Notice of Pre-AIA or AIA Status
This action is made in response to the amendments/remarks filed on 10/22/2025. This action is made FINAL.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 10/22/2025 has been entered. Claims 1-4, 6-10, 12-14, 16-18, and 20 remain pending in the application. Claims 5, 11, 15, and 19 have been cancelled.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 analysis:
Claims 1 is drawn to a method (i.e., process), Claim 12 is drawn to a system, and Claim 17 is drawn toward a computer program product (i.e., manufacture), which are all within the four statutory categories. (Step 1 – Yes, the claim falls into one of the statutory categories).
Step 2A analysis – Prong One:
Claim 1 recites:
A method, comprising:
receiving survey information associated with a patient, the survey information comprising quality of life answers for questions associated with a quality of life category;
automatically processing the survey information using an Artificial Intelligence (AI) alert decision support system to perform operations comprising:
generating an embedding using one-hot encoding for the survey information to reduce a dimensionality of the survey information; and
using a model corresponding to the quality of life category to predict a change in a quality of life score for the patient for the quality of life category based on the embedded survey information having the reduced dimensionality;
comparing the change in the quality of life score for the patient with a threshold; and
generating an alert based on the comparison of the change in the quality of life score for the patient with the threshold,
wherein receiving the survey information comprises:
collecting the survey information via an Internet of Things (IoT) device in concert with distributing a drug product to the patient via the IoT device.
The series of steps as recited above describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a provider receiving survey information about the health of a patient and using the survey information to predict a change in the health status of the patient. Additionally, collecting survey information in concert with distributing a drug product to the patient recites a method of organizing human activity, specifically an interaction between a provider and patient. Accordingly, the claim recites an abstract idea of managing interactions between people.
The series of steps as recited above also falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Generating an embedding, using a model and predicting a change in score for the patient, comparing changes in score, and generating an alert are all processes that can be done in the human mind using observation, evaluation, judgement, and opinion. Therefore, the claim recites an abstract idea of a mental process.
The series of steps as recited above also falls within the mathematical concepts grouping of abstract ideas. Generating an embedding using one-hot encoding is a mathematical calculation, and therefore is an abstract idea. See MPEP 2106.04(a)(2), “A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
Claims 12 and 17 recite/describe nearly identical steps as claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Step 2A analysis – Prong 2:
This judicial exception is not integrated into a practical application. Specifically, independent claims 1, 12, and 17 recite the following additional elements beyond the abstract idea: Artificial Intelligence (AI) alert decision support system, an Internet of Things (IoT) device, a processor, a memory, computer readable program code, and a non-transitory computer readable storage medium. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Specifically, the AI alert decision support system (DSS) may include other types of AI systems including, but not limited to, a multi-layer neural network, a deep learning system, a natural language processing system, and/or computer vision system (specification par. 36). The processor includes processor/computing systems that may be implemented as a single processor system, a multi-processor system, a multi-core processor system, or a network of stand-alone computer systems (specification par. 60). The memory may include a volatile memory device, such as dynamic random-access memory (DRAM) and static RAM (SRAM), or include a non-volatile memory device, such as flash memory and resistive RAM (RRAM) (specification par. 53). The computer readable program code may be written in a high-level programming language, such as Python, Java C, or C++ (specification par. 59). The storage medium may be a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an appropriate optical fiber with a repeater, a portable compact disc read-only memory (CDROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing (specification par. 68).
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1, 12, and 17 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
Step 2B analysis:
As discussed above in “Step 2A analysis – Prong 2”, the identified additional elements in Independent Claims 1, 12, and 17 are equivalent to adding the words “apply it” on a generic computer. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of “well- understood, routine, [and] conventional activities previously known to the industry.” Further, “the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention.”
The applicant’s specification discloses: the AI alert decision support system (DSS) may include other types of AI systems including, but not limited to, a multi-layer neural network, a deep learning system, a natural language processing system, and/or computer vision system (specification par. 36). The processor includes processor/computing systems that may be implemented as a single processor system, a multi-processor system, a multi-core processor system, or a network of stand-alone computer systems (specification par. 60). The memory may include a volatile memory device, such as dynamic random-access memory (DRAM) and static RAM (SRAM), or include a non-volatile memory device, such as flash memory and resistive RAM (RRAM) (specification par. 53). The computer readable program code may be written in a high-level programming language, such as Python, Java C, or C++ (specification par. 59). The storage medium may be a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an appropriate optical fiber with a repeater, a portable compact disc read-only memory (CDROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing (specification par. 68).
