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 the Claims
The office action is in response to the claims filed on September 24, 2025 for the application filed on April 25, 2024,which claims priority to European Patent Application No. 23170914.8 filed on May 1, 2023. Claims 1, 2, 8, 10, and 11 have been amended, and claims 5 and 15 have been cancelled. Claims 1 – 4 and 6 – 14 are currently pending and have been examined as discussed below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claims 11 – 4 and 6 – 14 are currently pending and have been examined as discussed below.
Information Disclosure Statement
The information disclosure statement (IDS) filed on April 25, 2024 has been entered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 4 and 6 – 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Examiners should determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance with the following flowchart under MPEP 2016(III).
Eligibility Step 1:
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim as a whole falls within one of the statutory categories of invention (i.e., a process, machine, manufacture, or composition of matter). See MPEP 2106.03. In the instant application, claims 1 – 4 and 6 – 7 are directed to a remote patient monitoring system (i.e., a machine); claims 8 – 9 are directed to a non-transitory computer-readable storage medium (i.e., an article of manufacture); and claims 10 – 14 are directed to a computer-implemented method (i.e., a process).
While each one of claims 1 – 4 and 6 – 14 appears to fall within one or more statutory categories of invention, the Office has determined that the full eligibility analysis is required because there is doubt as to whether the applicant is effectively seeking coverage for a judicial exception itself. The eligibility of each claim is not self-evident at least because each claim as a whole did not appear to clearly improve a technology or computer functionality. To the contrary, each claim as a whole appeared to merely apply one or more judicial exceptions on a computer.
Accordingly, it has been determined that each one of claims 1 – 4 and 6 – 14 as a whole falls within one or more statutory categories under Step 1, and the Office proceeds with the full eligibility analysis (the Alice/Mayo test described in MPEP 2106(III)) as discussed below.
Eligibility Step 2A, Prong One:
Under Step 2A, Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim is directed to one or more of the judicial exceptions (i.e., an abstract idea, law of nature, or natural phenomenon). See MPEP 2106.04(II)(A)(1). After evaluation, it has been determined that claims 1 – 4 and 6 – 14 are directed to judicial exceptions because claims 1 – 4 and 6 – 14 recite abstract ideas. (The Office will not determine that a claim is not directed to a judicial exception under Step 2A, Prong One for the mere reason that claim further recites one or more additional elements beyond the judicial exception.)
Independent claims 1, 8, and 10 are determined to be directed to a judicial exception including abstract ideas (i.e., mental process and/or CMOHA). Representative claim 1 recites the abstract ideas identified in bold as:
A remote patient monitoring system configured to provide engagement guidance in connection with a plurality of patients (mental process and/or CMOHA), the system comprising:
one or more processors; and
memory having stored thereon machine-readable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
obtain patient data for at least a first subset of patients from one or more data sources, wherein the patient data comprises one or more of the following: patient reported outcome measures; patient-reported experience measures; user interface usage data; and system usage data (mental process and/or CMOHA);
extract a plurality of experience level features for each patient of at least the first subset of patients from the patient data obtained (mental process and/or CMOHA);
determine a condition experience level for each patient of at least the first subset of patients based on the experience level features extracted for the corresponding patient (mental process and/or CMOHA);
generate an intervention priority score for each patient of at least the first subset of patients by applying a trained intervention priority model to the condition experience level determined for the corresponding patient, wherein each intervention priority score is indicative of a likelihood of the corresponding patient having a positive change in one or more clinical outcomes following one or more intervention actions (mental process, and/or CMOHA); and
automatically generate a recommended intervention plan for each patient of at least the first subset of patients based on the intervention priority scores generated for at least the first subset of patients, wherein each recommended intervention plan includes at least one intervention action (mental process and/or CMOHA).
Mental Process:
The abstract ideas identified in bold above, individually or in combination, may be practically performed in the human mind using observation, evaluation, judgment, and opinion. Furthermore, any one or more of these limitations in combination with computer components (i.e., “one or more processors,” ”memory,” “machine-readable instructions,” etc.) still amount to an abstract idea because no distinction should be made between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. See MPEP 2106.04(a)(2)(III). With the exception of generic computer-implemented steps, there is nothing in claims 1, 8, and 10 themselves that foreclose them from being performed by a human, mentally or with tools such as pen and paper. The limitations, individually or in combination, are directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform conventional computer processes. See MPEP 2106.04(a)(2)(III)(C)(3). Thus, these steps, individually or in combination, amount to an abstract idea in the "mental process" grouping.
CMOHA:
These limitations, individually or in combination, amount to acts of managing personal behavior (i.e., rules and instructions governing mental processes to be followed by a human). See MPEP 2106.04(a)(2)(II)). Furthermore, the limitations, in combination with the computer components (i.e., “one or more processors,” ”memory,” “machine-readable instructions,” etc.) still amount to an abstract idea because no distinction should be made between claims that recite mental processes performed by humans (i.e., and as required by rules or instructions to manage personal behavior) and claims that recite mental processes performed on a computer. See MPEP 2106.04(a)(2)(III). Thus, these steps, individually or in combination, amount to an abstract idea in the "CMOHA" grouping.
Accordingly, claims 1, 8, and 10 are directed to judicial exceptions under Step 2A, Prong One.
Dependent claims 2, 4, 6 – 7, 9, 11, and 13 – 14 are directed to one or more judicial exceptions (i.e., abstract idea exceptions) under Step 2A, Prong One of the full eligibility analysis as follows:
Mental Process:
Claims 2, 9, and 11 recite an abstract idea identified as “extracting a plurality of experience level features for each historical patient from the historical patient data obtained” and “determining a condition experience level for each historical patient based on the experience level features extracted for the corresponding historical patient.” These limitations merely define the trained intervention priority model applied to the condition experience level to generate the intervention priority score, which may be practically performed in the human mind using, observation, evaluation, judgment, and/or opinions. Thus, claims 2, 9, and 11 recite an abstract idea in the “mental process” grouping.
Claim 4 recites an abstract idea identified as “each recommended intervention plan includes at least one intervention action identified based on the intervention priority score generated for the corresponding patient.” This limitation merely defines the step of automatically-generating the recommended intervention plan, which may be practically performed in the human mind using, observation, evaluation, judgment, and/or opinions. Thus, claim 4 recites an abstract idea in the “mental process” grouping.
