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 .
DETAILED ACTION
This action is in reference to the communication filed on 23 DEC 2025.
Applicant elects Group I consisting of claims 1-8, 13-20.
Claims 1-8, 13-20 are present and have been examined.
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 1-8, 13-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-8, 13-20 the independent claim(s) 1, 13 each recite a method and a device, each of which are a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-8, 13-20, the independent claim(s) (claims 1, 13) is/are directed, in part, to:
Claim 1: Buchard teaches: A method
obtaining an actual event related to medical data of a patient and a virtual event generated based on the actual event;
determining a probability of the virtual event occurring, based on the medical data; and
based on the actual event, the virtual event, and the probability of the virtual event, training a model to determine a state value for a reward of an action that is performed in a current state of the patient
These claim elements are considered to be abstract ideas because they are directed mental processes – i.e. concepts performed in the human mind including observation, evaluation, judgment and opinion. Obtaining data is an observation, while determining and training based on values are examples of evaluation, judgement and/or opinion, in that they could be performed in the mind.
These claim elements are also considered to be abstract as they are at least nominally directed to mathematical concepts – i.e. mathematical relationships, formulas, equations, or calculations. Examiner finds that determining a probability, as well as the training of a model based on data, are examples of a mathematical relationship or calculation.
If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind, or mathematical relationships/formulas/equations/calculations, then it falls within the “mental processes” and/or the “mathematical concepts” categories of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements: Claim 1 recites an “electronic device” understood to be analogous to a computer, while claim 13 recites “an electronic device comprising a processor” to perform the claim steps. The electronic device(s) and processor are recited at a high-level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Examiner finds no improvement to the functioning of the computer or any other technology or technical field in the electronic devices/processor as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e).
Accordingly, this/these additional element(s) do(es) 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.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: Claim 1 recites an “electronic device” understood to be analogous to a computer, while claim 13 recites “an electronic device comprising a processor” to perform the claim steps. “When considered individually, the above identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in [xx]
[0131] The components described in the embodiments may be implemented by hardware components including, for example, at least one digital signal processor (DSP), a processor, a controller, an application-specific integrated circuit (ASIC), a programmable logic element, such as a field programmable gate array (FPGA), other electronic devices, or combinations thereof. At least some of the functions or the processes described in the embodiments may be implemented by software, and the software may be recorded on a recording medium. The components, the functions, and the processes described in the embodiments may be implemented by a combination of hardware and software.
[0132] The embodiments described herein may be implemented using a hardware component, a software component, and/or a combination thereof. A processing device may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller and an arithmetic logic unit (ALU), a DSP, a microcomputer, an FPGA, a programmable logic unit (PLU), a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. The processing device may run an operating system (OS) and one or more software applications that run on the OS. The processing device also may access, store, manipulate, process, and generate data in response to execution of the software. For purpose of simplicity, the description of a processing device is singular; however, one of ordinary skill in the art will appreciate that a processing device may include a plurality of processing elements and a plurality of types of processing elements. For example, the processing device may include a plurality of processors, or a single processor and a single controller. In addition, different processing configurations are possible, such as parallel processors.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-8, 14-20:
Dependent claims 5, 17 do not recite any additional abstract idea beyond those identified above, however, they do recite the non-abstract element of a database. Examiner finds that storing information within a database is generally found to be the equivalent of adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g), and as such this element does not constitute a practical application. Examiner finds nothing in the specification to constitute a finding of significantly more than the identified abstract idea(s), as the discussion in the specification appears to be purely functional (i.e. any database capable of storing data can perform the claimed limitations).
Dependent claims 2-4, 6-8, 14-16, 18-20 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as the training of the model, as well as additional information regarding the types of events and the probabilities therein. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-9, 13-20 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Buchard et al (US 20210327578 A1, hereinafter Buchard).
