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 .
Information Disclosure Statement
The information disclosure statement filed 07/11/2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information of “ZAINUDDIN Z et al.” referred to therein has not been considered.
Claim Objections
Claims 1-13 are objected to because of the following informalities:
Claim 1, line 1: “System” should be replaced with –A system–;
Claim 1, lines 2, 5-12: the bullet points should be deleted;
Claim 1, lines 2, 5-12: the first word of each line should be in lowercase;
Claim 1, lines 4-12: a semicolon should be at the end of each line;
Claim 1, line 12: “Tablet” should be in lowercase;
Claim 1, line 14: the semicolon should be replaced with a comma;
Claim 1, lines 19 and 20: “rolling window” and “Mexican Hat” should not be in italics;
Claim 1, line 20: “the Mexican Hat” should be replaced with –a Mexican Hat–;
Claim 1, line 21: the” should be replaced with –a–;
Claims 2-6, line 1: each recitation of “System” should be replaced with –The system–;
Claim 2, line 2: “including” should be replaced with –wherein the available data includes–;
Claim 4, lines 1-2: “What-if” and “Agnostic” should not be in italics;
Claim 7, line 1: “Non-invasive” should be replaced with –A non-invasive–;
Claim 7, line 3: “Store” should be replaced with –storing–;
Claim 7, lines 3, 5, 7-9: the bullet points should be deleted;
Claim 7, lines 4, 6, 7, 8, 9: the periods should be deleted;
Claim 7, lines 5, 7-8: “Generate” should be replaced with –generating–;
Claim 7, line 7: -–the– should be inserted before “user”;
Claim 7, line 8: -–the– should be inserted before “user”;
Claim 7, lines 13 and 14: “rolling window” and “Mexican Hat” should not be in italics;
Claims 8-13, line 1: “Non-invasive” should be replaced with –The non-invasive–;
Claim 8, line 2: “an activity” should be replaced with –the activity–;
Claim 9, line 2: “including” should be replaced with –wherein the available data includes–; and
Claim 11, line 2: “What-if” and “Agnostic” should not be in italics.
Appropriate correction is required.
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“A prediction generator module” because it uses a generic placeholder (i.e., module) that is coupled with functional language (i.e., prediction generator) without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
“Prediction generator” amounts to functional language because the term generator, in the context of ¶ [0020] of the published application, does not have any associated structure, material or acts, and the term only defines a function (i.e., “generator” is equivalent to “an element configured for generating”).
The published application describes a prediction model generator in Fig. 2 and ¶ [0020]. However, there is insufficient description of the algorithms for using the various machine learning techniques and the training data to arrive at the generation of the prediction. See MPEP 2181(II)(B). Therefore, the “prediction generator module” is being interpreted to correspond to any module configured to generate a prediction.
“A glucose model generator module” because it uses a generic placeholder (i.e., module) that is coupled with functional language (i.e., glucose model generator) without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
“Glucose model generator” amounts to functional language because the term generator, in the context of ¶ [0021] of the published application, does not have any associated structure, material or acts, and the term only defines a function (i.e., “generator” is equivalent to “an element configured for generating”).
The published application describes a glucose model generator in Fig. 3 and ¶ [0021]. Although the module includes a grammatical evolution module, a neural networks module, and a module of systems based on fuzzy logic, there is insufficient description of the algorithms for using the various modules to arrive at the generation of the glucose model. See MPEP 2181(II)(B). Therefore, the “glucose model generator module” is being interpreted to correspond to any module configured to generate a glucose model.
“A physiological variable generator module” because it uses a generic placeholder (i.e., module) that is coupled with functional language (i.e., physiological variable generator) without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
“physiological variable generator” amounts to functional language because the term generator, in the context of ¶ [0026] of the published application, does not have any associated structure, material or acts, and the term only defines a function (i.e., “generator” is equivalent to “an element configured for generating”).
The published application describes a physiological variable generator in Fig. 4 and ¶ [0026]. Although the module includes a grammatical evolution module, a neural networks module, and a module of systems based on fuzzy logic, there is insufficient description of the algorithms for using the various modules to arrive at the generation of the physiological variable. See MPEP 2181(II)(B). Therefore, the “physiological variable generator module” is being interpreted to correspond to any module configured to generate a physiological variable.
“An alarm generator module” because it uses a generic placeholder (i.e., module) that is coupled with functional language (i.e., physiological variable generator) without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
“alarm generator” amounts to functional language because the term generator, in the context of ¶ [0027] of the published application, does not have any associated structure, material or acts, and the term only defines a function (i.e., “generator” is equivalent to “an element configured for generating”).
The published application describes an alarm model generator in Fig. 5 and ¶¶ [0027]-[0028]. Although the module includes a time series generator, a wavelet filter, deep learning classification system, there is insufficient description of the algorithms for using the various modules to arrive at the generation of the alarm. See MPEP 2181(II)(B). Therefore, the “alarm generator module” is being interpreted to correspond to any module configured to generate a physiological variable.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
Claims 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
As indicated in MPEP 2161.01, the specification fails to support a claim that defines the invention in functional language specifying a desired result when the specification does not sufficiently identify how the invention achieves the claimed function. For there to be sufficient disclosure for a computer-implemented claim limitation, it is not enough that one skilled in the art could write a program to achieve the claimed function. Rather, the specification must disclose the algorithm, steps, or procedure for performing the claimed function in sufficient detail such that one of ordinary skill can reasonably conclude that the inventor invented the claimed subject matter. In other words, the algorithm, steps, or procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.
