DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed December 16, 2024.
Claims 1-20 are pending
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejection - 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 to under 35 U.S.C 101 as not being directed to eligible subject matter the grounds set out in detail below:
Independent Claims 1, 19, and 20:
Eligibility Step 1 (does the subject matter fall within a statutory category?): Independent Claims 1, 19, and 20 falls within the statutory category of method.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 19, and 20 claimed invention is directed to a judicial exception.
The claim elements in the independent claim 1 which set forth the abstract idea are:
A method for generating a prediction of a future health-related event associated with an individual, the method comprising:
identifying health-related data associated with the individual, the health-related data comprising a sequence of health-related events;
generating, for each health-related event in the sequence of health-related events, a respective embedded representation of the health-related event;
generating, using a sequence processing neural network and conditioned on at least the respective embedded representations of the health-related events, an output embedded representation of the future health-related event;
and processing at least the output embedded representation of the future health-related event…[…]…to generate the prediction of the future health-related event.
The claim elements in the independent claims 19 which set forth the abstract idea are:
A method for generating a prediction of one or more missing health-related events associated with an individual, the method comprising:
identifying incomplete health-related data associated with the individual, the incomplete health-related data comprising a sequence of health-related events with one or more gaps to be filled;
updating the sequence of health-related events by applying a mask for each of the one or more gaps;
generating, for each health-related event or mask in the sequence of health-related events, a respective embedded representation of the health-related event or the mask;
generating, for each mask in the sequence of health-related events, …[…]…and conditioned on at least the respective embedded representations of the health-related events, an output embedded representation of a missing health-related event;
and processing at least the output embedded representation of each missing health-related event …[…]…to generate the prediction of the one or more missing health-related events.
The claim elements in the independent claims 20 which set forth the abstract idea are:
A method for detecting one or more missing health-related events associated with an individual, the method comprising:
identifying health-related data associated with the individual at a first time, the health-related data comprising a sequence of health-related events;
generating a sequence of predictions of future health-related events that are likely to occur within a window of time after the first time;
identifying updated health-related data associated with the individual at a second time, updated health-related data comprising a sequence of health-related events that have occurred between the first time and the second time;
and processing the sequence of predictions of future health-related events and the updated health-related data to identify one or more health-related events in the sequence of future health-related events as missing health-related events, wherein the missing health-related events do not have a match within the updated health-related data.
which falls within “certain methods of organizing human activity” as following rules or instructions to predict a future and/or missing health related event based on data. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent Claim 1, 19, and 20 this judicial exception is not integrated into a practical application.
In Claim 1, 19, and 20 the additional elements are:
one or more de-embedding machine learning models
a sequence processing neural network
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (a) and (b) are merely applying the abstract idea as “apply-it” or an equivalent (e.g. using) to analyze data
Accordingly, claims 1, 19, and 20 does not integrate the abstract idea into a practical application.
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims 1, 19, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed above in step 2A prong 2 above, these additional elements, whether viewed individually or as an ordered combination, amount to no more than applying the abstract idea and thus insufficient to provide “significantly more”. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Dependent Claims 2-18:
Eligibility Step 1 (does the subject matter fall within a statutory category?):The dependent claims 2-18 fall within the statutory category of method.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-18 claimed invention are directed to a judicial exception.
Dependent claims 2-18 continue to limit the abstract idea in the independent claim by (1) types of health related data (2) further limiting the rules to analyze the data thus, inheriting the same abstract idea which falls within “certain methods of organizing human activity” as following rules or instructions to predict a future and/or missing health related event based on data. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): In Claims 2-18 this judicial exception is not integrated into a practical application.