Claims 6 and 14 recite an abstract idea identified as “the intervention priority score generated for each patient includes a plurality of sub-scores corresponding to one or a combination of potential intervention actions, each sub-score being indicative of a likelihood of the corresponding patient having a positive change in one or more clinical outcomes following the one or the combination of potential intervention actions.” This limitation merely further defines the step of generating the intervention priority score, which may be practically performed in the human mind using, observation, evaluation, judgment, and/or opinions. Thus, claims 6 and 14 recite an abstract idea in the “mental process” grouping.
Claim 7 recites an abstract idea identified as “the condition experience level determined for each patient has a first component comprising a score based on one or more current clinical values associated with the corresponding patient, and a second component comprising a score based on evidence of one or more historical intervention actions.” This limitation merely defines the step of determining the condition experience level, which may be practically performed in the human mind using, observation, evaluation, judgment, and/or opinions. Thus, claim 7 recites an abstract idea in the “mental process” grouping.
CMOHA:
Claims 2, 9, and 11 recite an abstract idea identified as “extracting a plurality of experience level features for each historical patient from the historical patient data obtained” and “determining a condition experience level for each historical patient based on the experience level features extracted for the corresponding historical patient.” This limitation merely defines the trained intervention priority model applied to the condition experience level to generate the intervention priority score, which amounts to an act of managing personal behavior, e.g., following rules or instructions. See MPEP 2106.04(a)(2)(II). Thus, claims 2, 9, and 11 recite an abstract idea in the “CMOHA” grouping.
Claim 4 recites an abstract idea identified as “each recommended intervention plan includes at least one intervention action identified based on the intervention priority score generated for the corresponding patient.” This limitation merely defines the step of automatically-generating the recommended intervention plan, which amounts to an act of managing personal behavior, e.g., following rules or instructions. See MPEP 2106.04(a)(2)(II). Thus, claim 4 recites an abstract idea in the “CMOHA” grouping.
Claims 6 and 14 recite an abstract idea identified as “the intervention priority score generated for each patient includes a plurality of sub-scores corresponding to one or a combination of potential intervention actions, each sub-score being indicative of a likelihood of the corresponding patient having a positive change in one or more clinical outcomes following the one or the combination of potential intervention actions.” This limitation merely defines the step of generating the intervention priority score, which amounts to an act of managing personal behavior, e.g., following rules or instructions. See MPEP 2106.04(a)(2)(II). Thus, claims 6 and 14 recite an abstract idea in the “CMOHA” grouping.
Claim 7 recites an abstract idea identified as “the condition experience level determined for each patient has a first component comprising a score based on one or more current clinical values associated with the corresponding patient, and a second component comprising a score based on evidence of one or more historical intervention actions.” This limitation merely defines the step of determining the condition experience level, which amounts to an act of managing personal behavior, e.g., following rules or instructions. See MPEP 2106.04(a)(2)(II). Thus, claim 7 recites an abstract idea in the “CMOHA” grouping.
Therefore, for at least these reasons, claims 2, 4, 6 – 7, 9, 11, and 13 – 14 recite judicial exceptions under Step 2A, Prong One.
Eligibility Step 2A, Prong Two:
Under Step 2A, Prong Two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the claims recite any additional limitations individually or in combination that integrate a judicial exception (i.e., the identified abstract ideas) into a practical application. After evaluation, it has been determined that claims 1 – 4 and 6 – 14 do not recite any additional elements individually or in combination that integrate the abstract ideas into a practical application.
Independent claims 1, 8, and 10 do not recite additional limitations beyond the judicial exceptions. Representative claim 1 recites the additional limitations identified in bold as:
A remote patient monitoring system configured to provide engagement guidance in connection with a plurality of patients, the system comprising:
one or more processors; and
memory having stored thereon machine-readable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
obtain patient data for at least a first subset of patients from one or more data sources, wherein the patient data comprises one or more of the following: patient reported outcome measures; patient-reported experience measures; user interface usage data; and system usage data;
extract a plurality of experience level features for each patient of at least the first subset of patients from the patient data obtained;
determine a condition experience level for each patient of at least the first subset of patients based on the experience level features extracted for the corresponding patient;
generate an intervention priority score for each patient of at least the first subset of patients by applying a trained intervention priority model to the condition experience level determined for the corresponding patient, wherein each intervention priority score is indicative of a likelihood of the corresponding patient having a positive change in one or more clinical outcomes following one or more intervention actions; and
automatically generate a recommended intervention plan for each patient of at least the first subset of patients based on the intervention priority scores generated for at least the first subset of patients, wherein each recommended intervention plan includes at least one intervention action.
Regarding the consideration under MPEP 2106.04(d)(2), claims 1, 8, and 10 recite the additional limitations identified as “a remote patient monitoring system,” “one or more processors,” “memory,” “machine-readable instructions,” “obtain patient data for at least a first subset of patients from one or more data sources,” and “a trained intervention priority model.” These additional limitations do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Thus, each one of claims 1, 8, and 10 as whole does not integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(a), claims 1, 8, and 10 do not purport to improve computer capabilities, but instead invokes computers merely as a tool. The claimed invention does not provide an improvement to technology, but rather provides an improvement in only the abstract idea itself. Thus, it is determined that the additional elements individually or in combination fail to integrate the abstract ideas into a practical application.
Regarding the consideration under MPEP 2106.05(b), claims 1, 8, and 10 merely add generic computer components (i.e., “one or more processors,” “memory,” “machine-readable instructions,” “a trained intervention priority model,” etc.) to perform conventional computer functions. It is important to note that a general purpose computer or generic computer components that apply a judicial exception, such as an abstract idea, by use of conventional computer functions do not qualify as a particular machine. See MPEP 2106.05(b)(1). Thus, each one of the claims as whole does not integrate the exception into a practical application.
Regarding the consideration under MPEP 2106.05(c), claims 1, 8, and 10 do not effect a transformation or reduction of a particular article to a different state or thing. For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea," has not been deemed a transformation. Claims 1, 8, and 10 recite “determine a condition experience level” (i.e., determining a score based on one or more current clinical values associated with the corresponding patient and determining a score based on evidence of one or more historical intervention actions) and “generate an intervention priority score.” These limitations amount to mere manipulation of basic mathematical constructs, i.e., the paradigmatic abstract idea. Thus, the claims do not integrate a judicial exception into a practical application.
