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 .
Priority
The instant application is a 371 of PCT/US22/17449 filed on 2/23/2022, which claims priority to U.S. Provisional Application 63/152,683 filed on 2/23/2021, and claims foreign priority under U.S.C. 119 to Application US63152683 filed on 2/23/2021. At this point in examination, the effective filing date of claims 1-18 is 2/23/2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 8/23/2023 are in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-6 are directed to a method (process) for mapping a metabolic phenotype. Claims 7-12 are directed to a system (machine). Claims 13-18 are directed to a non-transient computer-readable medium (machine). Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1, 7, and 13 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claims 1 and 7 further recite:
comparing the one or more in vivo glucose-metabolism traits of the subject to a corresponding one or more glucose-metabolism traits of one or more in silico entities (i.e., mental processes);
based on the comparing, determining at least one matching in silico entity for the one or more glucose-metabolism traits of the subject (i.e., mental processes);
assigning in vivo behavioral and demographic characteristics for the subject to the at least one matching in silico entity (i.e., mental processes).
Independent claim 13 further recites:
compare the one or more in vivo glucose-metabolism traits of the subject to a corresponding one or more glucose-metabolism traits of one or more in silico entities (i.e., mental processes);
based on the comparison, determine at least one matching in silico entity for the one or more glucose-metabolism traits of the subject (i.e., mental processes);
assign in vivo behavioral and demographic characteristics for the subject to the at least one matching in silico entity (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Dependent claims 2-6, 8-12, and 14-18 recite information further limiting the judicial exceptions indicated above.
Therefore, claims 1, 7, and 13 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claims 1 and 13 recite the additional non-abstract elements of data gathering:
obtaining one or more in vivo glucose-metabolism traits of a subject (claim 1);
receive one or more in vivo glucose-metabolism traits of a subject (claim 13).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claims 7 and 13 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a system comprising a processor and a processor-readable memory (claim 7);
a non-transient computer-readable medium (claim 13).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-18 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1, 7, and 13 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claims 1 and 13: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claims 7 and 13: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
[Step 2B: NO]
Therefore, claims 1-18 are patent ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 7, 10, 13, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Visentin et al. (Diabetes Technology & Therapeutics, 2014, 16(7), 428-434), in view of Cox et al. [US20200203020A1], as provided in the IDS filed 8/23/2023.
With respect to claims 1 and 7:
Claim 7 recites a system comprising a processor and a processor-readable memory.
Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F. 2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
Regarding the recited obtaining one or more in vivo glucose-metabolism traits of a subject, Visentin et al. discloses conducting a simulator assessment on the basis that for each real T1DM subject, a virtual subject exists that undergoes the same experimental scenario (i.e., same carbohydrate amount, insulin boluses, and basal pattern, given at the same time) behaves similarly from a clinical point of view (i.e., it shows a similar pattern and lies in the same clinically relevant zones [hypo-, eu-, and hyperglycemia]) (pg. 431, col. 1, para. 1). Also, further discloses testing this criterion by comparing the measured plasma glucose profile with those simulated in 100 in silico adults undergoing the same experimental scenario (pg. 431, col. 1, para. 3). This teaches obtaining in vivo plasma glucose traits of a subject.
Regarding the recited comparing the one or more in vivo glucose-metabolism traits of the subject to a corresponding one or more glucose-metabolism traits of one or more in silico entities, Visentin et al. discloses conducting a simulator assessment on the basis that for each real T1DM subject, a virtual subject exists that undergoes the same experimental scenario (i.e., same carbohydrate amount, insulin boluses, and basal pattern, given at the same time) behaves similarly from a clinical point of view (i.e., it shows a similar pattern and lies in the same clinically relevant zones [hypo-, eu-, and hyperglycemia]) (pg. 431, col. 1, para. 1). Also, further discloses testing this criterion by comparing the measured plasma glucose profile with those simulated in 100 in silico adults undergoing the same experimental scenario (pg. 431, col. 1, para. 3; pg. 431, Fig. 2). This teaches comparing in vivo plasma glucose levels of subjects to plasma glucose levels of in silico entities.
Regarding the recited based on the comparing, determining at least one matching in silico entity for the one or more glucose-metabolism traits of the subject, Visentin et al. discloses conducting a simulator assessment on the basis that for each real T1DM subject, a virtual subject exists that undergoes the same experimental scenario (i.e., same carbohydrate amount, insulin boluses, and basal pattern, given at the same time) behaves similarly from a clinical point of view (i.e., it shows a similar pattern and lies in the same clinically relevant zones [hypo-, eu-, and hyperglycemia]) (pg. 431, col. 1, para. 1). Also, further discloses testing this criterion by comparing the measured plasma glucose profile with those simulated in 100 in silico adults undergoing the same experimental scenario (pg. 431, col. 1, para. 3). Visentin et al. discloses that as a result, the distributions of most of the outcome metrics in in silico and real subjects are virtually identical (Table 2, second column) (pg. 431, Fig. 1; pg. 432, Table 2; pg. 433, col. 1, para. 1, lines 2-12). This teaches based on the comparing, determining matching in silico subjects for the blood glucose levels of the real subjects.
