DETAILED ACTION
Notices to Applicant
This communication is a non-final rejection. Claims 1-20, as filed 11/25/2024, are currently pending and have been considered below.
Priority is generally acknowledged to PCT/US2023/023673 (05/26/2023) which claims benefit to 63/347,291 (05/31/2022).
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Claim Objections
Claims 1 and 12 are objected to because of the following informalities. Claims 1 and 12 state “with respect those of the initial variable.” This appears to be a typographical mistake as the word “to” is needed after the word “respect”.
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter 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
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
1. A method, comprising:
storing time series data with a storage device, the time series data including at least one biophysical response over sequential time periods;
by operation of a computing device (additional element – merely applying the abstract idea with a computer)
establishing an initial variable, having an event value corresponding to each time period,
generating a plurality of assigned variables, each having an assigned event value corresponding to each time period with one assigned event value being different with respect those of the initial variable and the other assigned variables,
evaluating the initial and assigned variables with a probability function to determine the initial or assigned variable having a highest probability of event occurrences with respect to the biophysical responses in the time periods, and
using the highest probability initial or assigned variable as the initial variable, repeating the generating the assigned variables and evaluating of the initial and assigned variables until a highest probability initial or assigned variable has been determined; and
using the highest probability initial or assigned variable to predict the at least one biophysical response in a user (abstract idea – mathematical concepts and mental processes)
Claim 1 is presented as an exemplary claim but the same analysis applies to the other independent claim.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mathematical concepts, namely a mathematical optimization algorithm. The claims evaluate a probability function with an iterative loop to find a highest probability by establishing an initial variable, flipping binary variables, and evaluating probabilities. The claimed invention further recites a mental process, namely, an evaluation of the “highest probability.”
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. For example, claims 5-9 provide more details on the mathematical algorithm used to optimize the variable values.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, “by operation of a computing device” amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0066] and [0143], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, storing time series data amounts to mere data gathering and selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For example, claims 10 and 11 recite additional limitations regarding parallel computing which amount to invoking computers as a tool to perform the abstract idea. Claims 2-4 give more detail on the data that is analyzed by the system and add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception (i.e., storing data on a storage device and using a computing device for calculations), add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. For example, storing data on a storage device and using a computing device for calculations are amount to performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Claims 10, 11, 17, and 18 add basic parallel processing features that use computer components in their ordinary capacity to speed implement the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dalal (US20200245913A1) in view of Huang (Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit Dhillon Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:1550-1559, 2017).
Regarding claim 1, Dalal discloses: A method, comprising:
--storing time series data with a storage device, the time series data including at least one biophysical response over sequential time periods (“storing biophysical sensor signals and logged behavior corresponding to the biophysical sensor signals in a data storage device, the stored data comprising training data…the biophysical sensor signals include glucose sensor signals and the logged behavior includes logged food data,” [0023]; “training each of a plurality of different autoencoder (AE) temporal convolutional neural networks (CNNs) on historical time series data from one of a plurality of different data sources, wherein the plurality of different data sources comprises a continuous glucose monitor, a heart rate monitor, and a source of food data,” [0007]);
…
--generating a plurality of assigned variables, each having an assigned event value corresponding to each time period (“The method can comprise: until a convergence condition is achieved, iteratively: (i) generating the recommendation using the reinforcement learning algorithm, which recommendation comprises a recommended meal or physical activity, (ii) processing the recommendation using a biophysical reaction model to generate a predicted glucose response of the subject to following the recommendation, and (iii) applying a reward function to the predicted glucose response to generate a first reward and updating the reinforcement learning algorithm based on the first reward,” [0006]),
--evaluating the initial and assigned variables with a probability function to determine the initial or assigned variable having (“classifying the validated data set and query data sets with a probabilistic classifier conditioned on the data set values and target labels; and generating a quality score based on a classification result for all data values of one data set,” [0021]), and
--using the highest probability initial or assigned variable as the initial variable, repeating the generating the assigned variables and evaluating of the initial and assigned variables until a highest probability initial or assigned variable has been determined (“The method can comprise: until a convergence condition is achieved, iteratively: (i) generating the recommendation using the reinforcement learning algorithm, which recommendation comprises a recommended meal or physical activity, (ii) processing the recommendation using a biophysical reaction model to generate a predicted glucose response of the subject to following the recommendation, and (iii) applying a reward function to the predicted glucose response to generate a first reward and updating the reinforcement learning algorithm based on the first reward,” [0006]); and
--using the highest probability initial or assigned variable to predict the at least one biophysical response in a user (“the ML algorithms implemented on the ML servers 102 can be used to predict a subject's biophysical response (e.g., a glucose response) or make a recommendation (e.g., a diet or physical activity recommendation) that is configured to alter or maintain an aspect of the subject's health (e.g., glucose level),” [0104]).
