Computing technologies for diagnosis and therapy of language-related disorders

• US 9,489,854 B2
• Filed: 10/29/2014
• Issued: 11/08/2016
• 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
• Primary Examiner(s): Grant, Michael
• Application Number:
  US14/527,379
• Publication Number:
  US 20150118661A1
• Time in Patent Office: 741 Days
• Field of Search: 434/169
• US Class Current: 1/1
• 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 automatically via;

obtaining, by a first computer, a first set of criteria, wherein the first set of criteria is based at least in part on a first automated analysis of a patient data structure against a master data structure, wherein the patient data structure comprises a set of actual patient task responses, wherein the master data structure comprises a set of cell generation data and a set of predicted patient task responses for a plurality of patients;

storing, by the first computer, a first cell result in the patient data structure, wherein the first cell result is received from a second computer, wherein the second computer is at least one of implantable or wearable, wherein the second computer comprises a vibrator configured to vibrate such that a first sound is conducted along a jaw bone of a patient to an inner ear of the patient, wherein the patient is associated with the patient data structure, wherein the first cell result is based at least in part on the first computer selecting a first diagnostic shell based at least in part on the first set of criteria, generating a first diagnostic cell dynamically based at least in part on the first diagnostic shell, the set of actual patient task responses, and the set of cell generation data, and communicating the first diagnostic cell to the second computer such that the second computer is able to output the first diagnostic cell via the vibrator vibrating such that the first sound is conducted along the jaw bone to the inner ear, wherein the first sound comprises the first diagnostic cell, wherein the first diagnostic cell comprises a first diagnostic cell content which is created dynamically by the first computer based at least in part on an incorrect actual patient task response sourced from the set of actual patient task responses;

obtaining, by the first computer, a second set of criteria, wherein the second set of criteria is based at least in part on a second automated analysis of the patient data structure, including the first cell result, against the master data structure;

determining, by the first computer, based at least in part on the second set of criteria at least one of whether to generate a second diagnostic cell dynamically based at least in part on the first diagnostic shell such that the second computer is able to output the second diagnostic cell via the vibrator vibrating such that a second sound is conducted along the jaw bone to the inner ear, the set of actual patient task responses, and the set of cell generation data, whether to select a second diagnostic shell, or whether to provide a diagnosis of the language-related disorder to the second computer, wherein the first diagnostic shell and the second diagnostic shell are different in task type, wherein the second sound comprises the second diagnostic cell, wherein the second diagnostic cell comprises a second diagnostic cell content which is created dynamically by the first computer without relying on a second predetermined cell content.

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

3. The method of claim 1, further comprising:

providing therapy of the language-related disorder automatically based at least in part on the diagnosis via;

obtaining, by the first computer, a third set of criteria, wherein the third set of criteria is based at least in part on a third automated analysis of the patient data structure, including the first cell result, against the master data structure;

storing, by the first computer, a second cell result in the patient data structure, wherein the second cell result is received from the second computer, wherein the second cell result is based at least in part on the first computer selecting a first therapy shell based at least in part on the third set of criteria, generating a first therapy cell dynamically based at least in part on the first therapy shell, the set of actual patient task responses, and the set of cell generation data, and communicating the first therapy cell to the second computer;

obtaining, by the first computer, a fourth set of criteria, wherein the fourth set of criteria is based at least in part on a fourth automated analysis of the patient data structure, including the first cell result and the second cell result, against the master data structure;

determining, by the first computer, based at least in part on the fourth set of criteria at least one of whether to generate a second therapy cell dynamically based at least in part on the first therapy shell, the set of actual patient task responses, and the set of cell generation data, whether to select a second therapy shell, or whether to complete the therapy, 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 or the second therapy cell comprises 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, or a word amplification therapy shell.

6. The method of claim 3, further comprising:

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

rewarding, based at least in part on the gamifying, the patient via a reward system running on the first computer, wherein the patient operates the second 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 the third diagnostic shell is at least one of a syllabification diagnostic shell, a rapid naming diagnostic shell, or a word segmentation diagnostic shell, wherein the third diagnostic shell follows the second diagnostic shell.

