DETAILED ACTION
This action is in response to the initial filing filed on March 27, 2024 Claims 1-9 havebeen examined in this application.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-7 are drawn to a method and claim 9 is drawn to a device (i.e., a manufacture). As such, claims 1-7 and 9 are drawn to one of the statutory categories of invention (Step 1: YES).
Claim 8 is rejected under U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter, because under its broadest reasonable interpretation, claim 8 recites a computer program product without any structural recitations. As such, the computer program product could include a transitory media including forms of signal transmission, such as propagating electrical signals or carrier waves which is enumerated by the courts as non-statutory subject matter (see in re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007)). Further, without any structural recitation, a computer program does not fall within the groupings of a machine, a manufacture, a process, or a composition of matter. The applicant is required to provide structural recitations to amount to eligible subject matter (a manufacture), such as the program being stored on a computer-readable medium and limiting the computer-readable medium to only non-transitory embodiments. For this reason, the applicant is suggested to amend the claim to recite the computer program stored on a non-transitory computer readable medium for execution by the computer. As such, claims 8 is not drawn to one of the statutory categories of invention, however will continue to be included in this rejection for the sake of compact prosecution (Step 1: No).
Under Step 2A Prong 1, the claims are analyzed to determine whether the claims recite any judicial exceptions including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes).
Claims 1, 81, and 9, recite a risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning, comprising: S1, obtaining food images of pregnant women before eating; S2, processing the food images of the pregnant women before eating based on a convolutional neural network model, to obtain sugar intake, wherein an input of the convolutional neural network model comprises the food images of the pregnant women before eating, and an output of the convolutional neural network model is the sugar intake; S3, obtaining total sugar intake over a period of time based on a plurality of amounts of the sugar intake over the period of time;
S4, determining average daily sugar intake based on the total sugar intake over the period of time; and S5, determining a risk degree of the pregnant women suffering from the gestational diabetes mellitus by using a deep neural network model based on the average daily sugar intake and physiological indicators of the pregnant women, wherein an input of the deep neural network model comprises the average daily sugar intake and the physiological indicators of the pregnant women, and an output of the deep neural network model is the risk degree of the pregnant women suffering from the gestational diabetes mellitus. If claim limitations, under their broadest reasonable interpretation, include a mental process and/or certain methods of organizing human activity, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claims 1, 8, and 9 recite abstract ideas.
Representative Claim 1: A risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning, comprising:
S1, obtaining food images of pregnant women before eating;
S2, processing the food images of the pregnant women before eating based on a convolutional neural network model, to obtain sugar intake, wherein an input of the convolutional neural network model comprises the food images of the pregnant women before eating, and an output of the convolutional neural network model is the sugar intake;
S3, obtaining total sugar intake over a period of time based on a plurality of amounts of the sugar intake over the period of time;
S4, determining average daily sugar intake based on the total sugar intake over the period of time; and
S5, determining a risk degree of the pregnant women suffering from the gestational diabetes mellitus by using a deep neural network model based on the average daily sugar intake and physiological indicators of the pregnant women, wherein an input of the deep neural network model comprises the average daily sugar intake and the physiological indicators of the pregnant women, and an output of the deep neural network model is the risk degree of the pregnant women suffering from the gestational diabetes mellitus.
Representative Claim 8: A computer program product, comprising: a computer program, wherein when the computer program is executed by a processor, operations of the risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning according to any one of claims 1 to 7 are implemented.
Representative Claim 9: An electronic device, comprising: a memory; a processor; and a computer program, stored in the memory and configured to be executed by the processor to implement operations in the risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning according to any one of claims 1 to 7.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
The additional elements are instructions for applying the judicial exceptions with a generic computing device as, under their broadest reasonable interpretation, the additional elements of processors, non-transitory machine-readable storage device/memory device having instructions stored thereon, and an inertial measurement unit are generic computer components for performing the above method, per MPEP 2106.05(f). Under their broadest reasonable interpretation, the additional elements are generic components of a computing device used to apply the abstract idea.
Under their broadest reasonable interpretation, the recited steps of A risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning, comprising: obtaining images of food, processing images of food, determining total sugar intake over a period of time, determining average daily sugar intake over a period of time; and
determining a risk degree of the pregnant women based on the average daily sugar intake
(i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), then it also falls within the “Mental Processes” subject matter grouping of abstract ideas. The recited steps are a simulation that applies an abstract idea, specifically mental processes (observation (obtaining images of food) and/or evaluation (processing images, determining sugar intake or degree of risk)). If claim limitations, under their broadest reasonable interpretation, include a mental process and/or certain methods of organizing human activity (CMOHA), the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claims 1, 8, and 9 recite abstract ideas.