In Claims 2-18 the additional elements not already recited in the independent claims are:
an embedding neural network
a generative neural network
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (a) and (b) are merely applying the abstract idea as “apply-it” or an equivalent (e.g. using) to analyze data
Eligibility Step 2B (Does the claim amount to significantly more?): Dependent claims 2-18, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed above in step 2A prong 2 above, these additional elements, whether viewed individually or as an ordered combination, amount to no more than apply it thus insufficient to provide “significantly more”. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 and 11-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tomasev et. al (hereinafter Tomasev) (US11302446B2)
As per claim 1, Tomasev teaches:
A method for generating a prediction of a future health-related event associated with an individual, the method comprising: (abstract discloses, “Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future adverse health events using neural networks” and see Col. 1 lines 33-35 discloses, “This specification describes a system that makes predictions that characterize the likelihood that a specific adverse health event will occur to a patient in the future.”)
identifying health-related data associated with the individual, the health-related data comprising a sequence of health-related events; (Col. 1 lines 50-66 discloses, “Thus in one aspect there is provided a system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising receiving electronic health record data for a patient, the electronic health data comprising a plurality of features representing health events in an electronic health record for the patient, each of the plurality of features belonging to a vocabulary of possible features that comprises a plurality of possible numerical features and a plurality of possible discrete features. The operations may further comprise generating, from the electronic health record data, an input sequence comprising a respective feature representation at each of a plurality of time steps, wherein the plurality of time steps comprises a respective time window time step for each of a plurality (succession) of time windows.”)
generating, for each health-related event in the sequence of health-related events, a respective embedded representation of the health-related event; (Col. 4 lines 4-10 discloses, “In implementations the neural network comprises a deep embedding neural network to embed the features in the feature representation in an embedding space. The neural network output may then be generated from the embedded features. For example the deep embedding neural network may comprise a plurality of fully-connected layers (though other architectures may be used).” / examiner notes as previously cited the features are a sequence of health related events)
generating, using a sequence processing neural network and conditioned on at least the respective embedded representations of the health-related events, an output embedded representation of the future health-related event; (Col. 4 lines 4-10 discloses, “In implementations the neural network comprises a deep embedding neural network to embed the features in the feature representation in an embedding space. The neural network output may then be generated from the embedded features. For example the deep embedding neural network may comprise a plurality of fully-connected layers (though other architectures may be used).” And see Col. 7 lines In particular, the system 100 receives electronic health record data 102 for a patient, generates an input sequence 122 from the electronic health record data 102, and then processes the input sequence 122 using a neural network 110 to generate a neural network output 132. For example, the neural network output 132 can include a score, e.g., a probability, that characterizes a predicted likelihood that the adverse health event will occur to the patient within a fixed time period after the last time window in the electronic health record data 102. As another example, the neural network output 132 can include scores for multiple time periods, each starting after the last time window in the health record data 102 and each being a different length. The score for each time period can be a score, e.g., a probability, that characterizes the predicted likelihood that the adverse health event will occur to the patient within the corresponding time period after the last time window in the health record data 102.” And see Col 4 lines 20-23 discloses, “For example in some implementations the neural network may comprise a recurrent neural network e.g., with a plurality of recurrent neural network (RNN) layers and, optionally, highway connections.”)
and processing at least the output embedded representation of the future health-related event using one or more de-embedding machine learning models to generate the prediction of the future health-related event. (Col. 4 lines 4-10 discloses, “In implementations the neural network comprises a deep embedding neural network to embed the features in the feature representation in an embedding space. The neural network output may then be generated from the embedded features. For example the deep embedding neural network may comprise a plurality of fully-connected layers (though other architectures may be used).” And see Col. 7 lines In particular, the system 100 receives electronic health record data 102 for a patient, generates an input sequence 122 from the electronic health record data 102, and then processes the input sequence 122 using a neural network 110 to generate a neural network output 132. For example, the neural network output 132 can include a score, e.g., a probability, that characterizes a predicted likelihood that the adverse health event will occur to the patient within a fixed time period after the last time window in the electronic health record data 102. As another example, the neural network output 132 can include scores for multiple time periods, each starting after the last time window in the health record data 102 and each being a different length. The score for each time period can be a score, e.g., a probability, that characterizes the predicted likelihood that the adverse health event will occur to the patient within the corresponding time period after the last time window in the health record data 102.” And see Col 4 lines 20-23 discloses, “For example in some implementations the neural network may comprise a recurrent neural network e.g., with a plurality of recurrent neural network (RNN) layers and, optionally, highway connections.” And see Col. 10 lines 47-56 discloses, the lower dimensionality steps)
As per claim 2, Tomasev teaches:
The method of claim 1, wherein the health-related data comprises time information for each health-related event in the sequence of health-related events. (Col. 3 lines 23-45 discloses, “A further characteristic of EHR data which presents a challenge for neural network processing is that for a large proportion of the features there may be no very exact time stamp…[…]…Thus in implementations the plurality of time steps may include one or more surrogate time steps, each associated with a plurality of time window time steps that immediately precede the surrogate time step in the input sequence. For example there may be a surrogate time step at the conclusion of each day. Then generating the feature representation may comprise, for each of the surrogate time steps, determining whether the EHR data identifies any features (i) as occurring during a time interval spanned by the time windows corresponding to the plurality of time window time steps associated with the surrogate time steps without (ii) identifying a specific time window during which the feature occurred, and if so generating the feature representation for the surrogate time step from such features. In implementations the neural network processes such features at a current time step but the neural network output is not used for predicting an adverse health event until at least the next time step.”)