Visentin et al. does not disclose assigning in vivo behavioral and demographic characteristics for the subject to the at least one matching in silico entity.
However, Cox et al. discloses searching a database to find a digital model that best matches the input data received in operation by deploying a matching algorithm and personalizing the selected digital model such that the model closely resembles the anatomy and its physiological condition of the person, which can be achieved using input data received in operation or using additional input data (pg. 4-5, col. 2, para. [0041]-[0042]). The data used for personalization can include the person’s age, weight, gender, and lifestyle information pertaining to the person such as information regarding nutrition, smoking, alcohol consumption (pg. 5, col. 1, para. [0042], lines 1-16; pg. 6, col. 2, para. [0054], lines 16-18). This teaches assigning behavioral and demographic characteristics for a subject to a matching digital model.
It would have been prima facie obvious to one of ordinary skill in the art to modify the glucose-metabolism trait comparison method disclosed by Visentin et al. to incorporate assigning characteristics to an in silico entity disclosed by Cox et al. One would be motivated to incorporate assigning characteristics in the comparison method because Cox et al. discloses that it is desirable to generate personalized digital models as it facilitates the prediction of changes to a physical condition prior to the person having contacted a healthcare professional, which is beneficial in terms of effective treatment or management of medical conditions and can lead to significant efficiency improvements in medical care (pg. 4, col. 1-2, para. [0036], lines 12-20). Therefore, incorporating assignation of characteristics will improve efficiency in the comparison method. There is a likelihood of success, since in silico simulations and digital twins are well known techniques in the field of health sciences.
With respect to claims 4, 10, and 16:
Cox et al. does not disclose wherein: the determining and/or the assigning are performed, depending upon the availability of data therefor, in a single pass or iteratively.
However, Visentin et al. discloses conducting a simulator assessment on the basis that for each real T1DM subject, a virtual subject exists that undergoes the same experimental scenario (i.e., same carbohydrate amount, insulin boluses, and basal pattern, given at the same time) behaves similarly from a clinical point of view (i.e., it shows a similar pattern and lies in the same clinically relevant zones [hypo-, eu-, and hyperglycemia]) (pg. 431, col. 1, para. 1). Also, further discloses testing this criterion by comparing the measured plasma glucose profile with those simulated in 100 in silico adults undergoing the same experimental scenario (pg. 431, col. 1, para. 3). Visentin et al. discloses that as a result, the distributions of most of the outcome metrics in in silico and real subjects are virtually identical (Table 2, second column) (pg. 431, Fig. 1; pg. 432, Table 2; pg. 433, col. 1, para. 1, lines 2-12). The database used for model assessment consists of 24 T1DM adult subjects recruited at the Universities of Virginia, Charlottesville (n=11), Padova, Italy (n=7), and Montpellier, France (n=6) (pg. 429, col. 2, para. 5, lines 1-7). It would be obvious that the method described is done in a single pass by virtue of performing the method on the data presented.
With respect to claim 13:
Claim 13 recites a non-transient computer-readable medium.
Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F. 2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
Cox et al. does not disclose receive one or more in vivo glucose-metabolism traits of a subject.
However, Visentin et al. discloses conducting a simulator assessment on the basis that for each real T1DM subject, a virtual subject exists that undergoes the same experimental scenario (i.e., same carbohydrate amount, insulin boluses, and basal pattern, given at the same time) behaves similarly from a clinical point of view (i.e., it shows a similar pattern and lies in the same clinically relevant zones [hypo-, eu-, and hyperglycemia]) (pg. 431, col. 1, para. 1). Also, further discloses testing this criterion by comparing the measured plasma glucose profile with those simulated in 100 in silico adults undergoing the same experimental scenario (pg. 431, col. 1, para. 3). This teaches obtaining in vivo plasma glucose traits of a subject.
Cox et al. does not disclose compare the one or more in vivo glucose-metabolism traits of the subject to a corresponding one or more glucose-metabolism traits of one or more in silico entities.
