Abstract: A system and method for a patent search and analytics software tool that finds prior art for each claim limitation/element by breaking up claims into individual claim limitations. Once the claims are separated, the system finds the best prior art for each of the individual separate, different claim limitations/elements. Additionally, the software finds the best prior art for entire claims, including non-patent literature (NPL) searching. The system takes into account the limitations of the claim under consideration (query claim of query patent), the text of the art, the link structure of the citation network, and the patent classification and then constructs a network that consists of two types of nodes: (i) the art (patents and non-patent literature) and the (ii) classes of the patent classification. Each art node is linked to all the art nodes that it cites and is linked to all the classification nodes that it belongs to.

Abstract: A computer-implemented method performed at least in part by a graph database configured to store at least one graph dataset. The method includes receiving a graph query configured to be performed against a machine learning model, and communicating the graph query with a machine learning system that is configured to use the machine learning model to obtain model inference results and communicate those model inference results to the graph database application. The graph database provides query results based at least in part on the model inference results to an entity.

Abstract: Qscore is the most advanced tool available for ranking the potential commercial value of a patent or a portfolio of patents. Other ranking methods typically rely heavily on a patent's reference graph (citations to/from other patents). Qscore is far more sophisticated: using data mining tokenization techniques, Qscore takes into account multiple factors correlated with patent value. This document generally describes the method used to assign a quality score to each patent, which is used to bias the ranking of the results returned from the keyword-based searching in the analytics embodiment of the present invention. This quality score, denoted by Qscore, is designed to identify the patents that are not only relevant to the user's query but also possess some additional, query independent, quality characteristics. Consequently, Qscore can be considered as an information filtering aid—designed to identify the “good” information from a “sea” of information.

Abstract: A system and method for a patent search and analytics software tool that finds prior art for each claim limitation/element by breaking up claims into individual claim limitations. Once the claims are separated, the system finds the best prior art for each of the individual separate, different claim limitations/elements. Additionally, the software finds the best prior art for entire claims, including non-patent literature (NPL) searching. The system takes into account the limitations of the claim under consideration (query claim of query patent), the text of the art, the link structure of the citation network, and the patent classification and then constructs a network that consists of two types of nodes: (i) the art (patents and non-patent literature) and the (ii) classes of the patent classification. Each art node is linked to all the art nodes that it cites and is linked to all the classification nodes that it belongs to.

Abstract: A computer-implemented method performed at least in part by a graph database configured to store at least one graph dataset. The method includes receiving a graph query configured to be performed against a machine learning model, and communicating the graph query with a machine learning system that is configured to use the machine learning model to obtain model inference results and communicate those model inference results to the graph database application. The graph database provides query results based at least in part on the model inference results to an entity.

Abstract: Qscore is the most advanced tool available for ranking the potential commercial value of a patent or a portfolio of patents. Other ranking methods typically rely heavily on a patent's reference graph (citations to/from other patents). Qscore is far more sophisticated: using data mining tokenization techniques, Qscore takes into account multiple factors correlated with patent value. This document generally describes the method used to assign a quality score to each patent, which is used to bias the ranking of the results returned from the keyword-based searching in the analytics embodiment of the present invention. This quality score, denoted by Qscore, is designed to identify the patents that are not only relevant to the user's query but also possess some additional, query independent, quality characteristics. Consequently, Qscore can be considered as an information filtering aid—designed to identify the “good” information from a “sea” of information.

Abstract: The present invention is a patent search and analytics software tool (Zuse) that finds prior art for each claim limitation/element. The software automatically breaks up every claim into individual claim limitations. For example, our software can automatically break up a claim into about five (5) separate, different claim limitations/elements. Then, it can find the best prior art for each of the five (5) separate, different claim limitations/elements. This is very helpful when you cannot find a prior art reference for only part of a claim. Also, the software finds the best prior art for the entire claims. Our software also includes non-patent literature (NPL) searching. ZUSE identifies relevant prior art by taking into account the limitations of the claim under consideration (query claim of query patent), the text of the art, the link structure of the citation network, and the patent classification.

Abstract: A decentralized network, the network capable of deploying an automated system and method for determining the value of an intangible asset or intellectual property and developing a fair remuneration structure for licensing or purchasing the intangible asset or intellectual property by comparison to a dissected value of prior licensing and sale of transactions. Valuation determinants, including remuneration structures from prior transactions, registrability, and ability to withstand challenge are extracted, analyzed and weighted and loaded into a knowledge base. The knowledge base growing based on a decentralized and public facing approach. Remuneration structures are normalized and used to train predictive algorithms based on a market analysis and previous transactions. The algorithms are able both to learn from previous transactions and to assess the importance of particular valuation determinants in determining the value under particular circumstances.