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Here, the claim limitations are similar to receiving and sending information over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OJP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); See MPEP 2106.05(d)(ll)(i)).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the steps for provider prioritization amount to no more than using computer related devices to implement the abstract idea.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible. The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Independent claims - NO).
Dependent Claims
Dependent Claims 2-4, 6-10, 13-14, 18, and 20 are directed towards elements used to describe the alert communication, drug product distribution, and the AI algorithm. These elements include communicating the alert to the patient, caregiver, health care service provider, or pharmacist; quality of life categories including a health category, emotion category, and an activity category; collecting survey information in concert with distributing a drug product to a patient; receiving survey answers from the patient; receiving clinical information; generating an embedding; using a model to predict the quality of life score based on survey and clinical information; determining similarities between predicted scores and actual scores.
Each of these elements amounts to a form of managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Specifically, the alert communication between a provider, patient, and caregiver, distributing a drug product to a patient, and receiving survey answers and clinical information from the patient falls within the same abstract idea of managing interactions between people as identified in independent claims 1, 12, and 17.
The dependent claims are also directed toward the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. Generating an embedding, using a model and predicting a change in score for the patient, comparing changes in score, and generating an alert are all processes that can be done in the human mind using observation, evaluation, judgement, and opinion. Therefore, the claim falls within the same abstract idea of a mental process as identified in independent claims 1, 12, and 17.
Dependent claims 2-11, 13-16, and 18-20 recite the following additional elements: an Internet of Things (IOT) device, the Artificial Intelligence alert decision support system, and an AI QoL algorithm. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)). (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
As discussed above, the identified additional elements in dependent claims 2-11, 13-16, and 18-20 are equivalent to adding the words “apply it” on a generic computer. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
The dependent claims as a whole do not amount to significantly more than the judicial exception itself. The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the dependent claims are not patent eligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. (Step 2B - NO).
Therefore, Claims 1-20 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6-8, 12-14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rutowski et al. (WO 2019/246239) (hereinafter Rutowski) in view of Spekowius et al. (US 2022/0167893) (Hereinafter Spekowius), further in view of Hoon (KR 102155739 B1).
Regarding Claim 1, Rutowski discloses:
A method, comprising (Rutowski Abstract: The present disclosure provides systems and methods…):
receiving survey information associated with a patient ([79], [7]: The method can further comprise receiving data comprising the at least one response from the subject in response to transmitting the at least one query; the at least one query can comprise a plurality of queries and the at least one response can comprise a plurality of responses), the survey information comprising quality of life survey answers ([19] In some embodiments, the standardized test or questionnaire can be selected from the group consisting of PHQ-9, GAD-7, HAM-D, and BDI. The standardized test or questionnaire can be another similar test or questionnaire for assessing a patient's mental health state) for questions associated with a quality of life category (Figure 9, [207]: Each of question records 902 includes a question body 904, a classification 906, a quality 908, and an equivalence 910. Topic 906 includes data specifying a hierarchical topic category to which the question belongs.)
