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 .
Formal Matters
Applicant's response, filed 03 March 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-13 are currently pending and have been examined.
Claims 1, 4, and 11-13 have been amended.
Claims 1-13 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202210380666.9, filed on 11 October 2024.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 12 April 2022 claiming benefit to CN202210380666.9.
Claim Interpretation
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) 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 limitations are: a big data model preparation module, a data receiving module, a personalized model preparation module and a blood glucose prediction processing module in claim 10. The specification provides the hardware computer server and corresponding algorithms for each module in ¶ 00177-00184 and Fig. 2 that may be hardware, software or a combination thereof. Therefore, the claim limitations will be interpreted to be a hardware AND software computer program product stored on a memory (see MPEP § 2181(II)(B) wherein when the supporting disclosure for a computer-implemented invention discusses the implementation of the functionality of the invention through hardware, software, or a combination of both, a question can arise as to which mode of implementation supports the means-plus-function limitation. The language of 35 U.S.C. 112(f) requires that the recited "means" for performing the specified function shall be construed to cover the corresponding "structure or material" described in the specification and equivalents thereof. Therefore, by choosing to use a means-plus-function limitation and invoke 35 U.S.C. 112(f) applicant limits that claim limitation to the disclosed structure, i.e., implementation by hardware or the combination of hardware and software, and equivalents thereof. Therefore, the examiner should not construe the limitation as covering pure software implementation).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), 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), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (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).
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 1-13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-13 are drawn to a method, system, or manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for a medical blood glucose detection equipment combining a big data model and a personalized model in part performing the steps of training a big data blood glucose prediction model based on a preset big database and confirming a big data calibrated record for preprandial blood glucose calibration and postprandial blood glucose calibration; receiving a first collection data set of a specified object, the first collection data set comprising a first data type, and the first data type being a prediction type or a label type; when the first data type is the label type, updating a personalized database corresponding to the specified object according to the first collection data set; if the database is successfully updated, counting a total number of personalized data records of the personalized database to generate a corresponding first total number; if the first total number is greater than or equal to a preset first threshold value, performing optimal personalized data calibrated record screening in the personalized database based on the big data blood glucose prediction model to obtain a latest personalized data calibrated record; if the first total number is equal to a preset second threshold value, training a personalized blood glucose prediction model based on the big data blood glucose prediction model, the personalized database and the personalized data calibrated record; the first threshold value being smaller than the second threshold value; when the first data type is the prediction type, counting the total number of personalized data records in the personalized database to generate a corresponding second total number; if the second total number is smaller than the second threshold value, performing blood glucose prediction processing on the first collection data set based on the second total number, the big data calibrated record, the personalized data calibrated record and the big data blood glucose prediction model to generate corresponding first predicted blood glucose data; and if the second total number is equal to the second threshold value, performing personalized blood glucose prediction processing on the first collection data set based on the personalized data calibrated record, the big data blood glucose prediction model and the personalized blood glucose prediction model to generate corresponding second predicted blood glucose data.
Dependent claim 2 recites, in part, wherein a neural network of the big data blood glucose prediction model adopts a fully connected network structure; and a model structure of the personalized blood glucose prediction model is a support vector machine model structure, a random forest model structure or a decision tree model structure.
Dependent claim 3 recites, in part, wherein the big database comprises a plurality of big data records; the big data records comprise first preprandial sub-records and first postprandial sub-records; the first preprandial sub-record comprises a first collection time, a first time interval, a first optical data group, a first metabolic heat data group, a first physiological information data group and a first measured blood glucose level; the first postprandial sub-record comprises a second collection time, a second time interval, a second optical data group, a second metabolic heat data group, a second physiological information data group and a second measured blood glucose level; the first and second measured blood glucose levels correspond to preprandial and postprandial measured blood glucose levels, respectively; when the first data type is the label type, the first collection data set further comprises a first preprandial collection record and a first postprandial collection record; the first preprandial collection record comprises a third collection time, a third time interval, a third optical data group, a third metabolic heat data group, a third physiological information data group and a third measured blood glucose level; the first postprandial collection record comprises a fourth collection time, a fourth time interval, a fourth optical data group, a fourth metabolic heat data group, a fourth physiological information data group and a fourth measured blood glucose level; the third and fourth measured blood glucose levels correspond to preprandial and postprandial measured blood glucose levels, respectively; when the first data type is the prediction type, the first collection data set further comprises a fifth collection time, a fifth time interval, a fifth optical data group, a fifth metabolic heat data group and a fifth physiological information data group; the personalized database comprises a plurality of personalized data records; the personalized data records comprise second preprandial sub-records and second postprandial sub-records; the second preprandial sub-record comprises a sixth collection time, a sixth time interval, a sixth optical data group, a sixth metabolic heat data group, a sixth physiological information data group and a fifth measured blood glucose level; the second postprandial sub-record comprises a seventh collection time, a seventh time interval, a seventh optical data group, a seventh metabolic heat data group, a seventh physiological information data group and a sixth measured blood glucose level; the fifth and sixth measured blood glucose levels correspond to preprandial and postprandial measured blood glucose levels, respectively; and the maximum total number of records in the personalized database is the second threshold value.
