DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. According to the Amendment, filed 05 January 2026, the status of the claims is as follows:
Claims 1-18 are as originally filed; and
Claims 19 and 20 are withdrawn.
Election/Restrictions
3. Applicant’s election without traverse of Group I, claims 1-18, in the reply filed on 05 January 2026 is acknowledged.
Claims 19 and 20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05 January 2026.
Claim Interpretation
4. 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.
5. 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.
6. 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. Such claim limitation(s) is/are:
“therapy selection model” in claims 1, 3, 9, and 10-13; and
“machine learning model” in claim 11.
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.
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
7. 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.
8. 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.
9. Claims 1-18 are 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.
In claims 1, 3, 9, and 10-13, claim limitations “therapy selection model” and “machine learning model” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, 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. The original disclosure, filed 13 May 2022, merely describes “therapy selection model” as follows (see para. [0520]):
In system 3800, the therapy recommendation module 3802 is depicted as including a therapy selection model 3816 and an output module 3818. In some examples, the therapy selection model 3816 is a machine learning model. Any suitable machine learning technique can be used such as, for example, a supervised learning model, an unsupervised learning model, and/or a reinforcement learning model, which are discussed in more detail above. Generally, the therapy selection model 3816 processes the therapy selection data 3804 and patient data 3810 to select a cost-effective therapy 3820 for the patient from the available therapies 3806.
However, this description does not disclose corresponding structure for performing therapy recommendation to clearly link the structure to the function. In addition, the original disclosure merely describes “machine learning model” as follows (see para. [0520]):
In system 3800, the therapy recommendation module 3802 is depicted as including a therapy selection model 3816 and an output module 3818. In some examples, the therapy selection model 3816 is a machine learning model. Any suitable machine learning technique can be used such as, for example, a supervised learning model, an unsupervised learning model, and/or a reinforcement learning model, which are discussed in more detail above.
However, this description does not disclose corresponding structure for performing machine learning to clearly link the structure to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 2-9 and 11-18 are rejected due to their dependencies either directly or indirectly to base claims 1 and 10.
10. Claims 1-18 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.
In claims 1, 3, 9, and 10-13, claim limitations “therapy selection model” and “machine learning model” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, 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. The original disclosure, filed 13 May 2022, merely describes “therapy selection model” as follows (see para. [0520]):
In system 3800, the therapy recommendation module 3802 is depicted as including a therapy selection model 3816 and an output module 3818. In some examples, the therapy selection model 3816 is a machine learning model. Any suitable machine learning technique can be used such as, for example, a supervised learning model, an unsupervised learning model, and/or a reinforcement learning model, which are discussed in more detail above. Generally, the therapy selection model 3816 processes the therapy selection data 3804 and patient data 3810 to select a cost-effective therapy 3820 for the patient from the available therapies 3806.
However, this description does not disclose corresponding structure for performing therapy recommendation to clearly link the structure to the function. In addition, the original disclosure merely describes “machine learning model” as follows (see para. [0520]):
In system 3800, the therapy recommendation module 3802 is depicted as including a therapy selection model 3816 and an output module 3818. In some examples, the therapy selection model 3816 is a machine learning model. Any suitable machine learning technique can be used such as, for example, a supervised learning model, an unsupervised learning model, and/or a reinforcement learning model, which are discussed in more detail above.
However, this description does not disclose corresponding structure for performing machine learning to clearly link the structure to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 2-9 and 11-18 are rejected due to their dependencies either directly or indirectly to base claims 1 and 10.
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
11. 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.
12. Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, i.e. abstract idea, without significantly more.
Step 1 of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.03):
Claims 1-9 are directed to a “computer-implemented method”, which describes one of the four statutory categories of patentable subject matter, i.e. a process.
Claims 10-18 are directed to a “system”, which describes one of the four statutory categories of patentable subject matter, i.e. a machine.
Step 2A of the Revised Patent Subject Matter Eligibility Guidance (see MPEP 2106.04):
Claim(s) 1-9 recite the following mental process:
determining, by a therapy selection model, suitable therapies which are suitable to control glucose of the patient based on the patient data;
filtering, by the therapy selection model, the suitable therapies to select a cost-effective therapy for the patient based at least in part on therapy cost data, the therapy cost data including costs of the suitable therapies; and …
Based on broadest reasonable interpretation, these limitations are directed to receiving data and performing a mathematical operation, which can be done mentally or using pen and paper.