automatically processing the survey information using an Artificial Intelligence (AI) alert decision support system ([369] “The action generator 2833 applies a rule based model to determine which action within the question and adaptive action bank 2810 is appropriate. Alternatively, a machine learned model is applied in lieu of a rule based decision model.” This system including a decision model in conjunction with a machine learning model is interpreted as a decision support system) to perform operations comprising:
generating an embedding ([346] Embedding and clustering model 2556 maps words to prototypical words or word categories.); and
using a model ([79] The method can further comprise processing the data using a composite model comprising at least one or more semantic models to generate an assessment of the mental state of the subject) corresponding to the quality of life category (Figure 9, [207]: Each of question records 902 includes a question body 904, a classification 906, a quality 908, and an equivalence 910. Topic 906 includes data specifying a hierarchical topic category to which the question belongs.) to predict a change in a quality of life score for the patient for the quality of life category ([423] The scaled score maybe used to describe a severity of a mental state. [499] The dashboards may show predictions taken at various time points, charting a patient's progress with respect to treatment. [508] In addition, depression predictions over a time scale in which a patient is recovering from an illness or injury may be compared to the patient's health outcomes over that time scale, to see if treatment is improving the patient's depression or depression-related symptoms.) based on the embedded survey information [having the reduced dimensionality] ([346], [428]: The embedding model’s output is consumed by other language models, which are used in processing the score of the patient);
comparing the change in the quality of life score for the patient with a threshold ([427] The system may use a classification algorithm to do this, such as a neural network or an ensemble method. The binary classifier may output a number between 0 and 1. If a patient's score is above a threshold (e.g., 0.5), the patient may be classified as "depressed." If the patient's score is below the threshold, the patient may be classified as "not depressed."); and
generating an alert based on the comparison of the change in the quality of life score for the patient with the threshold ([173] In response to a change in the normalized score or confidence during the course of the screening, monitoring, or diagnosis, the electronic report can be updated substantially in real-time and be re-transmitted to the user),
wherein receiving the survey information comprises: collecting the survey information ([385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) via an Internet of Things (IoT) device ([160] The client device may be a cellular phone, tablet, laptop or desktop equipped with a microphone and optional camera, smart speaker in the home or other location, smart watch with a microphone and optional camera, or a similar device.).
However, Rutowski does not teach the following that is met by Spekowius:
collecting the survey information ([0048] “In embodiments, the answers to the questionnaire can be provided to the cognitive training device”) in concert with distributing a drug product to the patient via the IoT device ([0026], Figure 1, [0027]: A system for detecting and managing cognitive decline of a person using a cognitive training device receives data from the patient. The data used to describe the person’s behavior can include information describing how the person uses particular devices, such as, a medication dispenser. “The data can also include or be combined with physiological data of the person. The devices of the system 50 can be connected via any suitable Internet of Things (IoT) system.”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined the collection of survey information via an IOT device, as taught by Rutowski, with the medication dispensing tracking function as taught by Spekowius because including information on the drug dispensing behavior of the person can assist in characterizing the person’s behavior as abnormal and it is beneficial to extract information from a plurality of devices to detect the illness (Spekowius par. [0005], [0043]).
However, Rutowski and Spekowius do not teach the following that is met by Hoon:
generating an embedding using one-hot encoding for the survey information (Hoon Pg. 3, par. 1 and Pg. 4, Par. 3: a query can be processed in a form for learning in a word embedding or one-hot vector format; the recommendation unit 340 performs one-hot encoding on the text included in the product name on which the pre-processing has been performed, based on the syllable, and converts the text included in the product name into a vector as learning data of machine learning) to reduce a dimensionality of the survey information (Hoon Pg. 4, par. 4: there are many words that are difficult to say that it contains knowledge or meaning among the extracted entity names, and the dimension varies in the vectorization process due to the difference in the number of entity names extracted for each category. There is. Therefore, if you set a constraint that matches the dimension by limiting to the high-order number of entity names with high frequency of occurrence foreach category, as a result, entity names extracted from a number of categories can be vectorized through one-hot encoding, and input for learning).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined the method of generating the embedding as taught by Rutowski to include one-hot encoding as taught by Hoon since the claimed invention is only a combination of these well-known elements which would have performed the same function in combination as each did separately. For a question and answer formatted survey, the one-hot encoding performs the same function of organizing the survey information as the embedding taught in Rutowski, therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Regarding Claim 2, Rutowski discloses:
The method of Claim 1, further comprising: communicating the alert to the patient and/or an entity designated by the patient ([173] The electronic report may be output to the patient or a contact person associated with the patient, a healthcare provider, a healthcare payer, or another third-party).
Regarding Claim 3, Rutowski discloses:
The method of Claim 2, wherein the entity designated by the patient is a caregiver for the patient, a health care service provider, or a pharmacist ([173] The electronic report may be output to the patient or a contact person associated with the patient, a healthcare provider, a healthcare payer, or another third-party).