Dalal discloses a framework for predicting biophysical responses (i.e., predicting glucose levels) using machine learning but lacks the detailed framework set forth in the claims (i.e., greedy iterative logic). Dalal’s reinforcement learning has agents choosing actions from a probability distribution to achieve a goal such as glucose optimization, but Dalal does not expressly disclose that the method establishes an initial variable and then generates assigned variables that differ by one event value to find the highest probability. Dalal further fails to expressly disclose that the initial or assigned variable has the highest probability of event occurrences with respect to the biophysical responses in the time period and using the highest probability result as the new starting point for a subsequent round of single-value modifications until an optimal configuration is reached. Huang discloses:
--by operation of a computing device establishing an initial variable, having an event value corresponding to each time period (“Such dependencies are parametrized by factors in the language of graphical models, and the prediction of structured outputs can be formulated as a problem of Maximum-a Posteriori (MAP) inference, which finds the mode of the output distribution that is, an output configuration maximizing the summation of factor values,” page 1);
--generating a plurality of assigned variables, each having an assigned event value corresponding to each time period with one assigned event value being different with respect those of the initial variable and the other assigned variables (“The active sets are updated by finding a currently non-active coordinate with largest gradient magnitude,” page 4);
--evaluating the initial and assigned variables with a probability function to determine the initial or assigned variable having a highest probability of event occurrences with respect to the biophysical responses in the time periods (“These active sets are initialized as empty sets and are gradually augmented in an on-demand manner,” page 3).
One of ordinary skill in the art would have been motivated before the effective filing date to expand Dalal’s ML framework for glucose modeling to include the greedy MAP techniques of Huang because this would improve the speed and accuracy of the patient data analysis (see Huang Abstract: “Empirically, the proposed algorithms demonstrate orders of magnitude speedup over state-of-the-art MAP inference techniques on MAP inference problems including Segmentation, Protein Folding, Graph Matching, and Multilabel prediction with pairwise interaction…results in a single-loop algorithm of sublinear cost per iteration”).
Regarding claim 2, Dalal discloses: wherein the time series data comprises blood glucose values over the sequential time periods (“the first type sensor 120-0 can be a continuous glucose monitor (CGM), which can track a glucose level of a subject,” [0120]).
Regarding claim 3, Dalal discloses: wherein the event value corresponds to a meal (“which recommendation comprises a recommended meal or physical activity, (ii) processing the recommendation using a biophysical reaction model to generate a predicted glucose response of the subject to following the recommendation,” [0006]).
Regarding claim 4, Dalal discloses: wherein the event value corresponds to physical activity (“which recommendation comprises a recommended meal or physical activity, (ii) processing the recommendation using a biophysical reaction model to generate a predicted glucose response of the subject to following the recommendation,” [0006]; “the at least one recommendation is selected from a physical activity recommendation and a food recommendation,” [0014]).
Regarding claim 5, Dalal discloses a binary variable Z in [0242] but does not expressly disclose binary MAP inference or greedy variable augmentation. Huang further teaches: the initial variable is a binary value, with each event value corresponding to a bit location of the binary value, each bit location corresponding to each time period of the time series; and each assigned variable is a binary value, with each assigned event value corresponding to a bit location of the binary value, each bit location corresponding to each time period of the time series (“we introduce a variant of GDMM for binary MAP inference problems with a large number of factors,” Abstract; “Note that all variables are binary and all factors have only 4 states,” page 5).
One of ordinary skill in the art would have been motivated before the effective filing date to expand Dalal’s ML framework for glucose modeling to include the binary variables representing the presence or absence of an event in each time step of the series as taught by Huang because this would improve the speed and accuracy of the patient data analysis (see Huang Abstract: “Empirically, the proposed algorithms demonstrate orders of magnitude speedup over state-of-the-art MAP inference techniques on MAP inference problems including Segmentation, Protein Folding, Graph Matching, and Multilabel prediction with pairwise interaction”). Huang’s bit-by-bit improvements (i.e., starting at no events and flipping bits on demand) would further improve processing efficiency to achieve “sublinear costs per iteration” (Huang Abstract).
