DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-15 have been examined.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a profile receiver configured to receive a patient profile including basic information and cognitive information of a patient; a cognitive ability evaluating unit configured to calculate a function-specific cognitive score of the patient by evaluating a cognitive ability of the patient based on the patient profile; a training recommendation unit configured to recommend at least one training content through an artificial intelligence (AI) rehabilitation recommendation model; and a training conducting unit configured to calculate a result for the training content provided to the patient through a user terminal” in claim 1, “a prediction model configured to predict a result of training content of the patient; and a recommendation model configured to generate a recommended training content list based on a recommendation score calculated depending on a set target value” in claim 11, “the training recommendation unit is configured to: provide training content for reinforcing a sub-function of which the statistical value is low” in claim 12, “a result providing unit configured to provide a result for the training content; a profile updating unit configured to update the patient profile depending on a result of the training content; and a patient cognitive score predicting unit configured to generate a cognitive score prediction value by predicting the predefined cognitive score different from the function- specific cognitive score of the patient depending on a statistical value for the respective sub- function, wherein the patient cognitive score predicting unit is configured to: calculate accuracy of the cognitive score prediction value depending on a size of a parent population for calculating a statistical value for the respective sub-function” in claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation:
“[0040] Referring to FIG. 2, the patient-customized training content recommendation device 130 may be implemented to include a processor 210, a memory 230, a user input/output unit 250, and a network input/output unit 270.
[0041] The processor 210 may perform a procedure of operations of receiving a patient profile, calculating a function-specific cognitive score of a patient by evaluating the patient's cognitive ability based on the patient profile, recommending training content based on the obtained function-specific cognitive score, and receiving performance information about the provided training content, may manage read and write operations of the memory 230 throughout the procedure, and may schedule synchronization timing between a volatile memory and a non-volatile memory in the memory 230. The processor 210 may control the overall operation of the patient-customized training content recommendation device 130, and may be electrically connected to the memory 230, the user input/output unit 250, and the network input/output unit 270 to control the data flow therebetween. The processor 210 may be implemented as a central processing unit (CPU) of the patient-customized training content recommendation device 130.”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “a profile receiver configured to receive a patient profile including basic information and cognitive information of a patient; a cognitive ability evaluating unit configured to calculate a function-specific cognitive score of the patient by evaluating a cognitive ability of the patient based on the patient profile; a training recommendation unit configured to recommend at least one training content through an artificial intelligence (AI) rehabilitation recommendation model; and a training conducting unit configured to calculate a result for the training content provided to the patient through a user terminal” in claim 1, “a prediction model configured to predict a result of training content of the patient; and a recommendation model configured to generate a recommended training content list based on a recommendation score calculated depending on a set target value” in claim 11, “the training recommendation unit is configured to: provide training content for reinforcing a sub-function of which the statistical value is low” in claim 12, “a result providing unit configured to provide a result for the training content; a profile updating unit configured to update the patient profile depending on a result of the training content; and a patient cognitive score predicting unit configured to generate a cognitive score prediction value by predicting the predefined cognitive score different from the function- specific cognitive score of the patient depending on a statistical value for the respective sub- function, wherein the patient cognitive score predicting unit is configured to: calculate accuracy of the cognitive score prediction value depending on a size of a parent population for calculating a statistical value for the respective sub-function” in claim 14” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The current specification recites “The processor 210 may perform a procedure of operations of receiving a patient profile, calculating a function-specific cognitive score of a patient by evaluating the patient's cognitive ability based on the patient profile, recommending training content based on the obtained function-specific cognitive score, and receiving performance information about the provided training content” in [0041], however, it’s not clear if these parts of the apparatus (recommendation device 130) are the structure performing the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because claim 1 is directed to “a…recommendation device comprising: a cognitive ability evaluating unit configured to calculate a function-specific cognitive score of the patient by evaluating a cognitive ability of the patient based on the patient profile; a training recommendation unit configured to recommend at least one training content through an artificial intelligence (AI) rehabilitation recommendation model; and a training conducting unit configured to calculate a result for the training content provided to the patient through a user terminal”, and the “units” can be interpreted to be software per se. Software per se is not one of the four statutory categories.
