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This project focuses on the development and evaluation of an innovative intervention aiming to reduce CVD risk by promoting a healthy lifestyle among people with FH.MethodsThis project is designed as a randomised controlled trial in which individuals with FH will be assigned randomly to a control or intervention group. In the intervention group (n = 200), participants will receive a personalized intervention which is a combination of web-based tailored lifestyle advice and personal counselling by a lifestyle coach. The control group (n = 200) will receive care as usual. Primary outcomes are biological indicators of CVD risk: systolic blood pressure, glucose, BMI, waist circumference and lipids (triglycerides, total, LDL and HDL cholesterol). Secondary outcomes are: healthy lifestyle behaviour (with regard to smoking, physical activity, dietary pattern and compliance to statin therapy) and psychological correlates and determinants of healthy lifestyle behaviour (knowledge, attitude, risk perception, social influence, self-efficacy, cues to action, intention and autonomy). Measurement will take place at baseline, and at 3 and 12 months after randomisation. Additionally, a throughout process-evaluation will be conducted to assess and monitor intervention implementation during the trial.DiscussionResults of the PRO-FIT project will provide information about the effects and implementation of a healthy lifestyle intervention for individuals with FH. Our experiences with this intervention will be indicative about the suitability, feasibility and benefits of this approach for future interventions in other high-risk groups, such as Familial Combined Hypercholesterolemia (FCH) and diabetes.Trial registration numberNTR18992e:T8d9,ABSTRACTCONTEXT:A postgraduate and resident trainee mental health assistance center was created in September 1996 within our university.OBJECTIVE:To describe the clinical and demographic profile of its users.TYPE OF STUDY:Retrospective.SETTING:Universidade Federal de São Paulo — Escola Paulista de Medicina (Unifesp-EPM).METHODS:The study was carried between September 1996 and November 2002, when 233 semi-structured registration forms were filled out either by the psychologist or the psychiatrist during their first contact with the trainees, who were medical and nursing residents, and postgraduate students at specialization, master or doctoral levels. The registration forms included demographic, occupational and clinical data.RESULTS:The trainees were predominantly young (mean of 27 years old), single (82.0% of cases), women (79.4%), seeking help especially during the first year of training (63.1%). In 70.8% of the cases, they came to the service spontaneously. Such individuals showed greater adherence to the treatment than those who were referred by supervisors (p < 0.05). In 30% of the cases, the trainee sought psychological guidance or support at the service due to specific situational conflicts. Depression and anxiety disorders were the most frequent diagnoses; 22.3% of the trainees followed up mentioned a tendency towards suicidal thoughts. In comparison with other trainees, there was a higher prevalence of males among the medical residents (p < 0.01), with more cases of sleep disorders (p < 0.05), a smaller number of individuals refraining from the use of alcohol (p < 0.05) and a higher number of trainees requiring leave of absence (p < 0.001).DISCUSSION:The first year of training in health sciences is the most stressful, especially for women. Depression and anxiety symptoms are common, reflecting transitory self-limited deadaptation. However, the severity of the cases can also be evaluated in view of the large number of trainees who mentioned suicidal tendencies.CONCLUSIONS:This study emphasizes the need and importance of providing formal, structured and confidential mental health services for medical residents and postgraduate students from other health professions, in the training programs of academic institutions.2f:T6cb,The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.30:T7e3,BackgroundRibonucleotide reductases (RNRs) catalyse the only known de novo pathway for deoxyribonucleotide synthesis, and are therefore essential to DNA-based life. While ribonucleotide reduction has a single evolutionary origin, significant differences between RNRs nevertheless exist, notably in cofactor requirements, subunit composition and allosteric regulation. These differences result in distinct operational constraints (anaerobicity, iron/oxygen dependence and cobalamin dependence), and form the basis for the classification of RNRs into three classes.DescriptionIn RNRdb (Ribonucleotide Reductase database), we have collated and curated all known RNR protein sequences with the aim of providing a resource for exploration of RNR diversity and distribution. By comparing expert manual annotations with annotations stored in Genbank, we find that significant inaccuracies exist in larger databases. To our surprise, only 23% of protein sequences included in RNRdb are