Regarding Claim 4, Rutowski discloses:
The method of Claim 1, wherein the quality of life category is a health category, an emotion category, or an activity category ([207] Categories may correlate to (i) specific health diagnoses such as depression, anxiety, etc.; (ii) specific symptoms such as insomnia, lethargy, general disinterest, etc.; and/or (iii) aspects of a patient's treatment such as medication, exercise, etc.).
Regarding Claim 6, Rutowski discloses:
The method of Claim 1, wherein collecting the survey information ([385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) comprises:
receiving the answers to the quality of life survey questions ([Abstract]: The query may be configured to elicit at least one response from the subject. The query may be transmitted in an audio, visual, and/or textual format to the subject to elicit the response. Data comprising the response from the subject can be received. [385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) from the patient via the IoT device ([160] The client device may be a cellular phone, tablet, laptop or desktop equipped with a microphone and optional camera, smart speaker in the home or other location, smart watch with a microphone and optional camera, or a similar device.).
Regarding Claim 7, Rutowski discloses:
The method of Claim 1, further comprising:
receiving clinical information associated with the patient ([167] The system may provide demographic information, such as age, weight, occupation, height, ethnicity, medical history, psychological history, and gender to medical care professionals via client devices);
wherein automatically processing the survey information ([Abstract]: The [response] data can be processed using one or more individual, joint, or fused models), comprises:
using the model ([79] The method can further comprise processing the data using a composite model comprising at least one or more semantic models to generate an assessment of the mental state of the subject) corresponding to the quality of life category (Figure 9, [207]: Each of question records 902 includes a question body 904, a classification 906, a quality 908, and an equivalence 910. Topic 906 includes data specifying a hierarchical topic category to which the question belongs.) to predict the change in the quality of life score for the patient for the quality of life category ([423] The scaled score maybe used to describe a severity of a mental state. [499] The dashboards may show predictions taken at various time points, charting a patient's progress with respect to treatment. [508] In addition, depression predictions over a time scale in which a patient is recovering from an illness or injury may be compared to the patient's health outcomes over that time scale, to see if treatment is improving the patient's depression or depression-related symptoms.) based on the embedded survey information and the embedded clinical information([346], [428]: The embedding model’s output is consumed by other language models, which are used in processing the score of the patient).
Regarding Claim 8, Rutowski discloses:
The method of Claim 7, wherein at least a portion of the clinical information is measured via the IoT device ([167] The system may provide demographic information, such as age, weight, occupation, height, ethnicity, medical history, psychological history, and gender to medical care professionals via client devices. [161] A client device may collect additional data, such as biometric data).
Regarding Claim 12, Rutowski discloses:
A system ([81] a system), comprising:
A processor ([81] Another aspect of the present disclosure provides a system comprising one or more computer processors); and
a memory coupled to the processor and comprising computer readable program code embodied in the memory that is executable by the processor to perform operations comprising ([81] Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein):
receiving survey information associated with a patient ([79], [7]: The method can further comprise receiving data comprising the at least one response from the subject in response to transmitting the at least one query; the at least one query can comprise a plurality of queries and the at least one response can comprise a plurality of responses), the survey information comprising quality of life survey answers ([19] In some embodiments, the standardized test or questionnaire can be selected from the group consisting of PHQ-9, GAD-7, HAM-D, and BDI. The standardized test or questionnaire can be another similar test or questionnaire for assessing a patient's mental health state) for questions associated with a quality of life category (Figure 9, [207]: Each of question records 902 includes a question body 904, a classification 906, a quality 908, and an equivalence 910. Topic 906 includes data specifying a hierarchical topic category to which the question belongs.);
automatically processing the survey information using an Artificial Intelligence (AI) alert decision support system ([369] “The action generator 2833 applies a rule based model to determine which action within the question and adaptive action bank 2810 is appropriate. Alternatively, a machine learned model is applied in lieu of a rule based decision model.” This system including a decision model in conjunction with a machine learning model is interpreted as a decision support system) to perform operations comprising:
generating an embedding ([346] Embedding and clustering model 2556 maps words to prototypical words or word categories.) for the survey information; and
using a model ([79] The method can further comprise processing the data using a composite model comprising at least one or more semantic models to generate an assessment of the mental state of the subject) corresponding to the quality of life category (Figure 9, [207]: Each of question records 902 includes a question body 904, a classification 906, a quality 908, and an equivalence 910. Topic 906 includes data specifying a hierarchical topic category to which the question belongs.) to predict a change in a quality of life score for the patient for the quality of life category ([423] The scaled score maybe used to describe a severity of a mental state. [499] The dashboards may show predictions taken at various time points, charting a patient's progress with respect to treatment. [508] In addition, depression predictions over a time scale in which a patient is recovering from an illness or injury may be compared to the patient's health outcomes over that time scale, to see if treatment is improving the patient's depression or depression-related symptoms.) based on the embedded survey information [having the reduced dimensionality] ([346], [428]: The embedding model’s output is consumed by other language models, which are used in processing the score of the patient);
comparing the change in the quality of life score for the patient with a threshold ([427] The system may use a classification algorithm to do this, such as a neural network or an ensemble method. The binary classifier may output a number between 0 and 1. If a patient's score is above a threshold (e.g., 0.5), the patient may be classified as "depressed." If the patient's score is below the threshold, the patient may be classified as "not depressed."); and
generating an alert based on the comparison of the change in the quality of life score for the patient with the threshold ([173] In response to a change in the normalized score or confidence during the course of the screening, monitoring, or diagnosis, the electronic report can be updated substantially in real-time and be re-transmitted to the user).
