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 .
Claim Status
Claims 1-20 are currently pending and are herein under examination.
Claims 1-20 are rejected.
Claim 7 is objected.
Priority
The instant application does not claim priority to any prior filed application. As such, the effective filing date for claims 1-20 is 08/14/2023.
Information Disclosure Statement
The IDSs filed 08/14/2023, 02/24/2025, 09/11/2025, 12/13/2025 and 05/27/2026 follow the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of the list of references cited from these IDSs is included with this Office Action.
Drawings
The drawings filed 08/14/2023 are accepted.
Claim Objections
Claim 7 is objected to because line 3 appears that it should recite “stimulator” instead of “simulator”. Appropriate correction is required.
Claim Interpretation
35 USC 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Specifically, the claims recite the word “module”. MPEP 2181.I.A recites “the word 'module' does not provide any indication of structure because it sets forth the same black box recitation of structure for providing the same specified function as if the term ‘means’ had been used.” Below are the claims that invoke 35 USC 112(f):
Claims 1, 10 and 16 recite “a data collection module” for collecting a first set of patient data and a first set of treatment data. Claims 1, 10 and 16 recite “a clustering module” for clustering patient population into cohorts. Claims 1, 10 and 16 recite “a state discovery module” for generating states using a second set of patient data. Claims 1, 10 and 16 recite “an action discovery module” for generating actions using a second set of treatment data. Claims 1, 10 and 16 recite “a decision-making module” for determining probabilities with a transition from a first state to a second state. Claims 1, 10 and 16 recite “a recommendation module” generating a stimulator action policy for a patient. Claims 4, 13 and 19 recite “a decision-making module” that determines the plurality of probabilities using the Markov decision process, which recites sufficient structure for the module.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. These limitations are being interpreted as computer-implemented means-plus-function limitations, which requires an algorithm as the structure. Below are the algorithms in the specification that correspond to the claimed functions.
Data collection module. Structure for the data collection module is a computer processor because its function of collecting data is coextensive with a computer processor [97]. See MPEP 2181.II.B.
Clustering model. The following algorithms in the specification para. [98] and equivalents thereof will be the structure of the clustering module: “statistical methods such as K-means or density-based spatial clustering (DBSCAN) may be employed. The K-means method identifies ‘K’ number of centroids, and each data point may be associated with the nearest centroid, thus forming ‘K’ clusters or cohorts. DBSCAN is a machine learning algorithm that identifies clusters in a dataset based on the density of data points, distinguishing between high-density regions (clusters), lower density regions (noise), and transitional areas.”
State discovery module: The following algorithm in the specification para. [99] and equivalents thereof will be the structure of the state discovery module: “it may take the aggregated physiological data from patients in each cohort and apply clustering techniques again … This module may construct clusters, and their centroids, referred to as "states," which encapsulate the patient conditions most effectively”.
Action discovery module. The following algorithm in the specification para. [100] and equivalents thereof will be the structure of the action discovery module: “it may be configured to work with the neuromodulation data specific to each cohort. Leveraging clustering methodologies akin to those used earlier, like K-means or DBSCAN, it may work on the neuromodulation data and form clusters. The resulting cluster centroids, termed ‘actions,’ may illustrate the typical configuration of an SCS device for that specific cohort.”
Decision-making module. The following algorithm in the specification para. [101] and equivalents thereof will be the structure of the decision-making module: “model these state transitions using a Markov decision process (MDP) … The MDP may represent each patient's condition as a state and possible treatments as actions. The decision-making module may estimate the transition probabilities between states based on the action taken. For instance, it may calculate the likelihood of a patient moving from a "worst" state to a "best" state following a certain SCS device setting.”
Recommendation module. The specification discusses the recommendation module in para. [54] and [103]. However, these paras. do not explicitly disclose an algorithm to perform the claimed function. Rather, they generally recite leveraging probabilities or applying decision theory principles of optimization algorithm on the probabilities, but they do not constitute an algorithm. MPEP 2181.II.B recites “implicit or inherent disclosure of a class of algorithms for performing the claimed functions is not sufficient, and the purported ‘one-step’ algorithm is not an algorithm at all.” As such, any algorithm that performs the claimed function will read on this limitation.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
35 USC 112(a)
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 1-20 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 dependent from a rejected claim are also rejected, unless otherwise noted.