8. A system comprising:

a computer system configured to diagnose a language-related disorder automatically via;

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

storing a first cell result in the patient data structure, wherein the first cell result is received from a computing device over a communication network, wherein the computing device is at least one of implantable or wearable, wherein the computing device comprises a vibrator configured to vibrate such that a first sound is conducted along a jaw bone of a patient to an inner ear of the patient, wherein the patient is associated with the patient data structure, wherein the first cell result is based at least in part on the computer system selecting a first diagnostic shell based at least in part on the first set of criteria, generating a first diagnostic cell dynamically based at least in part on the first diagnostic shell, the set of actual patient task responses, and the set of cell generation data, and communicating the first diagnostic cell to the computing device over the communication network such that the computing device is able to output the first diagnostic cell via the vibrator vibrating such that the first sound is conducted along the jaw bone to the inner ear, wherein the first sound comprises the first diagnostic cell, wherein the first diagnostic cell comprises a first diagnostic cell content which is created dynamically by the computer system based at least in part on an incorrect actual patient task response sourced from the set of actual patient task responses;

obtaining a second set of criteria, wherein the second set of criteria is based at least in part on a second automated analysis of the patient data structure, including the first cell result, against the master data structure;

determining based at least in part on the second set of criteria at least one of whether to generate a second diagnostic cell dynamically based at least in part on the first diagnostic shell such that the computing device is able to output the second diagnostic cell via the vibrator vibrating such that a second sound is conducted along the jaw bone to the inner ear, the set of actual patient task responses, and the set of cell generation data, whether to select a second diagnostic shell, or whether to provide a diagnosis of the language-related disorder to the computing device over the communication network, wherein the first diagnostic shell and the second diagnostic shell are different in task type, wherein the second sound comprises the second diagnostic cell, wherein the second diagnostic cell comprises a second diagnostic cell content which is created dynamically by the computer system without relying on a second predetermined cell content.

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

10. The system of claim 8, wherein the computer system is further configured to provide therapy of the language-related disorder automatically based at least in part on the diagnosis via:

obtaining a third set of criteria, wherein the third set of criteria is based at least in part on a third automated analysis of the patient data structure, including the first cell result, against the master data structure;

storing a second cell result in the patient data structure, wherein the second cell result is received from the computing device over the communication network, wherein the second cell result is based at least in part on the computer system selecting a first therapy shell based at least in part on the third set of criteria, generating a first therapy cell dynamically based at least in part on the first therapy shell, the set of actual patient task responses, and the set of cell generation data, and communicating the first therapy cell to the computing device over the communication network;

obtaining a fourth set of criteria, wherein the fourth set of criteria is based at least in part on a fourth automated analysis of the patient data structure, including the first cell result and the second cell result, against the master data structure;

determining based at least in part on the fourth set of criteria at least one of whether to generate a second therapy cell dynamically based at least in part on the first therapy shell, the set of actual patient task responses, and the set of cell generation data, whether to select a second therapy shell, or whether to complete the therapy, 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 or the second therapy cell comprises 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, or a word amplification therapy shell.

13. The system of claim 10, further comprising:

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

rewarding, based at least in part on the gamifying, the patient via a reward system running on the computer system, wherein the patient operates the computing device.

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 the third diagnostic shell is at least one of a syllabification diagnostic shell, a rapid naming diagnostic shell, or a word segmentation diagnostic shell, wherein the third diagnostic shell follows the second diagnostic shell.