Claim limitations “A prediction generator module”, “A glucose model generator”, “A physiological variable generator module”, and “An alarm generator module” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function. MPEP 2181(II)(B) indicates mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting “software” without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure. In this case, the published application describes the modules at a high level of generality in Figs. 2-5 and ¶¶ [0020]-[0028]. However, there is insufficient description of the algorithms for using the various techniques and the training data to arrive at the claimed function. In order for one of ordinary skill to reasonably conclude that the inventor provided an adequate disclosure of the claimed subject matter, the specification would have to include more details regarding, for example, what features of the training data are used and how the machine learning algorithms and other techniques are utilized to perform the claimed functions.
Claim 1 recites “a learning system, to which a data augmentation phase is added with a rolling window and then a wavelet transform is applied with the Mexican Hat function and the Morlet function” in lines 19-21.The above limitation indicates that the data augmentation phase is added to the learning system. However, the specification does not disclose the algorithm, steps, or procedure for adding the data augmentation phase to the learning system. At most, ¶¶ [0028], [0044]-[0047] of the published application indicate that the data enhancement phase is performed to generate spectrograms. There is no indication of how the data augmentation phase is added to any “learning system”. Claim 7 recites a similar limitation in lines 12-15, so claim 7 is rejected on similar grounds.
Claim 1 recites “a wavelet transform is applied with the Mexican Hat function and the Morlet function” in lines 20-21. The specification does not disclose the algorithm, steps, or procedure for performing a wavelet transform based on both the Mexican Hat function and the Morlet function. The recitation indicates that a wavelet transform is applied with both the Mexican Hat function and the Morlet function. However, one of ordinary skill in the art would understand that a wavelet transform is performed by using ones one of the two functions. At most, the specification indicates that the use of the Mexican Hat function and the Morlet wavelet produce different results, which also indicates that the wavelet transform is based on only one of the two functions. See ¶ [0058] and Figs. 7-8 of the published application. Claim 7 recites a similar limitation in lines 12-15, so claim 7 is rejected on similar grounds.
Claims 2-6 and 8-13 are rejected by virtue of their dependence from claim 1 or 7, respectively.
Claim 2 recites “the prediction models are trained using available data previously collected from volunteers, including interstitial blood glucose data”. The specification does not disclose the algorithm, steps, or procedure for performing the training of the prediction models. At most, ¶ [0020] describes the “combined use of wavelet Transforms, Deep Learning, Genetic Programming, Evolutionary Grammars and Fuzzy Logic Based Systems” at a high level of generality, and the recitation does not provide sufficient description of how the elements are used together and are trained. In order for one of ordinary skill to reasonably conclude that the inventor invented the claimed subject matter, the specification would have to include more details regarding, for example, how the above elements are arranged in relation to each other, what the inputs to the algorithms are, and what features of the training data are used. Claim 9 recites similar limitations, so it is rejected on similar grounds. Claim 7 recites “Generate prediction models by measuring interstitial glucose and physiological variables in volunteer individuals different from the user” in lines 5-6, which does not have sufficient support in the specification for similar reasons.
Claim 4 recites “the glucose models are trained using What-if and Agnostic scenarios” in lines 1-2. The claim language suggests that the glucose models are trained using both scenarios. The specification does not disclose the algorithm, steps, or procedure for performing the training using both scenarios. In order for one of ordinary skill to reasonably conclude that the inventor invented the claimed subject matter, the specification would have to include more details regarding, for example, how the two scenarios are used to train the glucose models. At most, ¶ [0025] describes training the models using the What-if and Agnostic scenarios at a high-level of generality without describing how the two scenarios are used to train the glucose models. Claim 11 recites a similar limitation, so it is rejected on similar grounds.
Claim 7 recites “Generate prediction models by measuring interstitial glucose and physiological variables in volunteer individuals different from the user” in lines 5-6, “Generate blood glucose models from user interstitial glucose data” in line 7, “Generate models of user physiological variables” in line 8, and “Generate hypoglycemia and hyperglycemia alarm models” in line 9. However, the specification does not disclose the algorithm, steps, or procedure for generating the models. Paragraphs [0020]-[0027] describe the model generators, but the specification merely describe the generations at a high level of generality without providing sufficient details of the algorithm, steps, or procedure for generating the models. In order for one of ordinary skill to reasonably conclude that the inventor invented the claimed subject matter, the specification would have to include more details regarding, for example, what features of the training data are used, how the machine learning algorithms and other techniques are utilized to arrive at the model, and what the models actually do/are.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “A prediction generator module”, “A glucose model generator”, “A physiological variable generator module”, and “An alarm generator module” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. MPEP 2181(II)(B) indicates that mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting “software” without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. In this case, the published application describes the modules in Figs. 2-5 and ¶¶ [0020]-[0028]. However, there is insufficient description of the algorithms for using the various techniques and the training data to arrive at the claimed function. In order for one of ordinary skill to reasonably conclude that the inventor provided an adequate disclosure of the claimed subject matter, the specification would have to include more details regarding, for example, what features of the training data are used and how the machine learning algorithms and other techniques are utilized to perform the claimed functions.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 1 recites “System for prediction of glucose values and generation of hypoglycemia and hyperglycemia alerts” in lines 1-2. However, none of the limitations refer to the predicted glucose values or the generation of the hypoglycemia and hyperglycemia alerts. Therefore, it is unclear how the system achieves the prediction of the glucose values and the generation of the hypoglycemia and hyperglycemia alerts. Claim 7 recites similar limitations, so the claim is rejected on similar grounds.