Claims 1, 10 and 16 fail to comply with the written description requirement because they do not adequately link or associate adequately described particular structure, material, or acts to perform the function recited in the claim identified to invoke 35 U.S.C. 112(f) or pre- AIA 35 U.S.C. 112, sixth paragraph. As discussed in Claim Interpretation, claims 1, 10 and 16 recite a function for a recommendation module that invokes 35 U.S.C. 112(f). Neither the specification nor the drawings disclose sufficient algorithms or associated structures that perform the functions of this computer-implemented means-plus-function limitation. Thus, in accordance with MPEP § 2181.IV, the instant specification does not provide written description support for the recommendation module.
35 USC 112(b)
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims dependent from a rejected claim are also rejected, unless otherwise noted.
Claim 1 (lines 8 and 13), claim 10 (lines 11 and 16), and claim 16 (lines 11 and 16) recite “the cohort”. It is unclear if this refers to “a cohort in the plurality of cohorts”, or if it refers to another one of the cohorts within the plurality of cohorts. If referring to the former, change the phrase to “the cohort in the plurality of cohorts”.
Claim 2 (lines 2-3), claim 11 (lines 2-3), and claim 17 (lines 2-3) recite “the patient … the patient”. It is unclear if this refers to a patient in the patient population in claim 1 (line 3), claim 10 (line 6), and claim 16 (line 6), or if it refers to “a patient in the cohort” in claim 1 (line 13), claim 10 (line 16), and claim 16 (line 16). Clarify which patient is being referenced.
Claim 3 (line 3), claim 7 (line 2), claim 12 (line 3), and claim 18 (line 3) recite “the patient”. It is unclear if this refers to a patient in the patient population in claim 1 (line 3), claim 10 (line 6), and claim 16 (line 6), or if it refers to “a patient in the cohort” in claim 1 (line 13), claim 10 (line 16), and claim 16 (line 16). Clarify which patient is being referenced.
Claim 4 (line 3), claim 13 (line 3), and claim 19 (line 3) recite “the cohort”. It is unclear if this refers to “a cohort in the plurality of cohorts” in claim 1 (line 6), claim 10 (line 7), and claim 16 (line 7), or if it refers to another cohort within the plurality of cohorts. If referring to the former, change the phrase to “the cohort in the plurality of cohorts”.
Claim 8, line 2, recites “the patient data”. It is unclear which patient data is being referenced because claim 1 recites both “a first set of patient data” and “a second set of patient data”. Clarify which patient data is being referenced.
Claim 8, line 4, recites “a state … includes a combination of at least one”. It is unclear how the state can be simultaneously at least one and a combination. Should this recite “a state … includes a combination of at least [[one]] two” or “a state … includes
Claim 9, line 2, recites “the treatment data”. It is unclear which treatment data is being referenced because claim 1 recites both “a first set of treatment data” and “a second set of treatment data”. Clarify which treatment data is being referenced.
Claims 1, 10 and 16 recite a recommendation module that invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. See Claim Interpretation. The written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claims 1, 10 and 16 are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, claims 1-9 recite a method, claims 10-15 recite a product, and claims 16-20 recite a system. It is noted that specification para. [68] recites that computer readable storage media should not be construed as transitory signals per se. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claims 1, 10 and 16 recite “clustering … the patient population into a plurality of cohorts; generating … a plurality of states using a second set of patient data associated with a cohort in the plurality of cohorts; generating … a plurality of actions using a second set of treatment data associated with the cohort; determining … based on the plurality of actions, a plurality of probabilities associated with a transition from a first state to a second state in the plurality of states; and generating … based on the plurality of probabilities, a stimulator action policy for a patient in the cohort.”