As per claim 3, Tomasev teaches:
The method of claim 1, wherein generating an output embedded representation of the future health-related event comprises: processing a sequence of the respective embedded representations for the health-related events, wherein the respective embedded representations for the health-related events are ordered within the sequence according to a time at which each health-related event occurred. (Col. 7 lines57-60 discloses, “In the example of FIG. 1 and for ease of description, the electronic health record data 102 is depicted as a sequential representation of health events, with events being ordered by the time that the events occurred and represented by circles.” )
As per claim 4, Tomasev teaches:
The method of claim 1, wherein each health-related event is associated with an event type that is one of a plurality of event types, and wherein each respective embedded representation comprises one or more embeddings in a shared embedding space that is shared across the plurality of event types. (Col. 14 lines 23-29 discloses, “For example, the system can map each feature to a corresponding high-level concept, e.g., procedure, diagnosis prescription, laboratory test, vital sign, admission, transfer and so on. The system can then include in the feature representation at each time step a histogram of frequencies of each high-level concept among the features that occurred at the time step.” And see The deep embedding neural network 240 includes multiple fully-connected layers and is configured to embed the features in the feature representation in an embedding space. In other words, the deep embedding neural network 240 maps the feature representation into an ordered collection of numeric values, e.g., a vector, in an embedding space that has a fixed dimensionality. By doing so, the embedding neural network 240 transforms the high-dimensional and sparse input feature representation into a lower-dimensional continuous representation that makes subsequent prediction easier. In some implementations, there are residual connections between the fully-connected layers.” / examiner notes event types is interpreted as defined in the instant application disclosure e.g. page 7 lines 6-9 as labs, EHR data, procedures etc. )
As per claim 5, Tomasev teaches:
The method of claim 4, wherein the respective embedded representations for two or more of the plurality of event types comprise a different number of embeddings in the shared embedding space. (Col. 10 lines 12-56 and see Col. 14 lines 23-29 discloses, fixed dimensionality for values or vectors of features which have are a plurality of event types as previously cited)
As per claim 6, Tomasev teaches:
The method of claim 1, wherein generating, for each health-related event in the sequence of health-related events, a respective embedded representation of the health-related event comprises: for each health-related event in the sequence of health-related events: determining a respective event type for the health-related event; and processing the health-related event using an embedding function corresponding to the respective event type to generate the respective embedded representation of the health-related event. (see Col. 14 lines 23-29 and see Col. 10 and Col. 12-14 discloses, the use of neural network embedding functions and layers to take event types utilizing embedding and embedding space to determine scores and representations of the health related event for the future. )
As per claim 7, Tomasev teaches:
The method of claim 6, wherein for one or more of the respective event types, the corresponding embedding function is a learned function. (Col. 4 lines 23-25 discloses, “In other implementations the neural network may comprise a temporal convolutional neural network.” And see Col. 10 lines 19-22 discloses, “The neural network 110 includes a deep embedding neural network 240, a deep recurrent neural network 250, a set of main output layers 260, and optionally a set of auxiliary output layers 270.” / instant application defines the embedding neural network as being an e.g. CNN with layers see page 27 lines 20-25 instant application spec. )
As per claim 8, Tomasev teaches:
The method of claim 7, wherein the learned function is an embedding neural network. (Col. 4 lines 23-25 discloses, “In other implementations the neural network may comprise a temporal convolutional neural network.” And see Col. 10 lines 19-22 discloses, “The neural network 110 includes a deep embedding neural network 240, a deep recurrent neural network 250, a set of main output layers 260, and optionally a set of auxiliary output layers 270.”)
