Notice of Pre-AIA or AIA Status
This action is made in response to the Request for Continued Examination filed on 04/29/2026. This action is made NON-FINAL.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/29/2026 has been entered.
Response to Amendment
The amendment filed 04/29/2026 has been entered. Claims 1-4, 6-10, 12-14, 16-18, and 20 remain pending in the application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 6-10, 12-14, 16-18, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 12, and 17 recite “generating, by the AI alert decision support system, a sparse one-hot encoded vector representing the quality of life survey answers; transforming, by a machine-learned model corresponding to the quality of life category, the sparse one-hot encoded vector into a dense numerical embedding; and using, by the machine model corresponding to the quality of life category, the dense numerical embedding to predict a change in a quality of life score for the patient for the quality of life category over a future monitoring period.” These limitations are not described in the specification (i.e., introduce new matter). While the Applicant’s specification paragraph [0048] describes “the historical patient survey information from the database 130 may be embedded to generate low-dimensional vectors from the high-dimensional survey answer information” and “an encoding called one-hot encoding is used to generate the embedding by assigning 1 to a positive answer, 0 to a neutral answer, and -1 to a negative answer,” and paragraph [0050] discloses “an AI model… may be used [to] predict a change in a quality of life score… based on the embedded survey information,” the applicant’s specification is devoid of any description of generating a sparse one-hot encoded vector, transforming the sparse vector into a dense numerical embedding, and using the dense numerical embedding to predict a change in a quality of life score for the patient for the quality of life category over a future monitoring period. Additionally, claims 1, 12, and 17 recite “the IoT device being a drug-dispensing device with a user interface,” but the specification does not disclose any interface. The specification only discloses that the IoT device can be a drug product dispensing device. Therefore, the claims introduce new matter.
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, 12, and 17 recite the limitation "using, by the machine model…" in line 11, 15, and 15 respectively. There is insufficient antecedent basis for this limitation in the claim. Two lines before this limitation in each claim, it recites “transforming, by a machine-learned model.” If the machine model is the same as the machine-learned model, the limitation “using, by the machine model” should read as “using, by the machine-learned model,” for better clarity.
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 1-4, 6-10, 12-14, 16-18, and 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:
Claim 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, by the AI alert decision support system, a sparse one-hot encoded vector representing the quality of life survey answers;
transforming, by a machine-learned model corresponding to the quality of life category, the sparse one-hot encoded vector into a dense numerical embedding; and
using, by the machine model corresponding to the quality of life category, the dense numerical embedding to predict a change in a quality of life score for the patient for the quality of life category over a future monitoring period;
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, the IoT device being a drug-dispensing device with a user interface, the survey information being collected in response to an initiation of a scheduled dispensing operation.
The series of steps as recited above, excluding the underlined portions, 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, and subsequently alerting the patient when a change reaches a threshold. Accordingly, the claim recites an abstract idea of managing interactions between people.
The series of steps including: using the dense numerical embedding to predict a change in a quality of life score for the patient for the quality of life category over a future monitoring period and comparing the change in the quality of life score for the patient with a threshold 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. Additionally, generating a one-hot encoded vector can be performed in the human mind, with or without the use of a physical aid. 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 a sparse one-hot encoded vector and transforming the one-hot encoded vector into a dense numerical embedding 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 machine-learned model, 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 use of the AI alert decision support system and machine-learned model to carry out the abstract idea amounts to using a mathematical algorithm to apply the abstract idea, which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). 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). These are known algorithms. In addition, the use of the models to carry out the abstract idea amounts to using a mathematical algorithm to apply the abstract idea, which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). 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 limitations “receiving survey information associated with a patient” and “collecting the survey information via an Internet of Things (IoT) device, the IoT device being a drug-dispensing device with a user interface, the survey information being collected in response to an initiation of a scheduled dispensing operation” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
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).