correctly annotated across the key attributes of class, role and function, with 17% being incorrectly annotated across all three categories. This illustrates the utility of specialist databases for applications where a high degree of annotation accuracy may be important. The database houses information on annotation, distribution and diversity of RNRs, and links to solved RNR structures, and can be searched through a BLAST interface. RNRdb is accessible through a public web interface at http://rnrdb.molbio.su.se.ConclusionRNRdb is a specialist database that provides a reliable annotation and classification resource for RNR proteins, as well as a tool to explore distribution patterns of RNR classes. The recent expansion in available genome sequence data have provided us with a picture of RNR distribution that is more complex than believed only a few years ago; our database indicates that RNRs of all three classes are found across all three cellular domains. Moreover, we find a number of organisms that encode all three classes.31:T9b1,BackgroundAdvances in molecular biology and growing requirements from biomarker validation studies have generated a need for tissue banks to provide quality-controlled tissue samples with standardized clinical annotation. The NCI Cooperative Prostate Cancer Tissue Resource (CPCTR) is a distributed tissue bank that comprises four academic centers and provides thousands of clinically annotated prostate cancer specimens to researchers. Here we describe the CPCTR information management system architecture, common data element (CDE) development, query interfaces, data curation, and quality control.MethodsData managers review the medical records to collect and continuously update information for the 145 clinical, pathological and inventorial CDEs that the Resource maintains for each case. An Access-based data entry tool provides de-identification and a standard communication mechanism between each group and a central CPCTR database. Standardized automated quality control audits have been implemented. Centrally, an Oracle database has web interfaces allowing multiple user-types, including the general public, to mine de-identified information from all of the sites with three levels of specificity and granularity as well as to request tissues through a formal letter of intent.ResultsSince July 2003, CPCTR has offered over 6,000 cases (38,000 blocks) of highly characterized prostate cancer biospecimens, including several tissue microarrays (TMA). The Resource developed a website with interfaces for the general public as well as researchers and internal members. These user groups have utilized the web-tools for public query of summary data on the cases that were available, to prepare requests, and to receive tissues. As of December 2005, the Resource received over 130 tissue requests, of which 45 have been reviewed, approved and filled. Additionally, the Resource implemented the TMA Data Exchange Specification in its TMA program and created a computer program for calculating PSA recurrence.ConclusionBuilding a biorepository infrastructure that meets today's research needs involves time and input of many individuals from diverse disciplines. The CPCTR can provide large volumes of carefully annotated prostate tissue for research initiatives such as Specialized Programs of Research Excellence (SPOREs) and for biomarker validation studies and its experience can help development of collaborative, large scale, virtual tissue banks in other organ systems.32:T435,It has been proposed that faces are represented in the visual brain as points within a multi-dimensional “face space”, with the average at its origin. We adapted a psychophysical procedure that measures non-linearities in contrast transduction (by measuring discrimination around different reference/pedestal levels of contrast) to examine the encoding of facial-identity within such a notional space. Specifically we had subjects perform identity discrimination at various pedestal levels of identity (varying from average/0% to caricature/125% identity) to derive “identity dipper functions”. Results indicate that subjects are generally best at spotting identity change in neither average nor full-identity faces, but rather in faces containing an intermediate level of identity (which varies from face-to-face). The overall pattern of results is consistent with the neural encoding of faces involving a single modest non-linear transformation of identity that is consistent across faces and subjects, but that it scaled according to the distinctiveness of the face.33:T5d0,BackgroundIn systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.ResultsWe illustrate an approach, through the discussion of a purpose built software architecture, which allows disparate groups to reuse tools and access data sources in a common manner. The architecture allows for: the rapid development of distributed applications; interoperability, so it can be used by a wide variety of developers and computational biologists; development using standard tools, so that it is easy to maintain