Regarding the consideration under MPEP 2106.05(f), each one of the additional limitations in bold above is determined to be mere instructions to apply an abstract idea. These limitations are used to implement the abstract ideas recited at a high level of generality and are determined to be no more than mere instructions to implement the abstract ideas (i.e., CMOHA and mental processes) on generic computer components including the one or more processors, memory, machine-readable instructions, a trained intervention priority model. Accordingly, for these additional reasons, claims 1, 8, and 10 do not recite additional elements which integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(g), the additional limitations identified in bold as “obtain patient data for at least a first subset of patients from one or more data sources” is determined to not add more than insignificant extra-solution activity to the judicial exception. This limitation amounts to the extra-solution activity of pre-solution necessary data gathering, incidental to the primary process and thus merely a nominal or tangential addition to the claim. Thus, claims 1, 8, and 10 do not recite additional elements which integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(h), the additional limitations, individually or in combination, also amount to merely indicating a field of use or technological environment in which to apply the judicial exception. The additional limitations do no more than link the abstract ideas (i.e., the mental processes and/or CMOHAs identified above) to a particular technological environment (i.e., the field of medical data mining in epidemiology as opposed to any other field of data mining). Thus, the additional limitations fail to add an inventive concept to the claims.
Accordingly, in view of these considerations, the Office has determined that claims 1, 8, and 10 do not have one or more additional limitations, individually or in combination, that integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Dependent claims 2 – 4, 6 – 7, 9, and 11 – 14 present additional information in tandem with further details regarding elements from an associated one of independent claims 1, 8, and 10 and are therefore directed to one or more abstract ideas for similar reasons as given Under Step 2A, Prong One above. Claims 3 and 12 further recite one or more additional limitations, and these additional limitations fail to integrate the abstract idea into a practical application under Step 2A, Prong Two of the full eligibility analysis as follows:
Regarding the consideration under MPEP 2106.04(d)(2), claims 3 and 12 recite the additional limitations identified in bold as “one or more patient interfaces configured to be executed by or otherwise accessible via one or more patient devices, each patient interface being configured to send and receive data related to the remote monitoring of a corresponding patient” and “one or more coach interfaces configured to be executed by or otherwise accessible via one or more coach devices, each coach interface being configured to send and receive data related to the remote monitoring of a plurality of patients” do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Thus, each one of claims 3 and 12 as whole does not integrate the exception into a practical application.
Regarding the consideration under MPEP 2106.05(a), claims 3 and 12 do not purport to improve computer capabilities, but instead invokes computers merely as a tool. The claimed invention does not provide an improvement to technology, but rather provides an improvement in only the abstract idea itself. Thus, it is determined that the additional elements individually or in combination fail to integrate the abstract ideas into a practical application.
Regarding the consideration under MPEP 2106.05(b), claims 3 and 12 merely add generic computer components (i.e., “one or more patient interfaces,” “one or more patient devices,” “one or more coach interfaces,” “one or more coach devices,” etc.) to perform conventional computer functions. It is important to note that a general purpose computer or generic computer components that apply a judicial exception, such as an abstract idea, by use of conventional computer functions do not qualify as a particular machine. See MPEP 2106.05(b)(1). Thus, each one of claims 3 and 12 as whole does not integrate the exception into a practical application.
Regarding the consideration under MPEP 2106.05(c), claims 3 and 12 do not effect a transformation or reduction of a particular article to a different state or thing. For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. Claims 3 and 12 recite “one or more patient interfaces configured to be executed by or otherwise accessible via one or more patient devices, each patient interface being configured to send and receive data related to the remote monitoring of a corresponding patient” and “one or more coach interfaces configured to be executed by or otherwise accessible via one or more coach devices, each coach interface being configured to send and receive data related to the remote monitoring of a plurality of patients”. These limitations do not even amount to the mere manipulation of basic mathematical constructs, i.e., the paradigmatic abstract idea. Thus, claims 3 and 12 do not integrate a judicial exception into a practical application.
Regarding the consideration under MPEP 2106.05(f), each one of the additional limitations in bold above is determined to be mere instructions to apply an abstract idea. These limitations are used to implement the abstract ideas recited at a high level of generality and are determined to be no more than mere instructions to implement the abstract ideas (i.e., CMOHA and mental processes) on generic computer components including “one or more patient interfaces,” “one or more patient devices,” “one or more coach interfaces,” and “one or more coach devices.” Accordingly, for these additional reasons, claims 3 and 12 do not recite additional elements which integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(g), the additional limitations in bold as ““one or more patient interfaces configured to be executed by or otherwise accessible via one or more patient devices, each patient interface being configured to send and receive data related to the remote monitoring of a corresponding patient” and “one or more coach interfaces configured to be executed by or otherwise accessible via one or more coach devices, each coach interface being configured to send and receive data related to the remote monitoring of a plurality of patients” are determined to not add more than insignificant extra-solution activity to the judicial exception. These limitations are extra-solution activities including a respective one of pre-solution and post-solution activities and are incidental to the primary process. The additional limitation of “send and receive data related to the remote monitoring of a corresponding patient” is a well-known activity nominally and tangentially related to the invention and amount to necessary data gathering and outputting. Well-known pre-solution data gathering includes at least the limitation of “send data related to the remote monitoring of a corresponding patient.” Well-known post-solution data outputting includes at least the limitation of “receive data related to the remote monitoring of a corresponding patient.” Accordingly, for these additional reasons, claims 3 and 12 do not recite additional elements which integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(h), the additional limitations, individually or in combination, also amount to merely indicating a field of use or technological environment in which to apply the judicial exception. In the instant application, the additional limitations do no more than link the abstract ideas (i.e., the mathematical concepts, the mental processes and/or CMOHAs identified above) to a particular technological environment, i.e., the field of medical data mining epidemiology (as opposed to any other field of data mining). Thus, the additional limitations fail to add an inventive concept to the claims.
Therefore, for at least these reasons, each one of claims 1 – 4 and 6 – 14 as a whole (including additional limitations individually or in ordered combination) do not integrate a judicial exception (i.e., the identified abstract ideas) into a practical application under Step 2A, Prong Two.
Eligibility Step 2B:
Under Step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the claims include an element or a combination of elements that are sufficient to amount to significantly more than the judicial exception (i.e., whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry). See MPEP 2106.05(II). Dependent claims 2, 4, 6 – 7, 11, and 13 – 14 do not recite any additional limitations beyond the abstract idea determined in Step 2A, Prong One.
