DETAILED ACTION
This action is in response to the initial filing filed on November 14, 2024. Claims 1-20 have been examined and are currently 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 .
Inventorship
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.
Information Disclosure Statement
The Information Disclosure Statement filed on November 14, 2024 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Objections
Claim 2 is objected to because of the following informalities: Dependent claim 2 recites “the processor” in line 5 which lacks antecedent basis. Appropriate correction is required.
Claim 15 is objected to because of the following informalities: Dependent claim 15 recites “the processor” in line 5 which lacks antecedent basis. Appropriate correction is required.
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.
Claim(s) 1, 3, 8-12, 14, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lafon et al. US Publication 20230248320 A1 in view of Ferenc et al. US Publication 20190096525 A1 further in view of Mei et al. US Publication 20200155078 A1.
Claims, 1, 14, and 20:
As per claims 1, 14, and 20, Lafon teaches a method, system, and non-transitory computer readable medium comprising:
receiving monitored parameter data corresponding to data points collected over a number of days from a wearable device associated with a user through a network (paragraphs 0086 and 0041 “In this example, as shown at 1202, first data is received from one or more sensors of a wearable computing device. As noted above, the sensors may provide data related to respiration rate, heart rate, and heart rate variability, temperature, blood pressure, oxygen saturation, and the like. Additionally, as shown at 1204, second data may be provided by the user related to demographic or health information. For example, demographic information may include age, sex, geographic location, etc. Also, health information may include BMI, co-morbidities, and/or symptoms. As shown at 1206, the information may be collected and processed, using a trained neural network, to develop a Z-score. In such embodiments, the Z-scores may be processed into an image matrix and then evaluated, over time, to identify one or more symptoms indicative of an illness on a particular day. In various embodiments, the data is provided over a series of days, with changes in various factors providing the indicators of illness. Furthermore, in various embodiments, as shown at 1208, the data may be processed using a machine learning system, such as a trained linear classifier to predict a severity of the symptoms and/or illness for the user. The severity may be related to a likelihood the user will need intervention, such as hospitalization, as a result of the illness…”);
Lafon does not teach generating a plurality of tensors based on applying a convolutional neural network (CNN) to the monitored parameter data, wherein each of the plurality of tensors corresponds to a different day in the number of days. However, Ferenc teaches Patient Data Management System and further teaches, “As used herein, a tensor is a mathematical object that is analogous to, but more general than, a vector. In some embodiments, a tensor may be represented by an array of components that are functions of the coordinates of a space. A tensor can be represented as an organized multidimensional array of numerical values or scalars. For example, a one dimensional tensor can be a vector, a two dimensional tensor can be a matrix, and three dimensional tensor can be a cube of scalars.” (paragraph 0023), “The preprocessor applies the methods and steps described below and then feeds the tensor to a convolutional neural network (CNN). In one embodiment, the CNN has previously been trained to classify the tensor data according to disease type. After processing the tensor, the CNN may output a specific recommendation to the doctor based on the uploaded and pre-processed data. In the example above, the CNN may analyze the patient data and output a high probability that based on the patient's medical data, the patient likely has heart disease. Of course, this is just one example of the type of medical output that the system may provide to the doctor. The figure descriptions below describe the PDM system in greater detail.” (paragraph 0024), and “For example, each subsequence can represent a 3 day interval, with S0 occurring before S1, etc. Each subsequence includes episode sequences (i.e., chronologically ordered clinical episodes) that occurred within that 3 day interval.” (paragraph 0036). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include generating a plurality of tensors based on applying a convolutional neural network (CNN) to the monitored parameter data, wherein each of the plurality of tensors corresponds to a different day in the number of days as taught by Ferenc in order to apply convolutional neural network to analyze patient data with respect to days and times.