and does not require a large development effort; extensibility, so that new technologies and data types can be incorporated; and non intrusive development, insofar as researchers need not to adhere to a pre-existing object model.ConclusionBy using a relatively simple integration strategy, based upon a common identity system and dynamically discovered interoperable services, a light-weight software architecture can become the focal point through which scientists can both get access to and analyse the plethora of experimentally derived data.34:T898,Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components) are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM) remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM) image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM), mosaicking and registration (ir-tools), and large slice viewers (MosaicBuilder, Viking) can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina), terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally, this framework provides a mechanism for parallelization of ssTEM imaging, volume assembly, and data analysis across an international user base, enhancing the productivity of a large cohort of electron microscopists.35:Ta31,BackgroundAllium sativum., commonly known as garlic, is a species in the onion genus (Allium), which is a large and diverse one containing over 1,250 species. Its close relatives include chives, onion, leek and shallot. Garlic has been used throughout recorded history for culinary, medicinal use and health benefits. Currently, the interest in garlic is highly increasing due to nutritional and pharmaceutical value including high blood pressure and cholesterol, atherosclerosis and cancer. For all that, there are no comprehensive databases available for Expressed Sequence Tags(EST) of garlic for gene discovery and future efforts of genome annotation. That is why we developed a new garlic database and applications to enable comprehensive analysis of garlic gene expression.DescriptionGarlicESTdb is an integrated database and mining tool for large-scale garlic (Allium sativum) EST sequencing. A total of 21,595 ESTs collected from an in-house cDNA library were used to construct the database. The analysis pipeline is an automated system written in JAVA and consists of the following components: automatic preprocessing of EST reads, assembly of raw sequences, annotation of the assembled sequences, storage of the analyzed information into MySQL databases, and graphic display of all processed data. A web application was implemented with the latest J2EE (Java 2 Platform Enterprise Edition) software technology (JSP/EJB/JavaServlet) for browsing and querying the database, for creation of dynamic web pages on the client side, and for mapping annotated enzymes to KEGG pathways, the AJAX framework was also used partially. The online resources, such as putative annotation, single nucleotide polymorphisms (SNP) and tandem repeat data sets, can be searched by text, explored on the website, searched using BLAST, and downloaded. To archive more significant BLAST results, a curation system was introduced with which biologists can easily edit best-hit annotation information for others to view. The GarlicESTdb web application is freely available at .ConclusionGarlicESTdb is the first incorporated online information database of EST sequences isolated from garlic that can be freely accessed and downloaded. It has many useful features for interactive mining of EST contigs and datasets from each library, including curation of annotated information, expression profiling, information retrieval, and summary of statistics of functional annotation. Consequently, the development of GarlicESTdb will provide a crucial contribution to biologists for data-mining and more efficient experimental studies.36:T81e,BackgroundThere is a severe shortage of nurses in Australia. Policy makers and researchers are especially concerned that retention levels of nurses in the health workforce have worsened over the last decade. There are also concerns that rapidly growing private sector hospitals are attracting qualified nurses away from the public sector. To date no systematic analysis of trends in nursing retention rates over time has been conducted due to the lack of consistent panel data.ResultsA 1.4 percentage point improvement in retention has led to a 10% increase in the overall supply of nurses in NSW. There has also been a substantial aging of the workforce, due to greater retention and an increase in mature age entrants. The improvement in retention is found in all types of premises and is largest in nursing homes. There is a substantial amount of year to year movement in and out of the workforce and across premises. The shortage of nurses in public hospitals is due to a slowdown in entry rather than competition from the rapidly growing private sector hospitals.Policy ImplicationsThe finding of an improvement (rather than a worsening) in retention suggests that additional improvements may be difficult to achieve as further retention must involve individuals more and more dissatisfied with nursing relative to other opportunities. Hence policies targeting entry such as increased places in nursing programs and additional subsidies for training costs may be more effective in dealing with the workforce shortage. This is also the case for shortages in public sector hospitals as retention in nursing is found to be relatively high in this sector. However, the large amount of year to year movements across nursing jobs, especially among the younger nurses, also suggests that policies aimed at reducing job switches and increasing the number who return to nursing should also be pursued. 