Regarding independent claims 1, 8, and 10, and dependent claims 3 and 12, the Office carries over its identification of the additional elements from Step 2A, Prong Two so as to apply the same additional elements in Step 2B. See MPEP 2106.05(II). The Office further carries over conclusions from Step 2A, Prong Two on the considerations discussed in MPEP 2106.05(a) through (c), (e) through (h) so as to apply the same considerations in Step 2B.
Independent claims 1, 8, and 10 and dependent claims 3 and 12 recite limitations that are not enough to qualify as “significantly more” because those limitations simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry (i.e. ““one or more processors,” “memory,” “machine-readable instructions,” “one or more patient interfaces,” “one or more patient devices,” “one or more coach interfaces,” “one or more coach devices,” etc.). See MPEP 2106.05(d) and 2106.05(I)(A). Because the Office has determined that the additional elements, individually or in combination, are not unconventional under MPEP 2106.05(d), the Office cannot find that the additional elements are significantly more than the judicial exception. See MPEP 2106.05(g).
The courts have recognized certain computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See MPEP 2106.05(d)(II). A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. See MPEP 2106.05(d). The required factual determination must be expressly supported in writing. Appropriate forms of support include one or more of the following: (a) a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s); (b) a citation to one or more of the court decisions discussed in Subsection II of MPEP 2106.05(d) as noting the well-understood, routine, conventional nature of the additional element(s); (c) a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and (d) a statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s). In particular, Subsection II of MPEP 2106.05(d) states that the courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: (i) receiving or transmitting data over a network; (ii) performing repetitive calculations; and (iii) analyzing input data to provide output data. Regarding extra-solution activity, the limitations of “obtain patient data for at least a first subset of patients from one or more data sources” in independent claims 1, 8, and 10, and “send data related to the remote monitoring of a corresponding patient” and “receive data related to the remote monitoring of a corresponding patient” in dependent claims 3 and 12 amount to the insignificant pre-solution activity of mere data gathering under MPEP 2106.05(g)(3).
These limitations (i.e., when viewed individually, as a whole, and as an ordered combination) simply taking the well-understood process of telemedical diagnosis and treatment recommendation and implementing that process on a computer, which does not qualify as significantly more. The limitations (i.e., when viewed individually, as a whole, and as an ordered combination) represent insignificant conventional activities well-understood in the art of artificial intelligence and telemedical treatment recommendations, and narrowing the idea to generic computer components is an attempt to limit the use of the abstract idea to a particular technological environment. Furthermore, the additional elements or combination of elements in the dependent claims, other than the abstract idea per se, amount to no more than a recitation of:
A) Generic computer structure that serves to perform generic computer functions that serve to merely link the abstract idea to a particular technological environment (i.e., computer).
B) Generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. determining, analyzing, normalizing, and grouping).
Accordingly, each one of claims 1 – 4 and 6 – 14 as a whole (including additional limitations individually or in ordered combination) do not amount to significantly more than the judicial exception (e.g., the claims do not have one or more additional elements, individually or in combination with any other limitation, that represent well-understood, routine, conventional activities previously known to the industry).
Therefore, claims 1 – 4 and 6 – 14 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
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 1, 3 – 4, 6 – 8, 10, and 14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Talbot (U.S. Pub. No. 2018/0272065 A1) in view of NPL Bukhari and Kiana (U.S. Pub. No. 2011/0001605 A1).
Regarding independent claims 1, 8, and 10, Talbot teaches the limitations of representative claim 1 identified in bold as:
A remote patient monitoring system configured to provide engagement guidance in connection with a plurality of patients (Abstract of Talbot, [P]atient data management systems and methods are provided for monitoring a physiological condition of a patient. An exemplary method involves … providing an indication of a recommended therapy intervention for the patient based at least in part on a respective uplift metric value associated with the recommended therapy intervention.), comprising:
one or more processors (Paragraph [0068] of Talbot, [T]he processing system may be implemented using … one or more processors.);
memory having stored thereon machine-readable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (Paragraph [0068] of Talbot, [T]he computing device 102 generally represents a server... The server 102 generally includes a processing system and a data storage element (or memory) capable of storing programming instructions for execution by the processing system, that, when read and executed, cause processing system to create, generate, or otherwise facilitate the applications or software modules configured to perform or otherwise support the processes, tasks, operations, and/or functions described herein.) comprising:
obtain patient data for at least a first subset of patients from one or more data sources (Paragraph [0161] of Talbot, [T]he risk management process 1700 begins by receiving or otherwise obtaining measurement data and medical records data for a patient population), wherein the patient data comprises one or more of the following: patientreported outcome measures; patient-reported experience measures; user interface usage data; and system usage data;
extract a plurality of experience level features for each patient of at least the first subset of patients from the patient data obtained (Paragraph [0162] of Talbot, [S]tepwise feature selection, such as recursive feature elimination, is performed to identify which fields or attributes of the patient measurement data and medical records data are most correlative to or predictive of the occurrence of a particular condition within the patient population. In the instant application, the broadest reasonable interpretation of “extract a plurality of experience level features for each patient of at least the first subset of patients from the patient data obtained” reads on the activity in Talbot (Paragraph [0162]) of identifying fields or attributes of the patient measurement data and medical records data of the patient population.);
determine a condition experience level for each patient of at least the first subset of patients based on the experience level features extracted for the corresponding patient (Paragraph [0171] of Talbot, The uplift recommendation process 1800 receives or otherwise obtains historical patient data and medical records data for a patient population, and then analyzes the relationships between the historical patient data and the medical records data to identify different patient groups for modeling the impact on the patients' physiological condition for different therapy interventions ( tasks 1802, 1804, 1806). For example, the server 102 may retrieve historical patient data 120 and electronic medical records data 122 from the database 104 and then utilize machine learning to identify cohorts of patients where different therapy interventions or changes have a statistically significant improvement to an aspect of the physiological patients within that patient cohort, such as, for example, a reduction in A1C laboratory values, a reduction in glucose excursion events, an increase in the percentage of time sensor glucose measurements are within a target range, and/or the like. In the instant application, the broadest reasonable interpretation of “determine a condition experience level for each patient of at least the first subset of patients based on the experience level features extracted for the corresponding patient” reads on the activity in Talbot (Paragraph [0171) of identifying different therapy interventions or changes of therapy interventions (i.e., and associated cohorts of patients) based on the analyzing the relationships between the historical patient data and the medical records data with the associated fields or attributes.);
