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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 23, 2026 has been entered.
Status of Claims
In the response filed February 23, 2026, Applicant amended claims 1, 11, and 20. Claims 3, 6, 13, and 16 were previously canceled. Clams 1, 2, 4, 5, 7-12, 14, 15, and 17-20 are pending in the current application.
Response to Arguments
Applicant's arguments filed February 23, 2026 have been fully considered but they are not persuasive. First, Applicant asserts that the claims cannot be performed by a human mind because the operations of real-time updating and dynamic adjustment of model parameters require computational infrastructure such as machine learning and Bayesian network nodes. Examiner respectfully disagrees. Claims can recite a mental process even if they are claimed as being performed on a computer. In evaluating whether a claim that requires a computer recites a mental process, the broadest reasonable interpretation of the claim must be considered in light of the specification. For instance, the specification should be reviewed to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept (see MPEP 2106.04(a)(1)(III)(C)). Paragraphs [0040] and [0041] of Applicant’s specification discloses, “the inputs to the user profile collector 202 may also be sent to the feedback processing model. In some embodiments, the inputs are collected via the telehealth application 160 using multiple choice, sliders, text entry, and via open voice to text,” and Paragraph [0054] discloses, “The IT platform 200 may use parameter estimation and/or parameter updating to update the model. Parameter estimation may be used to update the probability weightings within the nodes to optimize for patient satisfaction. Parameter updating may be used to update the connections between the nodes to also optimize for patient satisfaction. In some embodiments, certain nodes within the model can be replaced by machine learning (ML).” In this case, the BRI of the claim in light of the specification recite the limitation of collecting user profile information through questionnaires, voice, or text. Moreover, the claims recite conventional machine learning models without specific improvements to the technology itself. There is nothing in the claims that prevent the collecting patient information and updating parameters from being performed by a human (e.g., using Bayesian or other statistical models for parameter updating). Second, Applicant asserts that the claim is integrated into a practical application because it applies machine learning techniques in a manner that imposes concrete execution constraints and effects a technical improvement in a distributed computing environment. Examiner respectfully disagrees. The servers, processing device, client device, communication network, and machine learning model in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. Moreover, the claims recite conventional machine learning models without specific improvements to the technology itself. The rejection is maintained.
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, 2, 4, 5, 7-12, 14, 15, and 17-20 are rejected under 35 U.S.C. 101 because the claims are not directed to patent eligible subject matter.
Claims 1, 2, 4, 5, 7-12, 14, 15, and 17-20 do fall within at least one of the four categories of patent eligible subject matter because the claims recite a machine (i.e., non-transitory computer-readable medium and apparatus) and process (i.e., a method).
Although claims 1, 2, 4, 5, 7-12, 14, 15, and 17-20 fall under at least one of the four statutory categories, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception (See MPEP 2106 I and II).
Claims 1, 2, 4, 5, 7-12, 14, 15, and 17-20 are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Part I: Step 2A, Prong One: Identify the Abstract Idea
Under step 2A, Prong One of the Alice framework, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). The determination consists of a) identifying the specific limitations in the claim that recite an abstract idea; and b) determining whether the identified limitations fall within at least one of the three subject matter groupings of abstract ideas (i.e., mathematical concepts, mental processes, and certain methods of organizing human activity).
The identified limitations of independent claim 1 (representative of independent claims 11 and 20) recite (in bold and italics):
obtaining a user profile dataset of a user, the user profile dataset comprising a plurality of user goals for a plurality of dayparts of a day, each daypart of the plurality of dayparts is respectively associated with one or more user goals of the plurality of user goals;
generating a machine learning platform, trained with training data to predict cannabis treatment responses that are respectively associated with the plurality of dayparts, on multiple servers configured in a cluster over a communication network by connecting a first machine learning model executing on a first server of the multiple servers and a second machine learning model executing on a second server of the multiple servers using connections, 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, wherein the training data comprises a plurality of user responses respectively associated with the plurality of dayparts, and wherein each user response of the plurality of user responses indicates differences between user goals and user experiences for the plurality of dayparts in a previous day;
generating, by a processing device, a plurality of treatment plans to treat a malady of the user based on the user profile dataset and the machine learning platform, each treatment plan is for using one or more cannabis products during a respective daypart of the plurality of dayparts of the day;
maintaining control over the communication network by limiting the effect a dataset available to the machine learning platform has on the communication network in response to determining that the dataset is incomplete, wherein determining that the dataset is incomplete comprises detecting, at runtime, absence of required probabilistic evidence values for one or more Bayesian network nodes corresponding to a respective daypart, and wherein limiting the effect comprises preventing propagation of probability updates across the model connections until prior probability values are substituted for the absent evidence values;
transmitting the plurality of treatment plans to a client device to cause the client device to display the plurality of treatment plans;
receiving feedback data comprising a plurality of user experience identifiers of a plurality of user experiences that are respectively associated with the plurality of dayparts, each user experience identifier indicating a unique experience of the user responsive to treating the malady of the user according to a respective treatment plan of the plurality of treatment plans;
calculating a plurality of differences between the plurality of user goals for the plurality of dayparts of the day and the plurality of user experiences; and
improving an accuracy of the machine learning platform during execution of the respective treatment plan by updating the connections, the first group of probability weightings, and the second group of probability weightings based on the plurality of differences, wherein updating comprises executing Bayesian parameter estimation to recompute posterior probability distributions for nodes corresponding to the plurality of dayparts and synchronizing the recomputed posterior probability distributions across the multiple servers via the communication network
The identified limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind (including observation, evaluation, judgement or opinion) but for the recitation of generic computer components. That is, other than reciting servers, processing device, client device, communication network, and machine learning platform (interpreted as a computer program using a CPU) nothing in the claim elements precludes the steps form practically being performed in the mind. For example, the identified limitations encompasses a healthcare professional observing patient response under a cannabis treatment plan and using a calculating the differences between the user goals. The training data comprises evaluating a plurality of user responses, which falls under mental processes. The claim limitations fall within the Mental Processes groupings of abstract ideas. Thus, the claimed invention recites a judicial exception.
