COMPUTING TECHNOLOGIES FOR DIAGNOSIS AND THERAPY OF LANGUAGE-RELATED DISORDERS

• US 20150118661A1
• Filed: 10/29/2014
• Published: 04/30/2015
• Est. Priority Date: 10/31/2013
• Status: Active Grant

• Current Assignee: Spring River Holdings Llc
• Original Assignee: Spring River Holdings Llc
• Inventors: Haruta, Pau-San, Haruta, Charisse Si-Fei, Haruta, Kieran Bing-Fei
• Granted Patent: US 9,489,854 B2
• Time in Patent Office: Days
• US Class Current: 434/169
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/4088 Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• CPC Class Codes: G09B 19/04 Speaking with audible presentation of the material to be studied G09B5/04
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present disclosure relates to computing technologies for diagnosis and therapy of language-related disorders. Such technologies enable computer-generated diagnosis and computer-generated therapy delivered over a network to at least one computing device. The diagnosis and therapy are customized for each patient through a comprehensive analysis of the patient'"'"'s production and reception errors, as obtained from the patient over the network, together with a set of correct responses at each phase of evaluation and therapy.

Claims

1. A method comprising:

diagnosing a language-related disorder via;

obtaining a first set of criteria via a first computer, wherein the first set of criteria is based on a first analysis of a patient data structure against a master data structure, wherein the patient data structure comprising a set of actual patient task responses, wherein the master data structure comprising a set of cell generation data and a set of predicted patient task responses for a plurality of patients;

storing a first result in the patient data structure via the first computer, wherein the first result is received from a second computer, wherein the first result is based on the first computer selecting a first diagnostic shell based on the first set of criteria, generating a first diagnostic cell based on the first diagnostic shell and the set of cell generation data, and communicating the first diagnostic cell to the second computer;

obtaining a second set of criteria via the first computer, wherein the second set of criteria is based on a second analysis of the patient data structure, including the first result, against the master data structure;

determining at least one of whether to generate a second diagnostic cell and whether to select a second diagnostic shell via the first computer, wherein the second diagnostic cell is based on the first diagnostic shell, wherein the first diagnostic shell and the second diagnostic shell are different in task type.

2. The method of claim 1, wherein the disorder is at least one of dyslexia, specific language impairment, auditory processing disorder, and aphasia.

3. The method of claim 1, further comprising:

providing therapy based on the diagnosing via;

obtaining a third set of criteria via the first computer, wherein the third set of criteria is based on a third analysis of the patient data structure, including the first result, against the master data structure;

storing a second result in the patient data structure via the first computer, wherein the second result is received from the second computer, wherein the second result is based on the first computer selecting a first therapy shell based on the third set of criteria, generating a first therapy cell based on the first therapy shell and the set of cell generation data, and communicating the first therapy cell to the second computer;

obtaining a fourth set of criteria via the first computer, wherein the fourth set of criteria is based on a fourth analysis of the patient data structure, including the first result and the second result, against the master data structure;

determining at least one of whether to generate a second therapy cell and whether to select a second therapy shell via the first computer, wherein the second therapy cell is based on the first therapy shell, wherein the first therapy shell and the second therapy shell are different in task type.

4. The method of claim 3, wherein at least one of the first therapy cell and the second therapy cell comprise a training unit and an evaluation unit.

5. The method of claim 3, wherein the first therapy shell and the second therapy shell is at least one of a phoneme discrimination therapy shell, a rapid word recognition therapy shell, and a word amplification therapy shell.

6. The method of claim 3, further comprising:

gamifying at least one of the first diagnostic cell, the second diagnostic cell, the first therapy cell, and the second therapy cell, wherein the gamifying is interactive and age based;

rewarding the gamifying via a reward system running on the first computer.

7. The method of claim 1, further comprising a third diagnostic shell, wherein the first diagnostic shell is a phoneme identification diagnostic shell, the second diagnostic shell is a sound-symbol matching diagnostic shell, and third diagnostic shell is at least one of a syllabification diagnostic shell, a rapid naming diagnostic shell, and a word segmentation diagnostic shell, wherein the third diagnostic shell follows the second diagnostic shell.

8. A system comprising:

a first computer facilitating a diagnosis of a language-related disorder via;

obtaining a first set of criteria, wherein the first set of criteria is based on a first analysis of a patient data structure against a master data structure, wherein the patient data structure comprising a set of actual patient task responses, wherein the master data structure comprising a set of cell generation data and a set of predicted patient task responses for a plurality of patients;

storing a first result in the patient data structure, wherein the first result is received from a second computer, wherein the first result is based on the first computer selecting a first diagnostic shell based on the first set of criteria, generating a first diagnostic cell based on the first diagnostic shell and the set of cell generation data, and communicating the first diagnostic cell to the second computer;

obtaining a second set of criteria, wherein the second set of criteria is based on a second analysis of the patient data structure, including the first result, against the master data structure;

determining at least one of whether to generate a second diagnostic cell and whether to select a second diagnostic shell, wherein the second diagnostic cell is based on the first diagnostic shell, wherein the first diagnostic shell and the second diagnostic shell are different in task type.