generate an intervention priority score for each patient of at least the first subset of patients by applying a trained intervention priority model to the condition experience level determined for the corresponding patient, wherein each intervention priority score is indicative of a likelihood of the corresponding patient having a positive change in one or more clinical outcomes following one or more intervention actions (Paragraph [0173] of Talbot, [T]he patient's medical records, measurement data, event log data, and/or current operating context may be utilized to identify which uplift models in the database 104 are likely to be most relevant to the individual patient being analyzed. Thereafter, the uplift recommendation process 1800 calculates or otherwise determines the impact or uplift metric associated with each respective therapy intervention for the patient based on the patient's measurement data and medical records data and the respective uplift models associated with the different therapy interventions (task 1816). In this regard, for each potential therapy intervention, the uplift recommendation process 1800 may calculate or otherwise determine an estimated A1C reduction or other estimation of the uplift or impact associated with the respective therapy intervention on the patient based on the patient's medical records, measurement data, and/or current operating context. In the instant application, the broadest reasonable interpretation of “generate an intervention priority score for each patient of at least the first subset of patients by applying a trained intervention priority model to the condition experience level determined for the corresponding patient, wherein each intervention priority score is indicative of a likelihood of the corresponding patient having a positive change in one or more clinical outcomes following one or more intervention actions” reads on the activity in Talbot (Paragraph [0173]) of determining the uplift metric associated with each respective therapy intervention for the patient by applying the uplift models to the patient's measurement data and medical records data, wherein the uplift recommendation process 1800 may calculate or otherwise determine , an estimated A1C reduction or other estimation of the uplift or impact associated with the respective therapy intervention on the patient.); and
automatically generate a recommended intervention plan for each patient of at least the first subset of patients based on the intervention priority scores generated for at least the first subset of patients, wherein each recommended intervention plan includes at least one intervention action (Paragraph [0174] of Talbot, [T]he uplift recommendation process 1800 determines a therapy intervention recommendation based on the uplift metrics and generating or otherwise providing indication of the recommended therapy intervention to the patient. [T]he uplift recommendation process 1800 identifies the therapy intervention having the maximum estimated impact or benefit (e.g., the largest estimated A1C reduction) as the recommended therapy intervention for the patient. In the instant application, the broadest reasonable interpretation of “automatically generate a recommended intervention plan for each patient of at least the first subset of patients based on the intervention priority scores generated for at least the first subset of patients, wherein each recommended intervention plan includes at least one intervention action” reads on the activity in Talbot (Paragraph [0174]) of identifying the therapy intervention having the maximum estimated impact or benefit (e.g., the largest estimated A1C reduction) as the recommended therapy intervention for the patient.).
Talbot does not appear to explicitly disclose, but NPL Bukhari teaches the limitation identified in bold as “the patient data comprises one or more of the following: patientreported outcome measures; patient-reported experience measures; user interface usage data; and system usage data” (Second Paragraph to Third Paragraph on Page 405 of NPL Bukhari. In the instant application, the broadest reasonable interpretation of “the patient data comprising one or more of the following patientreported outcome measures; and patient-reported experience measures” reads on the Patient-reported outcome measures (PROMs) in NPL Bukhari (Second Paragraph on Page 405) and the Patient-reported experienced measures (PREMs) in NPL Bukhari (Third Paragraph on Page 405).).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot to implement the patient data comprising one or more of the following patientreported outcome measures; and patient-reported experience measures, as taught by NPL Bukhari (Second Paragraph to Third Paragraph on Page 405) in order to improve the quality of care and to monitor outcomes of the treatment approach selected (First Paragraph on Page 405 of NPL Bukhari).
Talbot does not appear to explicitly disclose, but Kiana teaches the limitation identified in bold as “the patient data comprises one or more of the following: patientreported outcome measures; and patient-reported experience measures; user interface usage data; and system usage data” (Paragraph [0089] of Kiana. In the instant application, the broadest reasonable interpretation of “the patient data comprising one or more of the following: user interface usage data; and system usage data” reads on the context information in Kiana (Paragraph [0089]) including a patient name, a patients' unique hospital identification number, patient location, an identification number for a network interface module, time stamps for events occurring in the physiological monitoring system, environmental conditions such as changes to the state of the network and usage statistics of the network interface module, and identification information corresponding to the network.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot to implement the patient data comprising one or more of the following: user interface usage data; and system usage data, as taught by Kiana (Paragraph [0089]) in order to improve quality of care in a hospital or other patient care facility by improving communication between different clinical computer systems across the IT infrastructure (Paragraph [0278] of Kiana).
Regarding claim 3, Talbot as modified by NPL Bukhari and Kiani and applied to claim 1 teaches the limitations identified in bold as “one or more patient interfaces configured to be executed by or otherwise accessible via one or more patient devices, each patient interface being configured to send and receive data related to the remote monitoring of a corresponding patient” (Paragraph [0181] of Talbot, A GUI display may be generated or otherwise provided at the client device 106, 602 that indicates the recommended therapy to the patient or other user of the client device 106, 602.); and “one or more coach interfaces configured to be executed by or otherwise accessible via one or more coach devices, each coach interface being configured to send and receive data related to the remote monitoring of a plurality of patients” (Paragraph [0181] of Talbot, A GUI display may be generated or otherwise provided at the client device 106, 602 that indicates the recommended therapy to the patient or other user of the client device 106, 602.).
Regarding claim 4, Talbot as modified by NPL Bukhari and Kiani and applied to claim 1 teaches the limitations identified in bold as “each recommended intervention plan includes at least one intervention action identified based on the intervention priority score generated for the corresponding patient” (Paragraph [0174] of Talbot, [T]he uplift recommendation process 1800 determines a therapy intervention recommendation based on the uplift metrics and generating or otherwise providing indication of the recommended therapy intervention to the patient. In the instant application, the broadest reasonable interpretation of “each recommended intervention plan includes at least one intervention action identified based on the intervention priority score generated for the corresponding patient” reads on the therapy intervention recommendation of Talbot (Paragraph [0174]) including the recommended therapy intervention to the patient based on the generated uplift metrics for the corresponding patient).