As per claim 11, Tomasev teaches:
The method of claim 6, further comprising, for each health-related event in the sequence of health-related events: obtaining a corresponding event type encoding for the respective event type characterizing the respective event type; and updating the respective embedded representation using the corresponding event type encoding. (Col. 2 lines 37-48 discloses, “In implementations, therefore, presence features are generated which enable the neural network to distinguish between the absence of a numerical feature (value) and an actual value of zero. Put differently, a presence feature may be considered to capture a feature associated with an act of making a measurement, whatever the outcome. Thus, for example, a presence feature may be a binary feature. The presence features may also encode discrete features such as the implementation of diagnostic or other medical procedure codes. This approach facilitates better use of EHR data by a neural network for predicting the likelihood of an adverse health event.” And see Col. 10 lines 29-35 and Col. 13 lines 8-23 / examiner notes the disclosure teaches numerical features or values also taught as vectors encoded. The event types and encoding is interpreted as it is defined in the instant application specification page 28 lines 26-30 as feature vectors)
As per claim 12, Tomasev teaches:
The method of claim 6, further comprising, for each health-related event in the sequence of health-related events: updating the respective embedded representation by applying a corresponding learned transformation for the respective event type to the respective embedded representation. (Col. 12 lines 16-26 discloses, minimizing a loss function on a trained multi layer neural network such as cross entropy)
As per claim 13, Tomasev teaches:
The method of claim 1, wherein the sequence processing neural network has been trained to minimize a loss function that measures a distance between output embedded representations of the future health-related event generated by the sequence processing neural network for training samples, and target output embedded representations for the training samples. (Col. 12 lines 16-26 discloses, minimizing a loss function on a trained neural network such as cross entropy)
As per claim 14, Tomasev teaches:
The method of claim 1, wherein each of the one or more de-embedding machine learning models corresponds to a respective event type, and wherein processing at least the output embedded representation of the future health-related event using one or more de-embedding machine learning models to generate the prediction of the future health-related event comprises: obtaining a target event type; and processing the output embedded representation of the future health-related event using the de-embedding machine learning model corresponding to the target event type to generate the prediction of the future health-related event. (Col. 9 lines 62-67 and Col. 10 lines 1-67 and see Col. 11 lines 1-30 discloses the use of an RNN for sequential time based event data into vectors and de-embedding into scores for prediction of future health related event in which the historical event data is taken into account when generating predictions output by the RNN using embedded representations of predicted future health related events and see Col. 11 and Col. 12 and see Col. 14 lines 23-29 / examiner notes the target prediction is interpreted as the target event type as the event type is broadly defined in the instant application specification e.g. see page 7 of instant spec. and the cited prior art defines the event type as being labs, prescription diagnosis, etc. as the features analyzed to make target future event types )
As per claim 15, Tomasev teaches:
The method of claim 14, wherein each of the one or more de-embedding machine learning models corresponding to a respective event type has been trained to map an embedded representation of a health-related event to a prediction of the health-related event, wherein the future health-related event is of the respective event type. (Col. 9 lines 62-67 and Col. 10 lines 1-67 and see Col. 11 lines 1-30 discloses the use of an RNN for sequential time based event data into vectors and de-embedding into scores for prediction of future health related event in which the historical event data is taken into account when generating predictions output by the RNN using embedded representations of predicted future health related events and see Col. 11 and Col. 12 and see Col. 14 lines 23-29)
As per claim 16, Tomasev teaches:
The method of claim 1, wherein each of the one or more de-embedding machine learning models corresponds to a respective event type, and wherein processing at least the output embedded representation of the future health-related event using one or more de-embedding machine learning models to generate the prediction of the future health-related event comprises: processing the output embedded representation using a learned classifier function to generate a distribution over a plurality of event types; selecting a particular event type based on the distribution; and processing the output embedded representation using the de-embedding machine learning model corresponding to the particular event type to generate the prediction of the future health-related event. (Col. 9 lines 62-67 and Col. 10 lines 1-67 and see Col. 11 lines 1-30 discloses the use of an RNN for sequential time based event data into vectors and de-embedding into scores for prediction of future health related event in which the historical event data is taken into account when generating predictions output by the RNN using embedded representations of predicted future health related events and see Col. 11 and Col. 12 and see Col. 14 lines 23-29 / examiner notes the target prediction is interpreted as the target event type as the event type is broadly defined in the instant application specification e.g. see page 7 of instant spec. and the cited prior art defines the event type as being labs, prescription diagnosis, etc. as the features analyzed to make target future event type and using the distribution of probabilities and assigned scores of features representing event types for prediction )
As per claim 17, Tomasev teaches:
The method of claim 1, wherein the one or more de-embedding machine learning models comprise a generative neural network, and wherein processing at least the output embedded representation of the future health-related event using one or more de-embedding machine learning models to generate the prediction of the future health-related event comprises: processing the output embedded representation of the future health-related event using the generative neural network to generate the prediction of the future health-related event, wherein the generative neural network has been trained to generate a prediction of a future health-related event conditioned on the output embedded representation of the future health-related event. (Col. 9 lines 62-67 and Col. 10 lines 1-67 and see Col. 11 lines 1-30 discloses the use of an RNN for sequential time based event data into vectors and de-embedding into scores for prediction of future health related event in which the historical event data is taken into account when generating predictions output by the RNN using embedded representations of predicted future health related events)
As per claim 20, Tomasev teaches:
A method for detecting one or more missing health-related events associated with an individual, the method comprising: (abstract discloses, “Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future adverse health events using neural networks” and see Col. 1 lines 33-35 discloses, “This specification describes a system that makes predictions that characterize the likelihood that a specific adverse health event will occur to a patient in the future.” / examiner notes page 2 lines 11-13 of the instant application specification defines a future event which can be also a missing health related event)
identifying health-related data associated with the individual at a first time, the health-related data comprising a sequence of health-related events; (Col. 1 lines 50-66 discloses, “Thus in one aspect there is provided a system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising receiving electronic health record data for a patient, the electronic health data comprising a plurality of features representing health events in an electronic health record for the patient, each of the plurality of features belonging to a vocabulary of possible features that comprises a plurality of possible numerical features and a plurality of possible discrete features. The operations may further comprise generating, from the electronic health record data, an input sequence comprising a respective feature representation at each of a plurality of time steps, wherein the plurality of time steps comprises a respective time window time step for each of a plurality (succession) of time windows.”)
generating a sequence of predictions of future health-related events that are likely to occur within a window of time after the first time; (Col. 4 lines 28-42)
identifying updated health-related data associated with the individual at a second time, updated health-related data comprising a sequence of health-related events that have occurred between the first time and the second time; (Col. 13 lines 54-67 and Col. 14 lines 1-20)
and processing the sequence of predictions of future health-related events and the updated health-related data to identify one or more health-related events in the sequence of future health-related events as missing health-related events, wherein the missing health-related events do not have a match within the updated health-related data. (Col. 11 lines 47-67 and Col. 12 lines 1-7 / examiner notes that the interpretation under BRI is taken of a match within the health data as comparison to ground truth data as identifying if there is or is not a match to the updated health related future events)
Claim 19 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by FOSCHINI et. al (hereinafter FOSCHINI) (US20250069750A1)
As per claim 17, FOSCHINI teaches:
A method for generating a prediction of one or more missing health-related events associated with an individual, the method comprising: (abstract discloses, “Disclosed is a method comprising accessing, by a machine learning system, a set of data records for a plurality of users, the data records representative of physical statistics measured for each of the plurality of users over a time period. At least a subset of the data records comprises patterns of missing data for at least a portion of the time period.”)