This judicial exception is not integrated into a practical application because the additional limitation of “receiving patient data for a patient, the patient data including glucose data of the patient collected by a glucose monitor” in claim 1 add insignificant pre-solution activity to the abstract idea that merely collects data to be used by the mental process. Furthermore, the additional limitation of “outputting a therapy recommendation to control glucose for the patient that includes the cost-effective therapy” in claim 1 add insignificant post-solution activity to the mental process as it merely presents the result of the mental process of collecting and analyzing information, without more, and thus, is an ancillary part of such collection and analysis.
Claim(s) 10-18 recite the following mental process:
a therapy selection model to:
…
receive therapy selection data comprising available therapies and therapy cost data, the therapy cost data including costs of the available therapies; and
select a cost-effective therapy for the patient from the available therapies based on the glucose data of the patient and the therapy cost data; and …
Based on broadest reasonable interpretation, these limitations are directed to receiving data and performing a mathematical operation, which can be done mentally or using pen and paper.
This judicial exception is not integrated into a practical application because the additional limitations of “receive patient data for a patient, the patient data including glucose data of the patient collected by a glucose monitor” in claim 10 add insignificant pre-solution activity to the abstract idea that merely collects data to be used by the mental process. Furthermore, the additional limitation of “an output module to output the cost-effective therapy via a user interface” in claim 10 add insignificant post-solution activity to the mental process as it merely presents the result of the mental process of collecting and analyzing information, without more, and thus, is an ancillary part of such collection and analysis.
Step 2B of the Patent Subject Matter Eligibility Guidance (see MPEP 2106.05):
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered separately and in combination.
Analyzing the additional claim limitations individually, the additional limitations that are not directed to the mental process are “receiving patient data for a patient, the patient data including glucose data of the patient collected by a glucose monitor” in claim 1, and “receive patient data for a patient, the patient data including glucose data of the patient collected by a glucose monitor” in claim 10. Such limitations add insignificant pre-solution activity to the abstract idea that merely collects data to be used by the mental process.
Furthermore, the additional limitations of “outputting a therapy recommendation to control glucose for the patient that includes the cost-effective therapy” in claim 1, and “an output module to output the cost-effective therapy via a user interface” in claim 10, add insignificant post-solution activity to the mental process as it merely presents the result of the mental process of collecting and analyzing information, without more, and thus, is an ancillary part of such collection and analysis.
The additional limitations of dependent claims 2-9 and 11-18 are merely directed to and further narrow the scope of the mental process.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide computer implementation of the abstract idea using collected data without: improvement to the functioning of a computer or to any other technology or technical field; applying the mental process with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; applying or using the mental process in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment; or adding a specific limitation other than what is well-understood, routine, conventional activity in the field.
Claim Rejections - 35 USC § 102
13. 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.
14. 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.
15. Claims 1-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Testa et al., U.S. Patent Application Publication No. 2018/0005332 A1 (“Testa”).
