Methods and Systems for Applications for Z-numbers

• US 20130330008A1
• Filed: 07/29/2013
• Published: 12/12/2013
• Est. Priority Date: 09/24/2011
• Status: Active Grant

• Current Assignee: Z Advanced Computing, Inc.
• Original Assignee: Z Advanced Computing, Inc.
• Inventors: Zadeh, Lotfi A.
• Granted Patent: US 8,694,459 B2
• Time in Patent Office: Days
• US Class Current: 382/195
• CPC Class Codes: G05B 13/0275 using fuzzy logic only
• CPC Class Codes: G06F 16/90344 by using string matching techniques
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06N 7/02 using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00
• CPC Class Codes: G06N 7/023 Learning or tuning the parameters of a fuzzy system
• CPC Class Codes: G06T 7/12 Edge-based segmentation

Abstract

The current specification covers various new algorithms, methods, and systems for, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text recognition), background, relationship, position, pattern, and object), machine learning, training schemes, feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/fuzziness in language, clustering, and recognition, Natural Language Processing (NLP), Computing with Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, system diagnosis, medical diagnosis, biomedicine, large number of images analytics, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, data compression, and crowd-sourcing (using experts or humans).

Claims

1. A method for image recognition, said method comprising:

an input device receiving a first image of an object;

an image analytics module receiving said first image of said object from said input device;

said image analytics module examining said first image of said object;

said image analytics module recognizing said object within said first image;

a processing module searching for one or more characteristic elements of said object among parameters associated with images deposited in one or more storages;

said processing module finding said one or more characteristic elements of said object within a second image among said parameters associated with said images deposited in said one or more storages;

said processing module determining a Z-valuation as a value of an attribute for said finding said one or more characteristic elements of said object within said second image;

a computer module determining a relevancy factor for said second image based on said Z-valuation, said first image, and said second image;

a processor determining a source associated with said second image;

said processor communicating with a ranking module, in regard to said relevancy factor for said second image and said second image.

2. The method for image recognition as recited in claim 1, said method comprises:

displaying a targeted advertisement to a user on a computing, mobile, or communication device.

3. The method for image recognition as recited in claim 1, wherein said first image is a still image.

4. The method for image recognition as recited in claim 1, wherein said second image is a still image.

5. The method for image recognition as recited in claim 1, wherein said first image is a video frame image.

6. The method for image recognition as recited in claim 1, wherein said second image is a video frame image.

7. The method for image recognition as recited in claim 1, said method comprises:

filtering content for said images deposited in said storage.

8. The method for image recognition as recited in claim 1, said method comprises:

receiving a feedback from a user.

9. The method for image recognition as recited in claim 1, said method comprises:

providing a suggestion to a user.

10. The method for image recognition as recited in claim 1, said method comprises:

providing a coupon or discount to a user.

11. The method for image recognition as recited in claim 1, said method comprises:

determining a behavior or preference prediction, pattern, or history about a user.

12. The method for image recognition as recited in claim 1, said method comprises:

providing a feedback from a user'"'"'s friends.

13. The method for image recognition as recited in claim 1, said method comprises:

communicating with a text search engine or image search engine.

14. The method for image recognition as recited in claim 1, said method comprises:

marketing or suggesting a participating or partner web page.

15. The method for image recognition as recited in claim 1, said method comprises:

measuring or monitoring traffic, pattern of visitors, visitor count, and information regarding web sites.

16. The method for image recognition as recited in claim 1, said method comprises:

collecting personal information, preferences, statistics, and habits.

17. A method for image recognition, said method comprising:

an input device receiving a first image of an object;

an image analytics module receiving said first image of said object from said input device;

said image analytics module examining said first image of said object;

said image analytics module recognizing said object within said first image;

a processing module searching for one or more characteristic elements of an item similar or related to said object among parameters associated with images deposited in one or more storages;

said processing module finding said one or more characteristic elements of said item similar or related to said object within a second image among said parameters associated with said images deposited in said one or more storages;

said processing module determining a Z-valuation as a value of an attribute for said finding said one or more characteristic elements of said item similar or related to said object within said second image;

a computer module determining a relevancy factor for said second image based on said Z-valuation, said first image, and said second image;

a processor determining a source associated with said second image;

said processor communicating with a ranking module, in regard to said relevancy factor for said second image and said second image.

18. The method for image recognition as recited in claim 17, said method comprises:

displaying a targeted advertisement to a user on a computing, mobile, or communication device.

19. A method for image recognition, said method comprising:

an input device receiving a first image of an object;

an image analytics module receiving said first image of said object from said input device;

said image analytics module examining said first image of said object;

said image analytics module recognizing said object within said first image;

a processing module searching for one or more characteristic elements of said object among parameters associated with images deposited in one or more storages;

said processing module finding said one or more characteristic elements of said object within a second image among said parameters associated with said images deposited in said one or more storages;

said processing module determining a reliability valuation as a subset of a Z-valuation as a value of an attribute for said finding said one or more characteristic elements of said object within said second image;

a computer module determining a relevancy factor for said second image based on said reliability valuation as a subset of said Z-valuation, said first image, and said second image;

a processor determining a source associated with said second image;

said processor communicating with a ranking module, in regard to said relevancy factor for said second image and said second image.

20. The method for image recognition as recited in claim 19, said method comprises:

displaying a targeted advertisement to a user on a computing, mobile, or communication device.