Regarding claim 6, Dalal discloses reinforcement learning to iteratively update recommendations but not a bit-by-bit modification of the binary string starting from an all-zero state. Huang teaches: establishing the initial variable includes setting all bits of the initial variable to a value indicating no event, and generating the assigned variables includes changing a different bit in each assigned variable a value indicating an event (“These active sets are initialized as empty sets and are gradually augmented in an on-demand manner,” page 3; adding coordinates one-by-one based on their potential to improve the probability function: “The active sets are updated by finding a currently non-active coordinate with largest gradient magnitude,” page 4; “For each iteration, we add the first inactive f from the head until we have
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which can be done with O(1) amortized cost,” page 5).
The motivation to combine is the same as in claim 5.
Regarding claim 7, Dalal discloses: wherein evaluating the initial and assigned variables with a probability function comprises applying the initial and assigned variables to a probability prediction statistical model to generate a probability value for each initial and assigned variable (“classifying the validated data set and query data sets with a probabilistic classifier conditioned on the data set values and target labels,” [0021]; [0022]).
Regarding claim 8, Dalal discloses: training a statistical model with training sets of time series data of the least one biophysical response and corresponding events to generate the probability prediction statistical model (“training each of a plurality of different autoencoder (AE) temporal convolutional neural networks (CNNs) on historical time series data from one of a plurality of different data sources, wherein the plurality of different data sources comprises a continuous glucose monitor,” [0007]).
Regarding claim 9, Dalal discloses: wherein evaluating the initial and assigned variables with the probability function includes determining a conditional probability for one time period using conditional probabilities for all previous time periods (“RNNs, meanwhile, are neural networks with cyclical connections that can encode dependencies in time-series data, e.g., continuous glucose monitoring data, An RNN can include an input layer that is configured to receive a sequence of time-series inputs. An RNN can also include one or more hidden recurrent layers that maintain a state… The next state can depend on the previous state and the current input. The state can be maintained across time steps and can capture dependencies in the input sequence. Such an RNN can be used to encode times-series features of a subject's glucose levels, for example” [0110]).
Regarding claim 10, Dalal discloses: wherein: the computing device comprises a plurality of parallel processing paths (“a multiprocessing module configured to execute a plurality of machine learning processes in parallel,” [0013]) each processing path configured to determine a probability for a different initial or assigned variable (“classifying the validated data set and query data sets with a probabilistic classifier conditioned on the data set values and target labels,” [0021]);
--a variable selector coupled to each processing path and (“instantiate one of the machine learning processes to process the data of the data processing object and return processing results and execute the messaging function to return a message,” [0013]).
Dalal does not expressly disclose, but Huang teaches: configured to select the highest probability initial or assigned variable (“Such dependencies are parametrized by factors in the language of graphical models, and the prediction of structured outputs can be formulated as a problem of Maximum-a Posteriori (MAP) inference, which finds the mode of the output distribution that is, an output configuration maximizing the summation of factor values,” page 1); “These active sets are initialized as empty sets and are gradually augmented in an on-demand manner,” page 3).
The motivation to combine is the same as in claim 1.
Regarding claim 11, Dalal discloses: the computing device comprises a hardware accelerator unit selected from the group of: a graphics processing unit (GPU) and tensor processing unit (TPU); and the parallel processing paths are within the GPU or TPU (“The hardware can be general-purpose processors, graphics processing units (GPUs), application-specific integrated circuit (ASIC), or machine learning accelerators, to name a few examples,” [0103]).
Regarding claim 10, Dalal discloses parallel processing generally (“a multiprocessing module configured to execute a plurality of machine learning processes in parallel,” [0013]). Dalal does not expressly disclose, but Dassau teaches: wherein: the computing device comprises a plurality of parallel processing paths, each processing path configured to determine a probability for a different initial or assigned variable, and a variable selector coupled to each processing path and configured to select the highest probability initial or assigned variable (“These data are processed in parallel by a ROC component and a Kalman filter estimation algorithm,” page 297).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the parallel machine learning of Dalal and Huang to include the ROC component and Kalman filter estimation techniques of Dassau
Regarding claim 11, Dalal discloses: the computing device comprises a hardware accelerator unit selected from the group of: a graphics processing unit (GPU) and tensor processing unit (TPU); and the parallel processing paths are within the GPU or TPU (“The hardware can be general-purpose processors, graphics processing units (GPUs), application-specific integrated circuit (ASIC), or machine learning accelerators, to name a few examples,” [0103]).