Dependent Claims 2-7 further narrow the abstract ideas of stopping a game, displaying content, analyzing responses, enabling multiple users to participate in a game, customizing text, and narrating gameplay (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), then it also falls within the “Mental Processes” and is an abstract idea and then it also falls within the “Organizing Human Processes” subject matter grouping of abstract ideas and then also falls within the “Organizing Human Processes” subject matter grouping of abstract ideas.
Independent claim(s) 1, 8, and 9 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claims 1, 8, and 9 recite an abstract idea (Step 2A – Prong One: YES).
Under Step 2A Prong 2 the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application.
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using “obtaining photos/images”, “processing photos/images”, “obtaining sugar intake”, “determining average sugar intake”, and “determining a risk degree,” etc. (Claims 1, 8, and 9) are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of applying “obtaining photos/images”, “processing photos/images”, “obtaining sugar intake”, “determining average sugar intake”, and “determining a risk degree,” etc. Independent Claim(s) 1, 8, and 9, and dependent claims 2-7 are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components in a vehicle. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., obtaining photos/images, processing photos/images, obtaining sugar intake, determining average sugar intake, and determining a risk degree etc.). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s) of receiving input, verifying input, and displaying an image, (Claim(s) 1, 8, and 9), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claims 2-7 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself.
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent claim(s) 1, 8, and 9, and dependent claims 2-7 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of obtaining photos/images, processing photos/images, obtaining sugar intake, determining average sugar intake, and determining a risk degree (Claim(s) 1, 8, and 9), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea) i.e. selecting users (i.e. using a user interface) is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at paragraph [0046] acknowledges that “When a computer program is executed by the processor, the risk prediction method of the pregnant women suffering from the gestational diabetes mellitus based on the machine learning as provided above can be implemented” (i.e. conventional nature of using a computer and/or computer program). This additional element therefore does not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-7 are dependent from claims 1, 8, and 9 and include all the limitations of the independent claims, but fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). Therefore, the dependent claims recite the same abstract idea. The limitations of the dependent claims fail to amount to significantly more than the judicial exception. For example:
The limitations of claims 3, 6, and 7 recite clarifications of training methods for a convolutional neural network (CNN), types of physiological indicators, selecting training samples. Such clarifications, under their broadest reasonable interpretation, are merely defining/selecting a type of data to be manipulated which, per MPEP 2106.05(g), is insignificant extra-solution activity. Therefore, the limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amount to significantly more than the judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claim 2, 4, 5, 8, and 9 recite clarifications of obtaining food images, issuing a warning prompt when going above or below a threshold, a computer program, and an electronic device capable of storing and running a computer program. The limitations are further instructions for applying the judicial exceptions with a generic computing device/interface acting as an intermediary for performing the abstract ideas of obtaining images, issuing a warning prompt, and using a computer program, see MPEP 2106.05(f). Therefore, the limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amount to significantly more than the judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-9 are not eligible subject matter under 35 USC 101.
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.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Faccioli (US 2023/0140055 A1) in view of Wensheng (CN 114974585 A), in view of Chengwei (CN 111474137 A).
Regarding Claim 1, Faccioli discloses a risk prediction assessment model comprising:
S1, obtaining food images of pregnant women before eating ([0072] In certain embodiments, meal intake information CHO(k) may be provided by a user through manual entry, by providing a photograph through an application that is configured to recognize food types and quantities (e.g., potassium and glucose/carbohydrate content of foods), and/or by scanning a bar code or menu);
S2, processing the food images of the pregnant women before eating ([0070] application 106 obtains inputs 128 through one or more channels (e.g., manual user input, sensors/monitors, other applications executing on display device 107, EMRs, etc.). As mentioned previously, in certain embodiments, inputs 128 may be processed by the prediction module 116 and/or decision support engine 114 to output decision support outputs (e.g., alerts 130) (photos can be entered manually, as mentioned in the citation for S1, and are then processed)) based on a convolutional neural network model, to obtain sugar intake ([0100] convolution neural network (CNN));
S3, obtaining total sugar intake over a period of time based on a plurality of amounts of the sugar intake over the period of time ([0072] The glucose measurements g(k) may be measured by and received from at least a continuous glucose sensor (or multi-analyte sensor configured to measure at least glucose) that is a part of continuous analyte monitoring system 104 (obtaining total sugar intake over time));
S4, determining average daily sugar intake based on the total sugar intake over the period of time ([0041] In certain embodiments, a user's glucose metrics may include glucose levels, glucose level rate(s) of change, glucose trend(s), mean glucose (average sugar intake), glucose management indicator (GMI) (average sugar intake over time), glycemic variability, time in range (TIR), glucose clearance rate, etc.); and
S5, determining a risk degree of the pregnant women suffering from the gestational diabetes mellitus by using a deep neural network model based on the average daily sugar intake and physiological indicators of the pregnant women (Fig. 4, [0100] Some example alternatives to the use of an ARIMAX machine learning model 402 with a Kalman filter as the predictive filter 604 include at least (a) a non-linear physiological model of glucose-insulin dynamics or other non-linear black box model (e.g., deep neural network (DNN), convolution neural network (CNN)).