Claim 1 recites “the information” in lines 12-13. There is insufficient antecedent basis for this limitation in the claim because the claim does not recite “information”. The Examiner suggests amending line 3 of claim 1 such that it recites “collects information comprising heart rate,…”
Claim 1 recites “A mobile device or Tablet… stores local models and generates alarms in the activity wristband” in lines 12-15. It is unclear how the mobile device or table generates alarms in the activity wristband because they are separate elements. How does one element generate alarms in the second separate element. For the purposes of examination, the recitation will be interpreted to be “A mobile device or Tablet… stores local models andcauses generation of alarms in the activity wristband”.
Claim 1 recites “the alarm model generator” in line 16. There is insufficient antecedent basis for this limitation in the claim because it does not previously recite an alarm model generator. The Examiner suggests positively reciting an alarm model generator.
Claim 1 recites “the alarm model generator” in line 16. It is unclear what an alarm model is. The published application describes the alarm model generator in Fig. 5 and ¶ [0027], but it does not define what the alarm model is. There is no plain and ordinary meaning for alarm model, and one of ordinary skill in the art would not understand what it is based on the specification. For the purposes of examination, the recitation will not be given patentable weight.
Claim 1 recites “generate images corresponding to hypoglycemia or hyperglycemia situations, which are used to train a learning system” in lines 17-19. The grammatical structure of the recitation indicates that the hypoglycemia or hyperglycemia situations are used to train the learning system. However, ¶ [0027] of the published application indicates that the images are used to train a deep learning classification system. Therefore, in light of the specification, it is unclear whether the images or the situations are used to train the learning system. For the purposes of examination, the recitation will be interpreted to be “generate images corresponding to hypoglycemia or hyperglycemia situations, wherein the images are used to train a learning system”. Claim 7 recites a similar limitation in lines 11-13, so the claim is rejected on similar grounds.
Claim 1 recites “generate images corresponding to hypoglycemia or hyperglycemia situations, which are used to train a learning system, to which a data augmentation phase is added with a rolling window and then a wavelet transform is applied with the Mexican Hat function and the Morlet function”. The grammatical structure of the recitation indicates that the data augmentation is added to the learning system. However, ¶ [0028] of the published application suggests that the data augmentation is performed to generate spectrograms (i.e., the images). Therefore, in light of the specification, it is unclear whether (A) the data augmentation phase is added to the learning system or (B) the data augmentation is used to generate the images. Additionally, if the data augmentation phase is added to the learning system, it is unclear how a learning system (e.g., deep learning classification system of ¶ [0027]) receives a data augmentation phase. The specification does not provide clarification. Claim 7 recites a similar limitation in lines 11-15, so the claim is rejected on similar grounds.
Claim 1 recites “a wavelet transform is applied with the Mexican Hat function and with the Morlet function” in lines 20-21. The recitation indicates that a wavelet transform is applied with both the Mexican Hat function and the Morlet function. However, one of ordinary skill in the art would understand that a wavelet transform is performed by using ones one of the two functions. At most, the specification indicates that the use of the Mexican Hat function and the Morlet wavelet produce different results, which also indicates that the wavelet transform is based on only one of the two functions. Therefore, it is unclear whether a wavelet transform is applied using both functions or only one function. For the purposes of examination, the recitation will be interpreted to be a wavelet transform is applied with the Mexican Hat function or with the Morlet function. Claim 7 recites a similar limitation in lines 14-15, so the claim is rejected on similar grounds.
Claims 2-6 are rejected by virtue of their dependence from claim 1. Claims 8-13 are rejected by virtue of their dependence from claim 7.
Claim 2 recites “the prediction models” in line 1. There is insufficient antecedent basis for this limitation in the claim because the claim does not recite any prediction models. Additionally, it is unclear how the prediction models are related to the other elements of the system, so the prediction models seem wholly unrelated to the previously recited limitations.
Claim 3 recites “the glucose models” in line 1. There is insufficient antecedent basis for this limitation in the claim because the claim does not recite any glucose models. Additionally, it is unclear how the glucose models are related to the other elements of the system, so the glucose models seem wholly unrelated to the previously recited limitations..
Claim 3 recites “the glucose models are generated using different artificial intelligence techniques, such as genetic programming, deep learning and Takagi-Sugeno-Kang fuzzy rules” in lines 1-3.Due to the grammatical structure, it is unclear whether this limitation should be interpreted to be (A) each of a plurality of glucose models are generated using different artificial intelligence techniques or (B) glucose models are generated using artificial intelligence techniques that are different from some other technique. If the latter interpretation is the intended interpretation, it is unclear what the other technique is.
Claim 3 recites “artificial intelligence techniques, such as genetic programming, deep learning and Takagi-Sugeno-Kang fuzzy rules” in lines 2-3.The phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). The Examiner suggests deleting the recitation or reciting “wherein the artificial intelligence techniques comprises genetic programming, deep learning or Takagi-Sugeno-Kang fuzzy rules” in a dependent claim. For the purposes of examination, the recitation of “such as genetic programming, deep learning and Takagi-Sugeno-Kang fuzzy rules” will not be given patentable weight. Claim 10 recites a similar limitation, so it is rejected on similar grounds.
Claim 4 recites “the glucose models are trained using What-if and Agnostic scenarios” in lines 1-2, which indicates that the glucose models are trained using both scenarios. However, it is generally understood that a model is trained using only one scenario. Therefore, it is unclear whether the models are indeed trained on both scenarios or only one of the two scenarios. For the purposes of examination, the limitation will be interpreted to be “the glucose models are trained using What-if or Agnostic scenarios”. Claim 11 recites a similar limitation, so it is rejected on similar grounds.