Lafon does not teach processing the plurality of tensors, in a time sequential manner, by a long short-term memory (LSTM) network, wherein the time sequential manner corresponds to processing the tensors in chronological order by the LSTM based on the days corresponding to each tensor. However, Ferenc teaches Patient Data Management System and further teaches, “The computer 112, processes the patient monitoring data and test data 106, using a preprocessor 115. In one embodiment, the preprocessor 115 converts the data 106 into a three dimensional tensor 116. In some embodiments, the tensor 116, may have additional dimensions, for example, four or five dimensions. Next, the preprocessor 115, feeds the tensor 116, to a neural network 118. In some embodiments, the neural network 118, may comprise a convolutional neural network. In other embodiments the neural network 118, may comprise a different type of network such as a long or short term memory network.” (paragraph 0026) and “The process 200 next moves to step 206, where the preprocessor 115 allocates storage space for the data of step 203. The preprocessor 115 stores each source of clinical data into separate tensor slices 116. For example, each source of data may be from an electronic medical record, electronic health record, MAR etc.) The tensor slices 116 may be represented as sparse matrices to save storage space. In this example, each tensor slice 116 comprises multiple one dimensional arrays (“vectors”) and each vector represents a clinical episode. A clinical episode is one clinical activity, such as the results of a checkup, a prescription, a surgical outcome, a diagnosis, socio-demographics, regional climate information, etc. In other words, a clinical episode can be any information that may be useful in the diagnostic process. In this embodiment, each vector comprises clinical data points.” (paragraph 0032). Therefore, it would have been obvious to one ordinary skilled in the art at the time of filing to modify Lafon to include processing the plurality of tensors, in a time sequential manner, by a long short-term memory (LSTM) network, wherein the time sequential manner corresponds to processing the tensors in chronological order by the LSTM based on the days corresponding to each tensor as taught by Ferenc in order to analyze patient data in a specific format and time frame.
Lafon does not teach in response to processing the tensors, generating an embedding vector for the monitored parameter data. However, Ferenc teaches Patient Data Management System and further teaches, “The process 200 next moves to step 206, where the preprocessor 115 allocates storage space for the data of step 203. The preprocessor 115 stores each source of clinical data into separate tensor slices 116. For example, each source of data may be from an electronic medical record, electronic health record, MAR etc.) The tensor slices 116 may be represented as sparse matrices to save storage space. In this example, each tensor slice 116 comprises multiple one dimensional arrays (“vectors”) and each vector represents a clinical episode. A clinical episode is one clinical activity, such as the results of a checkup, a prescription, a surgical outcome, a diagnosis, socio-demographics, regional climate information, etc. In other words, a clinical episode can be any information that may be useful in the diagnostic process. In this embodiment, each vector comprises clinical data points.” (paragraph 0032). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include in response to processing the tensors, generating an embedding vector for the monitored parameter data as taught by Ferenc in order to generate a result based on the analysis of the data.
Lafon and Ferenc do not teach generating an output vector that indicates a prediction related to a plurality of health conditions for the user of the wearable device based on using an artificial intelligence algorithm and the embedding vector. However, Mei teaches Health Monitoring using Artificial Intelligence based on Sensor Data and further teaches, “At block 520, a neural network model is used to predict a risk of premonitory symptoms based on the sensor data. In one embodiment, the neural network model includes a convolutional neural network (CNN) model. For example, the neural network model can be a neural network model trained in accordance with the system/method described above with reference to FIG. 4. Further details regarding block 520 will be described below with reference to FIG. 7.” (paragraph 0069), “The output of the convolutional layer 720 is fed into a pooling layer 730. The pooling layer 730 uses a filter to down-sample the output of the convolutional layer 720. In one embodiment, the pooling layer 730 implements max pooling. Max pooling applies a filter to the output of the convolutional layer 720 and outputs the maximum number in every sub-region covered by the filter. However, other pooling functions (e.g., average pooling and/or L2-norm pooling) can also be used in accordance with the embodiments described herein. The pooling layer 730 can reduce computational costs of implementing the neural network for preemptive disease monitoring, and can reduce the effects of overfitting.” (paragraph 0077) and “The output of the pooling layer 730 is fed into a fully connected (FC) layer 740. The FC layer 740 can determine which features most correlate to a class (e.g., disease) by looking at which high level features most strongly correlate to the class. For example, the FC layer 740 can output an N dimensional vector, where N is the number of classes (e.g., diseases), and each number in the vector represents a probability of the class (e.g., softmax).” (paragraph 0078). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include generating an output vector that indicates a prediction related to a plurality of health conditions for the user of the wearable device based on using an artificial intelligence algorithm and the embedding vector as taught by Mei in order to determine a patient’s risk of developing a disease or illness.