In reference to claim 1, 13:
Buchard teaches: A method of operating an electronic device, the method comprising:
obtaining an actual event related to medical data of a patient and a virtual event generated based on the actual event (at least [fig 1 and related text] “An example use of the knowledge base 13 would be in automatic diagnostics, where the user 1, via mobile device 3, inputs symptoms they are currently experiencing, and the interface engine 11 identifies possible causes of the symptoms using the semantic triples from the knowledge base 13…The knowledge base 13 keeps track of the meaning behind medical terminology across different medical systems and different languages. In particular, the knowledge base 13 includes data patterns describing a plurality of semantic triples, each including a medical related subject, a medical related object, and a relation linking the subject and the object.” – i.e. the user’s entered data is the actual event, and the knowledge base is used to extrapolate potential causes, at [017-023] the determination is made as to if the triage decision is likely to be correct/incorrect);
determining a probability of the virtual event occurring, based on the medical data (at least [0120-0123] “In this case, the Q-target is obtained by considering the sequence of the event T until the end of the interaction. That is, we consider the events T.sub.j, T.sub.j+1, . . . , T.sub.k, for the states s.sub.j up to s.sub.k, with k+j the maximum number of questions. We then consider the probability P.sub.j of the event “The current triage decision is incorrect, and the next is correct, or both the current and next triage decision are incorrect, but the following triage decision is correct, or . . . ” and so on. We can rewrite P.sub.j as…” at [0165-0166] “The target Q-value for the information request action (the ask action) is based on the target state-action values for the triage actions. As the target Q-values for the triage actions are based on a probability (i.e. a probability that the triage action is correct), the target Q-value for the information request action can therefore be a probability that more information is needed based on the current Q-values for triage. “ see also [0170-0173] for discussion of probability of correct triage assignment given state):; and
based on the actual event, the virtual event, and the probability of the virtual event, training a model to determine a state value for a reward of an action that is performed in a current state of the patient (at least [fig 2, 3 and related text and 0173-0174, including table 1] figs 2 and 3 give examples of reinforcement training of the models based on the input and the probability of the level, at [0155] “It should be noted that, in many cases, the rewards will not need to be calculated at each step, as they will not change. Instead, the rewards are based on the probability of the respective triage level being correct based on a predetermined set of triage levels for the patient.”).
In reference to claim 2, 14:
Buchard further teaches wherein the training of the model comprises: in the case of the actual event, determining a predicted value of the reward as the state value (at least [fig 4 and related text] “This might be through requesting or generating questions for the patient, or might be through accessing information within a set of stored evidence (e.g. for training). The triage module 415 is configured to determine a triage level of the patient over one or more rounds of information retrieval. The triage module 415 includes a neural network that is configured to predict the value of certain actions in order to assist with this triage. “ see also [0155-0165] for discussion of reward state value); and
in the case of the virtual event, determining the state value so that the reward is predicted according to the probability of the virtual event (at least [095-0107] “Hence, for every vignette V.sub.i, each triage decision a∈custom-character is mapped to a reward equal to the normalized probability of that decision in the bag of expert decisions A.sub.i. Namely, denoting of r corresponding to the reward for action a as r.sub.a:…” - i.e. the reward is tied to the probability of the decision being correct, see also [0153-0155]).
In reference to claim 3, 15:
Buchard further teaches: wherein the training of the model comprises training the model to determine the state value by using a Bellman equation, which considers the probability of the virtual event (at least [056-061] Q-learning using Bellman equation).
In reference to claim 4, 16:
Buchard further teaches: wherein the training of the model further comprises, based on the state value, training the model to select a policy for determining an action appropriate for the current state (at least [055-057, 068-070] “In Q-Learning, the agent does not learn a policy function directly but instead learns a proxy state-action value function Q(s, a). This function approximates an optimal function Q*(s,a) defined as the maximum expected return achievable by any policy a over all possible trajectories τ, given that in state s the agent performs action a and the rest of the trajectory τ is generated by following n, denoted as τ˜π (with a slight abuse of notation).” – i.e. the policy is selected based on the optimized goal).