Dependent claim 4 recites, in part, wherein the training a big data blood glucose prediction model based on a preset big database and confirming a big data calibrated record for preprandial blood glucose calibration and postprandial blood glucose calibration comprises: extracting a plurality of big data records from the big database to form a big data set; performing first feature vector conversion processing on each of the first preprandial sub-records or the first postprandial sub-records in the big data set to generate first feature vectors corresponding to each sub-record; recording the first measured blood glucose levels or the second measured blood glucose levels corresponding to each of the first feature vectors as corresponding first feature blood glucose levels; selecting a big data record from the big data set as the big data calibrated record; taking two first feature vectors of the big data calibrated record as a corresponding big data preprandial calibration vector and a big data postprandial calibration vector, respectively, and taking two first feature blood glucose levels as a corresponding big data preprandial calibrated blood glucose level and a big data postprandial calibrated blood glucose level, respectively; recording all the other first feature vectors except the big data preprandial calibration vector and the big data postprandial calibration vector as second feature vectors; forming corresponding first model training vectors by each of the second feature vectors, the big data preprandial calibration vector, the big data postprandial calibration vector, the big data preprandial calibrated blood glucose level and the big data postprandial calibrated blood glucose level; based on the big data blood glucose prediction model, performing big data model prediction processing on any of the first model training vectors to obtain corresponding first predicted data, and generating corresponding current predicted blood glucose data according to the sum of the big data postprandial calibrated blood glucose level and the first predicted data; calculating a training error between the current predicted blood glucose data and the first feature blood glucose level corresponding to the current first model training vector using a loss function of the big data blood glucose prediction model; when the training error does not meet a preset reasonable error range, modulating the big data blood glucose prediction model based on the training error, and selecting the next first model training vector to continue training the modulated big data blood glucose prediction model; and stopping training when the training error meets the reasonable error range.
Dependent claim 5 recites, in part, wherein the step of updating a personalized database corresponding to the specified object according to the first collection data set comprises: counting the total number of personalized data records in the personalized database to generate a corresponding current total number; when the current total number is smaller than the second threshold value, adding a new personalized data record as a current record in the personalized database; when the current total number is equal to the second threshold value, taking the personalized data record with the earliest addition time in the personalized database as the current record; and extracting the first preprandial collection record from the first collection data set as the second preprandial sub-record of the current record and storing the same in the personalized database, and extracting the first postprandial collection record as the second postprandial sub-record of the current record and storing the same in the personalized database.
Dependent claim 6 recites, in part, wherein the step of performing optimal personalized data calibrated record screening in the personalized database based on the big data blood glucose prediction model to obtain a latest personalized data calibrated record comprises: performing first feature vector conversion processing on the second preprandial sub-record or the second postprandial sub-record of each of the personalized data records in the personalized database to generate third feature vectors corresponding to each sub-record; recording the fifth measured blood glucose levels or the sixth measured blood glucose levels corresponding to each of the third feature vectors as corresponding second feature blood glucose levels; sequentially extracting the personalized data records as a current calibrated record in the personalized database; taking two third feature vectors of the current calibrated record as a corresponding big data preprandial calibration vector and a big data postprandial calibration vector, respectively, and taking two second feature blood glucose levels as a corresponding big data preprandial calibrated blood glucose level and a big data postprandial calibrated blood glucose level, respectively; recording third feature vectors of other personalized data records except the current calibrated record as fourth feature vectors, and forming corresponding first model input vectors by each of the fourth feature vectors, the big data preprandial calibration vector, the big data postprandial calibration vector, the big data preprandial calibrated blood glucose level and the big data postprandial calibrated blood glucose level; based on the big data blood glucose prediction model, performing big data model prediction processing on each of the first model input vectors to obtain corresponding second predicted data, and generating corresponding first model predicted blood glucose data according to the sum of the big data postprandial calibrated blood glucose level and the second predicted data; calculating an absolute difference between each of the first model predicted blood glucose data and the second feature blood glucose level corresponding to a current first model input vector to obtain a corresponding first prediction error; calculating the sum of all the obtained first prediction errors to obtain a first prediction error sum corresponding to the current calibrated record; and taking a minimum prediction error sum as an optimal calibration personalized record group screening principle, and taking the personalized data record with the minimum first prediction error sum in the personalized database as the latest personalized data calibrated record.
