
| 20110004626 | Naeymi-Rad et al. | Jan 2011 | A1 |
| 20110047159 | Baid et al. | Feb 2011 | A1 |
| 20110055074 | Chen et al. | Mar 2011 | A1 |
| 20110060753 | Shaked et al. | Mar 2011 | A1 |
| 20110061013 | Bilicki et al. | Mar 2011 | A1 |
| 20110074811 | Hanson et al. | Mar 2011 | A1 |
| 20110078055 | Faribault et al. | Mar 2011 | A1 |
| 20110078173 | Seligmann et al. | Mar 2011 | A1 |
| 20110087519 | Fordyce, III et al. | Apr 2011 | A1 |
| 20110093327 | Fordyce, III et al. | Apr 2011 | A1 |
| 20110099133 | Chang et al. | Apr 2011 | A1 |
| 20110099628 | Lanxner et al. | Apr 2011 | A1 |
| 20110117878 | Barash et al. | May 2011 | A1 |
| 20110119100 | Ruhl et al. | May 2011 | A1 |
| 20110131122 | Griffin et al. | Jun 2011 | A1 |
| 20110137766 | Rasmussen et al. | Jun 2011 | A1 |
| 20110153384 | Horne et al. | Jun 2011 | A1 |
| 20110161096 | Buehler et al. | Jun 2011 | A1 |
| 20110167105 | Ramakrishnan et al. | Jul 2011 | A1 |
| 20110167493 | Song et al. | Jul 2011 | A1 |
| 20110170799 | Carrino et al. | Jul 2011 | A1 |
| 20110173032 | Payne et al. | Jul 2011 | A1 |
| 20110173093 | Psota et al. | Jul 2011 | A1 |
| 20110178842 | Rane et al. | Jul 2011 | A1 |
| 20110185316 | Reid et al. | Jul 2011 | A1 |
| 20110208565 | Ross et al. | Aug 2011 | A1 |
| 20110208724 | Jones et al. | Aug 2011 | A1 |
| 20110213655 | Henkin | Sep 2011 | A1 |
| 20110218934 | Elser | Sep 2011 | A1 |
| 20110218955 | Tang | Sep 2011 | A1 |
| 20110219450 | McDougal et al. | Sep 2011 | A1 |
| 20110225198 | Edwards et al. | Sep 2011 | A1 |
| 20110225586 | Bentley et al. | Sep 2011 | A1 |
| 20110231223 | Winters | Sep 2011 | A1 |
| 20110231305 | Winters | Sep 2011 | A1 |
| 20110238510 | Rowen et al. | Sep 2011 | A1 |
| 20110238553 | Raj et al. | Sep 2011 | A1 |
| 20110238570 | Li et al. | Sep 2011 | A1 |
| 20110251951 | Kolkowitz | Oct 2011 | A1 |
| 20110258158 | Resende et al. | Oct 2011 | A1 |
| 20110270604 | Qi et al. | Nov 2011 | A1 |
| 20110270705 | Parker | Nov 2011 | A1 |
| 20110270834 | Sokolan et al. | Nov 2011 | A1 |
| 20110289397 | Eastmond et al. | Nov 2011 | A1 |
| 20110291851 | Whisenant | Dec 2011 | A1 |
| 20110295649 | Fine | Dec 2011 | A1 |
| 20110307382 | Siegel et al. | Dec 2011 | A1 |
| 20110310005 | Chen et al. | Dec 2011 | A1 |
| 20110314007 | Dassa et al. | Dec 2011 | A1 |
| 20110314024 | Chang et al. | Dec 2011 | A1 |
| 20120011238 | Rathod | Jan 2012 | A1 |
| 20120011245 | Gillette et al. | Jan 2012 | A1 |
| 20120019559 | Siler et al. | Jan 2012 | A1 |
| 20120022945 | Falkenborg et al. | Jan 2012 | A1 |
| 20120036013 | Neuhaus et al. | Feb 2012 | A1 |
| 20120036434 | Oberstein | Feb 2012 | A1 |
| 20120050293 | Carlhian et al. | Mar 2012 | A1 |
| 20120054284 | Rakshit | Mar 2012 | A1 |
| 20120059853 | Jagota | Mar 2012 | A1 |
| 20120066166 | Curbera et al. | Mar 2012 | A1 |
| 20120066296 | Appleton et al. | Mar 2012 | A1 |
| 20120072825 | Sherkin et al. | Mar 2012 | A1 |
| 20120079363 | Folting et al. | Mar 2012 | A1 |
| 20120084117 | Tavares et al. | Apr 2012 | A1 |
| 20120084135 | Nissan et al. | Apr 2012 | A1 |
| 20120084287 | Lakshminarayan et al. | Apr 2012 | A1 |
| 20120084866 | Stolfo | Apr 2012 | A1 |
| 20120106801 | Jackson | May 2012 | A1 |
| 20120117082 | Koperda et al. | May 2012 | A1 |