Regarding claims 6 and 14, Talbot as modified by NPL Bukhari and Kiani and applied to an associated one of claims 1 and 10 teaches the limitations identified in bold as “the intervention priority score generated for each patient includes a plurality of sub-scores corresponding to one or a combination of potential intervention actions, each sub-score being indicative of a likelihood of the corresponding patient having a positive change in one or more clinical outcomes following the one or the combination of potential intervention actions” (Paragraph [0173] of Talbot, [T]he uplift recommendation process 1800 calculates or otherwise determines the impact or uplift metric associated with each respective therapy intervention for the patient… In this regard, for each potential therapy intervention, the uplift recommendation process 1800 may calculate or otherwise determine an estimated A1C reduction or other estimation of the uplift or impact associated with the respective therapy intervention on the patient. In the instant application, the broadest reasonable interpretation of “the intervention priority score generated for each patient includes a plurality of sub-scores corresponding to one or a combination of potential intervention actions, each sub-score being indicative of a likelihood of the corresponding patient having a positive change in one or more clinical outcomes following the one or the combination of potential intervention actions” reads on the uplift metric in Talbot (Paragraph [00173]) determined for the patient including an estimated A1C reduction or other estimation of the uplift, which is calculated or otherwise determined for each potential therapy intervention.).
Regarding claim 7, Talbot as modified by NPL Bukhari and Kiani and applied to claim 1 teaches the limitations identified in bold as “the condition experience level determined for each patient has a first component comprising a score based on one or more current clinical values associated with the corresponding patient, and a second component comprising a score based on evidence of one or more historical intervention actions” (Paragraph [0172] of Talbot, [T]he server 102 identifies the sensor glucose measurement variables, medical record variables, and/or operating context variables that are correlative to or predictive of the improvement in the physiological condition and then calculates or otherwise determines an equation, function, or model for calculating the likely improvement in the physiological condition based on the identified subset of variables. For example, stepwise feature selection may be performed to identify which fields or attributes of patient measurement data and medical records data are most correlative to or predictive of the amount of A1C reduction within the patient cohort. An uplift model for calculating the estimated A1C reduction for patients within that particular patient cohort may then be determined as a function of the correlative subset of sensor glucose measurement variables, medical record variables, and/or operating context variables. In this regard, for each of the different patient cohorts identified for different potential therapy interventions, the server 102 may determine an uplift model for calculating a metric indicative of the impact of the respective therapy intervention on the respective cohort patients' physiological condition as a function of a subset of sensor glucose measurement variables, medical record variables, and/or operating context variables. In the instant application, the broadest reasonable interpretation of “the condition experience level determined for each patient has a first component comprising a score based on one or more current clinical values associated with the corresponding patient, and a second component comprising a score based on evidence of one or more historical intervention actions” reads on the variables in Talbot (Paragraph [0172]) correlative to or predictive of the improvement in the physiological condition, with stepwise feature selection performed to identify which fields or attributes of patient measurement data and medical records data are most correlative to or predictive of the amount of A1C reduction within the patient cohort, and the uplift model calculating the estimated A1C reduction for patients within that particular patient cohort determined as a function of the correlative subset of variables (e.g., sensor glucose measurement variables, medical record variables, and/or operating context variables).).
Claims 2, 9, and 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Talbot as modified by NPL Bukhari and Kiani and applied to an associated one of claims 1, 8, and 10, and in further view of Chang (U.S. Pub. No. 2022/0076831 A1).
Regarding claims 2, 9, and 11, Talbot as modified by NPL Bukhari and Kiani teaches the limitations identified in bold as:
obtaining historical patient data for a plurality of historical patients, wherein the historical patient data includes historical intervention action data and actual patient outcome data for the plurality of historical patients;
extracting a plurality of experience level features for each historical patient from the historical patient data obtained;
determining a condition experience level for each historical patient based on the experience level features extracted for the corresponding historical patient;
training the intervention priority model using the condition experience levels determined for each of the historical patients and the historical intervention action data for each of the historical patients as inputs and changes in actual patient outcome data for each of the historical patients as outputs; and
storing the trained intervention priority model (Paragraph [0164] of Talbot, [T]he server 102 may store or otherwise maintain the risk prediction models for the different medical conditions in the database 104 in association with the patient population demographic criteria for the respective model. In other embodiments, the server 102 may transmit or push the risk prediction models to one or more client electronic devices 106, 602. In this regard, in some embodiments, the server 102 may periodically update the risk prediction models (e.g., weekly, monthly, yearly, and/or the like) to reflect new or more recent data in the database 104.).
Talbot as modified by NPL Bukhari and Kiani does not appear to explicitly disclose, but Chang teaches the limitation of claims 2, 9, and 11 identified in bold as “obtaining historical patient data for a plurality of historical patients, wherein the historical patient data includes historical intervention action data and actual patient outcome data for the plurality of historical patients” (Paragraph [0012] of Chang, [A] method for generating an intervention recommendation by a clinical decision support system is provided. The method includes... receiving, by the clinical decision support system, a dataset of historical patient variables for a plurality of patients, wherein the [historical] patient variables comprise for each of the plurality of patients: a physiological state over time; an intervention; an outcome of the intervention, wherein the outcome comprises the utility of the intervention. In the instant application, the broadest reasonable interpretation of “obtaining historical patient data for a plurality of historical patients, wherein the historical patient data includes historical intervention action data and actual patient outcome data for the plurality of historical patients” reads on the activity in Chang (Paragraph [0012]) of receiving a dataset of historical patient variables for a plurality of patients, wherein the [historical] patient variables comprise for each of the plurality of patients: a physiological state over time; an intervention; an outcome of the intervention.)
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of obtaining historical patient data for a plurality of historical patients, wherein the historical patient data includes historical intervention action data and actual patient outcome data for the plurality of historical patients, as taught by Chang (Paragraph [0012]) in order to provide clinical decision support systems and models that provide recommendations for the most optimal intervention based on historical data (Paragraph [0004] of Chang).
Talbot as modified by NPL Bukhari and Kiani does not appear to explicitly disclose, but Chang teaches the limitation of claims 2, 9, and 11 identified in bold as “extracting a plurality of experience level features for each historical patient from the historical patient data obtained” (Paragraph [0012] of Chang, [A] method for generating an intervention recommendation by a clinical decision support system is provided. The method includes... parameterizing a policy function using K-nearest neighbors and mapping physiological states and interventions to outcomes using a Q-function critic, wherein a favorable outcome is identified as a reward. In the instant application, the broadest reasonable interpretation of “extracting a plurality of experience level features for each historical patient from the historical patient data obtained” reads on the activity in Chang (Paragraph [0012]) of mapping, using the obtained historical patient data for a plurality of historical patients, the physiological states and interventions to outcomes.)