Regarding claim 12, the claim is substantially similar to claim 1 and is rejected with the same reasoning.
Regarding claim 13, Dalal discloses: wherein the at least one computing device comprises at least one statistical model configured to generate probability values for received initial variables and assigned variables (“classifying the validated data set and query data sets with a probabilistic classifier conditioned on the data set values and target labels,” [0021]; “Biophysical model 1065 can include an ANN, or any other suitable statistical learning agent, with initially unknown parameters,” [0181]; “physiological model 1018 can include an ANN, or any other suitable statistical learning agent, having the same general structure as biophysical model 1065,” [0182]).
Regarding claim 14, Dalal discloses: wherein the at least one statistical model comprises at least one artificial neural network (ANN) (“The ML servers 102 can implement artificial neural networks (ANN) of various architectures as will be described herein. Such ANNs can perform various functions, including learning and inference operations on data received from data sources 116, 118, and 120 as well as other data residing on the date store 122,” [0103]; “Biophysical model 1065 can include an ANN, or any other suitable statistical learning agent, with initially unknown parameters. Biophysical model 1065 can receive, as training data over a period of time, data source 1008-1 (second sensor) and optionally, third data source 1008-2. In response to such data, biophysical model 1065 can generate a simulated future observable 1057,” [0181]).
Regarding claim 15, Dalal discloses: wherein the at least one ANN comprises a recurrent ANN (“RNNs, meanwhile, are neural networks with cyclical connections that can encode dependencies in time-series data, e.g., continuous glucose monitoring data, An RNN can include an input layer that is configured to receive a sequence of time-series inputs. An RNN can also include one or more hidden recurrent layers that maintain a state. At each time step, each hidden recurrent layer can compute an output and a next state for the layer. The next state can depend on the previous state and the current input,” [0110]).
Regarding claim 16, Dalal discloses: wherein the at least one recurrent ANN is selected from the group of: a long short-term memory and gated current unit (“One example of an RNN is an LSTM, which can be made of LSTM units. An LSTM unit can be made of a cell, an input gate, an output gate, and a forget gate. The cell can be responsible for keeping track of the dependencies between the elements in the input sequence,” [0111]).
Regarding claim 17, Dalal discloses: wherein the at least one computing device comprises at least one hardware accelerator unit having a plurality of processing paths, each processing path configured to determine the probability of the initial variable or one of the assigned variables (“a multiprocessing module configured to execute a plurality of machine learning processes in parallel; and a data object decorating function comprising instructions executable by the multiprocessing module and configured to: create a data object in the system memory,” [0113]; “The hardware can be general-purpose processors, graphics processing units (GPUs), application-specific integrated circuit (ASIC), or machine learning accelerators, to name a few examples,” [0103]).
Regarding claim 18, Dalal discloses: wherein the at least one hardware accelerator is selected from the group of: a graphics processing unit and a tensor processing unit (“The hardware can be general-purpose processors, graphics processing units (GPUs), application-specific integrated circuit (ASIC), or machine learning accelerators, to name a few examples,” [0103]).
Regarding claim 19, Dalal discloses: wherein the time series data comprises a blood glucose values over the sequential time periods (“the first type sensor 120-0 can be a continuous glucose monitor (CGM), which can track a glucose level of a subject,” [0120]).
Regarding claim 20, Dalal discloses: wherein the event value is selected from the group of: a meal and a physical activity (“which recommendation comprises a recommended meal or physical activity,” [0006]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dassau (Dassau E, Bequette BW, Buckingham BA, Doyle FJ 3rd. Detection of a meal using continuous glucose monitoring: implications for an artificial beta-cell. Diabetes Care. 2008 Feb;31(2):295-300. doi: 10.2337/dc07-1293. Epub 2007 Oct 31. PMID: 17977934.) discloses a parallelized data processing in the context of CGM (“These data are processed in parallel by a ROC component and a Kalman filter estimation algorithm,” page 297).
Zhong (US20180271455A1) discloses “the conversational interaction application 712 may configure itself to generate or otherwise provide a reminder at the time of day when the probability of a hypoglycemic event is highest based on the ensemble predicted values and reliability metrics and then generate or otherwise provide a conversational response 1512 confirming or otherwise indicating the reminder has been set,” [0153].
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/JOSHUA B BLANCHETTE/Primary Examiner, Art Unit 3624