15. A computer-readable storage device storing a set of instructions for execution via a processing circuit, wherein the set of instructions is instructive for the processing circuit to implement a method, wherein the method comprises:

diagnosing dyslexia automatically via;

obtaining, by a first computer, a first set of criteria, wherein the first set of criteria is based at least in part on a first automated analysis of a patient data structure against a master data structure, wherein the patient data structure comprises a set of actual patient task responses, wherein the master data structure comprises a set of cell generation data and a set of predicted patient task responses for a plurality of patients;

storing, by the first computer, a first cell result in the patient data structure, wherein the first cell result is received from a second computer over a communication network, wherein the second computer is at least one of implantable or wearable, wherein the second computer comprises a vibrator configured to vibrate such that a first sound is conducted along a jaw bone of a patient to an inner ear of the patient, wherein the patient is associated with the patient data structure, wherein the first cell result is based at least in part on the first computer selecting a first diagnostic shell based at least in part on the first set of criteria, generating a first diagnostic cell dynamically based at least in part on the first diagnostic shell, the set of actual patient task responses, and the set of cell generation data, and communicating the first diagnostic cell to the second computer over the communication network such that the second computer is able to output the first diagnostic cell via the vibrator vibrating such that the first sound is conducted along the jaw bone to the inner ear, wherein the first sound comprises the first diagnostic cell, wherein the first diagnostic cell comprises a first diagnostic cell content which is created dynamically by the first computer based at least in part on an incorrect actual patient task response sourced from the set of actual patient task responses;

obtaining, by the first computer, a second set of criteria, wherein the second set of criteria is based at least in part on a second automated analysis of the patient data structure, including the first cell result, against the master data structure;

determining, by the first computer, based at least in part on the second set of criteria at least one of whether to generate a second diagnostic cell dynamically based at least in part on the first diagnostic shell such that the second computer is able to output the second diagnostic cell via the vibrator vibrating such that a second sound is conducted along the jaw bone to the inner ear, the set of actual patient task responses, and the set of cell generation data, whether to select a second diagnostic shell, or whether to provide a diagnosis of the dyslexia to the second computer over the communication network, wherein the first diagnostic shell and the second diagnostic shell are different in task type, wherein the second sound comprises the second diagnostic cell, wherein the second diagnostic cell comprises a second diagnostic cell content which is created dynamically by the first computer without relying on a second predetermined cell content.

16. The computer-readable storage device of claim 15, wherein the method further comprises:

providing therapy of the dyslexia automatically based at least in part on the diagnosis via;

obtaining, by the first computer, a third set of criteria, wherein the third set of criteria is based at least in part on a third automated analysis of the patient data structure, including the first cell result, against the master data structure;

storing, by the first computer, a second cell result in the patient data structure, wherein the second cell result is received from the second computer over the communication network, wherein the second cell result is based at least in part on the first computer selecting a first therapy shell based at least in part on the third set of criteria, generating a first therapy cell dynamically based at least in part on the first therapy shell, the actual patient task responses, and the set of cell generation data, and communicating the first therapy cell to the second computer over the communication network;

obtaining, by the first computer, a fourth set of criteria, wherein the fourth set of criteria is based at least in part on a fourth automated analysis of the patient data structure, including the first cell result and the second cell result, against the master data structure;

determining, by the first computer, based at least in part on the fourth set of criteria at least one of whether to generate a second therapy cell dynamically based at least in part on the first therapy shell, the set of actual patient task responses, and the set of cell generation data, whether to select a second therapy shell, or whether to complete the therapy, wherein the first therapy shell and the second therapy shell are different in task type.

17. The computer-readable storage device of claim 16, wherein at least one of the first therapy cell or the second therapy cell comprises a training unit and an evaluation unit.

18. The computer-readable storage device 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, or a word amplification therapy shell.

19. The computer-readable storage device of claim 16, wherein the method further comprises:

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

rewarding, based at least in part on the gamifying, the patient via a reward system running on the first computer, wherein the patient operates the computing device.

20. The computer-readable storage device of claim 15, wherein the method further comprises 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 the third diagnostic shell is at least one of a syllabification diagnostic shell, a rapid naming diagnostic shell, or a word segmentation diagnostic shell, wherein the third diagnostic shell follows the second diagnostic shell.