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Once a user takes a poll, he/she will not have the option to"]},"substantiveFound":2,"substantiveTotal":6,"substantiveCoverage":0.3333333333333333,"representativeClaimNumber":1},{"cpc":[],"date":"1993-08-25T00:00:00Z","title":"Electronic voting system and relative method","areaFit":0,"assignee":"ITALTEST S.r.l.","coverage":0.3333333333333333,"elements":[{"text":"A poll-based networking system, comprising:","found":true,"label":"1pre","mapping":{"claimText":"A poll-based networking system, comprising:","claimElementLabel":"1pre"},"support":"generic_context","subClauses":[],"elementType":"substantive","mappingNote":"Generic context — no specific mapping applied"},{"text":"a data processing system having one or more processors and a memory, the memory being specifically encoded with instructions such that when executed, the instructions cause the one or more processors to perform operations of:","blurb":" teaches a computer with a processing unit and program/data memory, providing the 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A system as claimed in Claim 1, characterized by the fact that each said"],"descriptionText":["more detail later on with reference to Fig.3. 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115.","sourceParagraph":"¶[0039]","chatCitationToken":"{{spec:0039:\"Once the user submits a vote, in an exemplary embodiment of the invention, ITV client 116a, 116b can submit the vote to content provider NOC 101 via content distributor network facilities 115.\"}}","claimElementLabel":"1d"},"claimSpan":{"end":70,"start":0},"paragraph":"¶[0039]","claimAnchor":"receiving and storing a selected answer for the first polling question","sourceParagraph":"¶[0039]","chatCitationToken":"{{spec:0039:\"Once the user submits a vote, in an exemplary embodiment of the invention, ITV client 116a, 116b can submit the vote to content provider NOC 101 via content distributor network facilities 115.\"}}"},{"blurb":" teaches storing each user's vote at the content provider or client until needed.","quote":"each users' vote can be stored by content provider NOC 101 or by an ITV client application (not shown) or on ITV client 116a, 116b until the end of the broadcast or until when 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and","paragraph":"¶[0039]","disclosureQuote":"the compatibility measuring engine 114 applies the calculation rules to compare the identified user data and the friend's data.","sourceParagraph":"¶[0039]","chatCitationToken":"{{spec:0039:\"the compatibility measuring engine 114 applies the calculation rules to compare the identified user data and the friend's data.\"}}","claimElementLabel":"1e"},"claimSpan":{"end":73,"start":0},"paragraph":"¶[0039]","claimAnchor":"comparing the selected answer against the selected answers of other users","sourceParagraph":"¶[0039]","chatCitationToken":"{{spec:0039:\"the compatibility measuring engine 114 applies the calculation rules to compare the identified user data and the friend's data.\"}}"},{"blurb":" teaches determining compatibility by counting overlapping tastes or calculating similarity between data sets.","quote":"the compatibility measure may be determined using one or more of multiple possible methods: (a) counting the % of overlapping tastes (e.g., “we both like Hamlet”); (b) calculating a “Cosine Similarity” measure between the data sets","subId":"1e#1","mapping":{"subId":"1e#1","claimSpan":{"end":189,"start":111},"claimText":"comparing the selected answer against the selected answers of other users, based on the unique identification, to generate a likelihood of match between the user and each of the other users; and","paragraph":"¶[0028]","disclosureQuote":"the compatibility measure may be determined using one or more of multiple possible methods: (a) counting the % of overlapping tastes (e.g., “we both like Hamlet”); (b) calculating a “Cosine Similarity” measure between the data sets","sourceParagraph":"¶[0028]","chatCitationToken":"{{spec:0028:\"the compatibility measure may be determined using one or more of multiple possible methods: (a) counting the % of overlapping tastes (e.g., “we both like Hamlet”); (b) calculating a “Cosine Similarity” measure between the data sets\"}}","claimElementLabel":"1e"},"claimSpan":{"end":189,"start":111},"paragraph":"¶[0028]","claimAnchor":"to generate a likelihood of match between the user and each of the other