Author: claimsbot

1. A computer-implemented method for generating an exterior geometry of a building based on a corresponding collection of interior geometry, the method comprising:
receiving a collection of interior geometry data of a building, wherein the interior geometry data of the building corresponds to one or more levels, wherein each of the one or more levels is associated with a corresponding vertical span, and wherein the each of the one or more levels corresponds to a respective one or more 2-D section polygons;
extruding into one or more 2.5-D section polygons, the one or more 2-D section polygons, wherein the extruding comprises assigning to each of the one or more 2-D section polygons, the corresponding vertical span associated with the each of the one or more levels to which the each of the one or more 2-D section polygons correspond; and
constructing, based on one or more of the extruded 2.5-D section polygons, a 2.5-D merged polygon set, wherein an outer shell of the 2.5-D merged polygon set corresponds to an exterior geometry corresponding to the building.
2. The computer-implemented method of claim 1, wherein the collection of interior geometry data of the building is grouped logically based on a grouping algorithm.
3. The computer-implemented method of claim 1, further comprising:
grouping the one or more 2-D section polygons based on the corresponding one of the one or more levels.
4. The computer-implemented method of claim 1, wherein the constructing the 2.5-D merged polygon set comprises:
stacking the one or more of the 2.5-D section polygons;
merging at least two stacked 2.5-D section polygons; and
simplifying the merged at least two stacked 2.5-D section polygons.
5. The computer-implemented method of claim 4, wherein the merging of the at least two stacked 2.5-D section polygons is based on a Z range of each of the at least two stacked 2.5-D section polygons.
6. The computer-implemented method of claim 4, wherein the merging of the at least two stacked 2.5-D section polygons is based on a footprint of each of the at least two stacked 2.5-D section polygons.
7. The computer-implemented method of claim 4, wherein the stacking of the one or more of the 2.5-D section polygons is based on an order value, wherein the order value indicates a logical location of each one of the one or more levels, relative to each other.
8. The computer-implemented method of claim 1, wherein each of the one or more levels is a logical grouping of a portion of the collection of interior geometry data, wherein the logical grouping is based on a height with respect to a ground level.
9. The computer-implemented method of claim 1, wherein each of the one or more 2-D section polygons is defined by one or more sets of latitudinal and longitudinal coordinates.
10. The computer-implemented method of claim 1, wherein the one or more 2-D section polygons are generated based on a floor plan of the building.
11. The computer-implemented method of claim 1, further comprising:
updating an existing geometry of the building using the exterior geometry corresponding to the building.
12. A system for generating an exterior geometry of a building based on a corresponding collection of interior geometry, the system comprising:
a memory comprising instructions for generating an exterior geometry of a building based on a corresponding collection of interior geometry; and
a processor configured to execute the instructions to:
receive a collection of interior geometry data of a building, wherein the interior geometry data of the building corresponds to one or more levels, wherein each of the one or more levels is associated with a corresponding vertical span, and wherein the each of the one or more levels corresponds to a respective one or more 2-D section polygons;
extrude into one or more 2.5-D section polygons, the one or more 2-D section polygons, wherein the extrusion comprises assignment to each of the one or more 2-D section polygons, of the corresponding vertical span associated with the each of the one or more levels to which the each of the one or more 2-D section polygons correspond; and
construct, based on one or more of the extruded 2.5-D section polygons, a 2.5-D merged polygon set, wherein an outer shell of the 2.5-D merged polygon set corresponds to an exterior geometry corresponding to the building, and wherein the construction of the 2.5-D merged polygon set comprises stacking of the one or more of the 2.5-D section polygons, merging of at least two stacked 2.5-D section polygons, and simplification of the merged at least two stacked 2.5-D section polygons.
13. The system of claim 12, wherein the processor is further configured to execute the instructions to:
group the one or more 2-D section polygons based on the corresponding one of the one or more levels.
14. The system of claim 12, wherein the merging of the at least two stacked 2.5-D section polygons is based on a Z range of each of the at least two stacked 2.5-D section polygons.