wherein receiving the survey information comprises: collecting the survey information ([385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) via an Internet of Things (IoT) device ([160] The client device may be a cellular phone, tablet, laptop or desktop equipped with a microphone and optional camera, smart speaker in the home or other location, smart watch with a microphone and optional camera, or a similar device.).
However, Rutowski does not teach the following that is met by Spekowius:
collecting the survey information ([0048] “In embodiments, the answers to the questionnaire can be provided to the cognitive training device”) in concert with distributing a drug product to the patient via the IoT device ([0026], Figure 1, [0027]: A system for detecting and managing cognitive decline of a person using a cognitive training device receives data from the patient. The data used to describe the person’s behavior can include information describing how the person uses particular devices, such as, a medication dispenser. “The data can also include or be combined with physiological data of the person. The devices of the system 50 can be connected via any suitable Internet of Things (IoT) system.”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined the collection of survey information via an IOT device, as taught by Rutowski, with the medication dispensing tracking function as taught by Spekowius because including information on the drug dispensing behavior of the person can assist in characterizing the person’s behavior as abnormal and it is beneficial to extract information from a plurality of devices to detect the illness (Spekowius par. [0005], [0043]).
However, Rutowski and Spekowius do not teach the following that is met by Hoon:
generating an embedding using one-hot encoding for the survey information (Hoon Pg. 3, par. 1 and Pg. 4, Par. 3: a query can be processed in a form for learning in a word embedding or one-hot vector format; the recommendation unit 340 performs one-hot encoding on the text included in the product name on which the pre-processing has been performed, based on the syllable, and converts the text included in the product name into a vector as learning data of machine learning) to reduce a dimensionality of the survey information (Hoon Pg. 4, par. 4: there are many words that are difficult to say that it contains knowledge or meaning among the extracted entity names, and the dimension varies in the vectorization process due to the difference in the number of entity names extracted for each category. There is. Therefore, if you set a constraint that matches the dimension by limiting to the high-order number of entity names with high frequency of occurrence foreach category, as a result, entity names extracted from a number of categories can be vectorized through one-hot encoding, and input for learning).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined the method of generating the embedding as taught by Rutowski to include one-hot encoding as taught by Hoon since the claimed invention is only a combination of these well-known elements which would have performed the same function in combination as each did separately. For a question and answer formatted survey, the one-hot encoding performs the same function of organizing the survey information as the embedding taught in Rutowski, therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Regarding Claim 13, Rutowski discloses:
The system of Claim 12, wherein the operations further comprise:
communicating the alert to the patient and/or an entity designated by the patient ([173] The electronic report may be output to the patient or a contact person associated with the patient, a healthcare provider, a healthcare payer, or another third-party).
Regarding Claim 14, Rutowski discloses:
The system of Claim 12, wherein the quality of life category is a health category, an emotion category, or an activity category ([207] Categories may correlate to (i) specific health diagnoses such as depression, anxiety, etc.; (ii) specific symptoms such as insomnia, lethargy, general disinterest, etc.; and/or (iii) aspects of a patient's treatment such as medication, exercise, etc.)