| 20120131512 | Takeuchi et al. | May 2012 | A1 |
| 20120144335 | Abeln et al. | Jun 2012 | A1 |
| 20120158585 | Ganti | Jun 2012 | A1 |
| 20120159307 | Chung et al. | Jun 2012 | A1 |
| 20120159362 | Brown et al. | Jun 2012 | A1 |
| 20120159399 | Bastide et al. | Jun 2012 | A1 |
| 20120173381 | Smith | Jul 2012 | A1 |
| 20120173985 | Peppel | Jul 2012 | A1 |
| 20120196557 | Reich et al. | Aug 2012 | A1 |
| 20120196558 | Reich et al. | Aug 2012 | A1 |
| 20120208636 | Feige | Aug 2012 | A1 |
| 20120215784 | King et al. | Aug 2012 | A1 |
| 20120215898 | Shah et al. | Aug 2012 | A1 |
| 20120221511 | Gibson et al. | Aug 2012 | A1 |
| 20120221553 | Wittmer et al. | Aug 2012 | A1 |
| 20120221580 | Barney | Aug 2012 | A1 |
| 20120226523 | Weiss | Sep 2012 | A1 |
| 20120226590 | Love | Sep 2012 | A1 |
| 20120245976 | Kumar et al. | Sep 2012 | A1 |
| 20120246148 | Dror | Sep 2012 | A1 |
| 20120254129 | Wheeler et al. | Oct 2012 | A1 |
| 20120278249 | Duggal et al. | Nov 2012 | A1 |
| 20120290879 | Shibuya et al. | Nov 2012 | A1 |
| 20120296907 | Long et al. | Nov 2012 | A1 |
| 20120310831 | Harris et al. | Dec 2012 | A1 |
| 20120310838 | Harris et al. | Dec 2012 | A1 |
| 20120311684 | Paulsen et al. | Dec 2012 | A1 |
| 20120323888 | Osann, Jr. | Dec 2012 | A1 |
| 20120330973 | Ghuneim et al. | Dec 2012 | A1 |
| 20130006426 | Healey et al. | Jan 2013 | A1 |
| 20130006725 | Simanek et al. | Jan 2013 | A1 |
| 20130016106 | Yip et al. | Jan 2013 | A1 |
| 20130018796 | Kolhatkar et al. | Jan 2013 | A1 |
| 20130024307 | Fuerstenberg et al. | Jan 2013 | A1 |
| 20130024339 | Choudhuri et al. | Jan 2013 | A1 |
| 20130046842 | Muntz et al. | Feb 2013 | A1 |
| 20130054306 | Bhalla | Feb 2013 | A1 |
| 20130057551 | Ebert et al. | Mar 2013 | A1 |
| 20130060786 | Serrano et al. | Mar 2013 | A1 |
| 20130061169 | Pearcy et al. | Mar 2013 | A1 |
| 20130073377 | Heath | Mar 2013 | A1 |
| 20130073454 | Busch | Mar 2013 | A1 |
| 20130078943 | Biage et al. | Mar 2013 | A1 |
| 20130086482 | Parsons | Apr 2013 | A1 |
| 20130096988 | Grossman et al. | Apr 2013 | A1 |
| 20130101159 | Chao et al. | Apr 2013 | A1 |
| 20130110746 | Ahn | May 2013 | A1 |
| 20130110822 | Ikeda et al. | May 2013 | A1 |
| 20130111320 | Campbell et al. | May 2013 | A1 |
| 20130117651 | Waldman et al. | May 2013 | A1 |
| 20130150004 | Rosen | Jun 2013 | A1 |
| 20130151148 | Parundekar et al. | Jun 2013 | A1 |
| 20130151388 | Falkenborg et al. | Jun 2013 | A1 |
| 20130151453 | Bhanot et al. | Jun 2013 | A1 |
| 20130157234 | Gulli et al. | Jun 2013 | A1 |
| 20130160120 | Malaviya et al. | Jun 2013 | A1 |
| 20130166348 | Scotto | Jun 2013 | A1 |
| 20130166480 | Popescu et al. | Jun 2013 | A1 |
| 20130166550 | Buchmann et al. | Jun 2013 | A1 |
| 20130176321 | Mitchell et al. | Jul 2013 | A1 |
| 20130179420 | Park et al. | Jul 2013 | A1 |
| 20130185245 | Anderson | Jul 2013 | A1 |
| 20130185307 | El-Yaniv et al. | Jul 2013 | A1 |
| 20130211985 | Clark et al. | Aug 2013 | A1 |
| 20130224696 | Wolfe et al. | Aug 2013 | A1 |
| 20130226318 | Procyk | Aug 2013 | A1 |
| 20130226953 | Markovich et al. | Aug 2013 | A1 |
| 20130232045 | Tai et al. | Sep 2013 | A1 |
| 20130238616 | Rose et al. | Sep 2013 | A1 |
| 20130238664 | Hsu et al. | Sep 2013 | A1 |
| 20130246170 | Gross et al. | Sep 2013 | A1 |