However, Visentin et al. discloses conducting a simulator assessment on the basis that for each real T1DM subject, a virtual subject exists that undergoes the same experimental scenario (i.e., same carbohydrate amount, insulin boluses, and basal pattern, given at the same time) behaves similarly from a clinical point of view (i.e., it shows a similar pattern and lies in the same clinically relevant zones [hypo-, eu-, and hyperglycemia]) (pg. 431, col. 1, para. 1). Also, further discloses testing this criterion by comparing the measured plasma glucose profile with those simulated in 100 in silico adults undergoing the same experimental scenario (pg. 431, col. 1, para. 3; pg. 431, Fig. 2). This teaches comparing in vivo plasma glucose levels of subjects to plasma glucose levels of in silico entities.
Cox et al. does not disclose based on the comparison, determine at least one matching in silico entity for the one or more glucose-metabolism traits of the subject.
However, Visentin et al. discloses conducting a simulator assessment on the basis that for each real T1DM subject, a virtual subject exists that undergoes the same experimental scenario (i.e., same carbohydrate amount, insulin boluses, and basal pattern, given at the same time) behaves similarly from a clinical point of view (i.e., it shows a similar pattern and lies in the same clinically relevant zones [hypo-, eu-, and hyperglycemia]) (pg. 431, col. 1, para. 1). Also, further discloses testing this criterion by comparing the measured plasma glucose profile with those simulated in 100 in silico adults undergoing the same experimental scenario (pg. 431, col. 1, para. 3). Visentin et al. discloses that as a result, the distributions of most of the outcome metrics in in silico and real subjects are virtually identical (Table 2, second column) (pg. 431, Fig. 1; pg. 432, Table 2; pg. 433, col. 1, para. 1, lines 2-12). This teaches based on the comparing, determining matching in silico subjects for the blood glucose levels of the real subjects.
Visentin et al. does not disclose assign in vivo behavioral and demographic characteristics for the subject to the at least one matching in silico entity.
However, Cox et al. discloses searching a database to find a digital model that best matches the input data received in operation by deploying a matching algorithm and personalizing the selected digital model such that the model closely resembles the anatomy and its physiological condition of the person, which can be achieved using input data received in operation or using additional input data (pg. 4-5, col. 2, para. [0041]-[0042]). The data used for personalization can include the person’s age, weight, gender, and lifestyle information pertaining to the person such as information regarding nutrition, smoking, alcohol consumption (pg. 5, col. 1, para. [0042], lines 1-16; pg. 6, col. 2, para. [0054], lines 16-18). This teaches assigning behavioral and demographic characteristics for a subject to a matching digital model.
Claims 2-3, 8-9, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Visentin et al. (Diabetes Technology & Therapeutics, 2014, 16(7), 428-434) and Cox et al. [US20200203020A1] as applied to claims 1, 4, 7, 10, 13, and 16 above, in view of Ahlqvist et al. (bioRxiv, 2017, 1-43).
Visentin et al. and Cox et al. are applied to claims 1, 4, 7, 10, 13, and 16 above.
With respect to claims 2, 8, and 14:
Visentin et al. and Cox et al. do not disclose wherein: said glucose-metabolism traits comprise one or more of (a) hemoglobin A1c (HbA1c), (b) fasting glucose, (c) C-peptide, (d) HOMA2-B, (e) HOMA-IR, or (f) any combination thereof.
However, Ahlqvist et al. discloses applying cluster analysis in newly diagnosed diabetic patients from the Swedish ANDIS (All New Diabetics in Scania) cohort using five variables (GAD-antibodies, BMI, HbA1c, HOMA2-B, and HOMA2-IR) (pg. 3, Methods, lines 1-3). This teaches glucose-metabolism traits such as hemoglobin A1c, HOMA2-B, and HOMA-IR.
It would have been prima facie obvious to one of ordinary skill in the art to modify the glucose-metabolism trait comparison method disclosed by Visentin et al. and Cox et al. to incorporate the glucose-metabolism traits disclosed by Ahlqvist et al. One would be motivated to incorporate the glucose-metabolism traits into the comparison method because Ahlqvist et al. discloses that combined information from variables central to the development of diabetes is superior to the measurement of only one metabolite, glucose, and that combining the clustering analyses with information in the healthcare system provides first steps towards a more precise, clinically useful stratification for diabetes (pg. 15, para. 3). This means incorporating a combination of glucose-metabolism traits will improve precision in the comparison method. There is a likelihood of success, since in silico simulations of diabetes subjects, digital twins, and clustering analyses of diabetes subjects are well known techniques in the field of health sciences.
With respect to claims 3, 9, and 15:
Visentin et al. and Cox et al. do not disclose wherein: said behavioral and demographic characteristics comprise one or more of (1) age of the subject, (m) duration of diabetes for the subject, (n) body mass index (BMI) of the subject, (o) body weight (BW) of the subject, or (p) any combination thereof.