Regarding Claim 16, Rutowski discloses:
The system of Claim 15, wherein collecting the survey information ([385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) comprises:
receiving the answers to the quality of life survey questions from the patient ([Abstract]: The query may be configured to elicit at least one response from the subject. The query may be transmitted in an audio, visual, and/or textual format to the subject to elicit the response. Data comprising the response from the subject can be received. [385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) via the IoT device ([160] The client device may be a cellular phone, tablet, laptop or desktop equipped with a microphone and optional camera, smart speaker in the home or other location, smart watch with a microphone and optional camera, or a similar device.).
Regarding Claim 17, Rutowski discloses:
A computer program product ([558] The computer programs typically comprise one or more instructions set at various times in various memory and storage devices in a computer, and when read and executed by one or more processing units or processors in a computer, cause the computer to perform operations to execute elements involving the various aspects of the disclosure), comprising:
a non-transitory computer readable storage medium comprising computer readable program code embodied in the medium that is executable by a processor to perform operations ([80] Another aspect of the present disclosure provides a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more computer processors, implements any of the methods above or elsewhere herein.) comprising:
receiving survey information associated with a patient ([79], [7]: The method can further comprise receiving data comprising the at least one response from the subject in response to transmitting the at least one query; the at least one query can comprise a plurality of queries and the at least one response can comprise a plurality of responses), the survey information comprising quality of life survey answers ([19] In some embodiments, the standardized test or questionnaire can be selected from the group consisting of PHQ-9, GAD-7, HAM-D, and BDI. The standardized test or questionnaire can be another similar test or questionnaire for assessing a patient's mental health state) for questions associated with a quality of life category (Figure 9, [207]: Each of question records 902 includes a question body 904, a classification 906, a quality 908, and an equivalence 910. Topic 906 includes data specifying a hierarchical topic category to which the question belongs.);
automatically processing the survey information using an Artificial Intelligence (AI) alert decision support system ([369] “The action generator 2833 applies a rule based model to determine which action within the question and adaptive action bank 2810 is appropriate. Alternatively, a machine learned model is applied in lieu of a rule based decision model.” This system including a decision model in conjunction with a machine learning model is interpreted as a decision support system) to perform operations comprising:
generating an embedding ([346] Embedding and clustering model 2556 maps words to prototypical words or word categories.) for the survey information; and
using a model ([79] The method can further comprise processing the data using a composite model comprising at least one or more semantic models to generate an assessment of the mental state of the subject) corresponding to the quality of life category (Figure 9, [207]: Each of question records 902 includes a question body 904, a classification 906, a quality 908, and an equivalence 910. Topic 906 includes data specifying a hierarchical topic category to which the question belongs.) to predict a change in a quality of life score for the patient for the quality of life category ([423] The scaled score maybe used to describe a severity of a mental state. [499] The dashboards may show predictions taken at various time points, charting a patient's progress with respect to treatment. [508] In addition, depression predictions over a time scale in which a patient is recovering from an illness or injury may be compared to the patient's health outcomes over that time scale, to see if treatment is improving the patient's depression or depression-related symptoms.) based on the embedded survey information [having the reduced dimensionality] ([346], [428]: The embedding model’s output is consumed by other language models, which are used in processing the score of the patient);
comparing the change in the quality of life score for the patient with a threshold ([427] The system may use a classification algorithm to do this, such as a neural network or an ensemble method. The binary classifier may output a number between 0 and 1. If a patient's score is above a threshold (e.g., 0.5), the patient may be classified as "depressed." If the patient's score is below the threshold, the patient may be classified as "not depressed."); and
generating an alert based on the comparison of the change in the quality of life score for the patient with the threshold ([173] In response to a change in the normalized score or confidence during the course of the screening, monitoring, or diagnosis, the electronic report can be updated substantially in real-time and be re-transmitted to the user).
wherein receiving the survey information comprises: collecting the survey information ([385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) via an Internet of Things (IoT) device ([160] The client device may be a cellular phone, tablet, laptop or desktop equipped with a microphone and optional camera, smart speaker in the home or other location, smart watch with a microphone and optional camera, or a similar device.).