As to Claim 1, Testa teaches the following:
A computer-implemented method (see “The present invention relates generally to managing diabetes treatment for cost and effectiveness.” in para. [0005]) comprising:
receiving patient data for a patient, the patient data including glucose data of the patient collected by a glucose monitor (see “User interface 102 includes an input device 105 (e.g. touchscreen buttons or mouse) to receive patient data from user …” in para. [0034]; see “The left side of the sample UI is titled PATIENT INFORMATION, and is designed to obtain data about the patient's medical history including A1C LEVEL, COST SENSITIVITY, ADHERENCE, ALREADY TAKING, SOLUTION MUST CONTAIN, ALLERGY, DRUG INTOLERANCE, FORGOT TO TAKE, INSURER, HAS COUPON FOR, COMORBID, PHYSICAL INTOLERANCE, SEX, INSULIN RESISTANCE, INSULIN PRODUCTION, BODY MASS INDEX, MAX # OF INTERVENTIONS, AND DISPLAY SOLUTIONS.” in para. [0035]; and see “A1C LEVEL asks for the patient's current blood glucose level expressed as a percent. This is also commonly referred to in the literature as the “hemoglobin a1c”, “HbA1c” or “glycohemoglobin” level. It is also most commonly referred to as a percent.” in para. [0036]);
determining, by a therapy selection model (“solution generator module 120” and “solution scoring model 122”) 120/122, suitable therapies which are suitable to control glucose of the patient based on the patient data (see “Referring to FIGS. 3-6, a solution generator module 120 is configured to determine a list of solutions as a function of the patient data and the therapy data. Determining the list of solutions includes eliminating solutions as a function of the patient data to generate a list of compatible solutions and returning list of compatible solutions as the list of solutions.” in para. [0080]; and see “SOLUTION GENERATOR MODULE—The SOLUTION GENERATOR MODULE generates unique combinations of therapies from the THERAPY TABLE 104. This unique combination of therapies is called a solution or therapy. The mechanism by which the unique combination of therapies is generated may be deterministic or random.” in para. [0081]);
filtering, by the therapy selection model 120/122, the suitable therapies to select a cost-effective therapy for the patient based at least in part on therapy cost data, the therapy cost data including costs of the suitable therapies (see “PRICE AND REVENUE OPTIMIZATION MODULE—The PRICE AND REVENUE OPTIMIZATION MODULE is be used by doctors, hospitals, drug manufacturers, and other entities to optimize the price or revenue generated by diabetes treatments. The PRICE AND REVENUE OPTIMIZATION MODULE allows these entities to determine how many times a specific treatment is recommended over a population, such as a hospital's entire population of patients.” in para. [0094]); and
outputting (via “reporter 112”) a therapy recommendation (“rank ordered lists of solutions and provides the aggregated table”) to control glucose for the patient that includes the cost-effective therapy (see “The reporter 112 aggregates on a patient by patient basis the rank ordered lists of solution received from the decision engine 106 and provides the aggregated rank ordered lists of solutions to the data source, each rank ordered list of solutions indicated as corresponding to one of the patients of the plurality of patients. That is, the reporter 112 aggregates the table of patient data and rank ordered lists of solutions and provides the aggregated table has an output from the system 100.” in para. [0065]).
As to Claim 2, Testa teaches the following:
wherein the patient data for the patient further includes a risk metric (“side effects module”) 140 indicating a relative level of risk to the patient to experience an adverse health event based on the glucose data of the patient (see “In this implementation of the UI, the individual components for diabetes include an a1c score; cost sensitivity score; body mass index (BMI) score; adherence score; side effect score; macro class score; insulin resistance score; and insulin production score.” in para. [0056]; see “Examples of information about a therapy which may be stored in the THERAPY TABLE 104 include: [0069] therapy name (e.g., “Glimeperide”) [0070] dosage (e.g., “1 milligram daily” or “Low”) [0071] therapeutic effect (e.g., “Reduces blood glucose levels 1.2 percent on average”) [0072] cost under various medical insurance plans (e.g., “AETNA $75”) [0073] side effects (e.g., “May cause nausea”) [0074] administration method (e.g., “Injectable” or “oral”) [0075] frequency (e.g., “Once per day”) ” in para. [0068]; and see fig. 15).
As to Claim 3, Testa teaches the following:
wherein the therapy selection model 120/122 selects the cost-effective therapy for the patient based at least in part on the risk metric of the patient (see “SOLUTION SUBSCORING MODULE 124—This component of the system determines a score for a particular attribute of a solution, such as its cost or side effects. For the treatment of Type 2 Diabetes, for example, the system may contain these SOLUTION SUBSCORING MODULES: [0088] A1C reduction module 133 and algorithm (see FIG. 8-9) [0089] Cost sensitivity module and algorithm 134 (see FIGS. 10-11) [0090] Mechanism Module 136 and algorithm (see FIG. 12) [0091] Body Mass Index effects module 138 and algorithm (see FIG. 13) [0092] Side effects module 140 and algorithm (see FIG. 14)” in para. [0087]).