users","sourceParagraph":"¶[0028]","chatCitationToken":"{{spec:0028:\"the compatibility measure may be determined using one or more of multiple possible methods: (a) counting the % of overlapping tastes (e.g., “we both like Hamlet”); (b) calculating a “Cosine Similarity” measure between the data sets\"}}"},{"blurb":" teaches calculating a compatibility measure for each user-friend pair.","quote":"a compatibility measure is determined or calculated for each pair wise combination of the user and one of their network friends.","subId":"1e#1","mapping":{"subId":"1e#1","claimSpan":{"end":189,"start":145},"claimText":"comparing the selected answer against the selected answers of other users, based on the unique identification, to generate a likelihood of match between the user and each of the other users; and","paragraph":"¶[0039]","disclosureQuote":"a compatibility measure is determined or calculated for each pair wise combination of the user and one of their network friends.","sourceParagraph":"¶[0039]","chatCitationToken":"{{spec:0039:\"a compatibility measure is determined or calculated for each pair wise combination of the user and one of their network friends.\"}}","claimElementLabel":"1e"},"claimSpan":{"end":189,"start":145},"paragraph":"¶[0039]","claimAnchor":"between the user and each of the other users","sourceParagraph":"¶[0039]","chatCitationToken":"{{spec:0039:\"a compatibility measure is determined or calculated for each pair wise combination of the user and one of their network friends.\"}}"},{"blurb":" teaches comparing metric values for the user and each friend to obtain compatibility.","quote":"the values of the metric are compared to obtain a measure of the similarity or compatibility between the user and each friends","subId":"1e#1","mapping":{"subId":"1e#1","claimSpan":{"end":189,"start":111},"claimText":"comparing the selected answer against the selected answers of other users, based on the unique identification, to generate a likelihood of match between the user and each of the other users; and","paragraph":"¶[0040]","correspondences":[{"kind":"relationship","subId":"1e#1","claimSpan":{"end":189,"start":145},"claimPhrase":"between the user and each of the other users","explanation":"The similarity or compatibility evaluation is made between the user and each friend.","referenceSpan":{"end":126,"start":93},"referencePhrase":"between the user and each friends"}],"disclosureQuote":"the values of the metric are compared to obtain a measure of the similarity or compatibility between the user and each friends","sourceParagraph":"¶[0040]","chatCitationToken":"{{spec:0040:\"the values of the metric are compared to obtain a measure of the similarity or compatibility between the user and each friends\"}}","claimElementLabel":"1e","unmappedClaimPhrases":["selected answer","selected answers of other users","based on the unique identification","likelihood of match"],"correspondenceSchemaVersion":1},"claimSpan":{"end":189,"start":111},"paragraph":"¶[0040]","claimAnchor":"to generate a likelihood of match between the user and each of the other users","correspondences":[{"kind":"relationship","subId":"1e#1","claimSpan":{"end":189,"start":145},"claimPhrase":"between the user and each of the other users","explanation":"The similarity or compatibility evaluation is made between the user and each friend.","referenceSpan":{"end":126,"start":93},"referencePhrase":"between the user and each friends"}],"sourceParagraph":"¶[0040]","chatCitationToken":"{{spec:0040:\"the values of the metric are compared to obtain a measure of the similarity or compatibility between the user and each friends\"}}","correspondenceSchemaVersion":1}],"paragraph":"¶[0028]","subClauses":[{"end":111,"text":"comparing the selected answer against the selected answers of other users, based on the unique identification, ","start":0,"subId":"1e#0"},{"end":194,"text":"to generate a likelihood of match between the user and each of the other users; and","start":111,"subId":"1e#1"}],"elementType":"substantive","mappingNote":"Cell verification: Comparison is disclosed, but selected answers, unique identification, and likelihood generation are not shown.","sourceParagraph":"¶[0028]","chatCitationToken":"{{spec:0028:\"during the calculation, the data about each friend are compared to the same category or related data about the user\"}}","unmappedClaimPhrases":["selected answer","selected answers of other users","based on the unique identification","likelihood of match"]},{"text":"displaying to the user the user profiles of other users that have a likelihood of match within a predetermined threshold.","found":false,"label":"1f","support":"not_found","subClauses":[{"end":121,"text":"displaying to the user the user profiles of other users that have a likelihood of match within a predetermined threshold.","start":0,"subId":"1f#0"}],"elementType":"substantive","mappingNote":"Cell verification: quote does not teach this element — Friend profiles are presented, but no predetermined likelihood-of-match threshold is disclosed.","sourceParagraph":"¶[0034]","chatCitationToken":"{{spec:0034:\"the friend's profile information, such as an image 351 (e.g., any type of a graphic representation, avatar, photo, etc.), identification information 353 (e.g., name, identifier, nick name), city and state, etc., associated with the friend may be presented to the user\"}}"}],"patentId":"US20080270038A1","deepCharted":true,"cellVerified":true,"patentNumber":"20080270038","scoreDetails":{"caveat":"Coverage is an LLM-supported element-hit fraction for triage; 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The method of claim 1, further"],"descriptionText":["who are part of the same social network.