The limitations “receiving survey information associated with a patient” and “collecting the survey information via an Internet of Things (IoT) device, the IoT device being a drug-dispensing device with a user interface, the survey information being collected in response to an initiation of a scheduled dispensing operation” were found to be insignificant extra-solution activity. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A.. 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, 16, 18, and 20 are directed towards elements used to describe the alert communication, drug product distribution, and the AI algorithm. These elements include (claim 2, 3, 13) communicating the alert to the patient, caregiver, health care service provider, or pharmacist; (claim 4, 14, and 18) quality of life categories including a health category, emotion category, and an activity category; (claim 6, 16, and 20) receiving the answers to the quality of life survey questions from the patient via the IoT device; (claim 7) receiving clinical information; generating an embedding; using a machine-learned model to predict the quality of life score based on survey and clinical information; (claim 8) at least a portion of the clinical information is measured via the IoT device; (claim 9) determining similarities between predicted scores and actual scores, updating the AI QoL algorithm based on loss function results, generating the machine-learned model corresponding to the quality of life category based on the updated QoL algorithm; (claim 10) the AI QoL algorithm is configured to perform a dynamic factor analysis.
The elements of claims 2-4, 6-7, 13-14, 16, 18, and 20 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 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 7 and 9 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, predicting a change in score for the patient, and determining similarities between predicted scores and actual scores 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 2-4, 6-10, 13-14, 16, 18, and 20 recite the following additional elements: an Internet of Things (IOT) device, the Artificial Intelligence alert decision support system, using the machine-learned model, 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. The use of the artificial intelligence and machine-learned models to carry out the abstract idea amounts to using a mathematical algorithm to apply the abstract idea, which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). 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).
The limitations “receiving the answers to the quality of life survey questions from the patient via the IoT device”, “receiving clinical information”, “at least a portion of the clinical information is measured via the IoT device” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
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.
The limitations “receiving the answers to the quality of life survey questions from the patient via the IoT device”, “receiving clinical information”, “at least a portion of the clinical information is measured via the IoT device” were found to be insignificant extra-solution activity. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A.. 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 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 1-4, 6-10, 12-14, 16-18, and 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 Wang et al. (CN 108829671 A), further in view of Ramaci (US 2019/0228850).
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:
a machine-learned model corresponding to the quality of life category ([346], [428]: The embedding model’s output is consumed by other language models, which are used in processing the score of the patient)
using, by the machine model corresponding to the quality of life category, embedding to predict a change in a quality of life score for the patient for the quality of life category over a future monitoring period ([421] each marginal score acts to fine-tune a prediction of depression, or of another mental state, as well as to make the prediction more robust. Marginal predictions may increase confidence measures for predictions of mental states in this way, after a particular number of queries and responses (correlated with a particular intermediate mental state). [423] The scaled score may be 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. [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 explicitly disclose the following that is met by Wang:
generating, by the AI alert decision support system, a sparse one-hot encoded vector representing the quality of life survey answers (Pg. 2, para. 4-5 and Pg. 8, para. 12: The continuous value characteristics may include values or ranges of values represented by the user selected in the choice of questions, for example: the user selects the age is 25 years in the questionnaire, the user selects the monthly income of similar fixed value between 12000 to 15000 RMB in the questionnaire, discrete value or continuous value. The text information may include text content answered by the user in the question answering questions of the questionnaire, the text content can be input by the user or the user selectively input by the predefined text content. Input data of the model can be one-hot coded to represent different parameters.)
transforming, by a machine-learned model, the sparse one-hot encoded vector into a dense numerical embedding (Pg. 2, para. 4-5, Pg. 8, para. 12: The continuous value characteristics may include values or ranges of values represented by the user selected in the choice of questions, for example: the user selects the age is 25 years in the questionnaire, the user selects the monthly income of similar fixed value between 12000 to 15000 RMB in the questionnaire, discrete value or continuous value. The text information may include text content answered by the user in the question answering questions of the questionnaire, the text content can be input by the user or the user selectively input by the predefined text content. Input data of the model can be one-hot coded to represent different parameters. The deep model can extract effective features from the one-hot coded vector through an embedding layer. ); and
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined Rutowski with Wang to include the transformation of the sparse one-hot encoded vector into a dense numerical embedding, because it improved the accuracy and efficiency of the prediction effect of the models (See Wang Pg. 14, para. 5).