As to Claim 4, Testa teaches the following:
wherein the cost-effective therapy has a lowest cost of the suitable therapies which are suitable to control the glucose of the patient (see “COST SENSITIVITY asks the patient to rate how much money they can afford to pay for treatment of this disease. The sample UI implementation uses an integer 0-to-10 Likert scale, but other implementations (such as asking the user to enter a maximum dollar amount) are easily adapted. In this sample UI implementation, a value of 0 means the patient is able to afford any treatment that may be suggested. A value of 10 indicates the patient strongly prefers the lowest cost treatments that are possible.” in para. [0037]; and see “The display of each individual component scores is meant to explain the reasoning behind the therapy recommendation to the medical professional and patient. For example, a high score in the area of cost sensitivity means that this therapy recommendation is particularly good at addressing the patient's cost concerns. The calculation of these scores is dependent on the implementation goals and many different methods can be used.” in para. [00]; and see figs. 10 and 11).
As to Claim 5, Testa teaches the following:
wherein the therapy cost data further includes cost savings of the suitable therapies (see “COST SENSITIVITY asks the patient to rate how much money they can afford to pay for treatment of this disease. The sample UI implementation uses an integer 0-to-10 Likert scale, but other implementations (such as asking the user to enter a maximum dollar amount) are easily adapted. In this sample UI implementation, a value of 0 means the patient is able to afford any treatment that may be suggested. A value of 10 indicates the patient strongly prefers the lowest cost treatments that are possible.” in para. [0037]; and see “The display of each individual component scores is meant to explain the reasoning behind the therapy recommendation to the medical professional and patient. For example, a high score in the area of cost sensitivity means that this therapy recommendation is particularly good at addressing the patient's cost concerns. The calculation of these scores is dependent on the implementation goals and many different methods can be used.” in para. [00]; and see figs. 10 and 11).
As to Claim 6, Testa teaches the following:
wherein the suitable therapies include one or more of wearing a glucose monitor, automated coaching, insulin therapy using an insulin pen, or insulin therapy using an insulin pump (see “[0068] THERAPY TABLE 104—The THERAPY TABLE 104 stores information about each therapy that can be used to treat the patient's medical condition under consideration by the system. Each entry in the THERAPY TABLE 104 contains information about one therapy. Examples of information about a therapy which may be stored in the THERAPY TABLE 104 include: [0069] therapy name (e.g., “Glimeperide”) [0070] dosage (e.g., “1 milligram daily” or “Low”) [0071] therapeutic effect (e.g., “Reduces blood glucose levels 1.2 percent on average”) [0072] cost under various medical insurance plans (e.g., “AETNA $75”) [0073] side effects (e.g., “May cause nausea”) [0074] administration method (e.g., “Injectable” or “oral”) [0075] frequency (e.g., “Once per day”) [0076] mechanism of action (e.g., “Basal insulin”) [0077] contraindications (e.g., “May cause renal failure”) [0078] effect on body mass index (e.g., “Moderate weight gain”, “Significant weight gain”) macro class (e.g., “Sensitizer”)” in para. [0068]-[0078]).
As to Claim 7, Testa teaches the following:
wherein the cost-effective therapy comprises insulin therapy using an insulin pen or an insulin pump (see para. [0068]-[0078]).
As to Claim 8, Testa teaches the following:
wherein the suitable therapies include insulin therapies using different types of insulin, each of the different types of insulin having a different cost (see para. [0068]-[0078]; and see “Similarly, cost and insurance reimbursement data may be stored in the same therapy table 104, or the cost and insurance reimbursement data may be separated out into another table database accessible by the decision engine 106.” in para. [0079]).
As to Claim 9, Testa teaches the following:
wherein the cost-effective therapy selected by the therapy selection model comprises an insulin therapy with a lowest cost (see “The display of each individual component scores is meant to explain the reasoning behind the therapy recommendation to the medical professional and patient. For example, a high score in the area of cost sensitivity means that this therapy recommendation is particularly good at addressing the patient's cost concerns.” in para. [0057]).