However, Rutowski does not teach the following that is met by Spekowius:
collecting the survey information ([0048] “In embodiments, the answers to the questionnaire can be provided to the cognitive training device”) in concert with distributing a drug product to the patient via the IoT device ([0026], Figure 1, [0027]: A system for detecting and managing cognitive decline of a person using a cognitive training device receives data from the patient. The data used to describe the person’s behavior can include information describing how the person uses particular devices, such as, a medication dispenser. “The data can also include or be combined with physiological data of the person. The devices of the system 50 can be connected via any suitable Internet of Things (IoT) system.”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined the collection of survey information via an IOT device, as taught by Rutowski, with the medication dispensing tracking function as taught by Spekowius because including information on the drug dispensing behavior of the person can assist in characterizing the person’s behavior as abnormal and it is beneficial to extract information from a plurality of devices to detect the illness (Spekowius par. [0005], [0043]).
However, Rutowski and Spekowius do not teach the following that is met by Hoon:
generating an embedding using one-hot encoding for the survey information (Hoon Pg. 3, par. 1 and Pg. 4, Par. 3: a query can be processed in a form for learning in a word embedding or one-hot vector format; the recommendation unit 340 performs one-hot encoding on the text included in the product name on which the pre-processing has been performed, based on the syllable, and converts the text included in the product name into a vector as learning data of machine learning) to reduce a dimensionality of the survey information (Hoon Pg. 4, par. 4: there are many words that are difficult to say that it contains knowledge or meaning among the extracted entity names, and the dimension varies in the vectorization process due to the difference in the number of entity names extracted for each category. There is. Therefore, if you set a constraint that matches the dimension by limiting to the high-order number of entity names with high frequency of occurrence foreach category, as a result, entity names extracted from a number of categories can be vectorized through one-hot encoding, and input for learning).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined the method of generating the embedding as taught by Rutowski to include one-hot encoding as taught by Hoon since the claimed invention is only a combination of these well-known elements which would have performed the same function in combination as each did separately. For a question and answer formatted survey, the one-hot encoding performs the same function of organizing the survey information as the embedding taught in Rutowski, therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Regarding Claim 18, Rutowski discloses:
The computer program product of Claim 17, wherein the quality of life category is a health category, an emotion category, or an activity category ([207] Categories may correlate to (i) specific health diagnoses such as depression, anxiety, etc.; (ii) specific symptoms such as insomnia, lethargy, general disinterest, etc.; and/or (iii) aspects of a patient's treatment such as medication, exercise, etc.).
Regarding Claim 20, Rutowski discloses:
The computer program product of Claim 17, wherein collecting the survey information ([385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) comprises:
receiving the answers to the quality of life survey questions from the patient ([Abstract]: The query may be configured to elicit at least one response from the subject. The query may be transmitted in an audio, visual, and/or textual format to the subject to elicit the response. Data comprising the response from the subject can be received. [385], Figure 29: The client response data is collected… captured by the client device’s camera(s) and microphones(s)) via the IoT device ([160] The client device may be a cellular phone, tablet, laptop or desktop equipped with a microphone and optional camera, smart speaker in the home or other location, smart watch with a microphone and optional camera, or a similar device.).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Rutowski et al. (WO 2019/246239) (Hereinafter Rutowski) in view of Spekowius et al. (US 2022/0167893) (Hereinafter Spekowius), further in view Hoon (KR 102155739 B1), further in view of Amarasingham et al. (US 2013/0262357) (Hereinafter Amarasingham).