| 20130246537 | Gaddala | Sep 2013 | A1 |
| 20130246597 | Iizawa et al. | Sep 2013 | A1 |
| 20130251233 | Yang et al. | Sep 2013 | A1 |
| 20130262328 | Federgreen | Oct 2013 | A1 |
| 20130262527 | Hunter et al. | Oct 2013 | A1 |
| 20130263019 | Castellanos et al. | Oct 2013 | A1 |
| 20130267207 | Hao et al. | Oct 2013 | A1 |
| 20130268520 | Fisher et al. | Oct 2013 | A1 |
| 20130279757 | Kephart | Oct 2013 | A1 |
| 20130282696 | John et al. | Oct 2013 | A1 |
| 20130290011 | Lynn et al. | Oct 2013 | A1 |
| 20130290825 | Arndt et al. | Oct 2013 | A1 |
| 20130297619 | Chandrasekaran et al. | Nov 2013 | A1 |
| 20130304770 | Boero et al. | Nov 2013 | A1 |
| 20130311375 | Priebatsch | Nov 2013 | A1 |
| 20130318594 | Hoy et al. | Nov 2013 | A1 |
| 20130325826 | Agarwal et al. | Dec 2013 | A1 |
| 20130339218 | Subramanian et al. | Dec 2013 | A1 |
| 20140006109 | Callioni et al. | Jan 2014 | A1 |
| 20140012724 | O'Leary et al. | Jan 2014 | A1 |
| 20140012796 | Petersen et al. | Jan 2014 | A1 |
| 20140019936 | Cohanoff | Jan 2014 | A1 |
| 20140032506 | Hoey et al. | Jan 2014 | A1 |
| 20140033010 | Richardt et al. | Jan 2014 | A1 |
| 20140040371 | Gurevich et al. | Feb 2014 | A1 |
| 20140047357 | Alfaro et al. | Feb 2014 | A1 |
| 20140058914 | Song et al. | Feb 2014 | A1 |
| 20140059038 | McPherson et al. | Feb 2014 | A1 |
| 20140068487 | Steiger et al. | Mar 2014 | A1 |
| 20140095273 | Tang et al. | Apr 2014 | A1 |
| 20140095363 | Caldwell | Apr 2014 | A1 |
| 20140095509 | Patton | Apr 2014 | A1 |
| 20140108068 | Williams | Apr 2014 | A1 |
| 20140108380 | Gotz et al. | Apr 2014 | A1 |
| 20140108985 | Scott et al. | Apr 2014 | A1 |
| 20140123279 | Bishop et al. | May 2014 | A1 |
| 20140129261 | Bothwell et al. | May 2014 | A1 |
| 20140136285 | Carvalho | May 2014 | A1 |
| 20140143009 | Brice et al. | May 2014 | A1 |
| 20140149272 | Hirani et al. | May 2014 | A1 |
| 20140149436 | Bahrami et al. | May 2014 | A1 |
| 20140156484 | Chan et al. | Jun 2014 | A1 |
| 20140156527 | Grigg et al. | Jun 2014 | A1 |
| 20140157172 | Peery et al. | Jun 2014 | A1 |
| 20140164502 | Khodorenko et al. | Jun 2014 | A1 |
| 20140189536 | Lange et al. | Jul 2014 | A1 |
| 20140195515 | Baker et al. | Jul 2014 | A1 |
| 20140195887 | Ellis et al. | Jul 2014 | A1 |
| 20140222521 | Chait | Aug 2014 | A1 |
| 20140222752 | Isman et al. | Aug 2014 | A1 |
| 20140222793 | Sadkin et al. | Aug 2014 | A1 |
| 20140229554 | Grunin et al. | Aug 2014 | A1 |
| 20140267294 | Ma | Sep 2014 | A1 |
| 20140267295 | Sharma | Sep 2014 | A1 |
| 20140278730 | Muhart | Sep 2014 | A1 |
| 20140279824 | Tamayo | Sep 2014 | A1 |
| 20140310282 | Sprague et al. | Oct 2014 | A1 |
| 20140316911 | Gross | Oct 2014 | A1 |
| 20140333651 | Cervelli et al. | Nov 2014 | A1 |
| 20140337772 | Cervelli et al. | Nov 2014 | A1 |
| 20140344230 | Krause et al. | Nov 2014 | A1 |
| 20140358789 | Boding et al. | Dec 2014 | A1 |
| 20140358829 | Hurwitz | Dec 2014 | A1 |
| 20140366132 | Stiansen et al. | Dec 2014 | A1 |
| 20150019394 | Unser et al. | Jan 2015 | A1 |
| 20150046870 | Goldenberg et al. | Feb 2015 | A1 |
| 20150073929 | Psota et al. | Mar 2015 | A1 |
| 20150073954 | Braff | Mar 2015 | A1 |
| 20150089424 | Duffield et al. | Mar 2015 | A1 |