\n\nWith the rapid increase in interest in social networks and social networking applications, users of such","their compatibility with friends. Subsequently, a compatibility measure between the user and the friend is determined. The determined compatibility","collaborative filtering or recommendation processes.\n\nModified Cosine-Based Similarity\n\nIn one embodiment, the cosine similarity measure may be"]},"substantiveFound":2,"substantiveTotal":6,"substantiveCoverage":0.3333333333333333,"representativeClaimNumber":1},{"cpc":[],"date":"2008-10-23T00:00:00Z","title":"Systems and Method of Network Operation and Information Processing, Including Data Acquisition, Processing and Provision, Including Data Acquisition, Processing and Provision and/or Interoperability Features","areaFit":0,"assignee":"FEEVA TECHNOLOGY. 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of:","start":150,"subId":"1a#2"}],"elementType":"substantive","mappingNote":"Cell verification: quote does not teach this element — Architecture/platform language does not disclose processors, memory, or encoded executable instructions.","sourceParagraph":"¶[0076]","chatCitationToken":"{{spec:0076:\"a software architecture or software platform, for realizing embodiments of the invention, may structured as set forth in FIG. 1B.\"}}"},{"text":"receiving user information from a user to generate a unique user profile for the user;","blurb":" teaches acquiring user profile and identifier information and saving it to a database, including building an existing profile.","found":true,"label":"1b","quote":"the DTD Server 160 acquires user profile and selective anonymous user identifier information, and begins saving this information to a database, this information can be new or simply building upon an existing profile","mapping":{"subId":"1b#0","claimSpan":{"end":85,"start":0},"claimText":"receiving user information from a user to generate a unique user profile for the user;","paragraph":"¶[0118]","correspondences":[{"kind":"identity","subId":"1b#0","claimSpan":{"end":26,"start":10},"claimPhrase":"user information","explanation":"User-profile and identifier data constitute information associated with the user.","referenceSpan":{"end":92,"start":28},"referencePhrase":"user profile and selective anonymous user identifier information"}],"disclosureQuote":"the DTD Server 160 acquires user profile and selective anonymous user identifier information, and begins saving this information to a database, this information can be new or simply building upon an existing profile","sourceParagraph":"¶[0118]","chatCitationToken":"{{spec:0118:\"the DTD Server 160 acquires user profile and selective anonymous user identifier information, and begins saving this information to a database, this information can be 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Location-centric information for each user logged-on to the network is relayed to a server, and user-profile information for each user is retrieved from a database. In some embodiments, if a user-profile is not present in the database, a new entry and profile-information may be created in the database.","subId":"1b#0","mapping":{"subId":"1b#0","claimSpan":{"end":85,"start":42},"claimText":"receiving user information from a user to generate a unique user profile for the user;","paragraph":"¶[0013]","correspondences":[{"kind":"identity","subId":"1b#0","claimSpan":{"end":72,"start":60},"claimPhrase":"user profile","explanation":"The quoted user-profile information is the claimed profile associated with a user.","referenceSpan":{"end":181,"start":143},"referencePhrase":"user-profile information for each user"}],"disclosureQuote":"gathering user-related information from users. Location-centric information for each user logged-on to the network is relayed to a server, and user-profile information for each user is retrieved from a database. In some embodiments, if a user-profile is not present in the database, a new entry and profile-information may be created in the database.","sourceParagraph":"¶[0013]","chatCitationToken":"{{spec:0013:\"gathering user-related information from users. Location-centric information for each user logged-on to the network is relayed to a server, and user-profile information for each user is retrieved from a database. In some embodiments, if a user-profile is not present in the database, a new entry and profile-information may be created in the database.