As to Claim 10, Testa teaches the following:
A system (see “The present invention relates generally to managing diabetes treatment for cost and effectiveness.” in para. [0005]) comprising:
a therapy selection model (“solution generator module 120” and “solution scoring model 122”) 120/122 to:
receiving patient data for a patient, the patient data including glucose data of the patient collected by a glucose monitor (see “User interface 102 includes an input device 105 (e.g. touchscreen buttons or mouse) to receive patient data from user …” in para. [0034]; see “The left side of the sample UI is titled PATIENT INFORMATION, and is designed to obtain data about the patient's medical history including A1C LEVEL, COST SENSITIVITY, ADHERENCE, ALREADY TAKING, SOLUTION MUST CONTAIN, ALLERGY, DRUG INTOLERANCE, FORGOT TO TAKE, INSURER, HAS COUPON FOR, COMORBID, PHYSICAL INTOLERANCE, SEX, INSULIN RESISTANCE, INSULIN PRODUCTION, BODY MASS INDEX, MAX # OF INTERVENTIONS, AND DISPLAY SOLUTIONS.” in para. [0035]; and see “A1C LEVEL asks for the patient's current blood glucose level expressed as a percent. This is also commonly referred to in the literature as the “hemoglobin a1c”, “HbA1c” or “glycohemoglobin” level. It is also most commonly referred to as a percent.” in para. [0036]);
receive therapy selection data comprising available therapies and therapy cost data (see “Referring to FIGS. 3-6, a solution generator module 120 is configured to determine a list of solutions as a function of the patient data and the therapy data. Determining the list of solutions includes eliminating solutions as a function of the patient data to generate a list of compatible solutions and returning list of compatible solutions as the list of solutions.” in para. [0080]; and see “SOLUTION GENERATOR MODULE—The SOLUTION GENERATOR MODULE generates unique combinations of therapies from the THERAPY TABLE 104. This unique combination of therapies is called a solution or therapy. The mechanism by which the unique combination of therapies is generated may be deterministic or random.” in para. [0081]), the therapy cost data including costs of the available therapies (see “COST SENSITIVITY asks the patient to rate how much money they can afford to pay for treatment of this disease. The sample UI implementation uses an integer 0-to-10 Likert scale, but other implementations (such as asking the user to enter a maximum dollar amount) are easily adapted. In this sample UI implementation, a value of 0 means the patient is able to afford any treatment that may be suggested. A value of 10 indicates the patient strongly prefers the lowest cost treatments that are possible.” in para. [0037]; and see “The display of each individual component scores is meant to explain the reasoning behind the therapy recommendation to the medical professional and patient. For example, a high score in the area of cost sensitivity means that this therapy recommendation is particularly good at addressing the patient's cost concerns. The calculation of these scores is dependent on the implementation goals and many different methods can be used.” in para. [00]; and see figs. 10 and 11); and
select a cost-effective therapy for the patient from the available therapies based on the glucose data of the patient and the therapy cost data (see “PRICE AND REVENUE OPTIMIZATION MODULE—The PRICE AND REVENUE OPTIMIZATION MODULE is be used by doctors, hospitals, drug manufacturers, and other entities to optimize the price or revenue generated by diabetes treatments. The PRICE AND REVENUE OPTIMIZATION MODULE allows these entities to determine how many times a specific treatment is recommended over a population, such as a hospital's entire population of patients.” in para. [0094]); and
an output module (“reporter”) 112 to output the cost-effective therapy via a user interface (“user interface”) 102 (see “In another embodiment, the user interface 102 is a batch interface including a parser 110 and a reporter 112. … The reporter 112 aggregates on a patient by patient basis the rank ordered lists of solution received from the decision engine 106 and provides the aggregated rank ordered lists of solutions to the data source, each rank ordered list of solutions indicated as corresponding to one of the patients of the plurality of patients. That is, the reporter 112 aggregates the table of patient data and rank ordered lists of solutions and provides the aggregated table has an output from the system 100.” in para. [0065]).
