Abstract: Disclosed are a method, machine-readable code, and a system for matching one or more citation tags with citation-rich documents or with professionals who are associated with a group of citation tags. The method takes a user input that can be converted to one or more primary search tags, and accesses a matrix of pair-wise tag co-occurrence values that are related to the co-occurrence of each pair of tags extracted from documents contained in a collection of citation-rich documents, to identify, for each primary tag received those secondary tags whose pair-wise co-occurrence values with respect to the primary tag is above a selected threshold value. A tag search vector constructed from the secondary tags and optionally, the primary vectors, is used in a database search to identify those documents or professionals having the highest tag-matching score with respect to the tag search vector, and these results are then displayed to the user.

Abstract: Disclosed are a method, machine-readable code, and a database for use in identifying, among a group of patent practitioners, one or more practitioners having expertise related to a given invention or technology. In the method, a search query related to the given invention or technology is used to identify one or more texts of patent abstracts or claims or patent class definitions having high term matches with the user-input query. The identified text(s) are linked to patent-class tags associated with the texts, and the identified tags are linked to one or more members of a group of patent practitioners who wrote and/or prosecuted patents having the patent-class assignments.

Abstract: A computer method, system and code, for representing a natural-language document in a vector form suitable for text manipulation operations are disclosed. The method involves determining (a) for each of a plurality of terms selected from one of (i) non-generic words in the document, (ii) proximately arranged word groups in the document, and (iii) a combination of (i) and (ii), a selectivity value of the term related to the frequency of occurrence of that term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively. The document is represented as a vector of terms, where the coefficient assigned to each term includes a function of the selectivity value determined for that term.

Abstract: High-throughput screening method and apparatus arm described. The method includes placing cells on a substrate defining a plurality of discrete microwells, at a well density of greater than about 100/cm2, with the number of cells in each well being less than about 1000, and where the cells in each well have been exposed to a selected agent. The change in conductance in each well is determined by applying a low-voltage, AC signal across a pair of electrodes placed in that well, and synchronously measuring the conductance across the electrodes, to monitor the level of growth or metabolic activity of cells contained in each well. Also disclosed is an apparatus for carrying out the screening method.

Abstract: Disclosed is an automated system, machine-readable storage medium embodying computer-executable code, and method for generating descriptive words and optionally, multi-word groups derived from a digitally encoded, natural-language input text that describes a concept, invention, or event in a selected field. The system includes (a) an electronic digital computer, (b) a database of words and optionally, word-groups derived from a plurality of texts, and (c) machine-readable storage medium embodying computer-executable code for accessing the database. The database provides, or can be used to calculate, a selectivity value for each of the words and optionally, word groups contained in or derived from the input text. Words and optionally, word groups having an above-threshold selectivity value are selected as descriptive terms from the input text.

Abstract: Disclosed are a computer-readable code, system and method for classifying a target document in the form of a digitally encoded natural-language text as belonging to one or more of two or more different classes. For each of a plurality of non-generic words and/or words groups characterizing the target document, there is determined a selectivity value calculated as the frequency of occurrence of that term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively, and the document is represented as a vector of terms, where the coefficient assigned to each term is a function of the selectivity value determined for that term. There is then determined, for each of the plurality of sample texts having associated classification identifiers, a match score related to the number of descriptive terms present in or derived from that text that match those in the target text.

Abstract: Disclosed are a computer-readable code, system and method for classifying a target document in the form of a digitally encoded natural-language text as belonging to one or more of two or more different classes. Each of a plurality of non-generic words and optionally, words groups characterizing the target document is selected as a descriptive term if the term has an above-threshold selectivity value in at least one library of texts in a field, where the selectivity value of a term is a measure of the field-specificity of that term. There is then determined, for each of the plurality of sample texts having associated classification identifiers, a match score related to the number of descriptive terms present in or derived from that text that match those in the target text. From the selected matched texts, and the associated classification identifiers, a classification determination of the target document is made.

Abstract: A computer method for representing a natural-language document in a vector form suitable for text manipulation operations is disclosed. The method involves determining (a) for each of a plurality of terms composed of non-generic words and, optionally, proximately arranged word groups in the document, a selectivity value of the term related to the frequency of occurrence of that term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively. The document is represented as a vector of terms, where the coefficient assigned to each term includes a function of the selectivity value determined for that term, and optionally related to the inverse document frequency of that word in one or more libraries of texts. Also disclosed are a computer-readable code for carrying out the method, a computer system that employs the code, and a vector produced by the method.

Abstract: A computer method for representing a natural-language document in a vector form suitable for text manipulation operations is disclosed. The method involves determining (a) for each of a plurality of terms composed of non-generic words and, optionally, proximately arranged word groups in the document, a selectivity value of the term related to the frequency of occurrence of that term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively. The document is represented as a vector of terms, where the coefficient assigned to each term includes a function of the selectivity value determined for that term, and optionally related to the inverse document frequency of that word in one or more libraries of texts. Also disclosed are a computer-readable code for carrying out the method, a computer system that employs the code, and a vector produced by the method.

Abstract: A method and system for registering volumetric scan data of a patient surgical site with actual patient position are disclosed. In practicing the method, position data relating to (i) the x,y,z coordinates of a detector screen in fixed coordinate system, and the x,y, coordinates of patient features on the screen are used to determine the x,y,z coordinates of the patient features in the fixed coordinate system. These coordinates are then matched with the coordinates of the same patient features taken from pre-op scan data, e.g., CT scan data, to place the CT scan data in the fixed coordinate system.

Abstract: A computer method for representing a natural-language document in a vector form suitable for text manipulation operations is disclosed. The method involves determining (a) for each of a plurality of terms selected from one of (i) non-generic words in the document, (ii) proximately arranged word groups in the document, and (iii) a combination of (i) and (ii), a selectivity value of the term related to the frequency of occurrence of that term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively. The document is represented as a vector of terms, where the coefficient assigned to each term includes a function of the selectivity value determined for that term, and optionally related to the inverse document frequency of that word in one or more libraries of texts.

Abstract: Disclosed are a computer-readable code, system and method for comparing a target concept, invention, or event with each of a plurality of texts. Each of a plurality of non-generic words and optionally, words groups characterizing the target concept, invention, or event, is selected as a vector term if the term has an above-threshold selectivity value in at least one library of texts in a field, where the selectivity value of a term is a measure of the field-specificity of that term. There is then determined, for each of the plurality of texts, a match score related to the number of vector terms present in or derived from that text that match those in the target concept, invention, or event. Texts having the highest match scores are selected.

Abstract: Disclosed are a computer-readable code, system and method for classifying a target document in the form of a digitally encoded natural-language text as belonging to one or more of two or more different classes. For each of a plurality of non-generic words and/or words groups characterizing the target document, there is determined a selectivity value calculated as the frequency of occurrence of that term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively, and the document is represented as a vector of terms, where the coefficient assigned to each term is a function of the selectivity value determined for that term. There is then determined, for each of the plurality of sample texts having associated classification identifiers, a match score related to the number of descriptive terms present in or derived from that text that match those in the target text.

Abstract: Disclosed are a computer-readable code, system and method for classifying a target document in the form of a digitally encoded natural-language text as belonging to one or more of two or more different classes. Each of a plurality of non-generic words and optionally, words groups characterizing the target document is selected as a descriptive term if the term has an above-threshold selectivity value in at least one library of texts in a field, where the selectivity value of a term is a measure of the field-specificity of that term. There is then determined, for each of the plurality of sample texts having associated classification identifiers, a match score related to the number of descriptive terms present in or derived from that text that match those in the target text. From the selected matched texts, and the associated classification identifiers, a classification determination of the target document is made.

Abstract: Disclosed are a computer-readable code, system and method for comparing a target concept, invention, or event with each of a plurality of texts. Each of a plurality of non-generic words and optionally, words groups characterizing the target concept, invention, or event, is selected as a descriptive term if the term has an above-threshold selectivity value in at least one library of texts in a field, where the selectivity value of a term is a measure of the field-specificity of that term. There is then determined, for each of the plurality of texts, a match score related to the number of descriptive terms present in or derived from that text that match those in the target concept, invention, or event. Texts having the highest match scores are selected.

Abstract: Disclosed is an automated system, machine-readable code, and method for generating descriptive words and optionally, multi-word groups derived from a digitally encoded, natural-language input text that describes a concept, invention, or event in a selected field. The system includes (a) an electronic digital computer, (b) a database of words and optionally, word-groups derived from a plurality of texts, and (c) computer-readable code for accessing the database. The database provides, or can be used to calculate, a selectivity value for each of the words and optionally, word groups contained in or derived from the input text. Words and optionally, word groups having an above-threshold selectivity value are selected as descriptive terms from the input text.

Abstract: Disclosed is a computer-accessible database composed of a list of non-generic words contained in a plurality of digitally encoded texts. Associated with each term is a selectivity value or values that are related to the frequency of occurrence of that word in at least one library of texts in a field, relative to the frequency of occurrence of the same word in one or more libraries of texts in one or more other fields, respectively. Also associated with each term are one or more text identifiers identifying one or more of the digitally processed texts containing that word. Each text identifier may be further associated with sentence and word-number identifiers that identify the sentence and word number(s) of a given database word.

Abstract: High-throughput screening method and apparatus arm described. The method includes placing cells on a substrate defining a plurality of discrete microwells, at a well density of greater than about 100/cm2, with the number of cells in each well being less than about 1000, and where the cells in each well have been exposed to a selected agent. The change in conductance in each well is determined by applying a low-voltage, AC signal across a pair of electrodes placed in that well, and synchronously measuring the conductance across the electrodes, to monitor the level of growth or metabolic activity of cells contained in each well. Also disclosed is an apparatus for carrying out the screening method.

Abstract: High-throughput screening method and apparatus are described. The method includes placing cells on a substrate defining a plurality of discrete microwells, at a well density of greater than about 100/cm2, with the number of cells in each well being less than about 1000, and where the cells in each well have been exposed to a selected agent. The change in conductance in each well is determined by applying a low-voltage, AC signal across a pair of electrodes paced in that well, and synchronously measuring the conductance across the electrodes, to monitor the level of growth or metabolic activity of cells contained in each well. Also disclosed is an apparatus for carrying out the screening method.

Abstract: A method and system for registering volumetric scan data of a patient surgical site with actual patient position are disclosed. In practicing the method, position data relating to (i) the x,y,z coordinates of a detector screen in fixed coordinate system, and the x,y, coordinates of patient features on the screen are used to determine the x,y,z coordinates of the patient features in the fixed coordinate system. These coordinates are then matched with the coordinates of the same patient features taken from pre-op scan data, e.g., CT scan data, to place the CT scan data in the fixed coordinate system.