15. The system of claim 12, wherein the merging of the at least two stacked 2.5-D section polygons is based on a footprint of each of the at least two stacked 2.5-D section polygons.
16. The system of claim 12, wherein the stacking of the one or more of the 2.5-D section polygons is based on an order value, wherein the order value indicates a logical location of each one of the one or more levels, relative to each other.
17. The system of claim 12, wherein each of the one or more 2-D section polygons is defined by one or more sets of latitudinal and longitudinal coordinates, and wherein the one or more 2-D section polygons are generated based on a floor plan of the building.
18. The system of claim 12, wherein the processor is further configured to execute the instructions to:
update an existing geometry of the building using the exterior geometry corresponding to the building.
19. A machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method for generating an exterior geometry of a building based on a corresponding collection of interior geometry, the method comprising:
receiving a collection of interior geometry data of a building, wherein the interior geometry data of the building corresponds to one or more levels, wherein each of the one or more levels is associated with a corresponding vertical span, and wherein the each of the one or more levels corresponds to a respective one or more 2-D section polygons;
extruding into one or more 2.5-D section polygons, the one or more 2-D section polygons, wherein the extruding comprises assigning to each of the one or more 2-D section polygons, the corresponding vertical span associated with the each of the one or more levels to which the each of the one or more 2-D section polygons correspond; and
constructing, based on one or more of the extruded 2.5-D section polygons, a 2.5-D merged polygon set, wherein an outer shell of the 2.5-D merged polygon set corresponds to an exterior geometry corresponding to the building, and wherein the constructing the 2.5-D merged polygon set comprises stacking the one or more of the 2.5-D section polygons, merging at least two stacked 2.5-D section polygons, and simplifying the merged at least two stacked 2.5-D section polygons.
20. The machine-readable storage medium of claim 7, wherein the method further comprises:
updating an existing geometry of the building using the exterior geometry corresponding to the building.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A computer-implemented method of identifying text content in images, the method comprising:
receiving an input image;
splitting the image into a plurality of character blocks, each character block in the plurality of character blocks containing pixel information that may represent one or more characters;
calculating a probability that a character block in the plurality of character blocks includes a character;
forming a candidate sequence of character blocks from the plurality of character blocks, the candidate sequence of character blocks representing a candidate match for a search term; and
comparing the candidate sequence of character blocks to the search term to determine if the search term is present in the candidate sequence of character blocks.
2. The method of claim 1 wherein comparing the candidate sequence of character blocks comprises calculating a similarity of a line of text in the candidate sequence of character blocks to the search term.
3. The method of claim 1 wherein the search term comprises a word or phrase indicative of spam.
4. The method of claim 1 wherein the search term comprises a link to a phishing site.
5. The method of claim 1 wherein the search term comprises company confidential or prohibited information.
6. The method of claim 1 wherein the one or more characters comprise an ASCII character.
7. A computer having a memory and a processor configured to execute computer-readable program code in the memory, the memory storing:
an email processing engine configured to receive an email and extract an image from the email; and
a pure adversarial optical character recognition (OCR) module configured to receive a search term and the image and to search the image for the search term;
wherein the OCR module is configured to split the image into a plurality of character blocks, calculate a probability that a character block in the plurality of character blocks includes a character, create a candidate sequence of character blocks from the plurality of character blocks to form a candidate match for the search term, and compare the candidate sequence of character blocks to the search term to determine if the search term is present in the candidate sequence of character blocks.
8. The computer of claim 7 wherein the email processing engine is configured for antispam.
9. The computer of claim 7 wherein the email processing engine is configured for antiphishing.
10. The computer of claim 7 wherein the email processing engine is configured to check the email for confidential or prohibited information.
11. The computer of claim 7 wherein the OCR module is further configured to provide information on a location of the search term in the image if the search term is found in the image.