Dependent claim 7 recites, in part, wherein the step of training a personalized blood glucose prediction model based on the big data blood glucose prediction model, the personalized database and the personalized data calibrated record comprises: performing first feature vector conversion processing on the second preprandial sub-record or the second postprandial sub-record of each of the personalized data records in the personalized database to generate fifth feature vectors corresponding to each sub-record; recording the fifth measured blood glucose levels or the sixth measured blood glucose levels corresponding to each of the fifth feature vectors as corresponding third feature blood glucose levels; taking two fifth feature vectors of the personalized data calibrated record as a corresponding big data preprandial calibration vector and a big data postprandial calibration vector, 10respectively, and taking two third feature blood glucose levels as a corresponding big data preprandial calibrated blood glucose level and a big data postprandial calibrated blood glucose level, respectively; recording the fifth feature vectors of other personalized data records except the personalized data calibrated record in the personalized database as sixth feature vectors; forming corresponding second model training vectors by each of the sixth feature vectors, the big data preprandial calibration vector, the big data postprandial calibration vector, the big data preprandial calibrated blood glucose level and the big data postprandial calibrated blood glucose level; performing big data model prediction processing on each of the second model training vectors based on the big data blood glucose prediction model to obtain corresponding third predicted data, and generating corresponding big data predicted blood glucose data according to the sum of the big data postprandial calibrated blood glucose level and the third predicted data; according to a preset big data-personalized feature association status, extracting part of feature data from the sixth feature vectors corresponding to each of the second model training vectors to form corresponding seventh feature vectors, extracting part of feature data from the big data preprandial calibration vector to form a corresponding personalized preprandial calibration vector, and extracting part of feature data from the big data postprandial calibration vector to form a corresponding personalized postprandial calibration vector; forming corresponding third model training vectors by the seventh feature vectors, the personalized preprandial calibration vector, the personalized postprandial calibration vector, the big data preprandial calibrated blood glucose level, the big data postprandial calibrated blood glucose level and the big data predicted blood glucose data; performing personalized model prediction processing on any of the third model training vectors based on the personalized blood glucose prediction model to obtain corresponding fourth predicted data, and generating corresponding current predicted blood glucose data according to the sum of the big data predicted blood glucose data and the fourth predicted data; calculating an absolute difference between the current predicted blood glucose data and the third feature blood glucose level corresponding to a current third model training vector as a current measurement error; modulating the personalized blood glucose prediction model when the current measurement error does not match a preset reasonable error range; and then selecting the next third model training vector to train the personalized blood glucose prediction model until training with the last third model training vector is completed.
Dependent claim 8 recites, in part, wherein the step of, according to a preset big data- personalized feature association status, extracting part of feature data from the sixth feature vectors corresponding to each of the second model training vectors to form corresponding seventh feature vectors, extracting part of feature data from the big data preprandial calibration vector to form a corresponding personalized preprandial calibration vector, and extracting part of feature data from the big data postprandial calibration vector to form a corresponding personalized postprandial calibration vector, comprises: identifying whether each status bit of the big data-personalized feature association status is an association status, and if so, adding matching feature data of each status bit in the sixth feature vectors, the big data preprandial calibration vector and the big data postprandial calibration vector to the corresponding seventh feature vectors, the personalized preprandial calibration vector and the personalized postprandial calibration vector, the big data-personalized feature association status comprising first, second, third and fourth status bits, the matching feature data of the first status bit being time interval feature data, the matching feature data of the second status bit being optical feature sequences, the matching feature data of the third status bit being metabolic heat feature sequences, and the matching feature data of the fourth status bit being physiological feature sequences.