However, the combination of Rutowski and Wang does not explicitly disclose the following that is met by Ramaci:
the IoT device being a drug-dispensing device with a user interface, the survey information being collected in response to initiation of a scheduled dispensing operation (Abstract, [0006], [0015], [0048] an advanced pill dispensing apparatus with one or more user-interfaces. Upon a user intent, the relational agent can execute an algorithm or a pathway consisting of a series of questions that proceed in a state-machine manner, based upon yes or no responses, or specific response choices provided to the user. A user interacts with the relational agent by providing responses or answers to clinically validated questionnaires or instruments. The questionnaires enable the monitoring of patient behaviors, medication compliance, medication adherence, medication persistence, wellness, symptoms, adverse events monitoring, and the like.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Rutowski and Wang, with the drug dispensing device with an interface that collects survey information in response to a dispensing action, as taught by Ramaci, because it is more conductive to eliciting information, providing feedback, and actively engaging patients for medication management and it can provide an assessment for the need for any intervention or behavior modification required by either the user, caregivers, family members, or healthcare team/providers (See Ramaci [0015] and [0048]).
Regarding Claim 2, the combination of Rutowski, Wang, and Ramaci discloses the method of claim 1, and Rutowski further teaches:
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, the combination of Rutowski, Wang, and Ramaci discloses the method of claim 2, and Rutowski further teaches:
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, the combination of Rutowski, Wang, and Ramaci discloses the method of claim 1, and Rutowski further teaches:
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, the combination of Rutowski, Wang, and Ramaci discloses the method of claim 1, and Rutowski further teaches:
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, the combination of Rutowski, Wang, and Ramaci discloses the method of claim 1, and Rutowski further teaches:
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 machine-learned model ([79] and [485] 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. Automatic work-flow generation logic includes deep learning machine logic) 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, the combination of Rutowski, Wang, and Ramaci discloses the method of claim 7, and Rutowski further teaches:
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:
a machine-learned model corresponding to the quality of life category ([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 explicitly disclose the following that is met by Wang:
generating, by the AI alert decision support system, a sparse one-hot encoded vector representing the quality of life survey answers (Pg. 2, para. 4-5 and Pg. 8, para. 12: The continuous value characteristics may include values or ranges of values represented by the user selected in the choice of questions, for example: the user selects the age is 25 years in the questionnaire, the user selects the monthly income of similar fixed value between 12000 to 15000 RMB in the questionnaire, discrete value or continuous value. The text information may include text content answered by the user in the question answering questions of the questionnaire, the text content can be input by the user or the user selectively input by the predefined text content. Input data of the model can be one-hot coded to represent different parameters.)
transforming, by a machine-learned model, the sparse one-hot encoded vector into a dense numerical embedding (Pg. 2, para. 4-5, Pg. 8, para. 12: The continuous value characteristics may include values or ranges of values represented by the user selected in the choice of questions, for example: the user selects the age is 25 years in the questionnaire, the user selects the monthly income of similar fixed value between 12000 to 15000 RMB in the questionnaire, discrete value or continuous value. The text information may include text content answered by the user in the question answering questions of the questionnaire, the text content can be input by the user or the user selectively input by the predefined text content. Input data of the model can be one-hot coded to represent different parameters. The deep model can extract effective features from the one-hot coded vector through an embedding layer. ); and
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined Rutowski with Wang to include the transformation of the sparse vector into a dense numerical embedding, because it improved the accuracy and efficiency of the prediction effect of the models (See Wang Pg. 14, para. 5).
However, the combination of Rutowski and Wang does not explicitly disclose the following that is met by Ramaci:
the IoT device being a drug-dispensing device with a user interface, the survey information being collected in response to initiation of a scheduled dispensing operation (Abstract, [0006], [0015], [0048] an advanced pill dispensing apparatus with one or more user-interfaces. Upon a user intent, the relational agent can execute an algorithm or a pathway consisting of a series of questions that proceed in a state-machine manner, based upon yes or no responses, or specific response choices provided to the user. A user interacts with the relational agent by providing responses or answers to clinically validated questionnaires or instruments. The questionnaires enable the monitoring of patient behaviors, medication compliance, medication adherence, medication persistence, wellness, symptoms, adverse events monitoring, and the like.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Rutowski and Wang, with the drug dispensing device with an interface that collects survey information in response to a dispensing action, as taught by Ramaci, because it is more conductive to eliciting information, providing feedback, and actively engaging patients for medication management and it can provide an assessment for the need for any intervention or behavior modification required by either the user, caregivers, family members, or healthcare team/providers (See Ramaci [0015] and [0048]).
Regarding Claim 13, the combination of Rutowski, Wang, and Ramaci discloses the system of claim 12, and Rutowski further teaches:
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, the combination of Rutowski, Wang, and Ramaci discloses the system of claim 12, and Rutowski further teaches:
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, the combination of Rutowski, Wang, and Ramaci discloses the system of claim 12, and Rutowski further teaches:
The system of Claim 12, 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:
a machine-learned model corresponding to the quality of life category ([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 explicitly disclose the following that is met by Wang:
generating, by the AI alert decision support system, a sparse one-hot encoded vector representing the quality of life survey answers (Pg. 2, para. 4-5 and Pg. 8, para. 12: The continuous value characteristics may include values or ranges of values represented by the user selected in the choice of questions, for example: the user selects the age is 25 years in the questionnaire, the user selects the monthly income of similar fixed value between 12000 to 15000 RMB in the questionnaire, discrete value or continuous value. The text information may include text content answered by the user in the question answering questions of the questionnaire, the text content can be input by the user or the user selectively input by the predefined text content. Input data of the model can be one-hot coded to represent different parameters.)
transforming, by a machine-learned model, the sparse one-hot encoded vector into a dense numerical embedding (Pg. 2, para. 4-5, Pg. 8, para. 12: The continuous value characteristics may include values or ranges of values represented by the user selected in the choice of questions, for example: the user selects the age is 25 years in the questionnaire, the user selects the monthly income of similar fixed value between 12000 to 15000 RMB in the questionnaire, discrete value or continuous value. The text information may include text content answered by the user in the question answering questions of the questionnaire, the text content can be input by the user or the user selectively input by the predefined text content. Input data of the model can be one-hot coded to represent different parameters. The deep model can extract effective features from the one-hot coded vector through an embedding layer.); and
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined Rutowski with Wang to include the transformation of the sparse vector into a dense numerical embedding, because it improved the accuracy and efficiency of the prediction effect of the models (See Wang Pg. 14, para. 5).
However, the combination of Rutowski and Wang does not explicitly disclose the following that is met by Ramaci:
the IoT device being a drug-dispensing device with a user interface, the survey information being collected in response to initiation of a scheduled dispensing operation (Abstract, [0006], [0015], [0048] an advanced pill dispensing apparatus with one or more user-interfaces. Upon a user intent, the relational agent can execute an algorithm or a pathway consisting of a series of questions that proceed in a state-machine manner, based upon yes or no responses, or specific response choices provided to the user. A user interacts with the relational agent by providing responses or answers to clinically validated questionnaires or instruments. The questionnaires enable the monitoring of patient behaviors, medication compliance, medication adherence, medication persistence, wellness, symptoms, adverse events monitoring, and the like.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the method as taught by Rutowski and Wang, with the drug dispensing device with an interface that collects survey information in response to a dispensing action, as taught by Ramaci, because it is more conductive to eliciting information, providing feedback, and actively engaging patients for medication management and it can provide an assessment for the need for any intervention or behavior modification required by either the user, caregivers, family members, or healthcare team/providers (See Ramaci [0015] and [0048]).
Regarding Claim 18, the combination of Rutowski, Wang, and Ramaci discloses the computer program product of claim 17, and Rutowski further teaches:
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, the combination of Rutowski, Wang, and Ramaci discloses the computer program product of claim 17, and Rutowski further teaches:
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 Wang et al. (CN 108829671 A), further in view of Ramaci (US 2019/0228850), further in view of Amarasingham et al. (US 2013/0262357) (Hereinafter Amarasingham).
Regarding Claim 9, the combination of Rutowski, Wang, and Ramaci discloses the method of claim 1, and Rutowski further teaches:
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, Wang, and Ramaci do not disclose the following that is met by Amarasingham:
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, the combination of Rutowski, Wang, and Ramaci discloses the method of claim 9, and Rutowski further teaches:
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).
Response to Arguments
Applicant's arguments filed 04/29/2026 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. Regarding the methods of organizing human activity arguments, the method is fundamentally 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, and subsequently alerting the patient when a change reaches a threshold. The use of an AI alert decision support system is recited at a high level of generality, and includes 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). These are known algorithms, and the use of the algorithms to carry out the abstract idea amounts to using a mathematical algorithm to apply the abstract idea, which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). Therefore, this does not provide a technological improvement. Additionally, collecting survey information via an IoT device, the IoT device being a drug-dispensing device, is insignificant extra solution activity, as it is necessary data gathering. Applicant also argues that a provider cannot reasonably perform one hot encoding and embedding, however, a person could reasonably perform these steps as a mental process. A person can predict a change in a quality of life score using embeddings, and can compare the change in the quality of life score to a threshold in their mind.
Regarding Step 2A – Prong 2, applicant argues the amended claims integrate the claimed subject matter into a practical application by improving the functioning of a device or technical system. However, the examiner respectfully disagrees. The generation of an alert at or near the time of drug distribution does not provide an improvement to the technology, and more specifically, does not provide an improvement to the operation of the IoT device, as the IoT device is merely being used to collect the survey information. Generating an alert when a change in quality of life score reaches a threshold is a limitation that falls within the methods of organizing human activity grouping of abstract ideas, as it is a task a provider would commonly do when their patient is showing a change in their quality of life. Thus the claims are not integrated into a practical application.
Regarding Step 2B, applicant argues that the ordered combination of IoT-based survey capture synchronized with drug dispensing, AI embedding, category-specific model, and threshold-based alerting is not conventional, and as a result, amounts to significantly more than the judicial exception, however, the examiner respectfully disagrees. The survey capture using the IoT device is mere data gathering that is insignificant extra-solution activity, as it is similar to sending and receiving information over a network. 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. 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.
Applicant’s arguments, see pg. 16 of Applicant’s Remarks, filed 04/29/2026, with respect to the rejection of claims 1-4, 6-8, 12-14, 16-18, and 20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wang et al., in further view of Ramaci. Applicant argues that Spekowius did not provide any description that the training device used to collect the survey information is the same device used to dispense medication. Examiner agrees. However, the newly applied reference Ramaci, discloses an advanced pill dispensing apparatus with one or more user-interfaces. Upon a user intent, the relational agent can execute an algorithm or a pathway consisting of a series of questions that proceed in a state-machine manner, based upon yes or no responses, or specific response choices provided to the user. A user interacts with the relational agent by providing responses or answers to clinically validated questionnaires or instruments. The questionnaires enable the monitoring of patient behaviors, medication compliance, medication adherence, medication persistence, wellness, symptoms, adverse events monitoring, and the like (See Ramaci [0006], [0015], [0048]).
Conclusion
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.
Reddy et al., AI-IoT based Smart Pill Expert System, discloses a pill dispensing system with a user interface that can provide real-time diagnosis and support for an individual.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS K VAN DUZER whose telephone number is (571)270-5832. The examiner can normally be reached Monday thru Thursday 8-5 CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.K.V./Examiner, Art Unit 3682
/EVANGELINE BARR/Primary Examiner, Art Unit 3682