| 20150095773 | Gonsalves et al. | Apr 2015 | A1 |
| 20150100897 | Sun et al. | Apr 2015 | A1 |
| 20150100907 | Erenrich et al. | Apr 2015 | A1 |
| 20150106379 | Elliot et al. | Apr 2015 | A1 |
| 20150134512 | Mueller | May 2015 | A1 |
| 20150135256 | Hoy et al. | May 2015 | A1 |
| 20150161611 | Duke et al. | Jun 2015 | A1 |
| 20150178825 | Huerta | Jun 2015 | A1 |
| 20150188872 | White | Jul 2015 | A1 |
| 20150227868 | Saraf | Aug 2015 | A1 |
| 20150227869 | Saraf | Aug 2015 | A1 |
| 20150338233 | Cervelli et al. | Nov 2015 | A1 |
| 20150379413 | Robertson et al. | Dec 2015 | A1 |
| 20160004764 | Chakerian et al. | Jan 2016 | A1 |
| 20160044054 | Stiansen et al. | Feb 2016 | A1 |
| 20160086185 | Adjaoute | Mar 2016 | A1 |
| 20160117486 | Blumenfeld et al. | Apr 2016 | A1 |
| 20160321661 | Hammond | Nov 2016 | A1 |
| 20170147654 | Saperstein et al. | May 2017 | A1 |
| 20170221063 | Mathura et al. | Aug 2017 | A1 |
| 20180075563 | Ananthanpillai et al. | Mar 2018 | A1 |
| 20180173769 | Saperstein et al. | Jun 2018 | A1 |
| 20230394605 | Man | Dec 2023 | A1 |
| Number | Date | Country |
|---|---|---|
| 102546446 | Jul 2012 | CN |
| 103167093 | Jun 2013 | CN |
| 102054015 | May 2014 | CN |
| 102014103482 | Sep 2014 | DE |
| 102014204827 | Sep 2014 | DE |
| 102014204830 | Sep 2014 | DE |
| 102014204834 | Sep 2014 | DE |
| 102014215621 | Feb 2015 | DE |
| 1191463 | Mar 2002 | EP |
| 1672527 | Jun 2006 | EP |
| 2487610 | Aug 2012 | EP |
| 2551799 | Jan 2013 | EP |
| 2555153 | Feb 2013 | EP |
| 2778977 | Sep 2014 | EP |
| 2835745 | Feb 2015 | EP |
| 2835770 | Feb 2015 | EP |
| 2838039 | Feb 2015 | EP |
| 2846241 | Mar 2015 | EP |
| 2851852 | Mar 2015 | EP |
| 2858014 | Apr 2015 | EP |
| 2858018 | Apr 2015 | EP |
| 2863326 | Apr 2015 | EP |
| 2863346 | Apr 2015 | EP |
| 2869211 | May 2015 | EP |
| 2889814 | Jul 2015 | EP |
| 2891992 | Jul 2015 | EP |
| 2892197 | Jul 2015 | EP |
| 2911078 | Aug 2015 | EP |
| 2963595 | Jan 2016 | EP |
| 2985729 | Feb 2016 | EP |
| 3142057 | Mar 2017 | EP |
| 2516155 | Jan 2015 | GB |
| 2518745 | Apr 2015 | GB |
| 2012778 | Nov 2014 | NL |
| 2013306 | Feb 2015 | NL |
| 624557 | Dec 2014 | NZ |
| WO 2000009529 | Feb 2000 | WO |
| WO 2005104736 | Nov 2005 | WO |
| WO 2005116851 | Dec 2005 | WO |
| WO 2008011728 | Jan 2008 | WO |
| WO 2009061501 | May 2009 | WO |
| WO 2010000014 | Jan 2010 | WO |
| WO 2010030913 | Mar 2010 | WO |
| WO 2013010157 | Jan 2013 | WO |
| WO 2013102892 | Jul 2013 | WO |
| WO 2013126281 | Aug 2013 | WO |
| Entry |
|---|
| US 8,712,906 B1, 04/2014, Sprague et al. (withdrawn) |
| “A First Look: Predicting Market Demand for Food Retail using a Huff Analysis,” TRF Policy Solutions, Jul. 2012, pp. 30. |
| “A Quick Guide to UniProtKB Swiss—Prot & TrEMBL,” Sep. 2011, pp. 2. |
| “A Word About Banks and the Laundering of Drug Money,” Aug. 18, 2012, http://www.golemxiv.co.uk/2012/08/a-word-about-banks-and-the-laundering-of-drug-money/. |
| Acklen, Laura, “Absolute Beginner's Guide to Microsoft Word 2003,” Dec. 24, 2003, pp. 15-18, 34-41, 308-316. |
| Alfred, Rayner “Summarizing Relational Data Using Semi-Supervised Genetic Algorithm-Based Clustering Techniques”, Journal of Computer Science, 2010, vol. 6, No. 7, pp. 775-784. |