Part I: Step 2A, prong two: additional elements that integrate the judicial exception into a practical application
Under step 2A, Prong Two of the Alice framework, the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. In particular, the claims are evaluated to determine if there are additional elements or a combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception.
This judicial exception is not integrated into a practical application. The servers, processing device, client device, communication network, and machine learning model in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. The claim is directed to an abstract idea.
Dependent claims 2, 4, 5, 7-10,12, 14, 15, and 17-19, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea:
Claims 2 and 12: scoring, based on the plurality of treatment plans, each cannabis product of a group of cannabis products stored in an inventory; and selecting a first cannabis product of the group of cannabis products based on a score associated with the first cannabis product (mental processes).
Claims 4 and 14: wherein the feedback data comprises a first feedback data associated with a first treatment plan and a second feedback data associated with a second treatment plan, and further comprising: transmitting, to the client device, a first query for the first feedback data associated with the first treatment plan; and transmitting, to the client device, a second query for the second feedback data associated with the second treatment plan (mental processes).
Claims 5 and 15: wherein the feedback data is received while the user is experiencing effects of the one or more cannabis products (mental processes).
Claims 7 and 17: generating, by the processing device, an adjusted treatment plan based on a first difference of the plurality of differences, the user profile dataset, and the machine learning platform; and transmitting the adjusted treatment plan to the client device to cause the client device to display the adjusted treatment plan (mental processes).
Claims 8 and 18: wherein a first treatment plan of the plurality of treatment plans includes a first dose recommendation of a first cannabis product of the one or more cannabis products and a product attribute of the first cannabis product, and the adjusted treatment plan includes at least one of an adjusted dose recommendation of the first cannabis product, an adjusted product attribute of the first cannabis product, or instructions for a non-cannabis treatment (mental processes).
Claim 9: wherein each user experience identifier of the plurality of user experience identifiers indicates at least one of: a relaxation level of the user, an energy level of the user, a mental level of the user, a euphoric level of the user, a mood level of the user, or whether the user experienced undesired side effects (mental processes).
Claims 10 and 19: wherein a first daypart of the plurality of dayparts is associated with a first day type and a first goal of the plurality of user goals, and a second daypart of the plurality of dayparts is associated with a second day type and a second goal of the plurality of user goals, and further comprising: generating, by the processing device, a first treatment plan of the plurality of treatment plans based on the first goal associated with the first day type; and generating, by the processing device, a second treatment plan of the plurality of treatment plans based on the second goal associated with the second day type (mental processes).
Since these claims are directed to an abstract idea, the Office must determine whether the remaining limitations “do significantly more” than describe the abstract idea.
Part II. Determine whether any Element, or Combination, Amounts to“Significantly More” than the Abstract Idea itself
Under Part II, the steps of the claims, when considered individually and as an ordered combination, do not improve another technology or technical field, do not improve the functioning of the computer itself, and are not enough to qualify as "significantly more". For example, the steps require no more than a conventional computer to perform generic computer functions. As stated above in Prong Two, the servers, processing device, client device, communication network, and machine learning model in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Therefore, based on the two-part Mayo analysis, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself. Claims 1, 2, 4, 5, 7-12, 14, 15, and 17-20, when considered individually and as an ordered combination, are rejected as ineligible subject matter under 35 U.S.C. 101.
Dependent claims 2, 4, 5, 7-10,12, 14, 15, and 17-19 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional claims do no recite significantly more than an abstract idea.
Conclusion
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/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685