Dependent claim 9 recites, in part, wherein the step of performing blood glucose prediction processing on the first collection data set based on the second total number, the big data calibrated record, the personalized data calibrated record and the big data blood glucose prediction model to generate corresponding first predicted blood glucose data comprises: determining whether the second total number is smaller than the first threshold value; if the second total number is smaller than the first threshold value, performing first feature vector conversion processing on the first preprandial sub-record and the first postprandial sub- record of the big data calibrated record, taking two obtained feature vectors as a corresponding big data preprandial calibration vector and a big data postprandial calibration vector, and taking the first measured blood glucose level and the second measured blood glucose level of the big data calibrated record as a corresponding big data preprandial calibrated blood glucose level and a big data postprandial calibrated blood glucose level; if the second total number is greater than or equal to the first threshold value, performing first feature vector conversion processing on the second preprandial sub-record and the second postprandial sub-record of the personalized data calibrated record, taking two obtained feature vectors as a corresponding big data preprandial calibration vector and a big data postprandial calibration vector, and taking the fifth measured blood glucose level and the sixth measured blood glucose level of the personalized data calibrated record as the corresponding big data preprandial calibrated blood glucose level and the big data postprandial calibrated blood glucose level; performing first feature vector conversion processing on the first collection data set to generate corresponding eighth feature vectors; forming corresponding second model input vectors by the eighth feature vectors, the big data preprandial calibration vector, the big data postprandial calibration vector, the big data preprandial calibrated blood glucose level and the big data postprandial calibrated blood glucose level; performing big data model prediction processing on the second model input vectors based on the big data blood glucose prediction model to obtain corresponding fifth predicted data; and generating the corresponding first predicted blood glucose data according to the sum of the big data postprandial calibrated blood glucose level and the fifth predicted data.
Dependent claim 10 recites, in part, wherein the step of performing personalized blood glucose prediction processing on the first collection data set based on the personalized data calibrated record, the big data blood glucose prediction model and the personalized blood glucose prediction model to generate corresponding second predicted blood glucose data comprises: performing first feature vector conversion processing on the second preprandial sub-record and the second postprandial sub-record of the personalized data calibrated record to obtain two ninth feature vectors, taking the two ninth feature vectors as a corresponding big data preprandial calibration vector and a big data postprandial calibration vector, respectively, and taking the fifth and sixth measured blood glucose levels of the personalized data calibrated record as a corresponding big data preprandial calibrated blood glucose level and a big data postprandial calibrated blood glucose level, respectively; performing first feature vector conversion processing on the first collection data set to generate corresponding tenth feature vectors; forming corresponding third model input vectors by the tenth feature vectors, the big data preprandial calibration vector, the big data postprandial calibration vector, the big data preprandial calibrated blood glucose level and the big data postprandial calibrated blood glucose level; performing big data model prediction processing on the third model input vectors based on the big data blood glucose prediction model to obtain corresponding sixth predicted data; generating corresponding big data predicted blood glucose data according to the sum of the big data postprandial calibrated blood glucose level and the sixth predicted data; according to a preset big data-personalized feature association status, extracting part of feature data from the tenth feature vectors to form corresponding eleventh feature vectors, extracting part of feature data from the big data preprandial calibration vector to form a corresponding personalized preprandial calibration vector, and extracting part of feature data from the big data postprandial calibration vector to form a corresponding personalized postprandial calibration vector; forming corresponding fourth model input vectors by the eleventh feature vectors, the personalized preprandial calibration vector, the personalized postprandial calibration vector, the big data preprandial calibrated blood glucose level, the big data postprandial calibrated blood glucose level and the big data predicted blood glucose data; performing personalized model prediction processing on the fourth model input vectors based on the personalized blood glucose prediction model to obtain corresponding seventh predicted data; and generating the corresponding second predicted blood glucose data according to the sum of the big data predicted blood glucose data and the seventh predicted data.
Dependent claim 11 recites, in part, train a big data blood glucose prediction model based on a preset big database and confirm a big data calibrated record for preprandial blood glucose calibration and postprandial blood glucose calibration; receive a first collection data set of a specified object, the first collection data set comprising a first data type, and the first data type being the prediction type or the label type; when the first data type is the label type, update a personalized database corresponding to the specified object according to the first collection data set; if the database is successfully updated, count a total number of personalized data records of the personalized database to generate a corresponding first total number; if the first total number is greater than or equal to a preset first threshold value, perform optimal personalized data calibrated record screening in the personalized database based on the big data blood glucose prediction model to obtain a latest personalized data calibrated record; if the first total number is equal to a preset second threshold value, train a personalized blood glucose prediction model based on the big data blood glucose prediction model, the personalized database and the personalized data calibrated record; the first threshold value being smaller than the second threshold value; when the first data type is the prediction type, count the total number of personalized data records in the personalized database to generate a corresponding second total number; if the second total number is smaller than the second threshold value, perform blood glucose prediction processing on the first collection data set based on the second total number, the big data calibrated record, the personalized data calibrated record and the big data blood glucose prediction model to generate corresponding first predicted blood glucose data; and if the second total number is equal to the second threshold value, perform personalized blood glucose prediction processing on the first collection data set based on the personalized data calibrated record, the big data blood glucose prediction model and the personalized blood glucose prediction model to generate corresponding second predicted blood glucose data.