Claims 2, 11 and 17 recite “determining a current state of the patient using time-series patient data associated with the patient; and determining the stimulator action policy by selecting one or more actions with a highest probability to transition from the current state to another state.”
Claims 3, 12 and 18 recite “determining a current setting of a stimulator using time-series treatment data associated with the patient; and determining the stimulator action policy by selecting a sequence of adjustments to the stimulator with the highest probability to transition from the current state to another state.”
Claims 4, 13 and 19 recite “generating a Markov decision process using time-series patient data and time-series treatment data associated with the cohort; and determining the plurality of probabilities using the Markov decision process.”
Claims 5, 14 and 20 recite “receiving a user feedback on the stimulator action policy and adjusting the Markov decision process based on the user feedback.”
Claims 6 and 15 recite “wherein a cohort represents a group of patients who share a similarity in at least one of a physiological feature, a pain diagnosis, a stimulator device type, and an implant location.”
Claim 8 recites “wherein: the patient data includes at least one of a pain level, an activity level, a sleep quality, and a mood level; and a state in the plurality of states includes a combination of at least one of the pain level, the activity level, the sleep quality, and the mood level.”
Claim 9 recites “wherein: the treatment data includes at least one of a stimulator frequency, a stimulator current, a stimulator voltage, and a stimulator intensity; and the stimulator action policy includes adjusting at least one of the stimulator frequency, the stimulator current, the stimulator voltage, and the stimulator intensity.”
Limitations reciting a mental process.
Claims 1-6 and 8-20 contain limitations recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the broadest reasonable interpretation (BRI) of the limitations in these claims that recite a mental process.
Referring to claims 1, 10 and 16 and as discussed in Claim Interpretation, the clustering module, state discovery module, and action discovery module include performing k-means clustering wherein the clusters, states, and actions are the centroids. A human can practically perform the mathematical operations of a k-means clustering using pen and paper.
Claims 1, 4, 10, 13, 16 and 19 recite generating a Markov decision process (MDP) and determining a plurality of probabilities using the MDP which include designing a MDP based on patient data then calculating a probability transition matrix, which can be done on pen and paper. Specification para. [58] describes mental processes to perform these limitations. The BRI of generating a stimulator action policy based on probabilities includes performing on pen and paper the calculations of Bellman equation to solve a MDP.
Claims 2 include evaluating patient data and determining a state of a patient such as “low activity” then selecting an action based on a highest transition probability. Claims 3 includes analyzing past and present settings of a stimulator then using a current state to identify a sequence of adjustments likely to lead to a state transition based on a high transition probability. Claim 5 includes collecting data and altering the variables within a MDP based on feedback, which can be done on pen and paper. See specification para. [58]. Claims 6 and 8 are included in the abstract idea in claims 1 of clustering the patient population into cohorts and generating a plurality of states. Claim 9 is included in the abstract idea of generating a plurality of actions and generating a stimulation action policy.
Limitations reciting a mathematical concept.
Claims 1, 4, 10, 13, 16 and 19 recite limitations that equate to a mathematical concept because they are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The paragraphs below discuss the broadest reasonable interpretation (BRI) of the limitations in these claims that recite a mathematical concept.
Referring to claims 1, 10 and 16 and as discussed in Claim Interpretation, the clustering module, state discovery module, and action discovery module all include performing k-means clustering wherein the clusters, states, and actions are the centroids. The centroid is a number (i.e. mean). As such, these limitations recite calculating a mean using k-means clustering, in light of the specification.
Claims 1, 4, 10, 13, 16 and 19 recite determining a plurality of probabilities using a MDP based on a plurality of actions. This necessitates calculating transition probabilities using a MDP. The BRI of generating a stimulator action policy includes solving a MDP using Bellman equation.
As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claims 1-10 recite “A computer-implemented method:”
Claims 1, 10 and 16 recite “collecting, by a data collection module, a first set of patient data and a first set of treatment data associated with a patient population treated with spinal cord stimulation; by a clustering module; by a state discovery module; by an action discovery module; by a decision-making module; by a recommendation module;”
Claim 7 recites “presenting the stimulator action policy to the patient; and automatically adjusting a simulator setting based on the stimulator action policy.”