Claims 2-14 inherit the deficiencies of claim 1 through dependency and are therefore also rejected.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-14 are drawn to a device (system) which is within the four statutory categories (i.e. machine). Claim 15 is drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
As per claim 15, claim recites:
“receiving a patient profile including basic information and cognitive information of a patient;
calculating a function-specific cognitive score of the patient by evaluating a cognitive ability of the patient based on the patient profile;
recommending at least one training content through an AI rehabilitation recommendation model; and
calculating a result for the training content provided to the patient through the user terminal”.
The limitations of “receiving a patient profile…, calculating a function-specific cognitive score…, and calculating a result…” are directed to an abstract idea of “certain methods of organizing human activity”. This is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic user terminal (processor) device does not take the claim out of the methods of organizing human interactions grouping. Thus, the claim recites an abstract idea.
The specification describes the user terminal as a generic computing device, for instance, the specification recites “In this specification, a 'device according to an embodiment of the present disclosure' includes all various devices capable of providing results to a user by performing arithmetic processing. For example, the apparatus according to an embodiment of the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in any one form.” in [0030] and “Here, for example, the computer may include a notebook computer, a desktop computer, a laptop computer, a tablet PC, a slate PC, and the like, which are equipped with a web browser.” in [0031].
The limitation of “recommending at least one training content through an AI rehabilitation recommendation model” corresponds to performing mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
Claim 1 similarly recites “receiving a patient profile…, calculating a function-specific cognitive score…, and calculating a result…” and these limitations are directed to an abstract idea of an “certain methods of organizing human activity”, whereas recommending at least one training content through an AI rehabilitation recommendation model” corresponds to an abstract idea of a “mathematical concept”.
Claims 2-14 are ultimately dependent from claim 1 and include all the limitations of claim 1. Therefore, claims 2-14 recite the same abstract idea. Claims 2-14 describe a further limitation regarding the basis for calculating the cognitive ability and recommend a training for the user. These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claims are provided below with markings separating abstract elements from the additional limitations, wherein the bolded style represents the additional limitations beyond abstract idea, and remaining limitations are directed to the abstract idea as discussed below:
1. A patient-customized training content recommendation device comprising: a profile receiver configured to receive a patient profile including basic information and cognitive information of a patient; a cognitive ability evaluating unit configured to calculate a function-specific cognitive score of the patient by evaluating a cognitive ability of the patient based on the patient profile; a training recommendation unit configured to recommend at least one training content through an artificial intelligence (AI) rehabilitation recommendation model; and a training conducting unit configured to calculate a result for the training content provided to the patient through a user terminal.
2. The patient-customized training content recommendation device of claim 1, wherein the basic information of the patient includes a current status, an age, a gender, and a highest level of education of the patient, and wherein the cognitive information includes a result for the training content, and includes a separately pre-defined cognitive score when there is no training content already performed by the patient.
3. The patient-customized training content recommendation device of claim 2, wherein the cognitive ability evaluating unit is configured to: define the function-specific cognitive score depending on a sub-function split stepwise; and calculate a statistical value for the respective sub-function.
4. The patient-customized training content recommendation device of claim 3, wherein the cognitive ability evaluating unit is configured to define the function-specific cognitive score through: a first step of defining at least one sub-function; a second step of defining a detailed sub-function included in the at least one sub- function; and a third step of performing labeling on the sub-function and the detailed sub-function to generate a label code for the sub-function and the detailed sub-function.
5. The patient-customized training content recommendation device of claim 4, wherein the cognitive ability evaluating unit is configured to: calculate the function-specific cognitive score by summing the statistical value for each sub-function.
6. The patient-customized training content recommendation device of claim 3, wherein the AI rehabilitation recommendation model is configured to: generate a recommended training content list including a specific label code depending on an input of the specific label code, and wherein the training recommendation unit is configured to: recommend the recommended training content list as the training content.
7. The patient-customized training content recommendation device of claim 1, wherein the AI rehabilitation recommendation model is configured to: generate a recommended training content list based on a cluster, to which the patient belongs, by clustering at least one patient based on a performance result for specific training content.