\"}}","claimElementLabel":"1b","unmappedClaimPhrases":["unique"],"correspondenceSchemaVersion":1},"claimSpan":{"end":85,"start":42},"paragraph":"¶[0013]","claimAnchor":"generate a unique user profile for the user","correspondences":[{"kind":"identity","subId":"1b#0","claimSpan":{"end":72,"start":60},"claimPhrase":"user profile","explanation":"The quoted user-profile information is the claimed profile associated with a user.","referenceSpan":{"end":181,"start":143},"referencePhrase":"user-profile information for each user"}],"sourceParagraph":"¶[0013]","chatCitationToken":"{{spec:0013:\"gathering user-related information from users. Location-centric information for each user logged-on to the network is relayed to a server, and user-profile information for each user is retrieved from a database. In some embodiments, if a user-profile is not present in the database, a new entry and profile-information may be created in the database.\"}}","correspondenceSchemaVersion":1},{"blurb":" teaches creating a simple profile from information about an end user, without requiring personal information.","quote":"A simple profile can be created without knowing any personal information of an end user","subId":"1b#0","mapping":{"subId":"1b#0","claimSpan":{"end":85,"start":42},"claimText":"receiving user information from a user to generate a unique user profile for the user;","paragraph":"¶[0121]","correspondences":[{"kind":"identity","subId":"1b#0","claimSpan":{"end":72,"start":60},"claimPhrase":"user profile","explanation":"The quoted simple profile is a user profile in the context of an end user.","referenceSpan":{"end":16,"start":0},"referencePhrase":"A simple profile"}],"disclosureQuote":"A simple profile can be created without knowing any personal information of an end 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","start":0,"subId":"1c#0"},{"end":131,"text":"the first polling question having a finite set of answers and a unique identification;","start":45,"subId":"1c#1"}],"elementType":"substantive","mappingNote":"Cell verification: quote does not teach this element — Transmitting survey questions does not disclose finite answer sets or unique question identification.","sourceParagraph":"¶[0118]","chatCitationToken":"{{spec:0118:\"DTD Server 160 then transmits first data such as a terms and conditions (T&C) page with these survey questions to the user (step 240).\"}}"},{"text":"receiving and storing a selected answer for the first polling question;","found":false,"label":"1d","support":"not_found","subClauses":[{"end":71,"text":"receiving and storing a selected answer for the first polling question;","start":0,"subId":"1d#0"}],"elementType":"substantive","mappingNote":"Cell verification: quote does not teach this element — Selecting an accept button does not disclose receiving and storing the selected answer.","sourceParagraph":"¶[0118]","chatCitationToken":"{{spec:0118:\"The user may then answer the survey questions and acknowledge the terms and conditions, for example, by selecting an “accept” button (step 250).\"}}"},{"text":"comparing the selected answer against the selected answers of other users, based on the unique identification, to generate a likelihood of match between the user and each of the other users; and","found":false,"label":"1e","support":"not_found","subClauses":[{"end":111,"text":"comparing the selected answer against the selected answers of other users, based on the unique identification, ","start":0,"subId":"1e#0"},{"end":194,"text":"to generate a likelihood of match between the user and each of the other users; and","start":111,"subId":"1e#1"}],"elementType":"substantive","mappingNote":"Cell verification: quote does not teach this element — Querying identifiers and answered questions does not disclose answer comparison or likelihood-of-match 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The user may then answer the survey questions and acknowledge the terms and conditions, for example, by","location profile database to see if the profile tag is set to on or off. 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A method for polling, comprising: receiving responses to a poll from one or more poll respondents; using information in a database to determine the"],"descriptionText":["questions to the one or more poll respondents.\n\nIn some embodiments, providing the poll involves providing the poll to one or more poll respondents","to polling service 200.\n\nNext, as shown in FIG. 3C, polling service 200 receives the poll responses (step 320).\n\nPolling service 200 then determines"]},"substantiveFound":0,"substantiveTotal":6,"substantiveCoverage":0,"representativeClaimNumber":1}],"parentGapLabels":[]}],"methodology":{"funnel":{"charted":3,"evaluated":51,"retrieved":32700},"queries":["profile registration identity","user enrollment attribute record","personal profile creation","member attributes identity record","social profile registration","user attributes profile generation","poll question answer choices","finite response set question identifier","survey item enumerated responses","question ID 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