Abstract: The present invention is a patent search and analytics software tool (Zuse) that finds prior art for each claim limitation/element. The software automatically breaks up every claim into individual claim limitations. For example, our software can automatically break up a claim into about five (5) separate, different claim limitations/elements. Then, it can find the best prior art for each of the five (5) separate, different claim limitations/elements. This is very helpful when you cannot find a prior art reference for only part of a claim. Also, the software finds the best prior art for the entire claims. Our software also includes non-patent literature (NPL) searching. ZUSE identifies relevant prior art by taking into account the limitations of the claim under consideration (query claim of query patent), the text of the art, the link structure of the citation network, and the patent classification.

Abstract: Qscore is the most advanced tool available for ranking the potential commercial value of a patent or a portfolio of patents. Other ranking methods typically rely heavily on a patent's reference graph (citations to/from other patents). Qscore is far more sophisticated: using data mining tokenization techniques, Qscore takes into account multiple factors correlated with patent value. This document generally describes the method used to assign a quality score to each patent, which is used to bias the ranking of the results returned from the keyword-based searching in the analytics embodiment of the present invention. This quality score, denoted by Qscore, is designed to identify the patents that are not only relevant to the user's query but also possess some additional, query independent, quality characteristics. Consequently, Qscore can be considered as an information filtering aid—designed to identify the “good” information from a “sea” of information.

Abstract: Multiple parallel slave processes and a master process are assigned to a node executing an operating system such that the operating system maintains a ready queue comprising a list of one or more processes that are ready to be executed by at least one processing core. A parallel slave process takes an action that causes the operating system to keep the parallel slave process out of the ready queue. Based on receiving an indication that the parallel slave process is to be kept out of the ready queue, the master process sets the parallel slave process to a blocking state, selects a second parallel slave process that is in a runnable state but is currently kept from being in the ready queue, and takes an action that causes the operating system to add the parallel slave process that is in the runnable state to the ready queue.

Abstract: Multiple parallel slave processes and a master process are assigned to a node executing an operating system such that the operating system maintains a ready queue comprising a list of one or more processes that are ready to be executed by at least one processing core. A parallel slave process takes an action that causes the operating system to keep the parallel slave process out of the ready queue. Based on receiving an indication that the parallel slave process is to be kept out of the ready queue, the master process sets the parallel slave process to a blocking state, selects a second parallel slave process that is in a runnable state but is currently kept from being in the ready queue, and takes an action that causes the operating system to add the parallel slave process that is in the runnable state to the ready queue.

Abstract: Systems and methods for making demographic predictions for websites and web-pages. Embodiments include a system and a method of making demographic predictions for websites. The system and method select one or more websites with known demographic attributes for use as training websites, obtain demographic attributes data of the training websites, determine first features of web-pages of the training websites and develop a prediction model using the determined first features and the obtained demographic attributes data. The prediction model predicts one or more values for a target demographic attribute. The system and method determine second features of web-pages of a target website and apply the prediction model to the determined second features of the target website to predict one or more values for the target demographic attribute of the target website.

Abstract: Systems and methods for real-time location and inquiry based information delivery are described. Embodiments of a method for real-time location and inquiry based information delivery include receiving, using a processor and memory, a customer inquiry. The customer inquiry includes product information indicative of a customer's interest in a particular product. The method further receives location information indicative of the customer's current geographical location, determines additional product information based on the customer inquiry, identifies one or more incentives for presentation to the customer based on at least a portion of the product information and the location information. The customer inquiry is received prior to any solicitation of the customer's interest in the particular product.

Abstract: Systems and methods for making demographic predictions for websites and web-pages. Embodiments include a system and a method of making demographic predictions for websites. The system and method select one or more websites with known demographic attributes for use as training websites, obtain demographic attributes data of the training websites, determine first features of web-pages of the training websites and develop a prediction model using the determined first features and the obtained demographic attributes data. The prediction model predicts one or more values for a target demographic attribute. The system and method determine second features of web-pages of a target website and apply the prediction model to the determined second features of the target website to predict one or more values for the target demographic attribute of the target website.

Abstract: Systems and methods for predicting characteristics of a web user, determining a combination of websites to obtain a target demographic mix, determining a set of keywords to buy to obtain a target demographic mix, selecting websites from market research and designing websites to appeal to an audience with desired demographic characteristics. Systems and methods may include determining features of web-pages of ad-carrying, target websites, applying prediction models to the determined features of the ad-carrying, target websites to predict values of demographic attributes of the ad-carrying, target websites, receiving one or more inputs including a target demographic mix, receiving a number that indicates an amount of visitors of the ad-carrying, target websites, and determining a combination of websites that provide target demographic mix based on the predicted values of the demographic attributes and number of visitors of the ad-carrying, target websites.