Claim 5 recites “the spectrograms” in line 1 and “the alarm models” in lines 1-2. There are insufficient antecedent bases for these limitations in the claim because the claim does not recite any spectrograms or alarm models. Additionally, it is unclear how the spectrograms and alarm models are related to the other elements of the system, so the spectrograms and alarm models seem wholly unrelated to the previously recited limitations.
Claim 5 recites “severe hypoglycemia” in line 2 and “severe hyperglycemia” in line 3. The term The term “severe” is a relative term which renders the claim indefinite. The term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In this case, it is unclear when something starts/stops being considered severe/mild, and the specification does not provide a standard for ascertaining the requisite degree. Claim 12 recite similar limitations, so it is rejected on similar grounds.
Claim 6 recites “alarm signals are generated” in line 1, which is a method step. A single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, because it creates confusion as to when direct infringement occurs. (MPEP 2173.05(p) citing In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 97 USPQ2d 1737 (Fed. Cir. 2011)). Additionally, the alarm signal generation is not tied to any of the structural features of the system, so it is unclear what is generating the alarm signals. The Examiner suggests positively reciting which of the structural elements is configured to generate the alarm signal.
Claim 7 recites “the claimed system” in line 2. There is insufficient antecedent basis for this limitation in the claim. Although claim 1 includes a system, there is no indication that claim 7 is dependent upon claim 1.
Claim 7 recites “the activity wristband” in lines 3-4 and “the alarm model generator” in line 10. There are insufficient antecedent bases for these limitations in the claim. For the purposes of examination, the recitations will be interpreted to be “an activity wristband” and “an alarm model generator”.
Claim 7 recite “Generate prediction models by measuring interstitial glucose and physiological variables in volunteer individuals different from the user” in lines 5-6. The recitation suggests that measuring the variables results in the generation of the prediction models, which is indefinite because measuring variables does not inherently result in the generation of prediction models. Further processing steps are required. For the purposes of examination, the recitation will be interpreted to be “Generate prediction models based on interstitial glucose and physiological variables measured in volunteer individuals different from the user”.
Claim 7 recites “hypoglycemia and hyperglycemia alarm models” in line 9 and “alarm model generator” in line 10. It is unclear what these alarm models are. The published application describes the alarm model generator in Fig. 5 and ¶ [0027], but it does not define what the alarm model is. There is no plain and ordinary meaning for alarm model, and one of ordinary skill in the art would not understand what it is based on the specification. For the purposes of examination, the recitation will not be given patentable weight.
Claim 7 recites “characterized by the alarm model generator which takes data obtained from a continuous glucose meter and obtains a time series to generate images corresponding to hypoglycemia or hyperglycemia situations, which are used to train a learning system, to which a data enhancement phase is added with a rolling window and then a wavelet transform is applied with the Mexican Hat function and with the Morlet function, generating spectrograms” which is so grammatically awkward that the meaning is unclear. How is a method characterized by an alarm model generator? What is generating the spectrograms? Clarification is required.
Claim 8 recites “the physiological variable data” in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites “images” in line 11 and “spectrograms” in line 15. It is unclear whether these limitations are the same as, related to, or different from each other. The difference in terminology suggest that they are different. However, the specification at ¶ [0028] suggests that they are the same. The examiner suggests (A) replacing “images” in line 11 with “spectrograms”, and (B) inserting –the– before “spectrograms in line 15.
Claim 9 recites “the prediction models are trained using available data previously collected from volunteers, including interstitial glucose data.” Claim 7 recites “generate prediction models by measuring interstitial glucose and physiological variables in volunteer individuals different from the user” in lines 5-6. First, It is unclear how the “interstitial glucose data” of claim 9 is related to the “interstitial glucose and physiological variables” in claim 7. The specification at ¶ [0020] suggests that they are the same, but the different terminology suggests that they are different. Second, it is unclear how the “volunteers” of claim 9 are related to the “volunteer individuals” of claim 7. The specification does not create a distinction between the elements, which suggests that they are the same. However, the different terminology suggests that they are different. Clarification is required.
Claim 12 recites “the spectrograms generated in the alarm models” in lines 1-2. There is insufficient antecedent basis for this limitation in the claim because the claim does not recite any spectrograms generated in the alarm models. Although the claim 7 recites “generating spectrograms” in line 15, there is no indication that he alarm models generates the spectrograms.
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. Claims 1-13 do not include additional elements that integrate the exception into a practical application of the exception or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, p. 50, January 7, 2019), and the 2024 Guidance Update on Patent Subject Matter Eligibility (Federal Register, Vol. 89, No. 137 p. 58128, July 17, 2024).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is directed to a machine, which is a statutory category.
Step 2A - Prong 1: Claim 1 is directed to an abstract idea in the form of a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components.
In particular, claim 1 recites the following limitations:
[A1]: a prediction generator module;
[B1]: a glucose model generator module
[C1]: a physiological variable generator module;
[D1]: a pattern analyzer
[E1]: characterized in that the alarm model generator takes data obtained from a continuous glucose meter and obtains a time series to generate images corresponding to hypoglycemia or hyperglycemia situations, which are used to train a learning system, to which a data augmentation phase is added with a rolling window and then a wavelet transform is applied with the Mexican hat function and the Morlet function.