8. The patient-customized training content recommendation device of claim 7, wherein the AI rehabilitation recommendation model is configured to: generate, as the recommended training content list, training content performed by other patients in the cluster, to which the patient belongs, in addition to training content performed by the patient.
9. The patient-customized training content recommendation device of claim 7, wherein the AI rehabilitation recommendation model is configured to generate the recommended training content list through: a first step of determining whether a correct answer rate of training content performed by the patient is greater than or equal to a first reference; a second step of determining whether a reaction time for the training content performed by the patient is Less than a second reference, when the correct answer rate of the patient is greater than or equal to the first reference; and a third step of generating training content with increased difficulty as the recommended training content list when difficulty of the training content performed by the patient is not a maximum, when the reaction time of the patient is less than the second reference.
10. The patient-customized training content recommendation device of claim 7, wherein the AI rehabilitation recommendation model is configured to generate the recommended training content list through: a first step of determining whether a correct answer rate of training content performed by the patient is less than a first reference; a second step of determining whether the correct answer rate of the training content performed by the patient is greater than or equal to a third reference, when the correct answer rate of the patient is less than the first reference; and a third step of generating training content having previous difficulty as the recommended training content list when the correct answer rate of the patient is greater than or equal to the third reference, and generating training content having reduced difficulty as the recommended training content list when the correct answer rate of the patient is less than the third reference.
11. The patient-customized training content recommendation device of claim 1, wherein the AI rehabilitation recommendation model includes: a prediction model configured to predict a result of training content of the patient; and a recommendation model configured to generate a recommended training content list based on a recommendation score calculated depending on a set target value.
12. The patient-customized training content recommendation device of claim 5, wherein the training content includes: at least one cognitive training in which a training type, difficulty, a number of problems, and a solution time are set, and wherein the training recommendation unit is configured to: provide training content for reinforcing a sub-function of which the statistical value is low.
13. The patient-customized training content recommendation device of claim 12, wherein the training recommendation unit is configured to: recommend the at least one training content by selecting cognitive training including most label codes of detailed sub-function included in the sub-function, of which the statistical value is low, through the AI rehabilitation recommendation model.
14. The patient-customized training content recommendation device of claim 3, further comprising: a result providing unit configured to provide a result for the training content; a profile updating unit configured to update the patient profile depending on a result of the training content; and a patient cognitive score predicting unit configured to generate a cognitive score prediction value by predicting the predefined cognitive score different from the function- specific cognitive score of the patient depending on a statistical value for the respective sub- function, wherein the patient cognitive score predicting unit is configured to: calculate accuracy of the cognitive score prediction value depending on a size of a parent population for calculating a statistical value for the respective sub-function.
15. A patient-customized training content recommending method performed on a user terminal, the method comprising: receiving a patient profile including basic information and cognitive information of a patient; calculating a function-specific cognitive score of the patient by evaluating a cognitive ability of the patient based on the patient profile; recommending at least one training content through an AI rehabilitation recommendation model; and calculating a result for the training content provided to the patient through the user terminal.
These additional elements are hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a user terminal (processor) to perform both the evaluating and recommending steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Choun Seung Ho (hereinafter Ho) (KR 20220074141 A).
