DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to Applicant’s communication filed on April 17, 2025.
Claims 1, 11 and 20 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
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.
Step 1 analysis:
Claims 1, 11 and 20 are each directed to a method, a system, and a manufacture respectively and therefore all fall into one of the four statutory categories. (Step 1: Yes, the claims fall into one of the four statutory categories).
Step 2A analysis - Prong one:
The substantially similar independent method, system, and computer readable media claims, taking claim 1 as exemplary, recite the following: generating…[algorithms]…comprising a first group of probability weightings and…a second group of probability weightings; …determining that the …[algorithms]…produced an incomplete dataset and limiting an impact of the incomplete dataset; obtaining a user profile dataset comprising voice notes associated with a user; providing the user profile dataset to the first machine learning model, wherein the first…[algorithm]…is trained to diagnose users by generating probabilistic values for maladies based on user profiles of the users; generating, based on the first…[algorithm]…and the user profile of the user, a plurality of probabilistic values respectively associated with a plurality of maladies of the user, each probability value indicating a probability the user has the malady, wherein generating the plurality of probabilistic values comprises: analyzing the voice notes to determine a tone of the user; determining, based on the tone, a current stress level of the user, and determining changes in stress level of the user based on a comparison of the current stress level and a plurality of historical levels of the user that are associated with a plurality of historical treatment plans; selecting, based on the plurality of probabilistic values, the second…[algorithm]…from a set of…[algorithms]… respectively trained to predict cannabis treatment responses for a particular malady of the plurality of maladies; generating…a recommended treatment plan for using one or more cannabis products to treat the particular malady of the user based on the user profile dataset and the second…[algorithm]…by matching the cannabis treatment responses to physician recommendations; transmitting the recommended treatment plan…to display the recommended treatment plan; and improving an accuracy of the…[algorithms]…by updating the connections between the …[algorithms]…, the first group of probability weightings, and the second group of probability weightings based on the changes in stress level of the user.
The limitations above, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting an apparatus implemented by a processing device (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the processing device, client device, communication network and machine learning models, this claim encompasses a person interviewing a patient, determining possible maladies and stress level of the patient, determining and then recommending a treatment plan for the patient in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The limitations of “improving an accuracy of the machine learning platform by updating the connections, the first group of probability weightings, and the second group of probability weightings based on the changes in stress level of the user.” is a process that under broadest reasonable interpretation covers a mathematical concept that includes mathematical relationships, mathematical formulas or equations, and mathematical calculations but for the recitation of generic computer component language (discussed below at Step 2A2). That is, other than reciting the generic computer component language, the claim recites a procedure for updating algorithm connections and probability weightings that encompasses a mathematical concept. The Examiner notes that the mathematical concept need not be expressed in mathematical symbols. See MPEP § 2106.04(a)(2)(I). Accordingly, the claim recites an abstract idea.
The claim further recites “the first machine learning model is trained” and “the second machine learning model from a set of machine learning models respectively trained.” When given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning model to diagnose users and to predict cannabis treatment responses for a particular malady represents the creation of mathematical interrelationships between data (see Applicant’s specification paras 47, 52, 65, 71). As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the abstract idea.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. The combination analysis of all the abstract ideas still leads to the determination that the limitations as a whole are grouped as certain methods of organizing human activity. (Step 2A – Prong 1: Yes, the claims are abstract).
Step 2A analysis - Prong two:
Claims 1, 11 and 20 recite additional elements beyond the abstract idea. Claims 1, 11 and 20 recite a machine learning platform, first and second machine learning models, a set of machine learning models, a processing device, a client device and a communication network. Claim 11 further recites a memory. Claim 20 further recites non-transitory computer-readable storage medium, instructions and a host system. The instructions appear to be software.
This judicial exception is not integrated into a practical application. In particular, the claims recite a processing device, a client device, a communication network, a memory, non-transitory computer-readable storage medium, instructions and a host system which are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the processing device receives inputs, executes instructions, analyzes data, transmits results, causes information to be displayed, etc. (see Applicant’s specification pages 10 and 37-40).