As to Claim 11, Testa teaches the following:
wherein the therapy selection model 120/122 comprises a machine learning model (see “Moreover, the various logical blocks, modules, and circuits described herein may be implemented or performed with a general purpose processor (e.g., microprocessor, conventional processor, controller, microcontroller, state machine or combination of computing devices), a digital signal processor (“DSP”), an application specific integrated circuit (“ASIC”), a field programmable gate array (“FPGA”) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Similarly, steps of a method or process described herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two.” in para. [0102]; and see “It is contemplated that these devices or functions may also be implemented in virtual machines and spread across multiple physical computing devices.” in para. [0103]).
As to Claim 12, Testa teaches the following:
wherein the therapy selection model 120/122 selects the cost-effective therapy based on the glucose data of the patient (see “Each therapy recommendation in the OUTPUT section of the UI may also assign a separate score to each INDIVIDUAL COMPONENT of its decision-making process. In this implementation of the UI, the individual components for diabetes include an a1c score; cost sensitivity score; body mass index (BMI) score; adherence score; side effect score; macro class score; insulin resistance score; and insulin production score. The scale used in this implementation is from 0 to 100, with 100 the highest potential score, but other scoring systems or scales are possible.” in para. [0056]), the therapy cost data (see “SOLUTION SUBSCORING MODULE 124—This component of the system determines a score for a particular attribute of a solution, such as its cost or side effects. For the treatment of Type 2 Diabetes, for example, the system may contain these SOLUTION SUBSCORING MODULES: [0088] A1C reduction module 133 and algorithm (see FIG. 8-9) [0089] Cost sensitivity module and algorithm 134 (see FIGS. 10-11) [0090] Mechanism Module 136 and algorithm (see FIG. 12) [0091] Body Mass Index effects module 138 and algorithm (see FIG. 13) [0092] Side effects module 140 and algorithm (see FIG. 14)” in para. [0087]), and a risk metric of the patient, the risk metric indicating a relative level of risk to the patient to experience an adverse health event (see “In this implementation of the UI, the individual components for diabetes include an a1c score; cost sensitivity score; body mass index (BMI) score; adherence score; side effect score; macro class score; insulin resistance score; and insulin production score.” in para. [0056]; see “Examples of information about a therapy which may be stored in the THERAPY TABLE 104 include: [0069] therapy name (e.g., “Glimeperide”) [0070] dosage (e.g., “1 milligram daily” or “Low”) [0071] therapeutic effect (e.g., “Reduces blood glucose levels 1.2 percent on average”) [0072] cost under various medical insurance plans (e.g., “AETNA $75”) [0073] side effects (e.g., “May cause nausea”) [0074] administration method (e.g., “Injectable” or “oral”) [0075] frequency (e.g., “Once per day”) ” in para. [0068]; and see fig. 15).
As to Claim 13, Testa teaches the following:
wherein the therapy selection model 120/122 selects the cost-effective therapy for the patient by:
determining suitable therapies which are suitable to control glucose of the patient based on the patient data (see “Referring to FIGS. 3-6, a solution generator module 120 is configured to determine a list of solutions as a function of the patient data and the therapy data. Determining the list of solutions includes eliminating solutions as a function of the patient data to generate a list of compatible solutions and returning list of compatible solutions as the list of solutions.” in para. [0080]; and see “SOLUTION GENERATOR MODULE—The SOLUTION GENERATOR MODULE generates unique combinations of therapies from the THERAPY TABLE 104. This unique combination of therapies is called a solution or therapy. The mechanism by which the unique combination of therapies is generated may be deterministic or random.” in para. [0081]); and
filtering the suitable therapies to select the cost-effective therapy for the patient based at least in part on the therapy cost data (see “PRICE AND REVENUE OPTIMIZATION MODULE—The PRICE AND REVENUE OPTIMIZATION MODULE is be used by doctors, hospitals, drug manufacturers, and other entities to optimize the price or revenue generated by diabetes treatments. The PRICE AND REVENUE OPTIMIZATION MODULE allows these entities to determine how many times a specific treatment is recommended over a population, such as a hospital's entire population of patients.” in para. [0094]).