These elements [A1]-[E1] of claim 1 are directed to an abstract idea because they are processes that, under their broadest reasonable interpretation, are mere steps that are capable of being mentally performed with the aid of pen and paper. For example, a skilled artisan is capable of generating a prediction, generating a glucose model, generating a physiological variable, analyzing a pattern, reading time series data from a continuous glucose meter, use a rolling window on the time series data, performing a wavelet transform using a Mexican hat function or a Morlet function to generate spectrograms (i.e., images), and train a learning system using the images.
Step 2A - Prong Two: Claim 1 does not recite additional elements that integrate the judicial exception into a practical application. Claim 1 recites the following additional elements:
[A2]: An activity wristband that collects heart rate, physical activity, energy expenditure and electrocardiogram (ECG) data;
[B2]: A database;
[C2]: A web interface;
[D2]: A mobile device or Tablet with internet connection, which stores the information collected by the activity wristband, interfaces with the database and other blocks of the system; and stores local models and generates alarms in the activity wristband.
The elements [A2]-[D2] do not integrate the exception into a practical application of the exception.
The elements [A2] and [D2] do not integrate the exception into a practical application because the elements amount to merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well known elements - see MPEP 2106.04(d); MPEP 2106.05(g). Additionally or alternatively, the elements amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.04(d); 2106.05(h).
The elements [B2]-[D2] do not integrate the exception into a practical application of the exception because the elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - See MPEP 2106.04(d) and MPEP 2106.05(f).
Accordingly, each of the additional elements do not integrate the abstract into a practical application because they do not impose any meaningful limitations on practicing the abstract idea.
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. Claim 1 recites the following additional elements:
[A2]: An activity wristband that collects heart rate, physical activity, energy expenditure and electrocardiogram (ECG) data;
[B2]: A database;
[C2]: A web interface;
[D2]: A mobile device or Tablet with internet connection, which stores the information collected by the activity wristband, interfaces with the database and other blocks of the system; and stores local models and generates alarms in the activity wristband.
The elements [A2]-[D2] do not amount to significantly more than the judicial exception itself.
The elements [A2] and [D2] do not qualify as significantly more because the elements amount to merely adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering at a higher level of generality in conjunction with the abstract idea that uses conventional, routine, and well known elements - see MPEP 2106.05(g). Additionally or alternatively, the elements amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Additionally, the elements [A2] and [D2] amount to well-understood, routine, and conventional elements. For example, the element [A2] is disclosed in ¶ [0057] of US 2019/0196411 A1 (Yuen, Micahel); ¶ [0085] of US 2017/0251935 A1 (Yuen, Shelten); and ¶ [0028] of US 2020/0100693 A1 (Velo), wherein the plurality of disclosures depict the well-understood, routine, and conventional nature of the element. Additionally, the element [D2] is disclosed in ¶ [0098] of US 2014/0343462A1 (Burnet); ¶¶ [0017], [0137] of US 2020/0160248 A1 (Harmon); ¶ [0099] of US 2021/0321953 A1 (Panneer Selvam), wherein the plurality of disclosures depict the well-understood, routine, and conventional nature of the element.
The elements [B2]-[D2] do not qualify as significantly more because these elements are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (See MPEP 2106.05(d)(II); Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (See MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Independent claim 7 recites mirrored method limitations and is not patent eligible for substantially similar reasons.
Claims 2-6 depend from claim 1, and they recite the same abstract idea as claim 1. Claims 8-13 depend from claim 7, and they recite the same abstract idea as claim 7. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process) and/or append abstract ideas (that is, the claims only recite limitations that add further mental processes) except for the following limitations.
Claims 3 and 10 recite “the glucose models are generated using different artificial intelligence techniques such as genetic programming, deep learning and Takagi-Kang fuzzy rules”. However the above element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amount to merely adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.04(d); MPEP 2106.05(g) and/or simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry See MPEP 2106.05(d)(II). Additionally, the element is well-understood, routine, and conventional, as is evidenced by ¶ [0013] of US 2020/0352517 (Jos).
Claims 6 and 13 recite “alarm signals are generated for the four categories other than normoglycemia”. However the above element does not integrate the exception into a practical application of the exception or qualify as significantly more because the element amount to merely adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.04(d); MPEP 2106.05(g). Additionally, the element is well-understood, routine, and conventional, as is evidenced by ¶ [0002] of US 2019/0142317 A1 (Steedman).
In view of the above, the additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4 and 7-11 are rejected under 35 U.S.C. 103 as being unpatentable over Determination of Blood Glucose Concentration by Using Wavelet Transform and Neural Networks (Ashok) in view of US 2022/0361780 A1 (Erraguntla), US 2017/0323285 A1 (Xing), and A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients (Zainuddin).