| Amnet, “5 Great Tools for Visualizing Your Twitter Followers,” posted Aug. 4, 2010, http://www.amnetblog.com/component/content/article/115-5-grate-tools-for-visualizing-your-twitter-followers.html. |
| Ananiev et al., “The New Modality API,” http://web.archive.org/web/20061211011958/http://java.sun.com/developer/technicalArticles/J2SE/Desktop/javase6/modality/ Jan. 21, 2006, pp. 8. |
| Appacts, “Smart Thinking for Super Apps,” <http://www.appacts.com> Printed Jul. 18, 2013 in 4 pages. |
| Apsalar, “Data Powered Mobile Advertising,” “Free Mobile App Analytics” and various analytics related screen shots <http://apsalar.com> Printed Jul. 18, 2013 in 8 pages. |
| Bluttman et al., “Excel Formulas and Functions for Dummies,” 2005, Wiley Publishing, Inc., pp. 280, 284-286. |
| Boyce, Jim, “Microsoft Outlook 2010 Inside Out,” Aug. 1, 2010, retrieved from the internet https://capdtron.files.wordpress.com/2013/01/outlook-2010-inside_out.pdf. |
| Bugzilla@Mozilla, “Bug 18726—[feature] Long-click means of invoking contextual menus not supported,” http://bugzilla.mozilla.org/show_bug.cgi?id=18726 printed Jun. 13, 2013 in 11 pages. |
| Butkovic et al., “Using Whois Based Geolocation and Google Maps API for Support Cybercrime Investigations,” Recent Advances in Telecommunications and Circuits: Proceedings of the 11th International Conference on Applied Electromagnetics, Wireless and Optical Communications (ELECTROSCIENCE '13), Proceedings of the 2nd International Conference on Circuits, Systems, Communications, Computers and Applications (CSCCA '13), Proceedings of the 1st International Conference on Solid State Circuits (SSC '13), Proceedings of the 1st International Conference on Antennas & Propagation (ANPRO '13) :Dubrovnik, Croatia, Jun. 25-27, 2013, pp. 194-200, 2013. |
| Canese et al., “Chapter 2: PubMed: The Bibliographic Database,” The NCBI Handbook, Oct. 2002, pp. 1-10. |
| Capptain—Pilot Your Apps, <http://www.capptain.com> Printed Jul. 18, 2013 in 6 pages. |
| Celik, Tantek, “CSS Basic User Interface Module Level 3 (CSS3 UI),” Section 8 Resizing and Overflow, Jan. 17, 2012, retrieved from internet http://www.w3.org/TR/2012/WD-css3-ui-20120117/#resizing-amp-overflow retrieved on May 18, 2015. |
| Chaudhuri et al., “An Overview of Business Intelligence Technology,” Communications of the ACM, Aug. 2011, vol. 54, No. 8. |
| Chen et al., “Bringing Order to the Web: Automatically Categorizing Search Results,” CHI 2000, Proceedings of the SIGCHI conference on Human Factors in Computing Systems, Apr. 1-6, 2000, The Hague, The Netherlands, pp. 145-152. |
| Chen, “Design and application of criminal investigation DSS based on case reasoning”, 2010 2ndInternational Workshop on Database Technology and Applications, pp. 1-5, doi: 10.1109/DBTA.2010.5659104 (2010). |
| Chung, Chin-Wan, “Dataplex: An Access to Heterogeneous Distributed Databases,” Communications of the ACM, Association for Computing Machinery, Inc., vol. 33, No. 1, Jan. 1, 1990, pp. 70-80. |
| Cohn, et al., “Semi-supervised clustering with user feedback,” Constrained Clustering: Advances in Algorithms, Theory, and Applications 4.1 (2003): 17-32. |
| Conner, Nancy, “Google Apps: The Missing Manual,” May 1, 2008, pp. 15. |
| Countly Mobile Analytics, <http://count.ly/> Printed Jul. 18, 2013 in 9 pages. |