12. The computer of claim 7 wherein the OCR module is further configured to provide a location of the search term in the image when the search term is found to be similar to a line of text in the candidate sequence of character-blocks.
13. A computer-implemented method of identifying text content in images, the method comprising:
extracting an image from an email; and
searching the image for presence of a search term by splitting the image into a plurality of character blocks, each character block in the plurality of character blocks containing pixel information that may represent a character, calculating a probability that a character block in the plurality of character blocks includes a character, forming a candidate sequence of character blocks from the plurality of character blocks to form a candidate match for the search term, and comparing the candidate sequence of character blocks to the search term to determine if the search term is present in the candidate sequence of character blocks.
14. The method of claim 13 wherein determining if the search term is present in the candidate sequence of character blocks comprises calculating a similarity of a line of text to the search term.
15. The method of claim 13 wherein the search term comprises a word or phrase indicative of spam.
16. The method of claim 13 wherein the search term comprises a link to a phishing site.
17. The method of claim 13 wherein the search term comprises company confidential or prohibited information.

1. A system for parsing a natural-language query and searching web documents, the system comprising:
a server; and
a memory coupled to the server, the memory to store logical instructions that are executed by the processor to perform operations comprising:
parsing a received query to create a semantic structure of the query;
identifying one or more queried entities or queried relations within the semantic structure;
retrieving one or more matching entities or relations based on a comparison of the semantic structure with a knowledge base;
selecting one entity or relation from the one or more matching entities or relations as a default entity or default relation based on a statistical measurement; and
retrieving a plurality of search results based on the default entity or relation.
2. The system of claim 1, wherein the operations further comprise executing a natural language processing engine (NLPE) to generate the semantic structure of the query.
3. The system of claim 1, wherein the operations further comprise ranking the plurality of search results based on a semantic ranking function.
4. The system of claim 3, wherein the ranking is based in part on a combination of a well-known factor of a page and a semantic-related measurement between the one or more queried entities or queried relations and the page.
5. The system of claim 3, wherein the operations further comprise displaying the plurality of search results with a corresponding plurality of semantic tags.
6. The system of claim 1, wherein the operations further comprise displaying one or more refinements on a search interface, the one or more refinements being based on an analysis of one or more of the default entity or default relation.
7. The system of claim 6, wherein the one or more refinements comprise one or more of an ambiguity refinement, a social refinement, a similarity refinement, a specific refinement, or a general refinement.
8. The system of claim 7, wherein the ambiguity refinement comprises displaying the default entity as a found entity, and displaying a list of ambiguous entities that are similarly named to the default entity.
9. The system of claim 7, wherein the social refinement comprises searching for social media pages of the default entity and displaying links to the social media pages along with the plurality of search results.
10. The system of claim 7, wherein the similarity refinement comprises searching for and displaying entities similar to the default entity.
11. The system of claim 7, wherein the specific refinement comprises searching for and displaying entities that are more specific than the default entity.
12. The system of claim 7, wherein the general refinement comprises searching for and displaying entities that are more general than the default entity.
13. The system of claim 1, further comprising identifying a query type based on a comparison of the semantic structure of the query with a plurality of commonly-asked question templates.
14. The system of claim 1, wherein the operations further comprise comparing the queried entity or relation with the knowledge base using one or more constraint entities or constraint relations.
15. The system of claim 1, wherein the retrieval of the plurality of search results is based in part on an index linking an entity, a relation, or a category, with a web address.
16. A method for constructing a knowledge base, comprising:
initializing the knowledge base with a plurality of external semantic resources;
constructing an indexing database to link one or more entities retrieved from the plurality of external semantic resources; and
at regular intervals, updating the knowledge base using an indexing process.
17. The method of claim 16, wherein constructing the indexing database further comprises:
parsing a document to retrieve a semantic structure of the document; and
generating a plurality of indices based on one or more of an entity, a category, or a relation within the semantic structure of the document.