However, Ahlqvist et al. discloses applying cluster analysis in newly diagnosed diabetic patients from the Swedish ANDIS (All New Diabetics in Scania) cohort using five variables (GAD-antibodies, BMI, HbA1c, HOMA2-B, and HOMA2-IR) (pg. 3, Methods, lines 1-3). This teaches behavioral and demographic characteristics comprising body mass index (BMI) of the subject.
Claims 5, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Visentin et al. (Diabetes Technology & Therapeutics, 2014, 16(7), 428-434) and Cox et al. [US20200203020A1] as applied to claims 1, 4, 7, 10, 13, and 16 above, in view of Friedrich (JAMA, 2009, 301(15), 1525-1527).
Visentin et al. and Cox et al. are applied to claims 1, 4, 7, 10, 13, and 16 above.
With respect to claims 5, 11, and 17:
Visentin et al. and Cox et al. do not disclose wherein: the subject comprises a human or an animal, relative to a number of corresponding images of an in silico entity therefor as stored in a database comprising a population of in silico entity images.
However, Friedrich discloses a computer simulator of the human metabolic system that is equipped with in silico images of 300 people with type 1 diabetes (pg. 1527, col. 1, para. 2). This teaches a computer simulator comprising in silico images of 300 human subjects.
It would have been prima facie obvious to one of ordinary skill in the art to modify the glucose-metabolism trait comparison method disclosed by Visentin et al. and Cox et al. to incorporate in silico entity images disclosed by Friedrich. One would be motivated to incorporate in silico entity images into the comparison method because Friedrich discloses that the in silico modeling developed by the UVA/Padova team is cheaper and quicker than animal testing (pg. 1527, col. 1, para. 2). Therefore, incorporating in silico entity images in the comparison method is less costly and contributes to faster comparisons. There is a likelihood of success, since in silico simulations of diabetes subjects and digital twins are well known techniques in the field of health sciences.
Claims 6, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Visentin et al. (Diabetes Technology & Therapeutics, 2014, 16(7), 428-434) and Cox et al. [US20200203020A1] as applied to claims 1, 4, 7, 10, 13, and 16 above, in view of Zhang (OSF, 2020, 1-10).
Visentin et al. and Cox et al. are applied to claims 1, 4, 7, 10, 13, and 16 above.
With respect to claims 6, 12, and 18:
Cox et al. does not disclose wherein: the determining at least one matching in silico entity for the one or more glucose-metabolism traits of the subject is based on a least magnitude Euclidean distance evaluated for at least (x) the subject and the at least one matching in silico entity for the one or more glucose-metabolism traits of the subject and (y) the subject and another in silico entity for the one or more glucose-metabolism traits of the subject.
However, Visentin et al. discloses conducting a simulator assessment on the basis that for each real T1DM subject, a virtual subject exists that undergoes the same experimental scenario (i.e., same carbohydrate amount, insulin boluses, and basal pattern, given at the same time) behaves similarly from a clinical point of view (i.e., it shows a similar pattern and lies in the same clinically relevant zones [hypo-, eu-, and hyperglycemia]) (pg. 431, col. 1, para. 1). Also, further discloses testing this criterion by comparing the measured plasma glucose profile with those simulated in 100 in silico adults undergoing the same experimental scenario (pg. 431, col. 1, para. 3). Visentin et al. discloses that as a result, the distributions of most of the outcome metrics in in silico and real subjects are virtually identical (Table 2, second column) (pg. 431, Fig. 1; pg. 432, Table 2; pg. 433, col. 1, para. 1, lines 2-12). This teaches determining matching in silico subjects for the blood glucose levels of the real subjects.
Visentin et al. does not disclose using Euclidean distance to compare subject data.
However, Zhang discloses assessing the similarity of FFPE specimens of patients in the database with that of new patients by calculating Euclidean distance to compare their respective measured protein biomarker values (pg. 2, Abstract, lines 4-5; pg. 5, para. 3, lines 7-9; pg. 8, Fig. 1, lines 1-12). The entries with the shortest Euclidean distances are grouped together. This teaches using Euclidean distance to match patient data to database information.
It would have been prima facie obvious to one of ordinary skill in the art to modify the glucose-metabolism trait comparison method disclosed by Visentin et al. and Cox et al. to incorporate Euclidean distance matching disclosed by Zhang. One would be motivated to incorporate Euclidean distance matching in the comparison method because Zhang discloses analysis of medical histories of patient specimens mathematically should provide the most reliable outcome predictions and most effective treatment plan recommendation for new patients (pg. 3, para. 4, lines 2-4). This means incorporating Euclidean distance matching in the comparison method improves reliability and effectiveness of comparisons. There is a likelihood of success, since in silico simulations of diabetes subjects, digital twins, and Euclidean distance matching of patient data are well known techniques in the field of health sciences.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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