| Definition “Identify”, downloaded Jan. 22, 2015, 1 page. |
| Definition “Overlay”, downloaded Jan. 22, 2015, 1 page. |
| Delcher et al., “Identifying Bacterial Genes and Endosymbiont DNA with Glimmer,” BioInformatics, vol. 23, No. 6, 2007, pp. 673-679. |
| Distmo—App Analytics, <http://www.distimo.com/app-analytics> Printed Jul. 18, 2013 in 5 pages. |
| Dramowicz, Ela, “Retail Trade Area Analysis Using the Huff Model,” Directions Magazine, Jul. 2, 2005 in 10 pages, http://www.directionsmag.com/articles/retail-trade-area-analysis-using-the-huff-model/123411. |
| “The FASTA Program Package,” fasta-36.3.4, Mar. 25, 2011, pp. 29. |
| Flurry Analytics, <http://www.flurry.com/> Printed Jul. 18, 2013 in 14 pages. |
| GIS-NET 3 Public _ Department of Regional Planning. Planning & Zoning Information for Unincorporated LA County. Retrieved Oct. 2, 2013 from http://gis.planning.lacounty.gov/GIS-NET3_Public/Viewer.html. |
| Google Analytics Official Website—Web Analytics & Reporting, <http://www.google.com/analytics.index.html> Printed Jul. 18, 2013 in 22 pages. |
| Gorr et al., “Crime Hot Spot Forecasting: Modeling and Comparative Evaluation”, Grant 98-IJ-CX-K005, May 6, 2002, 37 pages. |
| Goswami, Gautam, “Quite Writly Said!,” One Brick at a Time, Aug. 21, 2005, pp. 7. |
| Griffith, Daniel A., “A Generalized Huff Model,” Geographical Analysis, Apr. 1982, vol. 14, No. 2, pp. 135-144. |
| Gu et al., “Record Linkage: Current Practice and Future Directions,” Jan. 15, 2004, pp. 32. |
| Hansen et al., “Analyzing Social Media Networks with NodeXL: Insights from a Connected World”, Chapter 4, pp. 53-67 and Chapter 10, pp. 143-164, published Sep. 2010. |
| Hardesty, “Privacy Challenges: Analysis: It's Surprisingly Easy to Identify Individuals from Credit-Card Metadata,” MIT News On Campus and Around the World, MIT News Office, Jan. 29, 2015, 3 pages. |
| Hibbert et al., “Prediction of Shopping Behavior Using a Huff Model Within a GIS Framework,” Healthy Eating in Context, Mar. 18, 2011, pp. 16. |
| Hogue et al., “Thresher: Automating the Unwrapping of Semantic Content from the World Wide Web,” 14th International Conference on World Wide Web, WWW 2005: Chiba, Japan, May 10-14, 2005, pp. 86-95. |
| Hua et al., “A Multi-attribute Data Structure with Parallel Bloom Filters for Network Services”, HiPC 2006, LNCS 4297, pp. 277-288, 2006. |
| Huff et al., “Calibrating the Huff Model Using ArcGIS Business Analyst,” ESRI, Sep. 2008, pp. 33. |
| Huff, David L., “Parameter Estimation in the Huff Model,” ESRI, ArcUser, Oct.-Dec. 2003, pp. 34-36. |
| “HunchLab: Heat Map and Kernel Density Calculation for Crime Analysis,” Azavea Journal, printed from www.azavea.com/blogs/newsletter/v4i4/kernel-density-capabilities-added-to-hunchlab/ on Sep. 9, 2014, 2 pages. |
| Kahan et al., “Annotea: an Open RDF Infrastructure for Shared Web Annotations”, Computer Networks, Elsevier Science Publishers B.V., vol. 39, No. 5, dated Aug. 5, 2002, pp. 589-608. |
| Keylines.com, “An Introduction to KeyLines and Network Visualization,” Mar. 2014, <http://keylines.com/wp-content/uploads/2014/03/KeyLines-White-Paper.pdf> downloaded May 12, 2014 in 8 pages. |
| Keylines.com, “KeyLines Datasheet,” Mar. 2014, <http://keylines.com/wp-content/uploads/2014/03/KeyLines-datasheet.pdf> downloaded May 12, 2014 in 2 pages. |