18. The method of claim 16, wherein the updating the knowledge base using the indexing process further comprises:
updating any existing entities and relations in the knowledge base;
storing non-existing entities and relations as a list of candidates; and
adding to the knowledge base any non-existing entities and relations that have a high occurrence within the list of candidates.
19. A non-transitory computer-readable medium for storing computer-executable instructions that are executed by a processor to perform operations comprising:
parsing a received query to create a semantic structure of the query;
identifying one or more queried entities or queried relations within the semantic structure;
retrieving one or more matching entities or relations based on a comparison of the semantic structure with a knowledge base;
selecting one entity or relation from the one or more matching entities or relations as a default entity or default relation based on a statistical measurement; and
retrieving a plurality of search results based on the default entity or relation.
20. The computer-readable medium of claim 19, wherein the operations further comprise:
ranking the plurality of search results based on one or more of a well-known factor and a semantic relationship of entities within the page.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A composition for treating a parasitic infection in a subject comprising two or more compounds selected from the group consisting of alpha-pinene, linalyl acetate, para-cymene, and thymol octanoate.
2. The composition of claim 1, comprising three or more compounds selected from the group consisting of alpha-pinene, linalyl acetate, para-cymene, and thymol octanoate.
3. The composition of claim 1, comprising alpha-pinene, linalyl acetate, para-cymene, and thymol octanoate.
4. The composition of claim 3, comprising 1-75% alpha-pinene, 1-75% linalyl acetate. 1-75% para-cymene and 1-75% thymol octanoate.
5. The composition of claim 4, comprising 20-30% by weight alpha-pinene, 20-30% by weight linalyl acetate, 20-30% by weight para-cymene, and 20-30% by weight thymol octanoate.
6. The composition of claim 5 comprising, comprising 25% by weight alpha-pinene, 25% by weight linalyl acetate, 25% by weight para-cymene, and 25% by weight thymol octanoate.
7. The composition of claim 1, wherein the parasitic infection is by a protozoan parasite.
8. The composition of claim 7, wherein the parasite is selected from the group consisting of an intestinal protozoan, a tissue protozoan, and a blood protozoan.
9. The composition of claim 7, wherein the parasite is at least one of Entamoeba hystolytica, Giardia lamblia, Cryptosporidium muris, Cryptosporidium parvum, Trypanosomatida gambiense, Trypanosomatida rhodesiense, Trypanosomatida crusi, Leishmania mexicana, Leishmania braziliensis, Leishmania tropica, Leishmania donovani, Toxoplasma gondii, Plasmodium vivax, Plasmodium ovale, Plasmodium malariae, Plasmodium falciparum, Trichomonas vaginalis, and Histomonas meleagridis.
10. The composition of claim 1, wherein the parasitic infection is by a helminthic parasite.
11. The composition of claim 10, wherein the parasite is a nematode.
12. The composition of claim 11, wherein the parasite is a member of class Adenophorea.
13. The composition of claim 11, wherein the parasite is a member of class Secernentea.
14. The composition of claim 11, wherein the parasite is at least one of: Trichuris trichiura, Ascaris lumbricoides, Enterobius vermicularis, Ancylostoma duodenale, Necator americanus, Strongyloides stercoralis, Wuchereria bancrofti, and Dracunculus medinensis.
15. The composition of claim 10, wherein the parasite is a trematode.
16. The composition of claim 15, the parasite is selected from the group consisting of a blood fluke, a liver fluke, an intestinal fluke, and a lung fluke.
17. The composition of claim 15, wherein the parasite is at least one of Schistosoma mansoni, Schistosoma haematobium, Schistosoma japonicum, Fasciola hepatica, Fasciola gigantica, Heterophyes heterophyes, Paragonimus westermani, and Opishorchis sinensis.
18. The composition of claim 10, wherein the parasite is a cestode.
19. The composition of claim 18, wherein the parasite is least one of Taenia solium, Taenia saginata, Hymenolepis nana, Echinococcus granulosus, and Diplyidium caninum.
20. A formulation comprising the composition of claim 1.
21-27. (canceled)