Claim 1 recites a patient-customized training content recommendation device comprising:
a profile receiver configured to receive a patient profile including basic information and cognitive information of a patient (Ho discloses “The cognitive function test module 110 tests the cognitive function of a user (subject) using a computerized cognitive function test kit, and generates the result as computerized test data. Here, the cognitive function test kit is, for example, an MMSE-K (Mini Mental State Examination - Korea, Korean version of the Simplified Mental State Test) kit, a screening test tool developed to simply evaluate a subject's cognitive function and dementia in a short time.” on page 3, par. 8-9);
a cognitive ability evaluating unit configured to calculate a function-specific cognitive score of the patient by evaluating a cognitive ability of the patient based on the patient profile (Ho discloses “…the user's level for each area can be indicated by calculating a score for each area and scoring (processing the corresponding question of the MMSE as a percentage). Based on the reference score (eg, 60 points), the user's level may be determined as 1st level if it is low and 2nd level if it is higher than the reference score…” on page 8, par. 11-12);
a training recommendation unit configured to recommend at least one training content through an artificial intelligence (AI) rehabilitation recommendation model (Ho discloses “The customized rehabilitation content recommendation unit 121 receives the cognitive function test data from the cognitive function test module 110, and analyzes it with artificial intelligence (AI) for optimal customized cognition that can strengthen a relatively weak area for the subject. Rehabilitation content is recommended. For example, when the orientation score is low, content for reinforcing orientation may be recommended, and when the language function score is low, content for reinforcing language function may be recommended.” on page 5, par. 2); and
a training conducting unit configured to calculate a result for the training content provided to the patient through a user terminal (Ho discloses “The customized rehabilitation content recommendation unit 121 receives the cognitive function test data from the cognitive function test module 110, and analyzes it with artificial intelligence (AI) for optimal customized cognition that can strengthen a relatively weak area for the subject. Rehabilitation content is recommended. For example, when the orientation score is low, content for reinforcing orientation may be recommended, and when the language function score is low, content for reinforcing language function may be recommended.” on page 5, par. 2).
Claim 2 recites the patient-customized training content recommendation device of claim 1, wherein the basic information of the patient includes a current status, an age, a gender, and a highest level of education of the patient, and wherein the cognitive information includes a result for the training content, and includes a separately pre-defined cognitive score when there is no training content already performed by the patient (Ho; page 5, par. 2, page 6, par. 11).
Claim 3 recites the patient-customized training content recommendation device of claim 2, wherein the cognitive ability evaluating unit is configured to: define the function-specific cognitive score depending on a sub-function split stepwise; and calculate a statistical value for the respective sub-function (Ho; page 8, par. 8 to page 9, par. 1).
Claim 4 recites the patient-customized training content recommendation device of claim 3, wherein the cognitive ability evaluating unit is configured to define the function-specific cognitive score through: a first step of defining at least one sub-function; a second step of defining a detailed sub-function included in the at least one sub- function; and a third step of performing labeling on the sub-function and the detailed sub-function to generate a label code for the sub-function and the detailed sub-function (Ho; page 8, par. 8 to page 9, par. 1).
Claim 5 recites the patient-customized training content recommendation device of claim 4, wherein the cognitive ability evaluating unit is configured to: calculate the function-specific cognitive score by summing the statistical value for each sub-function (Ho; page 8, par. 11 to page 9, par. 1).
Claim 6 recites the patient-customized training content recommendation device of claim 3, wherein the AI rehabilitation recommendation model is configured to: generate a recommended training content list including a specific label code depending on an input of the specific label code, and wherein the training recommendation unit is configured to: recommend the recommended training content list as the training content (Ho; page 5, par. 2).
Claim 7 recites the patient-customized training content recommendation device of claim 1, wherein the AI rehabilitation recommendation model is configured to: generate a recommended training content list based on a cluster, to which the patient belongs, by clustering at least one patient based on a performance result for specific training content (Ho; page 5, par. 2).
Claim 8 recites the patient-customized training content recommendation device of claim 7, wherein the AI rehabilitation recommendation model is configured to: generate, as the recommended training content list, training content performed by other patients in the cluster, to which the patient belongs, in addition to training content performed by the patient (Ho; page 5, par. 2).
Claim 9 recites the patient-customized training content recommendation device of claim 7, wherein the AI rehabilitation recommendation model is configured to generate the recommended training content list through: a first step of determining whether a correct answer rate of training content performed by the patient is greater than or equal to a first reference; a second step of determining whether a reaction time for the training content performed by the patient is Less than a second reference, when the correct answer rate of the patient is greater than or equal to the first reference; and a third step of generating training content with increased difficulty as the recommended training content list when difficulty of the training content performed by the patient is not a maximum, when the reaction time of the patient is less than the second reference (Ho; page 5, par. 2, page 5, par. 10-page 6, par. 5).