With regards to claim 1, Ashok teaches a system for prediction of glucose values (Abstract and Patients and Methods disclose a system and method for prediction of blood glucose concentration) comprising: A database (Patients and Methods: Decomposition of Signals disclose stored signals, which requires a database) A prediction generator module user(Patients and Methods: Preparing Data to Implement Neural Network Techniques disclose the prediction of blood glucose concentration was done using back propagation network (BPN) with gradient descent algorithm with radial basis function (RBF), with extreme learning machine; Results: Paragraph 7 indicates that the data of 450 patients were used for training the architecture, wherein the trained architecture amounts to a prediction generator module; Patients and Methods: Data Collection discloses retrieving sensor outputs, pathology, biochemistry, and lipid profile along with the corresponding developed system sensor outputs; Ashok: Results: Last Paragraph indicates that the scattered signals were collected from the interstitial fluid and blood vessels, which indicates that the acquired data includes interstitial blood glucose data) - A glucose model generator module (Results: Paragraph 7 indicates that the data of 225 patients were used for testing the architecture, wherein the testing architecture for generating the glucose prediction amounts of the 225 patients amounts to a glucose model generator module) - A pattern analyzer (Patients and Methods: Decomposition of Signals disclose wavelet transform techniques which analyze the signals using mathematical functions) characterized in that the alarm model generator takes data obtained from a continuous glucose meter and obtains a time series to generate images/spectrograms corresponding to hypoglycemia or hyperglycemia situations (Patients and Methods: Developing Laser System and Decomposition of Signals indicate that the system is configured to receive a time series of photodetector data, wherein the signals are then processed by wavelet transform, which necessarily results in a spectrogram (i.e., an image). Patients and Methods: Developing Laser System depict a He-Ne laser system Patients and Methods: Preparing Data to Implement Neural Network Techniques Indicates that the decomposed outputs (i.e, the image) are classified into group I: 0-150 mg/dl, group II: 151-250 mg/dl, group III: 251-350 mg/dl, group IV: 351-450 mg/dl, and group V: ≥451 mg/ dl, which include hypoglycemia and hyperglycemia situations), which are used to train a learning system (Results: Paragraph 7 indicates that the data of 450 patients were used for training the architecture)
Ashok is silent with regards to whether the system is for generation of hypoglycemia and hyperglycemia alerts.
In a system relevant to the problem of predicting glucose-related events, Erranguntla teaches generation of hypoglycemia and hyperglycemia alerts (Abstract and ¶ [0005] disclose issuing an alert to the user in response to a prediction including a prediction that a hypoglycemic event or a hyperglycemic event will occur). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Ashok to incorporate the generation of hypoglycemia and hyperglycemia alerts. The motivation would have been to allow the user to address the predicted event (¶ [0056] of Erranguntla).
The above combination is silent regarding:
An activity wristband that collects heart rate, physical activity, energy expenditure and electrocardiogram (ECG) data,
- A physiological variable generator module
- An alarm generator module
- A web interface
- A mobile device or Tablet with internet connection, which stores the information collected by the activity wristband, interfaces with the database and other blocks of the system; and stores local models and generates alarms in the activity wristband.
In a system relevant to the problem of glucose monitoring (¶ [0061] of Xing indicates the acquired biometric parameters include a glucose level), Xing teaches An activity wristband that collects heart rate, physical activity, energy expenditure and electrocardiogram (ECG) data (Fig. 1 and ¶¶ [0037] depict a wearable personal digital (WPD) device attached to a wrist for sensing and storing biometric data associated with the user (blood pressure, heart rate, temperature, and so forth); ¶ [0043] indicates the WPD 200 includes activity tracking sensors; ¶ [0065] indicates the determination of calories burned; ¶ [0062] indicates the biometric sensors may further include skin contact sensor data engine for monitoring a user electrocardiogram) - A physiological variable generator module (¶ [0062] discloses a skin contact sensor data engine for monitoring an electrocardiogram, wherein the data engine amounts to a physiological variable generator because it generates an electrocardiogram)- An alarm generator module (¶ [0061] discloses that the biometric sensors may be configured to produce the alarm, which indicates that there is an alarm generator module) - A web interface (¶ [0070] discloses the processor of the WPD may be further operable to access control data and supplementary data including hot links to websites and advertising data), - A mobile device or Tablet with internet connection (¶ [0038] discloses the WPD device is communicatively coupled with an external device such as a smartphone using a wireless network, such as a Wi-Fi Network), which stores the information collected by the activity wristband (¶ [0061] discloses that the external device receives and processes or displays the biometric parameters sensed by the biometric sensors), interfaces with the database and other blocks of the system (¶ [0008] indicates the external device is configured to communicate with the processor of the WPD); and stores local models (¶ [0061] indicates that the external device receives biometric parameters for further processing, which indicates that the external device stores the parameters which represent (i.e., model) the biometrics) and generates alarms in the activity wristband (¶ [0044] discloses the processor of the WPD receiving data from the external device for causing a notification). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above system to incorporate an activity wristband that collects heart rate, physical activity, energy expenditure and electrocardiogram (ECG) data,- a physiological variable generator module - an alarm generator module- a web interface - a mobile device or tablet with internet connection, which stores the information collected by the activity wristband, interfaces with the database and other blocks of the system; and stores local models and generates alarms in the activity wristband, as taught by Xing because it would have been the combination of prior art elements according to known methods to yield predictable results. Additionally or alternatively, the motivation would have been to make the glucose prediction system easier to use by providing more user interfaces.
The above combination is silent regarding a data augmentation phase is added with a rolling window and then a wavelet transform is applied with the Mexican Hat function and with the Morlet function.