identifying incomplete health-related data associated with the individual, the incomplete health-related data comprising a sequence of health-related events with one or more gaps to be filled; [0083] In a second operation 720, the system may identify gaps in the collected wearable data likely caused by natural missingness (naturally-occurring patterns of missing wearable device data). These patterns may be present due to patterns of wearable device disuse or downtime that may occur over a duration of typical use by a subject. Upon determining these patterns, the system may mask one or more portions of data collected from a subject, creating gaps to make the data appear similar to subject data with patterns of natural missingness.
updating the sequence of health-related events by applying a mask for each of the one or more gaps; ([0076] In a second operation 520, the self-supervised learning system may generate a training data set by using patterns of missing information (i.e., "missingness") in some of the data records of the set to mask other records of the set. For example, in some embodiments, the self-supervised learning system applies a missingness of each data record of the set to a next data record of the set, and repeats the process over one or more iterations to generate the training set. In other embodiments, the self-supervised learning system identifies pairs of data records of the set based a level of similarity and/or a level of overlap in missingness, and applies the missingness of a first data record of the pair to a second data record of the pair to generate the training set.)
generating, for each health-related event or mask in the sequence of health-related events, a respective embedded representation of the health-related event or the mask; ([0005] discloses, “The method comprises providing a set of time series wearable sensor data. The method also comprises generating a plurality of embeddings from the time series wearable data”)
generating, for each mask in the sequence of health-related events, using a sequence processing neural network and conditioned on at least the respective embedded representations of the health-related events, an output embedded representation of a missing health-related event; ([0120] discloses, “Embodiments of the disclosure may generate synthetic data by placing synthetic data values in gaps within a collected sequence of wearable sensor data (e.g., at places where data is missing or has been masked), while removing non-synthetic wearable sensor data values. This may be performed until the entire wearable dataset comprises synthetic data.” And see [0102] discloses, “The encoder sub-system 1070 may generate representations from the data. The encoder sub-system may comprise one or more machine learning algorithms. In some embodiments, one or more of the machine learning algorithms comprises a neural network (or artificial neural network (ANN)). A neural network may be a convolutional neural network (CNN) or recurrent neural network (RNN). A neural network may be a multilayer perceptron (MLP).”)
and processing at least the output embedded representation of each missing health-related event using one or more de-embedding machine learning models to generate the prediction of the one or more missing health-related events. ( [0043] Each physical statistic monitored by the self-supervised learning system 110 can be affected based on the behavior of a user 120 (e.g., whether the user is exercising, is asleep, etc.) and/or a health condition of the user 120 (e.g., whether the user is exhibiting normal health, has the flu, is suffering from allergies, etc.). As such, by analyzing the physical statistics monitored for a given user, predictions can be made relating to user behavior and/or user health condition. For example, in some embodiments, the physical statistic data module 210 can be used for training models to reflect different behaviors and/or health conditions, and to predict behaviors and/or health conditions for an individual user based on received physical statistic data for the user, in real time or near-real time. In some embodiments, the physical statistic data module 210 continuously receives physical statistic data from health sensors 125 or users and preprocesses the physical statistic data for evaluation in real time or near-real time (for example, for predicting the health condition of a user).” And see [0108] The head 1080 may process the representations 1080 to perform a downstream task. The head 1080 may comprise one or more machine learning algorithms configured to perform the downstream task. For example, the head 1080 may comprise one or more supervised and/or unsupervised machine learning algorithms The head 1080 may comprise, for example, support vector machines (SVM), a logistic regression, or a decision tree algorithm (e.g., gradient boosted trees, Adaboost, XGBoost, or random forests). The head may comprise one or more layers.” And see [0109] The head 1080 may comprise an activation function to produce a prediction output. The head may perform a regression task. The head may perform a classification task. The head may comprise a binary classifier. The head may comprise a multiclass classifier.”)
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.
Claims 9-10 and 18 are rejected to under 35 U.S.C. 103 as being unpatentable over Tomasev et. al (hereinafter Tomasev) (US11302446B2) in view of Kang et. al (hereinafter Kang) (CN117316362A)
As per claim 9, Tomasev does not teach
The method of claim 8, wherein the embedding neural network is pre-trained and frozen prior to training the sequence processing neural network.