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of extracting a plurality of experience level features for each historical patient from the historical patient data obtained, as taught by Chang (Paragraph [0012]) in order to provide clinical decision support systems and models that provide recommendations for the most optimal intervention based on historical data (Paragraph [0004] of Chang).
Talbot as modified by NPL Bukhari and Kiani does not appear to explicitly disclose, but Chang teaches the limitation of claims 2, 9, and 11 identified in bold as “determining a condition experience level for each historical patient based on the experience level features extracted for the corresponding historical patient” ([A] method for generating an intervention recommendation by a clinical decision support system is provided. The method includes... identifying, by the clinical decision support system, one or more optimal interventions from among the identified K-nearest neighbors based on a highest reward for the one or more optimal interventions. In the instant application, the broadest reasonable interpretation of “determining a condition experience level for each historical patient based on the experience level features extracted for the corresponding historical patient” reads on the activity in Chang (Paragraph [0012]) of identifying, by the clinical decision support system, one or more optimal interventions from among the identified K-nearest neighbors based on a highest reward for the one or more optimal interventions)
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of determining a condition experience level for each historical patient based on the experience level features extracted for the corresponding historical patient, as taught by Chang (Paragraph [0012]) in order to provide clinical decision support systems and models that provide recommendations for the most optimal intervention based on historical data (Paragraph [0004] of Chang).
Talbot as modified by NPL Bukhari and Kiani does not appear to explicitly disclose, but Chang teaches the limitation of claims 2, 9, and 11 identified in bold as “training the intervention priority model using the condition experience levels determined for each of the historical patients and the historical intervention action data for each of the historical patients as inputs and changes in actual patient outcome data for each of the historical patients as outputs” (Paragraph [0012] of Chang, [A] method for generating an intervention recommendation by a clinical decision support system is provided. The method includes... training an association model of the clinical decision support system with the dataset of historical patient variables for a plurality of patients. Paragraph [0072] of Chang, Database 215 may comprise the input data which may be used to train the system, as described and/or envisioned herein, such as the dataset of historical patient variables for a plurality of patients. This dataset may include, for example, for at least some of the patients: (i) a physiological state over time; (ii) an intervention; (iii) an outcome of the intervention, where the outcome comprises the utility of the intervention. In the instant application, the broadest reasonable interpretation of “training the intervention priority model using the condition experience levels determined for each of the historical patients and the historical intervention action data for each of the historical patients as inputs and changes in actual patient outcome data for each of the historical patients as outputs” reads on the activity in Chang (Paragraphs [0012] and [0072]) of training an association model using the dataset of historical patient variables for a plurality of patients (e.g., an intervention for at least some patients as inputs and an outcome of the intervention as outputs).)
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of training the intervention priority model using the condition experience levels determined for each of the historical patients and the historical intervention action data for each of the historical patients as inputs and changes in actual patient outcome data for each of the historical patients as outputs, as taught by Chang (Paragraphs [0012] and [0072]) in order to provide clinical decision support systems and models that provide recommendations for the most optimal intervention based on historical data (Paragraph [0004] of Chang).
Claims 12 – 13 are rejected under 35 U.S.C. 103(a) as being unpatentable over Talbot as modified by NPL Bukhari and Kiani and applied to claim 10 and in further view of Velado (U.S. Pub. No. 2020/0098464 A1).
Regarding claim 12, Talbot as modified by NPL Bukhari and Kiani does not appear to explicitly disclose, but Velado teaches the limitations identified in bold as:
presenting, via one or more coach interfaces of the remote patient monitoring system, the recommended intervention plans for at least some of the first subset of patients (Paragraphs [0087] and [0097] of Velado. In the instant application, the broadest reasonable interpretation of “presenting, via one or more coach interfaces of the remote patient monitoring system, the recommended intervention plans for at least some of the first subset of patients” reads on the activity in Velado (Paragraphs [0087] and [0097]) of presenting, via the GUI displays of the client application 808 at the client device 806 (i.e., for the patient’s doctor or other healthcare provider), the recommended activities for the patient.);
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of presenting, via one or more coach interfaces of the remote patient monitoring system, the recommended intervention plans for at least some of the first subset of patients, as taught by Velado (Paragraphs [0087] and [0097]) in order to facilitate improved glucose control that reduces patient workload (Paragraph [0004] of Velado).
receiving, via the one or more coach interfaces of the remote patient monitoring system, a selection of one or more intervention actions from the recommended intervention plans of at least one patient (Paragraph [0098] of Velado In the instant application, the broadest reasonable interpretation of “receiving, via the one or more coach interfaces of the remote patient monitoring system, a selection of one or more intervention actions from the recommended intervention plans of at least one patient” reads on the activities in Velado (Paragraph [0098]) of utilizing the GUI displays of the client application 808 at the client device 806 (i.e., for the patient’s doctor or other healthcare provider) to accept or confirm recommendations and communicating the recommendations from the client device to the infusion device.);
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of receiving, via the one or more coach interfaces of the remote patient monitoring system, a selection of one or more intervention actions from the recommended intervention plans of at least one patient, as taught by Velado (Paragraph [0098]) in order to facilitate improved glucose control that reduces patient workload (Paragraph [0004] of Velado).
automatically implementing, via one or more patient interfaces, at least one intervention action of the one or more selected intervention actions, wherein the at least one intervention action is implemented in connection with at least a first patient (Paragraph [0098] of Velado In the instant application, the broadest reasonable interpretation of “receiving, via the one or more coach interfaces of the remote patient monitoring system, a selection of one or more intervention actions from the recommended intervention plans of at least one patient” reads on the activities in Velado (Paragraph [0098]) of implementing processing or automation tasks at the remote device 814 to implement the infusion device to deliver a recommended bolus of insulin and or schedule a future delivery of insulin based on the patient's activity plan.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of automatically implementing, via one or more patient interfaces, at least one intervention action of the one or more selected intervention actions, wherein the at least one intervention action is implemented in connection with at least a first patient, as taught by Velado (Paragraph [0098]) in order to facilitate improved glucose control that reduces patient workload (Paragraph [0004] of Velado).