In the same field of endeavor of monitoring a blood glucose, Zainuddin teaches a learning system to which a data augmentation phase is added with a rolling window (II. MATERIAL AND METHODOLOGY: B. Feature Selection recites moving a window of Length L along the time-series with a step s at a time to find recurrent features, wherein the features are converted to PCs which are input into the WNN model) and then a wavelet transform is applied with the Mexican Hat function and with the Morlet function (II. MATERIAL AND METHODOLOGY: C. Blood Glucose Level Prediction Based on Wavelet Neural Network discloses the wavelet neural network based on the Mexican Hat, and Morlet wavelet families, wherein the WNN necessarily includes a wavelet transform based on the wavelet families). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the learning system of Ashok with the learning system to which a data augmentation phase is added with a rolling window and then a wavelet transform is applied with the Mexican Hat function and with the Morlet function, as taught by Zainuddin. Because both learning system are capable of being used for arriving at a glucose prediction, it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
With regards to claim 2, the above combination teaches or suggests the prediction models are trained using available data previously collected from volunteers, including interstitial blood glucose data (Ashok: Results: Paragraph 7 indicates that the data of 450 patients were used for training the architecture; Ashok: Results: Last Paragraph indicates that the scattered signals were collected from the interstitial fluid and blood vessels, which indicates that the acquired data includes interstitial blood glucose data)
With regards to claim 3, the above combination teaches or suggests the glucose models are generated using different artificial intelligence techniques such as genetic programming, deep learning and Takagi-Sugeno-Kang fuzzy rules (In view of the rejection under 35 U.S.C. §112(b), the recitation of “such as genetic programming, deep learning and Takagi-Sugeno-Kang fuzzy rules” is not being given patentable weight; Ashok: Patients and Methods: Preparing Data to Implement Neural Network Techniques disclose the prediction of blood glucose concentration was done using back propagation network (BPN) with gradient descent algorithm with radial basis function (RBF), which are different artificial intelligence techniques..
With regards to claim 4, the above combination teaches or suggests the glucose models are trained using What-if and Agnostic scenarios (In view of the rejection under 35 U.S.C. §112(b), the limitation is being interpreted to be “the glucose models are trained using What-if or Agnostic scenarios”; Ashok: Patients and Methods: Developing Decomposition of Signals depict using past data, which amounts to agnostic scenarios)
With regards to claim 7, Ashok teaches a non-invasive method for predicting glucose values using the claimed system (Abstract and Patients and Methods disclose a system and method for prediction of blood glucose concentration) comprising: generating prediction models by measuring interstitial glucose and physiological variables in volunteer individuals different from the user (Patients and Methods: Preparing Data to Implement Neural Network Techniques disclose the prediction of blood glucose concentration was done using back propagation network (BPN) with gradient descent algorithm with radial basis function (RBF), with extreme learning machine; Results: Paragraph 7 indicates that the data of 450 patients were used for training the architecture, wherein the trained architecture amounts to a prediction models; Patients and Methods: Data Collection discloses retrieving sensor outputs, pathology, biochemistry, and lipid profile along with the corresponding developed system sensor outputs; Ashok: Results: Last Paragraph indicates that the scattered signals were collected from the interstitial fluid and blood vessels, which indicates that the acquired data includes interstitial blood glucose data) - generate blood glucose models from user interstitial glucose data(Results: Paragraph 7 indicates that the data of 225 patients were used for testing the architecture, wherein the testing architecture for generating the glucose prediction amounts of the 225 patients amounts to a glucose model generation), generate hypoglycemia and hyperglycemia alarm models (in view of the rejection under 35 U.S.C. §112(b), the recitation is not being given patentable weight), and characterized in that the alarm model generator takes data obtained from a continuous glucose meter and obtains a time series to generate images/spectrograms corresponding to hypoglycemia or hyperglycemia situations (Patients and Methods: Developing Laser System and Decomposition of Signals indicate that the system is configured to receive a time series of photodetector data, wherein the signals are then processed by wavelet transform, which necessarily results in a spectrogram (i.e., an image). Patients and Methods: Developing Laser System depict a He-Ne laser system Patients and Methods: Preparing Data to Implement Neural Network Techniques Indicates that the decomposed outputs (i.e, the image) are classified into group I: 0-150 mg/dl, group II: 151-250 mg/dl, group III: 251-350 mg/dl, group IV: 351-450 mg/dl, and group V: ≥451 mg/ dl, which include hypoglycemia and hyperglycemia situations), which are used to train a learning system (Results: Paragraph 7 indicates that the data of 450 patients were used for training the architecture)
Ashok is silent with regards to whether the system is for generation of hypoglycemia and hyperglycemia alerts.
In a system relevant to the problem of predicting glucose-related events, Erranguntla teaches generation of hypoglycemia and hyperglycemia alerts (Abstract and ¶ [0005] disclose issuing an alert to the user in response to a prediction including a prediction that a hypoglycemic event or a hyperglycemic event will occur). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Ashok to incorporate the generation of hypoglycemia and hyperglycemia alerts. The motivation would have been to allow the user to address the predicted event (¶ [0056] of Erranguntla).
The above combination is silent regarding storing a user’s physiological variables and interstitial glucose data in the activity wristband, and generating models of user physiological variables
In a system relevant to the problem of glucose monitoring (¶ [0061] of Xing indicates the acquired biometric parameters include a glucose level), Xing teaches storing a user’s physiological variables and glucose data in the activity wristband, (Fig. 1 and ¶¶ [0037] depict a wearable personal digital (WPD) device attached to a wrist for sensing and storing biometric data associated with the user (blood pressure, heart rate, temperature, and so forth); ¶ [0043] indicates the WPD 200 includes activity tracking sensors; ¶ [0065] indicates the determination of calories burned; ¶ [0062] indicates the biometric sensors may further include skin contact sensor data engine for monitoring a user electrocardiogram; ¶ [0055] discloses the biometric parameters include glucose level) - generating models of user physiological variables (¶ [0062] discloses a skin contact sensor data engine for monitoring an electrocardiogram, wherein the electrocardiogram is a model of the user’s cardiac bioelectric variability). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above system to incorporate, based on the teachings of Xing, storing a user’s physiological variables and interstitial glucose data in the activity wristband, and generating models of user physiological variables because it would have been the combination of prior art elements according to known methods to yield predictable results. Additionally or alternatively, the motivation would have been to acquire additional physiological variables, thereby providing a more complete diagnostic picture of the patient.