The claim further recites the additional elements of using a machine learning platform made up of a first and second trained machine learning models and a set of machine learning models to diagnose users and to predict cannabis treatment responses for a particular malady. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Alternatively, or in addition, the implementation of the trained machine learning models to diagnose users and to predict cannabis treatment responses for a particular malady merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
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, 11, and 20 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application).
Step 2B analysis:
For the next step of the analysis, it must be determined whether the limitations present in the claims represent a patent-eligible application of the abstract idea. A claim directed to a judicial exception must be analyzed to determine whether the elements of the claim, considered both individually and as an ordered combination are sufficient to ensure that the claim as a whole amounts to significantly more than the 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.”
Applicant’s specification discloses the following:
Applicant describes embodiments of the disclosure at a very high level to include the use of a wide variety of networks, processors, computing devices, storage devices, machine learning models, host systems, operating systems, busses, firmware, software, circuitry and computer-readable storage medium (see Applicant’s specification paras 26, 31, 49, 98, 102-106, 114).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions appear to be implemented using conventional computer systemization.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processing device, a client device, a communication network, a memory, non-transitory computer-readable storage medium, instructions and a host system to perform all of the steps discussed above amount to no more than mere instructions to apply the exceptions using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims do not provide an inventive concept significantly more than the abstract idea.
Further, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a machine learning platform made up of a first and second trained machine learning models and a set of machine learning models to diagnose users and to predict cannabis treatment responses for a particular malady was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model(s)) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. 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. (Step 2B: No, the claims do not provide significantly more).
Dependent Claims 2-10 and 12-19 further define the abstract idea that is presented in independent Claims 1 and 11 respectively, and are further grouped as certain methods of organizing human activity and are abstract for the same reasons and basis as presented above. Further, Claims 2, 6-7, 12 and 16-17 recite an additional element beyond the abstract idea. Claims 2, 6-7, 12 and 16-17 recite a third machine learning model. This additional element was analyzed the same as the machine learning models above and was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model(s)) to a particular technological environment or field of use. Further, it is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. For example, as noted above, the Applicant’s specification indicates the use of known machine learning models. Accordingly, this additional element, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-4, 6-7, 10-14, 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Esmailian et al. (US 20200402662) in view of Holve et al. (US 20230274834), further in view of Wild et al. (Wild, B., Brenner, J., Joos, S., Samstag, Y., Buckert, M., & Valentini, J. (2020). Acupuncture in persons with an increased stress level—results from a randomized-controlled pilot trial. PLOS ONE, 15(7). https://doi.org/10.1371/journal.pone.0236004).
Regarding Claim 1, Esmailian discloses the following limitations:
obtaining a user profile dataset comprising voice notes associated with a user; (Esmailian discloses a monitoring wellness system 100 which may include a monitoring inference engine 104 which receives user generated sensor data 101, user input data 102, user CB regimen/prescription data 103 related to the patient, and/or patient over the counter medication/prescription data 115 (obtaining a user profile dataset). The user input data 102 related to the patient may include observational data that is provided, such as emotions, autonomously inferred from analyzing pitch and volume of the patient's voice (comprising voice notes). – paras 18-19, 21; FIGs 1, 3 item 102)
wherein generating the plurality of probabilistic values comprises: analyzing the voice notes to determine a tone of the user; (Esmailian discloses that the user input data may include for example, information, such as emotions, autonomously inferred from analyzing (analyzing the voice notes) pitch and volume (determine a tone) of the patient's voice (of the user). – paras 18, 31)
determining, based on the tone, a current stress level of the user, (Esmailian discloses that the user input data may include for example, information, such as emotions (determining a current stress level of the user), autonomously inferred from analyzing pitch and volume (based on the tone) of the patient's voice. – paras 18, 31)