Claim 10 recites “A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising:”
Claims 11-15 recite “the computer program product”.
Claim 16 recites “A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising:”
Claims 17-20 recite “the computer system”.
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B.
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)), insignificant extra-solution activity (MPEP § 2106.05(g)), and field of use limitations (MPEP § 2106.05(h)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claims 1-20 recite a computer-implemented method, a computer program product and a computer system as well as a clustering module, a state discovery module, an action discovery module, a decision-making module, and a recommendation module which are being interpreted as processors programmed with algorithms. There are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Claims 1, 10 and 16 collect data by a collection module. The BRI of this limitation includes using a processor to collect data, which invokes a computer as a tool to perform an existing process of receiving data and does not provide a practical application (MPEP 2106.05(f)(2)). This limitation also equates to insignificant, extra-solution activity because it gathers data necessary to perform the judicial exceptions in claims 1, 10 and 16.
Claim 7 recites presenting the stimulator action policy to the patient which equates to insignificant, extra-solution activity of necessary data outputting because it outputs the result of the judicial exception in claim 1 of generating a simulator action policy.
Claim 7 recites adjusting the stimulator setting based on the stimulator policy which equates to no more than a recitation of the words "apply it" (MPEP 2106.05(f)). The judicial exception of the stimulator action policy is applied by adjusting the stimulator setting. See MPEP 2106.05(f)(1) and (2) for particularity of the application and whether the application recites sufficient details to solve a problem.
As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B:
Claims found to be directed at a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claims 1-20 recite a computer-implemented method, a computer program product and a computer system as well as a clustering module, a state discovery module, an action discovery module, a decision-making module, and a recommendation module which are being interpreted as processors programmed with algorithms. There are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claims 1, 10 and 16 collect data by a collection module and claim 7 presents data. These limitations equate to receiving/transmitting data over a network, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Claims 10 and 16 recite storing instructions in memory, which the courts have established as a WURC function of a generic computer in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
When the additional elements of claims 1 and 7 are viewed in combination, they recite WURC limitations as taught by Bartlett et al. (“Bartlett”; US Patent no. 2 on IDS filed 02/24/2025; US 2023/0191130 A1), Schulte et al. (“Schulte”; FOR ref. no. 1 on IDS filed 08/14/2023; WO 2023/283568 A1), and Paydarfar et al. (“Paydarfar”; US 12,458,807 B2; effective filing date 10/23/2020). Bartlett discloses a reinforcement learning based closed-loop neuromodulation system that uses a Markov decision process (MDP) to adjust neuromodulation based on a policy function [17] [49] (FIG. 3). Schulte discloses a personalized therapy neurostimulation system, where an MDP formalizes a reinforcement problem by computing an optimal policy that adjusts neurostimulation [305-308]. Paydarfar uses a reinforcement learning framework where the agent is the stimulator and a brain circuit is the environment, which is presented as an MDP, and the agent acts by stimulating the brain circuit then uses reward and new state information to inform its next action (col. 24, lines 37-53).
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to WURC functions/components of a generic computer, mere instructions to implement an abstract idea on a computer, and to WURC limitation as taught by Bartlett and Schulte. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-20 are not patent eligible.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4-6, 8, 10-11, 13-17 and 19-20 are rejected under 35 U.S.C. 102 (a)(1) and (a)(2) as being anticipated by Gorman et al. (“Gorman”; WO 2022/265828 A1; publication date 22 Dec 2022).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claims 1, 10 and 16:
Claim 1: A computer-implemented method comprising: Claim 10: A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising: Claim 16: A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising:
Gorman manages health conditions by monitoring health data to generate intervention plans (abstract). FIG. 7 shows a computer with software stored in memory that performs the functions of the invention [115-116].
collecting, by a data collection module, a first set of patient data and a first set of treatment data associated with a patient population treated with spinal cord stimulation;
Gorman collects for each patient data comprising symptoms and treatments [91].