Regarding Claim 9, Rutowski discloses:
The method of Claim 1, wherein the AI alert decision support system ([369] “The action generator 2833 applies a rule based model to determine which action within the question and adaptive action bank 2810 is appropriate. Alternatively, a machine learned model is applied in lieu of a rule based decision model.” This system including a decision model in conjunction with a machine learning model is interpreted as a decision support system) includes
However, Rutowski, Spekowius, and Hoon do not disclose the following that is met by Amarasignham:
an AI QoL algorithm that is trained by determining similarities between predicted quality of life scores and actual quality of life scores based on embeddings of historical survey information associated with both the patient and a plurality of historical patients ([0042] The artificial intelligence model tuning process 40 may compare the actual observed outcome of the event to the predicted outcome then separately analyze the variables within the model that contributed to the incorrect outcome);
updating the AI QoL algorithm based on loss function results associated with the similarities ([0042] The artificial intelligence model tuning process 40 may periodically retrain a selected predictive model... It may then re-weigh the variables that contributed to this incorrect outcome, so that in the next reiteration those variables are less likely to contribute to a false prediction.); and
generating the model corresponding to the quality of life category based on the updated AI QoL algorithm ([0042] The artificial intelligence model tuning process 40 may automatically modify or improve a predictive model in three exemplary ways).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined the teachings of Rutowski, including the decision support system, with the training algorithm as described in Amarasingham because it allows for improved accurate outcome to allow for selection of the most accurate statistical methodology, variable count, variable selection, interaction terms, weights, and intercept for a local health system or clinic (Amarasingham specification [0042]).
Regarding Claim 10, Rutowski discloses:
The method of Claim 9, wherein the AI QoL algorithm is configured to perform a dynamic factor analysis ([170] the system may analyze unlabeled word clouds and search for patterns, in order to separate people into groups based on their mental states. [287] Associations between acoustic patterns in speech and health are in some cases applicable to different languages without retraining. They may also be retrained on data from that language).
Relevant Prior Art of Record Not Currently Being Applied
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Soza (US 2022/0165368) describes a health plan method that incorporates survey information and comparing against historical information for the patient.
Response to Arguments
Applicant's arguments filed 10/22/2025 have been fully considered but they are not persuasive. With respect to the previous rejection under 35 U.S.C. 101, the applicant argues the claimed invention is not directed to methods of organizing human activity or a mental process, however, the examiner respectfully disagrees. The claims recite that the method is that of a provider receiving survey information about the health of a patient and using the survey information to predict a change in the health status of the patient. Additionally, collecting survey information in concert with distributing a drug product to the patient recites a method of organizing human activity, specifically an interaction between a provider and patient. Regarding the mental processes, the claims describe steps which a person can perform in their mind, such as generating an embedding, using a model and predicting a change in score for the patient, comparing changes in score, collecting survey information, and generating an alert.
Regarding Step 2A – Prong 2, applicant argues generating an embedding using one-hot encoding for the survey information to reduce a dimensionality of the survey information and a model used to predict a change in a quality of life score based on the embedding integrate the claimed subject matter into a practical application. However, the examiner respectfully disagrees. As stated previously, generating an embedding using one-hot encoding can be performed in the human mind. Specifically, para. 48 of the specification states that “an encoding called one-hot encoding is used to generated the embedding by assigning 1 to a positive answer, 0 to a neutral answer, and -1 to a negative answer”, which encompasses a task a person can do in their mind. Similarly, using a model is a step that can also be performed in the human mind, and therefore also falls within the mental processes grouping of abstract ideas, thus the claims are not integrated into a practical application.
Regarding Step 2B, applicant argues that the claims recite additional elements that amount to significantly more than the judicial exception, however, the examiner respectfully disagrees. Generating an embedding using one hot encoding does not amount to significantly more because the limitation falls within the mental processes grouping of abstract ideas. Additionally, “collecting the survey information via an Internet of Things (IoT) device in concert with distributing a drug product to the patient via the IoT device” also does not amount to significantly more than the judicial exception because it is a method of organizing human activity, for example an interaction between a doctor and a patient.
Applicant’s arguments, see Remarks, Pg. 10-11, filed 10/22/2025, with respect to the rejection under 102(a)(2) of claims 1-4, 12-14, 17, and 18 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Spekowius, further in view of Hoon.
Applicant’s arguments with respect to 35 U.S.C. 103 have been fully considered but they are not persuasive. Applicant argues Rutowski does not provide any description of using a medication dispenser configured as an IoT device to collect survey information, however, the examiner respectfully disagrees. Rutowski par. 26-27 and Figure 1 discloses a system for detecting and managing cognitive decline of a person using a cognitive training device that receives data from the patient. The data used to describe the person’s behavior can include information describing how the person uses particular devices, such as, a medication dispenser. Thus, Rutowski describes collecting survey information from an IoT device in concert with dispensing medication through an IoT device.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.K.V./Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681