Regarding claim 13, Talbot as modified by NPL Bukhari, Kiani, and Velado and as applied to claim 12 teaches the limitations identified in bold as:
determining a change in one or more patient outcomes for at least the first patient following the implementation of at least the one intervention action (Paragraph [0110] of Velado. In the instant application, the broadest reasonable interpretation of “determining a change in one or more patient outcomes for at least the first patient following the implementation of at least the one intervention action” reads on the activity in Velado (Paragraph [0110]) of dynamically updating, as the patient adjusts the bolus amount slider indicator upward to increase the bolus amount the forecasted glucose values for the contemporaneous and subsequently hourly intervals to reflect the bolus amount.); and
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of determining a change in one or more patient outcomes for at least the first patient following the implementation of at least the one intervention action, as taught by Velado (Paragraph [0110]) in order to facilitate improved glucose control that reduces patient workload (Paragraph [0004] of Velado).
updating the trained intervention priority model using an updated training dataset that includes the at least one intervention action and the change in one or more patient outcomes for at least the first patient (Paragraph [0111] of Velado In the instant application, the broadest reasonable interpretation of “updating the trained intervention priority model using an updated training dataset that includes the at least one intervention action and the change in one or more patient outcomes for at least the first patient” reads on the activities in Velado (Paragraph [0111] of determining an updated patient-specific forecasting model based on historical patient data that postdates development of the current patient-specific forecasting model and re-training the forecasting model.);
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and artificial intelligence diagnostics at the time of filing to modify the system and method of Talbot as modified by NPL Bukhari and Kiani to include the activity of updating the trained intervention priority model using an updated training dataset that includes the at least one intervention action and the change in one or more patient outcomes for at least the first patient, as taught by Velado (Paragraph [0111]) in order to facilitate improved glucose control that reduces patient workload (Paragraph [0004] of Velado).
Response to Arguments
Applicant’s arguments (Fourth Paragraph on Page 8 of the Amendment filed September 24, 2025) and amendment with respect to the objection to the specification have been fully considered and are persuasive. The objection to the specification has been withdrawn.
Applicant’s arguments (Fifth Paragraph on Page 8 of the Amendment filed September 24, 2025) and amendment with respect to the objection to claims 2, 9, and 11 have been fully considered and are persuasive. The objection to claims 2, 9, and 11 has been withdrawn.
Applicant’s arguments (Sixth Paragraph on Page 8 to Third Paragraph on Page 9 of the Amendment filed September 24, 2025) with respect to the rejections of claims 1 – 15 under 35 U.S.C. § 101 have been considered but they are not persuasive.
In Applicant’s arguments (Last Paragraph on Page 8 and Third Paragraph on Page 9 of the Amendment filed September 2, 2025), Applicant argued the independent claims 1, 8, and 10 do not recite an abstract idea because the claims provide an improvement to technology, and therefore, claims 1, 8, and 10 are patent eligible. The Office respectfully disagrees.
In Applicant’s arguments (First Paragraph to Second Paragraph on Page 9 of the Amendment filed September 2, 2025), Applicant had ostensibly relied upon MPEP 2106.06 to argue that the eligibility of the claimed invention is self-evident because the claims improve technology or computer functionality (akin to the claimed inventions in Enfish and McRo – see MPEP 2106.06(b)). For purposes of efficiency in examination, examiners may use a streamlined eligibility analysis (Pathway A) when the eligibility of the claim is self-evident, e.g., because the claim clearly improves a technology or computer functionality. However, if there is doubt as to whether the applicant is effectively seeking coverage for a judicial exception itself, the full eligibility analysis (the Alice/Mayo test described in MPEP § 2106, subsection III) should be conducted to determine whether the claim integrates the judicial exception into a practical application or recites significantly more than the judicial exception.
In Enfish, the court held that claims to a self-referential table for a computer database are directed to clear improvements to computer-related technology and thus do not need the full eligibility analysis. In McRo, the court held that claims to automatic lip synchronization and facial expression animation were directed to improvements to other technologies or technological processes, beyond computer improvements and thus do not need the full eligibility analysis. In these cases, when the claims were viewed as a whole, their eligibility was self-evident based on the clear improvement, so no further analysis was needed. In the instant application, each one of claims 1, 8, and 10 as a whole does not improve technology or computer functionality, but rather merely improves an abstract idea itself by applying it to generic computer components.
In Applicant’s arguments (Third Paragraph on Page 9 of the Amendment filed September 2, 2025), Applicant argued that independent claims 1, 8, and 10: “provide engagement guidance in connection with multiple patients and in turn, improves the functioning of a RPM system itself”; “provide clear guidance on the prioritization of patients under remote monitoring and facilitate data-driven intervention actions that drive the biggest impact on patient outcomes.” Applicant argued that these features are “new to Remote Patient Monitoring (RPM) systems and thus improves the functioning of the computer itself.” However, these features and the recited solution are recited at a high level of generality such that the claims represent mere instructions to apply the abstract idea of providing engagement guidance and prioritization of patients on generic computer components performing conventional functions. Furthermore, the claimed activities (i.e., of extracting a plurality of experience level features for each patient of at least the first subset of patients from the patient data, determining a condition experience level for each patient based on the experience level features, generating an intervention priority score for each patient, and automatically generating a recommended intervention plan for each patient based on the intervention priority scores) are well-known, understood, routing and conventional activities, as taught by Talbot (U.S. Pub. No. 2018/0272065 A1). Therefore, the rejections of claims 1 – 15 under 35 U.S.C. § 101 are maintained.
Applicant’s arguments (Last Paragraph on Page 9 to Second Paragraph on Page 10 of the Amendment filed September 24, 2025) and amendment with respect to the rejections of claims 1, 3 – 8, 10, and 12 – 15 under 35 U.S.C. § 102 have been considered and are persuasive. The rejections of claims 1, 3 – 8, 10, and 12 – 15 have been withdrawn.
Applicant’s arguments (Third Paragraph to Fourth Paragraph on Page 10 of the Amendment filed September 24, 2025) and amendment with respect to the rejections of claims 2, 9, and 11 under 35 U.S.C. § 103 have been considered and are moot in view of the new grounds of rejection necessitated by the amendment.
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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/V.C.I./Examiner, Art Unit 3686
/DEVIN C HEIN/Examiner, Art Unit 3686
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