However, Faccioli is not relied upon disclosing a risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning; wherein an input of the convolutional neural network model comprises the food images of the pregnant women before eating, and an output of the convolutional neural network model is the sugar intake; wherein an input of the deep neural network model comprises the average daily sugar intake and the physiological indicators of the pregnant women, and an output of the deep neural network model is the risk degree of the pregnant women suffering from the gestational diabetes mellitus.
Wensheng teaches A risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning ([0010] A method for constructing an early risk prediction assessment model for metabolic syndrome (diabetes is a specific type of metabolic syndrome), [0013] Using extreme gradient boosting (XGBoost) (XGBoost is a machine learning library) combined with the Stacking framework to build a prediction model) during pregnancy); and wherein an input of the deep neural network model comprises the average daily sugar intake and the physiological indicators of the pregnant women, and an output of the deep neural network model is the risk degree of the pregnant women suffering from the gestational diabetes mellitus ([0010] A method for constructing an early risk prediction assessment model for metabolic syndrome (diabetes mellitus is encompassed in “metabolic syndrome” as mentioned in the art) during pregnancy (gestational), [0044] S42. The spectral preprocessing data of the training set and the actual sugar content preprocessing data of the training set are used as the training input of the convolutional neural network model (input is sugar content or intake if ingested)).
Faccioli and Wensheng are both considered to be analogous to the claimed invention, because they are in the same field of determining blood glucose values. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the applicant’s invention of obtaining images of food, processing images of food, and obtaining total sugar intake over a period of time based on a plurality of amounts of the sugar intake over the period of time, as disclosed by Faccioli, further including a risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning by Wensheng for the purpose of identifying high-risk groups, and applying scientific health intervention can interrupt the vicious circle of metabolic abnormalities between mother and child, and avoid risks to newborns (Wensheng, [0026]).
However, Wensheng is not relied upon teaching wherein an input of the convolutional neural network model comprises the food images of the pregnant women before eating, and an output of the convolutional neural network model is the sugar intake.
Chengwei teaches wherein an input of the convolutional neural network model comprises the food images of the pregnant women before eating, and an output of the convolutional neural network model is the sugar intake ([0007] S1. Select citrus, form a citrus sample (input is citrus which is food), [0015] S7. Using the convolutional neural network model established in the above steps to predict the citrus sugar content (sugar content of food is same as intake, “intake” is when food is eaten whereas “content” describes the food in general)).
Faccioli and Chengwei are both considered to be analogous to the claimed invention, because they are in the same field of determining glucose (sugar) content of food. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the applicant’s invention of obtaining images of food, processing images of food, and obtaining total sugar intake over a period of time based on a plurality of amounts of the sugar intake over the period of time, as disclosed by Faccioli, further including an input of the convolutional neural network model comprises the food images of the pregnant women before eating, and an output of the convolutional neural network model is the sugar intake by Chengwei for the purpose of non-destructive rapid testing of citrus sugar content (Chengwei, [0005]).
Regarding Claim 2, Faccioli discloses wherein the obtaining food images of pregnant women before eating, comprises: photographing food of pregnant women before eating based on a mobile phone to obtain the food images ([0062] In some embodiments, one of the plurality of display devices is a smartphone, such as display device 220 which represents a mobile phone, using a commercially available operating system (OS), and configured to display a graphical representation of the continuous sensor data (e.g., including current and historic data)).
Regarding Claim 3, Faccioli is not relied upon disclosing obtaining the convolutional neural network model by training using a gradient descent method.
However, Chengwei teaches the risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning according to claim 1, further comprising: obtaining the convolutional neural network model by training using a gradient descent method ([0032] the Gms prediction model was established by using the extreme gradient boosting (gradient boosting utilizes gradient descent method) tree combined with the Stacking framework).
Regarding Claim 4, Faccioli discloses issuing a warning prompt when the average daily sugar intake is greater than a first threshold (Fig. 7 Elements 704 and 706 Generating a hyperglycemic (high blood sugar) alert when above hyperglycemic threshold).