…the second machine learning model…respectively trained to predict cannabis treatment responses for a particular malady of the plurality of maladies; (Esmailian discloses that the monitoring inference engine receives user input, user cannabis regime/prescription data and sensor data to produce the personalized/particularized information (personality type of the user). Which is then fed to a clinical decision support inference engine (the second machine learning model) which may include prediction and recommendation techniques for treatments (e.g., doses, consumptions, methods, etc.) (trained to predict cannabis treatment responses) – paras 21-23, 32; FIG. 3, items 119, 320) (Examiner notes that the differences in the first, second, and third machine learning models is simply just nomenclature)
generating, by a processing device, a recommended treatment plan for using one or more cannabis products to treat the particular malady of the user based on the user profile dataset and the second machine learning model (Esmailian discloses that the output of the monitoring inference engine (based on the user profile dataset) is provided to the clinical decision support inference engine 119 (CDSIE) (the second machine learning model) which infers the most effective regimen/prescriptions for specific patient conditions (a recommended treatment plan for using one or more cannabis products to treat a malady of the user). – paras 28-29, 31-32, FIGs 1 and 3 item 119)
by matching the cannabis treatment responses to physician recommendations; (Esmailian discloses that the system may also consider other forms of medical records, such as physician notes and diagnosis and treatment plans (physician recommendations) and store them in a database. The stored data is filtered, synthesized and used as training and/or reconfiguration data to a clinical decision support inference engine (CDSIE) – paras 27-28)
transmitting the recommended treatment plan to a client device to cause the client device to display the recommended treatment plan; (Esmailian discloses that the output of the clinical decision support engine 119 (the recommended treatment plan) may also be made available to the clinician/educator 107 as a resource, for example providing recommendations, or to another machine that may be at least partly responsible for making clinical decisions. Information that users provide via the web, wearable device, and app interfaces (or otherwise) (a client device) goes into the database driving those interfaces, and a copy goes into the database hosting the machine learning pipelines. The current best set of inferences at any point may be applied to further optimizing the CDSIE itself, which is governed by the same database that hosts what users see on the website and/or the app (or otherwise) (cause the client device to display the recommended treatment plan). – paras 29, 49)
and improving an accuracy of the machine learning platform by updating…, the first group of probability weightings, and the second group of probability weightings based on the changes in stress level of the user. (Esmailian discloses that by repeatedly improving the training data through this process of recombining and statistically evaluating the data 314 (updating the first group of probability weightings, and the second group of probability weightings), the system may ultimately improve both the relevance of the data collected and the accuracy/efficiency of clinical decision making (improving an accuracy of the machine learning platform) by positioning users uniquely ahead of repetitive, one size fits all treatment. The system incrementally moves past traditional one case at a time memorization of effective treatments. The monitoring inference engine 104 further tracks the data points over time (the changes in stress level of the user). – paras 33, 35, 38)
Esmailian does not disclose the following limitations met by Holve:
A method comprising: generating a machine learning platform coupled to a communication network by creating connections between a first machine learning model and a second machine learning model, wherein the first machine learning model comprises a first group of probability weightings and the second machine learning model comprises a second group of probability weightings; (Holve teaches a condition detection system (a machine learning platform) connected to a network (coupled to a communication network) and comprising one or more machine learning models (a first machine learning model and a second machine learning model) to generate conditional probabilities for patients, such as the probability of a particular condition, disease or disorder. Model selection component is invoked by the condition detection component to select and train condition detection models (a first machine learning model and a second machine learning model). This selection may be done by looping through each of the feature sets to train one or more machine learning models using the feature set and to assess the accuracy of the trained machine learning model(s) (creating connections). The total number of outputted condition probabilities equals the total number of combinations that the condition detection model is applied to. The combinations may then be grouped into, for example, equally sized groups of answer combinations, such as quartiles, based on their condition probabilities. For example, one “condition probability group” may have probabilities 0 to 0.3 (a first group of probability weightings), another 0.3 to 0.53 (a second group of probability weightings), etc. – abstract; paras 25-26, 31, 38; FIGs. 1, 4)
maintaining a control over the communication network by determining that the machine learning platform produced an incomplete dataset and limiting an impact of the incomplete dataset on the communication network; (Holve teaches that in some cases, values for variables may be missing (determining that the machine learning platform produced an incomplete dataset). For example, a patient may have chosen not to answer a particular question in a survey, may never have been tested or measured for a particular attribute or condition, or may never have been presented with a corresponding question. In order to resolve these discrepancies (limiting an impact of the incomplete dataset on the communication network), the condition detection system may fill in values for predictive variables in records with missing values. – paras 18, 37; FIG. 3 item 330)
providing the user profile dataset to a first machine learning model, wherein the first machine learning model is trained to diagnose users by generating probabilistic values for maladies based on user profiles of the users; (Holve teaches a condition detection system that trains one or more machine learning models (a first machine learning model) to generate condition probabilities (trained to diagnose users by generating probabilistic values for maladies) for patients using training data collected from any number of sources as well as model selection. The condition probability system then surveys patients and/or their healthcare providers for information (providing the user profile dataset) about the patient via, for example, a questionnaire, and applies one or more trained models to the collected patient information (based on user profiles of the users) to detect conditions for the patient. – abstract; paras 13-15, 24, 40)
generating, based on the first machine learning model and the user profile of the user, a plurality of probabilistic values respectively associated with a plurality of maladies of the user, each probability value indicating a probability the user has the malady; (Holve teaches that the condition detection model (the first machine learning model) that applies collected patient data (the user profile of the user) generates a condition probability (generating… probabilistic values… indicating a probability the user has the malady) and then may repeat the process and generate the next probability (a plurality of probabilistic values respectively associated with a plurality of maladies). – abstract; paras 13-15, 40; FIG. 6, items 630, 650)
selecting, based on the plurality of probabilistic values, the second machine learning model from a set of machine learning models (Holve teaches a condition detection system that trains one or more machine learning models to generate condition probabilities for patients using training data collected from any number of sources as well as model selection (selecting…from a set of machine learning models). – abstract; paras 7, 21-22, 36; FIG. 1 item 114; FIG. 2 item 225; FIG. 4)
and improving an accuracy of the machine learning platform by updating the connections between the first machine learning model and the second machine learning model,… (Holve teaches that the condition detection system may provide real-time updates in response to updating or re-training one or more condition detection models (the first machine learning model and the second machine learning model) after receiving updated health records and further, periodically send updated condition probabilities and opportunities based on updated patient data and/or training data (updating the connections between the first machine learning model and the second machine learning model). In some examples, the condition detection model(s) is trained using collected data (e.g., health records) along with transformed versions of the underlying collected data using, for example, stochastic learning with backpropagation (SLBP) to adjust the weights of a neural network. In some cases, the use of this augmented training set may increase type I and/or type II errors while classifying. The condition detection system can reduce these errors by performing an iterative training algorithm, in which the condition detection model(s) is retrained with an updated training set containing the incorrectly classified records after condition detection has been performed (i.e., the records or transformed versions of those records for which a condition was incorrectly detected), which provides a condition detection model that can detect condition(s) (probabilities) in the underlying data while limiting the number of type I and/or type II errors (improving an accuracy of the machine learning platform by updating the connections). – paras 32, 34, 52)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the machine learning models as disclosed by Esmailian (see para 47) to incorporate applying collected patient information to trained machine learning models to generate condition probabilities, as selecting model(s) from one or more machine learning models, and filling in missing data as taught by Holve in order to provide better patient outcomes (see Holve paras 2 and 12).
Esmailian and Holve do not disclose the following limitations met by Wild:
and determining changes in stress level of the user based on a comparison of the current stress level and a plurality of historical levels of the user that are associated with a plurality of historical treatment plans; (X teaches an acupuncture (treatment plans) study in adult persons with increased stress levels. The effects on stress level (determining changes in stress level of the user) (measured by the Perceived Stress Questionnaire (PSQ-20)) and other variables were assessed at the end of treatment and a 3-month follow-up. The stress level of the participants was high at baseline and decreased over time, see FIG. 2 (based on a comparison of the current stress level and a plurality of historical levels of the user that are associated with a plurality of historical treatment plans). – pages 1, “methods”; page 2, “results”; page 15-16, FIG. 2)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the system and method to reduce aches, pains, anxiety, and sleep problems as disclosed by Esmailian (see para 14) to incorporate patient questionnaires as taught by Wild in order to assess changes in stress levels over time (see Wild page 1, “methods”).
Regarding Claim 2, this claim depends on claim 1, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 1 above regarding the set of machine learning models; thus, the same rejection applies. Further, Esmailian discloses the following limitations:
wherein selecting, based on the user profile dataset, the second machine learning model…comprises: providing the user profile dataset to a third machine learning model trained to diagnose maladies associated with at least one of pain, anxiety, or sleep patterns, (Esmailian discloses that the data stored in the database (DB) 108 may be provided as input to a data filtering and synthesis engine 116 that filters the data. Synthesized data from the data filtering and synthesis engine 116 is provided as training and/or reconfiguration data 109 to a clinical decision support inference engine (CDSIE) 119 (a third machine learning model). The data within the DB 108 includes the personalize/particularized information including for example sleep problems, aches, pains, and anxiety (trained to diagnose maladies associated with pain, anxiety, or sleep). – paras 14-17, 23, 31-34 and FIG. 3)
wherein the third machine learning model is trained with at least one of research data associated with cannabis, clinical trial data associated with cannabis, or data indicative of physician expertise and experience associated with cannabis. (Esmailian discloses that the DB 108 may also store population research data 111 (research data associated with cannabis). The system may also consider other form of medical records, such as…physician notes, diagnosis and treatment plans, to the extent they are made available by the patient and/or clinician (data indicative of physician expertise and experience associated with cannabis). In some implementations, the research data 111 may also include data from virtual clinician rounds (clinical trial data associated with cannabis). The virtual clinician rounds, based upon simulated and/or actual patients and patient conditions, may be used to obtain feedback from the clinician/educator 107 that is then added to the database for additional robustness. – para 27-29)
Regarding Claim 3, this claim depends on claim 1, which is rejected for the basis and reasons disclosed above. Further, Esmailian discloses the following limitations:
wherein the user profile dataset is indicative of at least one medical condition, a wellness condition, a cannabis use preference, and a user experience with cannabis. (Esmailian discloses that the monitoring wellness system 100 may receive user input data 102 related to the patient including observational data including survey answers, user emotion, etc. (a wellness condition), preferred consumption method (a cannabis use preference), user CB (cannabis) regimen/prescription data 103 related to the patient (a user experience with cannabis), medical classifications, such as ICD-10 and CPT code data (medical condition), – paras 18-20 and 41; FIG. 1 items 101, 102, and 103; claims 4-5)
Regarding Claim 4, this claim depends on claim 1, which is rejected for the basis and reasons disclosed above. Further, Esmailian does not disclose the following limitations met by Holve:
wherein the set of machine learning models each correspond to a Bayesian model. (Holve teaches that one of ordinary skill in the art will recognize that the disclosed technology may operate with any form of classification models (or classifiers), such as… Bayesian classifiers. Further, the component generates weights for the selected model(s) using, for example, a hyper parameterization process (e.g., Bayesian optimization…). – paras 22, 36, 38; FIG. 2 item 230)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system which includes machine learning models as disclosed by Esmailian (see para 47) to incorporate a Bayesian model as taught by Holve in order to provide better patient outcomes (see Holve paras 2 and 12).
Regarding Claim 6, this claim depends on claim 1, which is rejected for the basis and reasons disclosed above. Further, Esmailian discloses the following limitations:
providing the user profile dataset to a third machine learning model trained to generate model data for the recommended treatment plan, wherein the model data comprises at least one of an instruction for using the one or more cannabis products… (Esmailian discloses that the clinical decision support engine (CDSIE) 119 (a third machine learning model) may include prediction and recommendation techniques 320 for treatments 330 (e.g., dosages, consumption, methods, etc.) (an instruction for using the one or more cannabis products). Further, the patient dashboard may include product, dosing, frequency, and timing information relevant to a reduction in pain. – paras 29, 32, 51)
Regarding Claim 7, this claim depends on claim 1, which is rejected for the basis and reasons disclosed above. Further, Esmailian discloses the following limitations:
providing the user profile dataset to a third machine learning model trained to generate model data for the recommended treatment plan, wherein the model data comprises the one or more reasons the first machine learning model selected the one or more cannabis products to treat the malady of the user. (Esmailian discloses that the CDSIE 119 (a third machine learning model) may have additional capabilities to rank products based on their efficacy and/or patient testimonials (reasons the first machine learning model selected the one or more cannabis products). – paras 30-34)
Regarding Claim 10, this claim depends on claim 1, which is rejected for the basis and reasons disclosed above. Further, Esmailian does not disclose the following limitations met by Holve:
receiving physician approval of the recommended treatment plan prior to transmitting the recommended treatment plan to the client device. (Holve teaches that the condition detection system can simulate different answers from the patient’s questionnaire to find one or more hypothetical sets of answers to the questions that would result in a different probability and present those results to the patient in the form of opportunities for the patient to change their probability (in some cases under the supervision of a physician or other health care provider) (receiving physician approval…prior to transmitting the recommended treatment plan to the client device), such as a recommendation to change eating and/or exercise habits, prescription drugs, weight, etc. (the recommended treatment plan). – para 14)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the output of the most effective regimen/prescriptions for specific patient conditions as disclosed by Esmailian to incorporate the supervision of a physician as taught by Holve in order to provide the patient with better outcomes (see Holve para 14).
Regarding Claim 11, this claim recites substantially similar limitations to those recited in claim 1 above; thus, the same rejection applies. Further, Esmailian discloses the following limitations:
An apparatus comprising: a memory; and a processing device, operatively coupled to the memory, to: (Esmailian discloses a wellness system operating on a computing device including a processor and different engines containing memory capacities. – abstract and paras 54, 73)
Regarding Claim 12, this claim depends on claim 11, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 2 above; thus, the same rejection applies.
Regarding Claim 13, this claim depends on claim 11, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 3 above; thus, the same rejection applies.
Regarding Claim 14, this claim depends on claim 11, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 4 above; thus, the same rejection applies.
Regarding Claim 16, this claim depends on claim 11, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 6 above; thus, the same rejection applies.
Regarding Claim 17, this claim depends on claim 11, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 7 above; thus, the same rejection applies.
Regarding Claim 20, this claim recites substantially similar limitations to those recited in claim 1 above; thus, the same rejection applies. Further, Esmailian discloses the following limitations:
A non-transitory computer-readable storage medium including instructions that, when executed by a processing device of a host system, cause the processing device to: (Esmailian discloses that the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit (a processing device of a host system). Computer-readable media generally may correspond to tangible computer-readable storage media which is non-transitory. – paras 77-79)
Claims 5, 8-9, 15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Esmailian et al., in view of Holve et al., in view of Wild et al., further in view of West et al. (US 20200098458).
Regarding Claim 5, this claim depends on claim 1, which is rejected for the basis and reasons disclosed above. Further, Esmailian, Holve and Wild do not disclose the following limitations met by West:
maintaining, in a storage, a plurality of association between a plurality of cannabis product identifiers and a plurality of scores, (West teaches that the patient input includes a cannabis profile of the cannabis product(s) (a plurality of cannabis product identifiers) and a patient efficacy rating (a plurality of scores). The platform stores the cannabis treatment data in the patient record associated with the patient (maintaining, in a storage). – abstract; paras 7, 39, 42; FIG. 8B)
wherein each score comprises…a feeling state use score, or a malady score. (West teaches that the patient input includes a patient efficacy rating (a feeling state use score, or a malady score). Applicant’s specification states that the feeling state use score is based on patient feeling of “energy, mental clarity, relaxation, etc.” (see Applicant’s specification para 74) and therefore may be interpreted as efficacy ratings because West teaches that the rating may be based on patient satisfaction of treatment of improving post-traumatic stress disorder. – abstract; paras 7, 39, 42)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the wellness system as disclosed by Esmailian and Holve to incorporate storing patient inputs of cannabis product(s) and corresponding efficacy ratings as taught by West in order to acquire credible data of effective treatments, efficacies and best practices (see West paras 1-2, 45-46).
Regarding Claim 8, this claim depends on claim 1, which is rejected for the basis and reasons disclosed above. While Esmailian disclose the recommended treatment plan as applied to claim 1 above, Esmailian does not disclose the following limitations met by Holve:
determining that the feedback data is incomplete; (Holve teaches that, in some cases, values for variables may be missing. For example, a patient may have chosen not to answer a particular question in a survey, may never have been tested or measured for a particular attribute or condition, or may never have been presented with a corresponding question. – paras 18, 37)
and generating…even though the feedback data is incomplete. (Holve teaches that in order to resolve the discrepancies of missing variables, the condition detection system may fill in values for predictive variables in records with missing values and then moves on in the feature selection process (generating…even though the feedback data is incomplete). – paras 18, 37 and FIG. 3 item 330)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the wellness system as disclosed by Esmailian to incorporate detecting and filling in missing values as taught by Holve in order to provide better patient outcomes (see Holve paras 2 and 12).
Esmailian, Holve and Wild do not disclose the following limitations met by West:
receiving feedback data indicative of an experience of the user when using the one or more cannabis products based on the recommended treatment plan; (West teaches that after the patient obtains cannabis product(s) (the recommended treatment plan), the platform receives input of cannabis treatment data for the patient from the second client device. The input includes a cannabis profile of the cannabis product(s) and a patient efficacy rating (receiving feedback data indicative of an experience of the user when using the one or more cannabis products). – paras 7, 42-43)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the wellness system as disclosed by Esmailian and Holve to incorporate receiving patient efficacy ratings as taught by West in order to receive feedback from the patient on the results from the cannabis treatment without requiring a follow-up appointment (see West paras 2, 45-46).
Regarding Claim 9, this claim depends on claim 8, which is rejected for the basis and reasons disclosed above. Further, Esmailian discloses the following limitations:
obtaining uncertainty data associated with a prior iteration of the first machine learning model, wherein the generating the recommended treatment plan is further based on the uncertainty data. (Esmailian discloses FIG. 4 which shows the manner in which the data is recombined in preprocessing after the most recent full iteration of FIG. 3 provides new information about the relevance of previously tested combinations of data points. Each iteration i, serves as the collected training data for the next iteration (i+1) of the model in FIG. 3, as well periodically, the reconfiguration of user dashboards (shown in FIG. 1). – para 35-36, 40, 52; FIG. 3)
Regarding Claim 15, this claim depends on claim 11, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 5 above; thus, the same rejection applies.
Regarding Claim 18, this claim depends on claim 11, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claim 8 above; thus, the same rejection applies.
Regarding Claim 19, this claim depends on claim 18, which is rejected for the basis and reasons disclosed above. Additionally, it recites substantially similar limitations to those recited in claims 9 and 10 above; thus, the same rejections apply.
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.
Lai (Lai, M. (2019, December 13). Course handouts for Bayesian Data Analysis class. Chapter 12 Missing Data. https://bookdown.org/marklhc/notes_bookdown/missing-data.html) discloses a Bayesian modeling approach for handling missing data by treating the missing data as parameters with some prior information. For example, multiple imputation is a modern technique that uses previously observed data and the observed associations to predict the missing values. (see introduction paragraph; section 12.2.2; section 12.2.3).
Response to Arguments
Regarding rejections under 35 USC § 112(a) to Claims 1-20, Applicant’s arguments have been fully considered and are persuasive. Examiner has withdrawn the rejection.
Regarding rejections under 35 USC § 101 to Claims 1-20, Applicant does not present any arguments to address the 101 rejection. Examiner has updated the 101 rejection in light of latest amendments.
Regarding rejections under 35 USC § 103 to Claims 1-20, Applicant does not present any arguments regarding specific limitations not taught by the prior art. Examiner has updated the 103 rejection in light of latest amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/K.E.V./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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