The limitation of “patient population treated with spinal cord stimulation” is a product by process. It defines a process previously performed (i.e., treating patients with spinal cord stimulation) used to derive a product (a patient population). MPEP 2113.I recites "[e]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process.". As such, the patient data of Gorman reads on this limitation even though the patients were treated in a different way.
clustering, by a clustering module, the patient population into a plurality of cohorts;
Gorman uses “machine learning to predict the efficacy of particular treatments for individual patients based on aggregate data collected on cohorts of patients” [16]. Alternatively, the training data contains positive and negative cases, which is used to train a reinforcement learning model [40] [62].
generating, by a state discovery module, a plurality of states using a second set of patient data associated with a cohort in the plurality of cohorts; generating, by an action discovery module, a plurality of actions using a second set of treatment data associated with the cohort; determining, by a decision-making module based on the plurality of actions, a plurality of probabilities associated with a transition from a first state to a second state in the plurality of states;
Gorman recites “the modular implementation of the treatment recommendation engine 112 allows two actionable fail-back plans in the event of failing to obtain actionable results with the DQN. The first plan includes falling back to a standard RL approach taking the association analysis as a building block for the definition of a Markov Decision Process (MDP)” [43].
The association analysis produces state-action pairs, wherein the states are phenotypes derived from patient health data and the actions are treatments derived from patient information [66], and “the agent learns to map states (patient conditions/symptoms) to actions that maximize a reward (clinical outcome)” [95]. These teachings indicate that an MDP is generated using patient symptoms as states and patient treatments as actions (FIG 5) (a plurality of states using a second set of patient data; a plurality of actions using a second set of treatment data).
Gorman teaches that a MDP generates state-transition probabilities [95] (a plurality of probabilities associated with a transition from a first state to a second sate in a plurality of states).
and generating, by a recommendation module based on the plurality of probabilities, a stimulator action policy for a patient in the cohort.
Gorman recites “[u]nder the RL recommendation process, which is comprised of elements 642 and 644, the process may include applying RL analysis 642 to the received 602 new patient data. RL develops optimal value functions and decision-making policies in order to identify sequences of actions yielding the greatest probability of long-term favorable outcomes as conditions of uncertainty evolve over time. Interactions between a learning algorithm and its environment often occur within a Markov Decision Process containing states, actions, state-transition probabilities, and rewards” [110].
The sequence of actions in Gorman refers to treatment recommendations which may be pharmacological or hormonal treatments and supplements [99]. These treatments fall under the broadest reasonable interpretation of “stimulator”. Thus, the decision-making policy of Gorman in para. [110] is a stimulator action policy.
Claims 2, 11 and 17:
Gorman teaches determining a current state of the patient using time-series patient data associated with the patient when reciting “the association analysis engine 304B may also ascertain associations over time as a patient’s symptoms evolve and/or as the patient responds to a given course of interventions. In this manner, the association analysis engine 304B ascertains associations over a threshold period time, which may be used to compute patient trajectories. With this approach, the association analysis engine 304B obtains an informative subset of data consisting of phenotypes (including their corresponding attributes) and the most informative treatments at a given point of time” [41]. The association data is used in the MDP and reinforcement learning algorithm [43]. Gorman recites “the process enables further monitoring of patient status and trajectories to provide real-time recommendations such that patient progress and well-being can be evaluated at a plurality of steps in the patient’s health and recovery journey” [96].
Gorman teaches that estimating probabilities “allow[s] the assessment of the probability to transition to other states so that recommendations and interventions can be planned to maximize this transition probability to the most favorable state for the patient” [10] [72] (determining the stimulator action policy by selecting one or more actions with a highest probability to transition from the current state to another state).
Claims 4, 13 and 19:
Gorman generates an MDP using association data obtained over time from a training cohort, which contains patient symptoms and treatments [40] [43] [66] [91] (FIG. 5). The MDP generates state-transition probabilities [95].
Claims 5, 14 and 20:
Gorman recites “outcome stream 104 refers to data obtained after an intervention plan or protocol is recommended to and/or implemented by a patient. It refers to data regarding adherence to the recommended protocol, symptom data, psychosocial metrics data, and quality of life metrics data that can be monitored. Outcome data 104 may be obtained from a number of data sources, including information from diagnostics, lifestyle, activity, and so on ... Sources of outcome data for a new patient might include, for example, responses made by the patient from a standard questionnaire or physiologic data such as heart rate, blood pressure and genetic or hormonal test results, data on physical activity or sleep from wearable/sensor/smartphone data and multilevel data from physiologic levels to life-style or psychology” [45] (receiving a user feedback on the stimulator action policy). Gorman teaches “[t]hroughout the process user health data is collected and used to continuously adapt recommendations according to symptom presentation in a positive feedback loop” (adjusting the Markov decision process based on the user feedback) [16].
Alternatively, Gorman recites “[o]nce the recommendation system is ready and set up in production, when data from a new patient is available, it is processed to obtain phenotypes and associations that are then fed into the recommendation system to obtain a personalized protocol for this patient, as described in greater detail below. Once the protocol is ready, validated and accepted by the patient, the adherence to the protocol, symptoms, psychosocial metrics and quality of life metrics can be monitored. Thus, this new stream of data may be fed into the pipeline for updating the available datasets that shall be later used for upgrading or fine-tuning the recommendation engine” [78].
Claims 6 and 15:
The training data contains positives cases of women in perimenopause with specific estrogen levels (physiological feature) [61-62].
Claim 8:
The symptoms, which are used in the training data and as input for new patient data, can be mood changes [65], sleep data [44], physical activity [44]. These symptoms are used as states in the MDP [95].
Claim Rejections - 35 USC § 103
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 3, 7, 9, 12 and 18 are rejected under 35 USC 103 for being unpatentable overGorman et al. (“Gorman”; WO 2022/265828 A1; publication date 22 Dec 2022) in view of Blum et al. (“Blum”; EP 3,493,876 B1; filed 13 Jan 2017).
The limitations of claims 1-2, 10-11 and 16-17 have been taught above by Gorman under 35 USC 102 and are applied here under 35 USC 103. Bold and italicized text below are the limitations of the instant claims, and italicized text serves to map the prior art onto the instant claims.
Claims 3, 12 and 18:
Gorman teaches time series treatment data associated with a patient [40] [66] [91] (time-series treatment data associated with the patient). However, Gorman does not determine a current setting of a stimulator using the time series data.
Gorman recites “the probability to transition to other states so that recommendations and interventions can be planned to maximize this transition probability to the most favorable state for the patient” [9] [72], and “the agent learns to map states (patient conditions/symptoms) to actions that maximize a reward (clinical outcome). Actions affect both the immediate outcomes as well as all subsequent states. RL develops optimal value functions and decision-making policies in order to identify sequences of actions yielding the greatest probability of long-term favorable outcomes as conditions of uncertainty evolve over time” [95] (determining the stimulator action policy by selecting a sequence of adjustments to the stimulator with the highest probability to transition from the current state to another state).
Claim 7:
Gorman recites “[a]n outcome data interface 410 sends intervention plan recommendations to computing devices associated with the patient” [86] (presenting the stimulator action policy to the patient). Gorman recites “RL develops optimal value functions and decision-making policies in order to identify sequences of actions yielding the greatest probability of long-term favorable outcomes as conditions of uncertainty evolve over time” [95] (automatically adjusting … based on the stimulator action policy). However, Gorman does not automatically adjust a stimulator setting based on the stimulator action policy.
Claim 9:
Gorman teaches treatment data associated with a patient [40] [66] [91] (treatment data) and a stimulator action policy that adjusts treatments [95] (the stimulator action policy includes adjusting). However, Gorman does not disclose that the treatment data or stimulator action policy includes stimulator frequency, stimulator current, stimulator voltage, or stimulator intensity.