Regarding Claim 5, Faccioli discloses issuing a warning prompt when the average daily sugar intake is less than a second threshold (Fig. 7 Elements 708 and 710 Generating a hypoglycemic (low blood sugar) alert when below hypoglycemic threshold).
Regarding Claim 6, Faciioli discloses wherein the physiological indicators of the pregnant women comprise a body mass index, whether to take folic acid, a menarche age, a hemoglobin value, a leukocyte value, a platelet value, a serum creatinine value, a hepatitis B virus value, a hepatitis B virus surface antigen value, a serum alanine aminotransferase value, an albumin value, and a total bilirubin value ([0030] body mass index (BMI), [0021] albumin; … hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin EЕ, hemoglobin F, D-Punjab, hepatitis B virus; … specific antigens (hepatitis B virus, HIV-1); … white blood cells (leukocytes)).
However, Faccioli is not relied upon disclosing wherein the physiological indicators of the pregnant women comprise whether to take folic acid, a menarche age, a platelet value, a serum creatinine value, a serum alanine aminotransferase value, and a total bilirubin value.
Wensheng discloses wherein the physiological indicators of the pregnant women comprise wherein the physiological indicators of the pregnant women comprise whether to take folic acid, a menarche age, a platelet value, a serum creatinine value, a serum alanine aminotransferase value, and a total bilirubin value ([0037] menarche, [0039] Metabolism-related laboratory test data mainly include the following: hemoglobin, hematocrit, platelets, neutrophils, lymphocytes, eosinophil ferritin, partial thromboplastin time, prothrombin time, fibrinogen, D-dimer, glucose, triglycerides, total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, APOA1, APOB, homocysteine, uric acid, alanine aminotransferase, aspartate Aminotransferase, total protein, albumin, total bilirubin, direct bilirubin, creatinine)
Regarding Claim 7, Faccioli is not relied upon disclosing wherein the convolutional neural network model is obtained through a training process, and the training process comprises: obtaining a plurality of training samples, wherein the training samples comprises sample input data and labels corresponding to the sample input data, the sample input data is a sample food image, and the label is the sugar intake corresponding to the sample food image; and training an initial convolutional neural network model based on the plurality of training samples, to obtain the convolutional neural network model.
However, Chengwei teaches the risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning according to claim 1, wherein the convolutional neural network model is obtained through a training process, and the training process comprises: obtaining a plurality of training samples, wherein the training samples comprises sample input data and labels corresponding to the sample input data, the sample input data is a sample food image, and the label is the sugar intake corresponding to the sample food image; and training an initial convolutional neural network model based on the plurality of training samples, to obtain the convolutional neural network model ([0007] Select citrus (food), form a citrus sample, and divide the citrus sample into a training set and a validation set, [0044] The spectral preprocessing data of the training set and the actual sugar content preprocessing data of the training set are used as the training input of the convolutional neural network model, and are fed to the Gaussian noise layer).
Regarding Claim 8, Faccioli discloses a computer program product, comprising: a computer program, wherein when the computer program is executed by a processor, operations of the risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning according to any one of claims 1 to 7 are implemented ([0127] Although depicted as a single physical device, in embodiments, computing device 1000 may be implemented using virtual device(s), and/or across a number of devices, such as in a cloud environment. As illustrated, computing device 1000 includes a processor 1005, memory 1010, storage 1015, a network interface 1025, and one or more I/O interfaces 1020).
Regarding Claim 9, Faccioli discloses an electronic device, comprising: a memory; a processor; and a computer program, stored in the memory and configured to be executed by the processor to implement operations in the risk prediction method of pregnant women suffering from gestational diabetes mellitus based on machine learning according to any one of claims 1 to 7 ([0127] Although depicted as a single physical device, in embodiments, computing device 1000 may be implemented using virtual device(s), and/or across a number of devices, such as in a cloud environment. As illustrated, computing device 1000 includes a processor 1005, memory 1010, storage 1015, a network interface 1025, and one or more I/O interfaces 1020).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dehais et al. (US 2016/0163037 A1) an estimation of food volume and carbs, which can be used in a glucometer or insulin pump.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAMID TARIQ HAFIZ whose telephone number is (571) 272-4629. The examiner can normally be reached 7:30 AM - 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at 571-270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAMID TARIQ HAFIZ/
Examiner, Art Unit 3715
/KANG HU/Supervisory Patent Examiner, Art Unit 3715
1 Examiner notes that claim 8 is rejected under 35 U.S.C. 101 for failing Step 1 as discussed above for being directed to non-transitory subject matter. This rejection under steps 2A and 2B for being directed to ineligible subject matter is included herein for the sake of compact prosecution, assuming Applicant amends the claim to overcome Step 1.