| Keylines.com, “Visualizing Threats: Improved Cyber Security Through Network Visualization,” Apr. 2014, <http://keylines.com/wp-content/uploads/2014/04/Visualizing-Threats1.pdf> downloaded May 12, 2014 in 10 pages. |
| Kitts, Paul, “Chapter 14: Genome Assembly and Annotation Process,” The NCBI Handbook, Oct. 2002, pp. 1-21. |
| Kontagent Mobile Analytics, <http://www.kontagent.com/> Printed Jul. 18, 2013 in 9 pages. |
| Li et al., “Interactive Multimodal Visual Search on Mobile Device,” IEEE Transactions on Multimedia, vol. 15, No. 3, Apr. 1, 2013, pp. 594-607. |
| Liu, Tianshun, “Combining GIS and the Huff Model to Analyze Suitable Locations for a New Asian Supermarket in the Minneapolis and St. Paul, Minnesota USA,” Papers in Resource Analysis, 2012, vol. 14, pp. 8. |
| Localytics—Mobile App Marketing & Analytics, <http://www.localytics.com/> Printed Jul. 18, 2013 in 12 pages. |
| Madden, Tom, “Chapter 16: The BLAST Sequence Analysis Tool,” The NCBI Handbook, Oct. 2002, pp. 1-15. |
| Manno et al., “Introducing Collaboration in Single-user Applications through the Centralized Control Architecture,” 2010, pp. 10. |
| Manske, “File Saving Dialogs,” <http://www.mozilla.org/editor/ui_specs/FileSaveDialogs.html>, Jan. 20, 1999, pp. 7. |
| Microsoft—Developer Network, “Getting Started with VBA in Word 2010,” Apr. 2010, <http://msdn.microsoft.com/en-us/library/ff604039%28v=office.14%29.aspx> as printed Apr. 4, 2014 in 17 pages. |
| Microsoft Office—Visio, “About connecting shapes,” <http://office.microsoft.com/en-us/visio-help/about-connecting-shapes-HP085050369.aspx> printed Aug. 4, 2011 in 6 pages. |
| Microsoft Office—Visio, “Add and glue connectors with the Connector tool,” <http://office.microsoft.com/en-us/visio-help/add-and-glue-connectors-with-the-connector-tool-HA010048532.aspx?CTT=1> printed Aug. 4, 2011 in 1 page. |
| Mixpanel—Mobile Analytics, <https://mixpanel.com/> Printed Jul. 18, 2013 in 13 pages. |
| Mizrachi, Ilene, “Chapter 1: GenBank: The Nuckeotide Sequence Database,” The NCBI Handbook, Oct. 2002, pp. 1-14. |
| “Money Laundering Risks and E-Gaming: A European Overview and Assessment,” 2009, http://www.cf.ac.uk/socsi/resources/Levi_Final_Money_Laundering_Risks_egaming.pdf. |
| Nierman, “Evaluating Structural Similarity in XML Documents”, 6 pages, 2002. |
| Nolan et al., “MCARTA: A Malicious Code Automated Run-Time Analysis Framework,” Homeland Security (HST) 2012 IEEE Conference on Technologies for, Nov. 13, 2012, pp. 13-17. |
| Olanoff, Drew, “Deep Dive with the New Google Maps for Desktop with Google Earth Integration, It's More than Just a Utility,” May 15, 2013, pp. 1-6, retrieved from the internet: http://web.archive.org/web/20130515230641/http://techcrunch.com/2013/05/15/deep-dive-with-the-new-google-maps-for-desktop-with-google-earth-integration-its-more-than-just-a-utility/. |
| Open Web Analytics (OWA), <http://www.openwebanalytics.com/> Printed Jul. 19, 2013 in 5 pages. |
| Palmas et al., “An Edge-Bunding Layout for Interactive Parallel Coordinates” 2014 IEEE Pacific Visualization Symposium, pp. 57-64. |
| Perdisci et al., “Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces,” USENIX, Mar. 18, 2010, pp. 1-14. |