Dependent claim 12 recites, in part, realize the method of claim 1.
Dependent claim 13 recites, in part, execute the method of claim 1.
These steps of receiving, managing, and storing historical records of large patient data and a single particular patient to analyze for glucose values amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982) – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Additionally, the steps of comparing to thresholds and calculating data record values, training and generating representative models via particular optimization algorithms, and computing normalized/calibrated glucose records amount to a mathematical concept which includes mathematical relationships, mathematical formulas or equations, and mathematical calculations. The mathematical concept need not be expressed in mathematical symbols but not merely limitations that are based on or involve a mathematical concept (MPEP § 2106.04(a)(2)(I)(A) citing the abstract idea grouping for mathematical concepts for mathematical relationships). Examiner notes that, in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the use of a computer to train a model, including a random forest model, a support vector machine model, a decision tree, and/or a neural network model, utilizing the training embodiments offered in the instant specification amount to applying data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) and therefore are mere instructions to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014) consistent with Example 47 claim 2. While the model designs are left in the abstraction, note that their use may also be considered additional elements amounting to “apply it” under Step 2A Prong 2 and Step 2B. The techniques outlined, and Examiner notes the known methods of training to one of ordinary skill in the art, are mathematical algorithms of labeling and fitting data to a particular model representation.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1 and 11 recite receiving data collected by a medical physiological sensor of the medical blood glucose detection equipment. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Claim 11 recites a device for realizing the steps of the blood glucose prediction method combining the big data model and the personalized model of claim 1, comprising a dedicated hardware big data model preparation module, a medical data acquisition data receiving module, a dedicated hardware personalized model preparation module, and a dedicated hardware blood glucose prediction processing module. Claim 12 recites electronic equipments, comprising a medical-grade non-volatile memory, a dedicated embedded processor and a medical wireless transceiver, wherein the processor is configured to be coupled with the memory through a medical data bus, and read and execute medical blood glucose prediction instructions in the memory, so as to realize the method according to any one of wherein the processor is configured to be coupled with the memory, and read and execute instructions in the memory, so as to realize the method of claim 1, the transceiver is coupled with the processor through a medical data bus, and the processor controls the transceiver to send and receive medical blood glucose monitoring messages between the medical electronic equipment and a medical blood glucose detection sensor or terminal. Claim 13 recites non-transitory computer-readable storage medium for medical blood glucose detection equipment, which stores computer instructions for medical blood glucose prediction.
The specification notes that the computer and corresponding hardware may be any general purpose computer (see the instant specification in ¶ 00184-185). Therefore, the use of a computer and corresponding hardware is recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 11 recites a device for realizing the steps of the blood glucose prediction method combining the big data model and the personalized model of claim 1, comprising a dedicated hardware big data model preparation module, a medical data acquisition data receiving module, a dedicated hardware personalized model preparation module, and a dedicated hardware blood glucose prediction processing module. Claim 12 recites electronic equipments, comprising a medical-grade non-volatile memory, a dedicated embedded processor and a medical wireless transceiver, wherein the processor is configured to be coupled with the memory through a medical data bus, and read and execute medical blood glucose prediction instructions in the memory, so as to realize the method according to any one of wherein the processor is configured to be coupled with the memory, and read and execute instructions in the memory, so as to realize the method of claim 1, the transceiver is coupled with the processor through a medical data bus, and the processor controls the transceiver to send and receive medical blood glucose monitoring messages between the medical electronic equipment and a medical blood glucose detection sensor or terminal. Claim 13 recites non-transitory computer-readable storage medium for medical blood glucose detection equipment, which stores computer instructions for medical blood glucose prediction.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claims 1 and 11 recite receiving data collected by a medical physiological sensor of the medical blood glucose detection equipment. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. 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 conventional computer implementation.
Claims 1-13 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 03 March 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant first asserts that the claims recite a particular treatment or prophylaxis practical application under Step 2A Prong 2. Examiner disagrees. As there is no positively recited administration step of the treatment, but only the intended use to calibrate a record for preprandial and postprandial blood glucose, the claim does not qualify as a prophylaxis step under Step 2A Prong 2.
In order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used. Therefore, the rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857