The above combination is silent regarding a data augmentation phase is added with a rolling window and then a wavelet transform is applied with the Mexican Hat function and with the Morlet function.
In the same field of endeavor of monitoring a blood glucose, Zainuddin teaches a learning system to which a data augmentation phase is added with a rolling window (II. MATERIAL AND METHODOLOGY: B. Feature Selection recites moving a window of Length L along the time-series with a step s at a time to find recurrent features, wherein the features are converted to PCs which are input into the WNN model) and then a wavelet transform is applied with the Mexican Hat function and with the Morlet function (II. MATERIAL AND METHODOLOGY: C. Blood Glucose Level Prediction Based on Wavelet Neural Network discloses the wavelet neural network based on the Mexican Hat, and Morlet wavelet families, wherein the WNN necessarily includes a wavelet transform based on the wavelet families). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the learning system of Ashok with the learning system to which a data augmentation phase is added with a rolling window and then a wavelet transform is applied with the Mexican Hat function and with the Morlet function, as taught by Zainuddin. Because both learning system are capable of being used for arriving at a glucose prediction, it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
With regards to claim 8, the above combination teaches or suggests the physiological variable data is taken using an activity wristband worn by the user (Fig. 1 and ¶¶ [0037] of Xing depict a wearable personal digital (WPD) device attached to a wrist for sensing and storing biometric data associated with the user (blood pressure, heart rate, temperature, and so forth); ¶ [0043] of Xing indicates the WPD 200 includes activity tracking sensors; ¶ [0062] of Xing indicates the biometric sensors may further include skin contact sensor data engine for monitoring a user electrocardiogram; ¶ [0055] discloses the biometric parameters include glucose level).
With regards to claim 9, the above combination teaches or suggests the prediction models are trained using available data previously collected from volunteers, including interstitial blood glucose data (Ashok: Results: Paragraph 7 indicates that the data of 450 patients were used for training the architecture; Ashok: Results: Last Paragraph indicates that the scattered signals were collected from the interstitial fluid and blood vessels, which indicates that the acquired data includes interstitial blood glucose data)
With regards to claim 10, the above combination teaches or suggests the glucose models are generated using different artificial intelligence techniques such as genetic programming, deep learning and Takagi-Sugeno-Kang fuzzy rules (In view of the rejection under 35 U.S.C. §112(b), the recitation of “such as genetic programming, deep learning and Takagi-Sugeno-Kang fuzzy rules” is not being given patentable weight; Ashok: Patients and Methods: Preparing Data to Implement Neural Network Techniques disclose the prediction of blood glucose concentration was done using back propagation network (BPN) with gradient descent algorithm with radial basis function (RBF), which are different artificial intelligence techniques..
With regards to claim 11, the above combination teaches or suggests the glucose models are trained using What-if and Agnostic scenarios (In view of the rejection under 35 U.S.C. §112(b), the limitation is being interpreted to be “the glucose models are trained using What-if or Agnostic scenarios”; Ashok: Patients and Methods: Developing Decomposition of Signals depict using past data, which amounts to agnostic scenarios)
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Determination of Blood Glucose Concentration by Using Wavelet Transform and Neural Networks (Ashok) in view of US 2022/0361780 A1 (Erraguntla), US 2017/0323285 A1 (Xing), A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients (Zainuddin), as applied to respective claims 1 and 7 above, and further in view of US 2020/0070840 A1 (Gunaratne).
With regards to claims 5 and 12, the above combination is silent regarding whether the spectrograms generated in the alarm models correspond to five categories: severe hypoglycemia, hypoglycemia, normoglycemia, hyperglycemia and severe hyperglycemia.
In the same field of endeavor of monitoring blood glucose levels, Gunaratne teaches classifying blood glucose levels into five categories: severe hypoglycemia, hypoglycemia, normoglycemia, hyperglycemia and severe hyperglycemia (¶ [0046] discloses classifying glucose levels as severely hypoglycemic state, hypoglycemic state, euglycemic, hyperglycemic and severely hyperglycemic based on different ranges of glucose levels). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the classification of the spectrograms of Ashok to incorporate that they include the five categories: severe hypoglycemia, hypoglycemia, normoglycemia, hyperglycemia and severe hyperglycemia as taught by Gunaratne. The motivation would have been to use glycemic ranges which are more diagnostically relevant to a patient’s glycemic state, thereby improving the diagnostic analysis of the patient.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Determination of Blood Glucose Concentration by Using Wavelet Transform and Neural Networks (Ashok) in view of US 2022/0361780 A1 (Erraguntla), US 2017/0323285 A1 (Xing), A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients (Zainuddin) and US 2020/0070840 A1 (Gunaratne), as applied to respective claims 5 and 12above, and further in view of US 2022/0061706 A1 (Zade).
With regards to claims 6 and 13, the above combination is silent regarding whether
alarm signals are generated for the four categories other than normoglycemia.
In a system relevant to the problem of generating hypoglycemic and hyperglycemic alarms, Zade teaches providing alarm signals for categories other than normoglycemia (¶ [0024] discloses providing an accurate and effective warning or messaging process to alert patients to imminent negative BG conditions; also see ¶ [0085]). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate, based on the teachings of Zade, that alarm signals are generated for the four categories other than normoglycemia. The motivation would have been to communicate the glucose level prediction to the user.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL C KIM whose telephone number is (571)272-8637. The examiner can normally be reached M-F 8:00 AM - 5:00 PM EST.
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, Jacqueline Cheng can be reached at (571) 272-5596. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.C.K./Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights