John J Sidorowich

6532076, Mar 11, 2003

Apr 4, 2000

09/542724

John J. Sidorowich - Santa Cruz CA

Therma-Wave, Inc. - Fremont CA

G01B 1128

356630, 382141, 382145

An optical measuring device generates a plurality of measured optical data from inspection of a thin film stack. The measured optical data group naturally into several domains. In turn the thin film parameters associated with the data fall into two categories: local and global. Local âgenesâ represent parameters that are associated with only one domain, while global genes represent parameters that are associated with multiple domains. A processor evolves models for the data associated with each domain, which models are compared to the measured data, and a âbest fitâ solution is provided as the result. Each model of theoretical data is represented by an underlying âgenotypeâ which is an ordered set of the genes. For each domain a âpopulationâ of genotypes is evolved through the use of a genetic algorithm. The global genes are allowed to âmigrateâ among multiple domains during the evolution process.

6781706, Aug 24, 2004

Jan 22, 2003

10/349262

John J. Sidorowich - Santa Cruz CA

Therma-Wave, Inc. - Fremont CA

G01B 1128

356630, 382141

An optical measuring device generates a plurality of measured optical data from inspection of a thin film stack. The measured optical data group naturally into several domains. In turn the thin film parameters associated with the data fall into two categories: local and global. Local âgenesâ represent parameters that are associated with only one domain, while global genes represent parameters that are associated with multiple domains. A processor evolves models for the data associated with each domain, which models are compared to the measured data, and a âbest fitâ solution is provided as the result. Each model of theoretical data is represented by an underlying âgenotypeâ which is an ordered set of the genes. For each domain a âpopulationâ of genotypes is evolved through the use of a genetic algorithm. The global genes are allowed to âmigrateâ among multiple domains during the evolution process.

7050179, May 23, 2006

Mar 2, 2004

10/791256

John J. Sidorowich - Santa Cruz CA, US

Therma-Wave, Inc. - Fremont CA

G01B 11/28
G06K 9/00

356630, 382141, 382145, 706 13

An optical measuring device generates a plurality of measured optical data from inspection of a thin film stack. The measured optical data group naturally into several domains. In turn the thin film parameters associated with the data fall into two categories: local and global. Local “genes” represent parameters that are associated with only one domain, while global genes represent parameters that are associated with multiple domains. A processor evolves models for the data associated with each domain, which models are compared to the measured data, and a “best fit” solution is provided as the result. Each model of theoretical data is represented by an underlying “genotype” which is an ordered set of the genes. For each domain a “population” of genotypes is evolved through the use of a genetic algorithm. The global genes are allowed to “migrate” among multiple domains during the evolution process.

8108392, Jan 31, 2012

Oct 1, 2008

12/242957

David L. Marvit - San Francisco CA, US
Jawahar Jain - Los Altos CA, US
Stergios Stergiou - Sunnyvale CA, US
Alex Gilman - Fremont CA, US
B. Thomas Adler - Santa Cruz CA, US
John J. Sidorowich - Santa Cruz CA, US

Fujitsu Limited - Kawasaki

G06F 17/30

707737, 704 9

In one embodiment, identifying clusters of words includes accessing a record that records affinities. An affinity between a first and second word describes a quantitative relationship between the first and second word. Clusters of words are identified according to the affinities. A cluster comprises words that are sufficiently affine with each other. A first word is sufficiently affine with a second word if the affinity between the first and second word satisfies one or more affinity criteria. A clustering analysis is performed using the clusters.

8280892, Oct 2, 2012

Oct 1, 2008

12/242984

David L. Marvit - San Francisco CA, US
Jawahar Jain - Los Altos CA, US
Stergios Stergiou - Sunnyvale CA, US
Alex Gilman - Fremont CA, US
B. Thomas Adler - Santa Cruz CA, US
John J. Sidorowich - Santa Cruz CA, US
Yannis Labrou - Washington DC, US

Fujitsu Limited - Kawasaki-shi

G06F 17/30

707749, 704 9

In one embodiment, assigning tags to a document includes accessing the document, where the document comprises text units that include words. The following is performed for each text unit: a subset of words of a text unit is selected as candidate tags, relatedness is established among the candidate tags, and certain candidate tags are selected according to the established relatedness to yield a candidate tag set for the text unit. Relatedness between the candidate tags of each candidate tag set and the candidate tags of other candidate tag sets is determined. At least one candidate tag is assigned to the document according to the determined relatedness.

20090094020, Apr 9, 2009

Oct 1, 2008

12/243050

David L. Marvit - San Francisco CA, US
Jawahar Jain - Los Altos CA, US
Stergios Stergiou - Sunnyvale CA, US
Alex Gilman - Fremont CA, US
B. Thomas Adler - Santa Cruz CA, US
John J. Sidorowich - Santa Cruz CA, US
Albert Reinhardt - Albany CA, US
Yannis Labrou - Washington DC, US

Fujitsu Limited - Kanagawa

G06F 17/27
G06F 17/30

704 9, 707 5, 704E15018, 707E17015

In one embodiment, a set of target search terms for a search is received. Candidate terms are selected, where a candidate term is selected to reduce an ontology space of the search. The candidate terms are to a computer to recommend the candidate terms as search terms. In another embodiment, a document stored in one or more tangible media is accessed. A set of target tags for the document is received. Terms are selected, where a term is selected to reduce an ontology space of the document. The terms are sent to a computer to recommend the terms as tags.

20090094233, Apr 9, 2009

Oct 1, 2008

12/243267

David L. Marvit - San Francisco CA, US
Jawahar Jain - Los Altos CA, US
Stergios Stergiou - Sunnyvale CA, US
Alex Gilman - Fremont CA, US
B. Thomas Adler - Santa Cruz CA, US
John J. Sidorowich - Santa Cruz CA, US
Yannis Labrou - Washington DC, US

Fujitsu Limited - Kanagawa

G06F 17/30

707 5, 707E17001

In one embodiment, modeling topics includes accessing a corpus comprising documents that include words. Words of a document are selected as keywords of the document. The documents are clustered according to the keywords to yield clusters, where each cluster corresponds to a topic. A statistical distribution is generated for a cluster from words of the documents of the cluster. A topic is modeled using the statistical distribution generated for the cluster corresponding to the topic.

58646337, Jan 26, 1999

May 17, 1996

8/649576

Jon Opsal - Livermore CA
John J. Sidorowich - Aptos CA

Therma-Wave, Inc. - Fremont CA

G06K 900
G06F 1518

382141

An optical inspection device generates a plurality of measured optical data from inspection of a thin film stack. A processor evolves models of theoretical data, which are compared to the measured data, and a "best fit" solution is provided as the result. Each model of theoretical data is represented by an underlying "genotype" which is an ordered list of "genes. " Each gene corresponds to a selected thin film parameter of interest. Many such individual genotypes are created thereby forming a "population" of genotypes, which are evolved through the use of a genetic algorithm. Each genotype has a fitness associated therewith based on how much the theoretical data derived therefrom differs from the measured data. Individual genotypes are selected based on fitness, then a genetic operation is performed on the selected genotypes to produce new genotypes. Multiple generations of genotypes are evolved until an acceptable solution is obtained.