Blum programs settings of an implantable pulse generator (IPG) associated with a patient using closed loop programming (abstract) [5] (a stimulator). Blum receives clinical response values after the IPG, configured with a proposed set of stimulation parameters, stimulates a patient (claim 1, steps a-c). A revised proposed set of stimulation parameters are generated based on the clinical response values and based on a distance of each set of untested stimulation parameter values from one or more previously tested stimulation parameter values (claim 1, steps d-e). The revised proposed set of stimulation parameters are used to configure the IPG (claim step f) (automatically adjusting a stimulator setting based on the stimulator policy). Steps c to f are repeated (claim 1, step g) (determining a current setting of a stimulator using time-series treatment data associated with the patient). The stimulation parameter includes current amplitude and frequency [7] (treatment data includes a stimulator current) (the adjusting at least the stimulator current).
Obviousness rationale for claims 3, 7, 9, 12 and 18:
It would have been prima facie obvious to modify Gorman who identifies health conditions and provides/implements/updates intervention plans, comprising a sequence of actions based on patient symptoms and past treatments, by incorporating an IPG that has stimulation parameters such as current amplitude and frequency in order to treat chronic pain in a patient as taught by Blum [2]. This would result in determining a sequence of adjustments to the IPG based on the policy derived from the MDP of Gorman. There would have been a reasonable expectation of success to combine the computing device of Gorman with the IPG of Blum because both devices communicate to external devices, allowing for the recommendations of Gorman to be communicated to the IPG of Blum. See Gorman at FIG 1 and 8 as well as para. [49] [120] [125]. See Blum at FIG. 1. and at para. [4] [40].
Double Patenting
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/233,725 (hereinafter “App. ‘725”).
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are a broader variation of the claims in App. ‘725. The following table shows claims in App. ‘725 that read on the limitations of the instant claims:
Instant Application
App ‘725
Claims
Limitations
Claims
Limitations
1, 10 and 16
collecting, by a data collection module, a first set of patient data and a first set of treatment data associated with a patient population treated with spinal cord stimulation; clustering, by a clustering module, the patient population into a plurality of cohorts; generating, by a state discovery module, a plurality of states using a second set of patient data associated with a cohort in the plurality of cohorts; generating, by an action discovery module, a plurality of actions using a second set of treatment data associated with the cohort; determining, by a decision-making module based on the plurality of actions, a plurality of probabilities associated with a transition from a first state to a second state in the plurality of states; and generating, by a recommendation module based on the plurality of probabilities, a stimulator action policy for a patient in the cohort.
1, 10 and 16
collecting, by a data collection module, a first set of patient data and a first set of treatment data associated with a patient population treated with neuromodulation; clustering according to a set of vectors of features corresponding to the first set of patient data, by a clustering module, the patient population into a plurality of cohorts, wherein at least one cohort is clustered according to an implant location, the implant location being a feature in the set of vectors of features; generating, by a state discovery module, a plurality of states using a second set of patient data associated with a cohort in the plurality of cohorts; generating, by an action discovery module, a plurality of actions using a second set of treatment data associated with the cohort; determining, by a decision-making module based on the plurality of actions, a plurality of probabilities associated with a transition from a first state to a second state in the plurality of states; generating, by a recommendation module based on the plurality of probabilities, and based on a current setting of a neuromodulation device corresponding to the patient using time-series treatment data associated with the patient a neuromodulator action policy for a patient in the cohort
App. ‘725 claims 2, 11 and 17; 1-3, 10-12 and 16-18; 4, 13 and 19; 5, 14, and 20; 6 and 15; 7; 8; and 9 read on instant claims 2, 11 and 17; 3, 12 and 18; 4, 13 and 19; 5, 14 and 20; 6 and 15; 7; 8; and 9.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
No claims are allowed.
Notable, but not relied upon, prior art includes: Intelligent Healthcare Solution Recommendation Based on Reinforcement Learning (NPL ref. no. 2 on IDS filed 08/14/2023) discusses clustering data to generate states and actions.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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