9. The system of claim 8, wherein the disorder is at least one of dyslexia, specific language impairment, auditory processing disorder, and aphasia.

10. The system of claim 8, wherein the first computer facilitating a provision of therapy of the language-related disorder based on the diagnosis of the language-related disorder, wherein the facilitating the provision of therapy is via:

obtaining a third set of criteria via the first computer, wherein the third set of criteria is based on a third analysis of the patient data structure, including the first result, against the master data structure;

storing a second result in the patient data structure via the first computer, wherein the second result is received from the second computer, wherein the second result is based on the first computer selecting a first therapy shell based on the third set of criteria, generating a first therapy cell based on the first therapy shell and the set of cell generation data, and communicating the first therapy cell to the second computer;

obtaining a fourth set of criteria via the first computer, wherein the fourth set of criteria is based on a fourth analysis of the patient data structure, including the first result and the second result, against the master data structure;

determine at least one of whether to generate a second therapy cell and whether to select a second therapy shell via the first computer, wherein the second therapy cell is based on the first therapy shell, wherein the first therapy shell and the second therapy shell are different in task type.

11. The system of claim 10, wherein at least one of the first therapy cell and the second therapy cell comprise a training unit and an evaluation unit.

12. The system of claim 10, wherein the first therapy shell and the second therapy shell is at least one of a phoneme discrimination therapy shell, a rapid word recognition therapy shell, and a word amplification therapy shell.

13. The system of claim 10, further comprising:

gamifying at least one of the first diagnostic cell, the second diagnostic cell, the first therapy cell, and the second therapy cell, wherein the gamifying is interactive and age based;

rewarding the gamifying via a reward system running on the first computer.

14. The system of claim 8, further comprising a third diagnostic shell, wherein the first diagnostic shell is a phoneme identification diagnostic shell, the second diagnostic shell is a sound-symbol matching diagnostic shell, and third diagnostic shell is at least one of a syllabification diagnostic shell, a rapid naming diagnostic shell, and a word segmentation diagnostic shell, wherein the third diagnostic shell follows the second diagnostic shell.

15. A non-transitory, computer-readable storage medium storing a set of instructions for execution via a hardware processor, wherein the set of instructions instructing the hardware processor to implement a method, the method comprising:

diagnosing dyslexia via;

obtaining a first set of criteria via a first computer, wherein the first set of criteria is based on a first analysis of a patient data structure against a master data structure, wherein the patient data structure comprising a set of actual patient task responses, wherein the master data structure comprising a set of cell generation data and a set of predicted patient task responses for a plurality of patients;

storing a first result in the patient data structure via the first computer, wherein the first result is received from a second computer, wherein the first result is based on the first computer selecting a first diagnostic shell based on the first set of criteria, generating a first diagnostic cell based on the first diagnostic shell and the set of cell generation data, and communicating the first diagnostic cell to the second computer;

obtaining a second set of criteria via the first computer, wherein the second set of criteria is based on a second analysis of the patient data structure, including the first result, against the master data structure;

determining at least one of whether to generate a second diagnostic cell and whether to select a second diagnostic shell via the first computer, wherein the second diagnostic cell is based on the first diagnostic shell, wherein the first diagnostic shell and the second diagnostic shell are different in task type.

16. The medium of claim 15, wherein the method further comprising:

providing therapy based on the diagnosing via;

obtaining a third set of criteria via the first computer, wherein the third set of criteria is based on a third analysis of the patient data structure, including the first result, against the master data structure;

storing a second result in the patient data structure via the first computer, wherein the second result is received from the second computer, wherein the second result is based on the first computer selecting a first therapy shell based on the third set of criteria, generating a first therapy cell based on the first therapy shell and the set of cell generation data, and communicating the first therapy cell to the second computer;

obtaining a fourth set of criteria via the first computer, wherein the fourth set of criteria is based on a fourth analysis of the patient data structure, including the first result and the second result, against the master data structure;

determining at least one of whether to generate a second therapy cell and whether to select a second therapy shell via the first computer, wherein the second therapy cell is based on the first therapy shell, wherein the first therapy shell and the second therapy shell are different in task type.

17. The medium of claim 16, wherein at least one of the first therapy cell and the second therapy cell comprise a training unit and an evaluation unit.

18. The medium of claim 16, wherein the first therapy shell and the second therapy shell is at least one of a phoneme discrimination therapy shell, a rapid word recognition therapy shell, and a word amplification therapy shell.

19. The medium of claim 16, wherein the method further comprising:

gamifying at least one of the first diagnostic cell, the second diagnostic cell, the first therapy cell, and the second therapy cell, wherein the gamifying is interactive and age based;

rewarding the gamifying via a reward system running on the first computer.

20. The medium of claim 15, further comprising a third diagnostic shell, wherein the first diagnostic shell is a phoneme identification diagnostic shell, the second diagnostic shell is a sound-symbol matching diagnostic shell, and third diagnostic shell is at least one of a syllabification diagnostic shell, a rapid naming diagnostic shell, and a word segmentation diagnostic shell, wherein the third diagnostic shell follows the second diagnostic shell.