| Piwik—Free Web Analytics Software. <http://piwik.org/> Printed Jul. 19, 2013 in18 pages. |
| “Potential Money Laundering Warning Signs,” snapshot taken 2003, https://web.archive.org/web/20030816090055/http:/finsolinc.com/ANTI-MONEY%20LAUNDERING%20TRAINING%20GUIDES.pdf. |
| “Refresh CSS Ellipsis When Resizing Container—Stack Overflow,” Jul. 31, 2013, retrieved from internet http://stackoverflow.com/questions/17964681/refresh-css-ellipsis-when-resizing-container, retrieved on May 18, 2015. |
| Rouse, Margaret, “OLAP Cube,” <http://searchdatamanagement.techtarget.com/definition/OLAP-cube>, Apr. 28, 2012, pp. 16. |
| Shah, Chintan, “Periodic Connections to Control Server Offer New Way to Detect Botnets,” Oct. 24, 2013 in 6 pages, <http://www.blogs.mcafee.com/mcafee-labs/periodic-links-to-control-server-offer-new-way-to-detect-botnets>. |
| Shi et al., “A Scalable Implementation of Malware Detection Based on Network Connection Behaviors,” 2013 International Conference on Cyber-Enabled Distributed Computing Knowledge Discovery, IEEE, Oct. 10, 2013, pp. 59-66. |
| Sigrist, et al., “PROSITE, a Protein Domain Database for Functional Characterization and Annotation,” Nucleic Acids Research, 2010, vol. 38, pp. D161-D166. |
| Sirotkin et al., “Chapter 13: The Processing of Biological Sequence Data at NCBI,” The NCBI Handbook, Oct. 2002, pp. 1-11. |
| StatCounter—Free Invisible Web Tracker, Hit Counter and Web Stats, <http://statcounter.com/> Printed Jul. 19, 2013 in 17 pages. |
| Symantec Corporation, “E-Security Begins with Sound Security Policies,” Announcement Symantec, Jun. 14, 2001. |
| TestFlight—Beta Testing On The Fly, <http://testflightapp.com/> Printed Jul. 18, 2013 in 3 pages. |
| trak.io, <http://trak.io/> printed Jul. 18, 2013 in 3 pages. |
| Umagandhi et al., “Search Query Recommendations Using Hybrid User Profile with Query Logs,” International Journal of Computer Applications, vol. 80, No. 10, Oct. 1, 2013, pp. 7-18. |
| UserMetrix, <http://usermetrix.com/android-analytics> printed Jul. 18, 2013 in 3 pages. |
| Valentini et al., “Ensembles of Learning Machines”, M. Marinaro and R. Tagliaferri (Eds.): Wirn Vietri 2002, LNCS 2486, pp. 3-20. |
| Vose et al., “Help File for ModelRisk Version 5,” 2007, Vose Software, pp. 349-353. [Uploaded in 2 Parts]. |
| Wang et al., “Research on a Clustering Data De-Duplication Mechanism Based on Bloom Filter,” IEEE 2010, 5 pages. |
| Wiggerts, T.A., “Using Clustering Algorithms in Legacy Systems Remodularization,” ReverseEngineering, Proceedings of the Fourth Working Conference, Netherlands, Oct. 6-8, 1997, IEEE Computer Soc., pp. 33-43. |
| Wikipedia, “Federated Database System,” Sep. 7, 2013, retrieved from the internet on Jan. 27, 2015 http://en.wikipedia.org/w/index.php?title=Federated_database_system&oldid=571954221. |
| Wikipedia, “Multimap,” Jan. 1, 2013, https://en.wikipedia.org/w/index.php?title=Multimap&oldid=530800748. |
| Yang et al., “HTML Page Analysis Based on Visual Cues”, A129, pp. 859-864, 2001. |
| Number | Date | Country | |
|---|---|---|---|
| 20210373721 A1 | Dec 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62686994 | Jun 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16116629 | Aug 2018 | US |
| Child | 17445037 | US |