In reference to claim 5, 17:
Buchard further teaches: wherein the determining of the probability comprises, based on a database in which the medical data is stored, determining the probability through a model configured to compare the virtual event to distribution of actual events in the database. (at least [018, 044] “. A variety of metrics may be used when determine the predefined triage actions that may be deemed to be correct. For instance, appropriateness or safety may be judged based on a distribution of the correct triage actions in the predefined triage actions. Alternatively, a classifier or other model may be applied to the evidence, having been trained based on a predefined set of correct triage levels…] Learning a triage system from a detailed distribution of patient trajectories allows for the correction of inherent biases of expert-crafted systems and allows the system to be tailored for a specific target population. Given sufficient training data, the embodiments described herein can be designed to minimize the risk for the patients by striking the right balance between information gathering and decision-making.” At [0209] “One of the reasons to use the Dynamic Q-Learning over classic Q-Learning is to ensure that the Q-values correspond to a valid probability distribution. Using the classic DQN would produce unbounded Q-values for the asking action, because asking is not terminal, whereas the Q-values for the triage action would be bounded. In classic Q-Learning, only a careful process of reward shaping for the ask action could account for this effect.”).
In reference to claim 6, 18:
Buchard further teaches: wherein the virtual event is an event in which one or more features are selected from the actual event and an event that is generated by the selected one or more features and is different from the actual event (at least [0137] “An example use of the knowledge base 13 would be in automatic diagnostics, where the user 1, via mobile device 3, inputs symptoms they are currently experiencing, and the interface engine 11 identifies possible causes of the symptoms using the semantic triples from the knowledge base 13. Following triage, the system may also proceed to diagnose the patient, for instance, by requesting further information before determining a likely condition,” – the “actual” event are the symptoms entered by patient 1, while the virtual event is the “diagnosis” from knowledge base 13; the diagnosis is different from the event/symptom; see also [063, 067] for discussion of symptoms/state, see also [0134] “The question engine 17 may be implemented through a probabilistic graphical model that stores various potential symptoms, medical conditions and risk factors. The question engine may applies logical rules to the knowledge base 13 and probabilistic graphical model to deduce new information (infer information from the input information, the knowledge base 13 and the probabilistic graphical model). The question engine is configured to generate questions for the user to answer in order to obtain information to answer an overall question (e.g. “what is the triage level”). Each question is selected in order to reduce the overall uncertainty within the system.”)).
In reference to claim 7, 19:
Buchard further teaches: wherein the virtual event comprises information about a next state of the patient predicted for the action performed in the current state of the patient (at least [fig 3 and related text including 0150] “. As the triage action is terminal, and as the Q-values for triage actions are equal to the reward and are not dependent on the next state, there is no need to update the state by adding new evidence, but instead the next state may be deemed to be the same as the current state. Accordingly, the next state may be stored in the experience but may be set to be the same as the state for the experience.”)
In reference to claim 8, 20:
Buchard further teaches: wherein the medical data comprises information about the current state of the patient and the action (at least [fig 1 and related text] “The question engine 17 may be implemented through a probabilistic graphical model that stores various potential symptoms, medical conditions and risk factors. The question engine may applies logical rules to the knowledge base 13 and probabilistic graphical model to deduce new information (infer information from the input information, the knowledge base 13 and the probabilistic graphical model). The question engine is configured to generate questions for the user to answer in order to obtain information to answer an overall question (e.g. “what is the triage level”). Each question is selected in order to reduce the overall uncertainty within the system.”) – e.g. symptoms are the current state, and the “triage” assessment is the action to be taken in that the classification determines next steps).
Relevant Prior Art
The following prior art not relied upon is made a part of the record:
US 20200373017 A1 to Wang, discloses a means of reinforcement learning for clinical treatment outcomes among patients.
US 20220366245 A1 to Guez, discloses training a neural network with a reinforcement protocol, and a specific applicability to medical environments.
US 20240177015 A1 to Connolly, teaches training a model on a probability of an expected outcome/reward.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687