However, Kang teaches:
The method of claim 8, wherein the embedding neural network is pre-trained and frozen prior to training the sequence processing neural network. (page 3 para. 6 discloses, “Further, in S6, the input constructed based on the discharge medical order text features based on transfer learning is the patient discharge summary text, and the intermediate representation is obtained as the embedding of the text from the clinical text ICD coding model of the multi-filter residual convolutional neural network, and then Clinical text ICD encoding model pre-trained in multi-filter residual convolutional neural network using patient's unlabeled data, clinical text via frozen multi-filter residual convolutional neural network using discharge summary text of patient vt times The weight of the ICD encoding model is obtained. After the intermediate representation is obtained, it is passed through the SUM layer and used as the user's multi-view medical order text feature. It is transmitted to the output layer together with the module features from other views and then fed into the classifier for prediction.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tomasev’s teachings of a surgical system as previously cited with Kang’s teachings as previously cited, the motivation being Tomasev teaches the concern of overfitting and accuracy of modelling (e.g. see Col 1 and 10) thus it would be obvious to accelerate training, reduce computational costs, and prevent overfitting by combining with Kang with no unpredictable results to substitute one machine learning for Kang’s.
As per claim 10, Tomasev does not teach
The method of claim 8, wherein the embedding neural network is pre-trained, and wherein parameters for the embedding neural network are updated during training of the sequence processing neural network.
However, Kang does teach:
The method of claim 8, wherein the embedding neural network is pre-trained, and wherein parameters for the embedding neural network are updated during training of the sequence processing neural network. ((page 3 para. 6 discloses, “Further, in S6, the input constructed based on the discharge medical order text features based on transfer learning is the patient discharge summary text, and the intermediate representation is obtained as the embedding of the text from the clinical text ICD coding model of the multi-filter residual convolutional neural network, and then Clinical text ICD encoding model pre-trained in multi-filter residual convolutional neural network using patient's unlabeled data, clinical text via frozen multi-filter residual convolutional neural network using discharge summary text of patient vt times The weight of the ICD encoding model is obtained. After the intermediate representation is obtained, it is passed through the SUM layer and used as the user's multi-view medical order text feature. It is transmitted to the output layer together with the module features from other views and then fed into the classifier for prediction.” And see page 9 and 10 / examiner under BRI interprets the multi filter residual convolutional neural network to update its parameters through optimization such as batch as taught in the cited prior art)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tomasev’s teachings of a surgical system as previously cited with Kang’s teachings for the same reasons given above for claim 9.
As per claim 18, Tomasev does not teach
The method of claim 1, wherein identifying health-related data associated with the individual comprises receiving data representing one or more health-related events of the sequence from a user.
However, Kang does teach:
The method of claim 1, wherein identifying health-related data associated with the individual comprises receiving data representing one or more health-related events of the sequence from a user. (page 9 para. 3 discloses, “Label=D={x1,x2,...,xn} represents the ICD code manually entered by the doctor for this visit”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tomasev’s teachings of a surgical system as previously cited with Kang’s teachings for the same reasons given above for claim 9 as the claim recited choice data analyzed in the same manner.
Prior Art Cited But Not Relied Upon
Saripalli et. al – (US11404145B2)
Systems , apparatus , instructions , and methods for medical machine time - series event data processing are disclosed . An example time series event data processing apparatus includes memory storing instructions and one - dimensional time series healthcare - related data ; and at least one processor. The example at least one processor is to : execute artificial intelligence model ( s ) trained on aggregated time series data to at least one of a ) predict a future medical machine event , b ) detect a medical machine event , or c ) classify the medical machine event using the one - dimensional time series healthcare - related data ; when the artificial intelligence model ( s ) are executed to predict the future medical machine event , output an alert related to the predicted future medical machine event to trigger a next action ; and when the artificial intelligence model ( s ) are executed to detect and / or classify the medical machine event , label the medical machine event and output the labeled event to trigger the next action
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687