As to Claim 14, Testa teaches the following:
wherein the cost-effective therapy has a lowest cost of the suitable therapies which are suitable to control the glucose of the patient (see “COST SENSITIVITY asks the patient to rate how much money they can afford to pay for treatment of this disease. The sample UI implementation uses an integer 0-to-10 Likert scale, but other implementations (such as asking the user to enter a maximum dollar amount) are easily adapted. In this sample UI implementation, a value of 0 means the patient is able to afford any treatment that may be suggested. A value of 10 indicates the patient strongly prefers the lowest cost treatments that are possible.” in para. [0037]; and see “The display of each individual component scores is meant to explain the reasoning behind the therapy recommendation to the medical professional and patient. For example, a high score in the area of cost sensitivity means that this therapy recommendation is particularly good at addressing the patient's cost concerns. The calculation of these scores is dependent on the implementation goals and many different methods can be used.” in para. [00]; and see figs. 10 and 11).
As to Claim 15, Testa teaches the following:
wherein the suitable therapies include insulin therapies using different types of insulin, each of the different types of insulin having a different cost (see “COST SENSITIVITY asks the patient to rate how much money they can afford to pay for treatment of this disease. The sample UI implementation uses an integer 0-to-10 Likert scale, but other implementations (such as asking the user to enter a maximum dollar amount) are easily adapted. In this sample UI implementation, a value of 0 means the patient is able to afford any treatment that may be suggested. A value of 10 indicates the patient strongly prefers the lowest cost treatments that are possible.” in para. [0037]; and see “The display of each individual component scores is meant to explain the reasoning behind the therapy recommendation to the medical professional and patient. For example, a high score in the area of cost sensitivity means that this therapy recommendation is particularly good at addressing the patient's cost concerns. The calculation of these scores is dependent on the implementation goals and many different methods can be used.” in para. [00]; and see figs. 10 and 11).
As to Claim 16, Testa teaches the following:
wherein the cost-effective therapy comprises an insulin therapy with a lowest cost (see “The display of each individual component scores is meant to explain the reasoning behind the therapy recommendation to the medical professional and patient. For example, a high score in the area of cost sensitivity means that this therapy recommendation is particularly good at addressing the patient's cost concerns.” in para. [0057]).
As to Claim 17, Testa teaches the following:
wherein the therapy cost data further includes cost savings of the available therapies (see “COST SENSITIVITY asks the patient to rate how much money they can afford to pay for treatment of this disease. The sample UI implementation uses an integer 0-to-10 Likert scale, but other implementations (such as asking the user to enter a maximum dollar amount) are easily adapted. In this sample UI implementation, a value of 0 means the patient is able to afford any treatment that may be suggested. A value of 10 indicates the patient strongly prefers the lowest cost treatments that are possible.” in para. [0037]; and see “The display of each individual component scores is meant to explain the reasoning behind the therapy recommendation to the medical professional and patient. For example, a high score in the area of cost sensitivity means that this therapy recommendation is particularly good at addressing the patient's cost concerns. The calculation of these scores is dependent on the implementation goals and many different methods can be used.” in para. [00]; and see figs. 10 and 11).
As to Claim 18, Testa teaches the following:
wherein the cost-effective therapy comprises one or more of wearing a glucose monitor, automated coaching, insulin therapy using an insulin pen, or insulin therapy using an insulin pump (see “[0068] THERAPY TABLE 104—The THERAPY TABLE 104 stores information about each therapy that can be used to treat the patient's medical condition under consideration by the system. Each entry in the THERAPY TABLE 104 contains information about one therapy. Examples of information about a therapy which may be stored in the THERAPY TABLE 104 include: [0069] therapy name (e.g., “Glimeperide”) [0070] dosage (e.g., “1 milligram daily” or “Low”) [0071] therapeutic effect (e.g., “Reduces blood glucose levels 1.2 percent on average”) [0072] cost under various medical insurance plans (e.g., “AETNA $75”) [0073] side effects (e.g., “May cause nausea”) [0074] administration method (e.g., “Injectable” or “oral”) [0075] frequency (e.g., “Once per day”) [0076] mechanism of action (e.g., “Basal insulin”) [0077] contraindications (e.g., “May cause renal failure”) [0078] effect on body mass index (e.g., “Moderate weight gain”, “Significant weight gain”) macro class (e.g., “Sensitizer”)” in para. [0068]-[0078]).
Conclusion
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 03/04/2026