Claim 10 recites the patient-customized training content recommendation device of claim 7, wherein the AI rehabilitation recommendation model is configured to generate the recommended training content list through: a first step of determining whether a correct answer rate of training content performed by the patient is less than a first reference; a second step of determining whether the correct answer rate of the training content performed by the patient is greater than or equal to a third reference, when the correct answer rate of the patient is less than the first reference; and a third step of generating training content having previous difficulty as the recommended training content list when the correct answer rate of the patient is greater than or equal to the third reference, and generating training content having reduced difficulty as the recommended training content list when the correct answer rate of the patient is less than the third reference (Ho; page 5, par. 2, page 5, par. 10-page 6, par. 5).
Claim 11 recites the patient-customized training content recommendation device of claim 1, wherein the AI rehabilitation recommendation model includes: a prediction model configured to predict a result of training content of the patient; and a recommendation model configured to generate a recommended training content list based on a recommendation score calculated depending on a set target value (Ho; page 5, par. 2, page 5, par. 10-page 6, par. 5).
Claim 12 recites the patient-customized training content recommendation device of claim 5, wherein the training content includes: at least one cognitive training in which a training type, difficulty, a number of problems, and a solution time are set, and wherein the training recommendation unit is configured to: provide training content for reinforcing a sub-function of which the statistical value is low (Ho; page 8, par. 11 to page 9, par. 1).
Claim 13 recites the patient-customized training content recommendation device of claim 12, wherein the training recommendation unit is configured to: recommend the at least one training content by selecting cognitive training including most label codes of detailed sub-function included in the sub-function, of which the statistical value is low, through the AI rehabilitation recommendation model (Ho; page 9, par. 5-8).
Claim 14 recites the patient-customized training content recommendation device of claim 3, further comprising: a result providing unit configured to provide a result for the training content; a profile updating unit configured to update the patient profile depending on a result of the training content; and a patient cognitive score predicting unit configured to generate a cognitive score prediction value by predicting the predefined cognitive score different from the function- specific cognitive score of the patient depending on a statistical value for the respective sub- function, wherein the patient cognitive score predicting unit is configured to: calculate accuracy of the cognitive score prediction value depending on a size of a parent population for calculating a statistical value for the respective sub-function (Ho; page 5, par. 10-11).
Claim 15 recites a patient-customized training content recommending method performed on a user terminal, the method comprising:
receiving a patient profile including basic information and cognitive information of a patient (Ho discloses “The cognitive function test module 110 tests the cognitive function of a user (subject) using a computerized cognitive function test kit, and generates the result as computerized test data. Here, the cognitive function test kit is, for example, an MMSE-K (Mini Mental State Examination - Korea, Korean version of the Simplified Mental State Test) kit, a screening test tool developed to simply evaluate a subject's cognitive function and dementia in a short time.” on page 3, par. 8-9);
calculating a function-specific cognitive score of the patient by evaluating a cognitive ability of the patient based on the patient profile (Ho discloses “…the user's level for each area can be indicated by calculating a score for each area and scoring (processing the corresponding question of the MMSE as a percentage). Based on the reference score (eg, 60 points), the user's level may be determined as 1st level if it is low and 2nd level if it is higher than the reference score…” on page 8, par. 11-12);
recommending at least one training content through an AI rehabilitation recommendation model (Ho discloses “The customized rehabilitation content recommendation unit 121 receives the cognitive function test data from the cognitive function test module 110, and analyzes it with artificial intelligence (AI) for optimal customized cognition that can strengthen a relatively weak area for the subject. Rehabilitation content is recommended. For example, when the orientation score is low, content for reinforcing orientation may be recommended, and when the language function score is low, content for reinforcing language function may be recommended.” on page 5, par. 2); and
calculating a result for the training content provided to the patient through the user terminal (Ho discloses “The customized rehabilitation content recommendation unit 121 receives the cognitive function test data from the cognitive function test module 110, and analyzes it with artificial intelligence (AI) for optimal customized cognition that can strengthen a relatively weak area for the subject. Rehabilitation content is recommended. For example, when the orientation score is low, content for reinforcing orientation may be recommended, and when the language function score is low, content for reinforcing language function may be recommended.” on page 5, par. 2).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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/DILEK B COBANOGLU/ Primary Examiner, Art Unit 3687
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