Lafon and Ferenc do not teach and transmitting, to the user of the wearable device or to a healthcare provider, a notification message that is based on the prediction. However, Mei teaches Health Monitoring using Artificial Intelligence based on Sensor Data and further teaches, “At block 530, if there is a risk of premonitory symptoms, an alert is transmitted to one or more entities associated with the user. The one or more entities can include the user, one or more doctors associated with the user, etc. The alert can be transmitted to one or more electronic devices associated with the one or more entities. For example, the alert can be transmitted to at least one of the one or more wearable devices worn on the user. As another example, the alert can be transmitted as an electronic message delivered to the user (e.g., e-mail or text message).” (paragraph 0070) and “The wearable devices 820-1 and 820-2 are configured to communicate with a health monitoring processing device 830 via a network. For example, the health monitoring processing device can include, e.g., a server. The health monitoring processing device 830 is configured to receive or collect sensor data from the wearable devices 820-1 and 820-2, and perform health monitoring using artificial intelligence based on the sensor data. For example, the health monitoring processing device 830 can predict a risk of premonitory symptoms based on the sensor data by using a neural network model, and transmit an alert to one or more entities associated with the user based on the predicted risk. Further details regarding the system 800 are described above with reference to FIGS. 1-7.” (paragraph 0082). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include transmitting, to the user of the wearable device or to a healthcare provider, a notification message that is based on the prediction as taught by Mei in order to provide an alert or message regarding the patient’s risk of developing a disease.
Claims 3 and 16:
As per claims 3 and 16, Lafon, Ferenc, and Mei teach the method and system of claims 1 and 14 as described above and Ferenc teaches wherein generating the output vector that indicates the prediction comprises:
generating an input vector based on combining the embedding vector for the monitored parameter data with one or more additional embedding vectors associated with the user (paragraph 0037). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include generating an input vector based on combining the embedding vector for the monitored parameter data with one or more additional embedding vectors associated with the user as taught by Ferenc in order to generate a profile associated with a patient to find elements of importance corresponding to the patient.
Mei further teaches and generating the output vector based on applying the artificial intelligence algorithm to the input vector (paragraphs 0069-0070 and 0077-0078). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include generating the output vector based on applying the artificial intelligence algorithm to the input vector as taught by Mei in order to assess a patient.
Claims 8 and 19:
As per claims 8 and 19, Lafon, Ferenc, and Mei teach the method and system of claims 3 and 14 as described above and Lafon further teaches wherein the artificial intelligence algorithm comprises one or more of:
a multi-layer perceptron (MLP) algorithm;
a convolution neural network (CNN) (paragraph 0085);
or a recurrent neural network (RNN).
Claim 9:
As per claim 9, Lafon, Ferenc, and Mei teach the method of claim 3 as described above and Mei further teaches wherein the artificial intelligence algorithm comprises a number of convolutional layers and a plurality of fully connected layers, each fully connected layer corresponding to a different health condition in the plurality of health conditions (paragraphs 0075-0077). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include wherein the artificial intelligence algorithm comprises a number of convolutional layers and a plurality of fully connected layers, each fully connected layer corresponding to a different health condition in the plurality of health conditions as taught by Mei in order to analyze data and determine patterns or trends associated with predicted health conditions.
Claim 10:
As per claim 10, Lafon, Ferenc, and Mei teach the method of claim 1 as described above and Lafon further teaches wherein the wearable device is an activity tracker, and wherein the monitored parameter data comprises data points related to one or more of:
a heart rate (paragraph 0032);
an oxygen level;
an activity level comprising at least one of a number of steps, a number of flights climbed, or a duration of exercise;
or a number of calories burned.
Claim 11:
As per claim 11, Lafon, Ferenc, and Mei teach the method of claim 10 as described above and Lafon further teaches wherein the monitored parameter data further comprises information logged by a user manually (paragraphs 0035 and 0042).
Claim 12:
As per claim 12, Lafon, Ferenc, and Mei teach the method of claim 1 as described above and Mei further teaches wherein transmitting the notification message comprises transmitting the notification message to one of the wearable device or a mobile device associated with the wearable device to provide a user of the wearable device with a suggested action based on the prediction (paragraphs 0070 and 0082). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include wherein transmitting the notification message comprises transmitting the notification message to one of the wearable device or a mobile device associated with the wearable device to provide a user of the wearable device with a suggested action based on the prediction- as taught by Mei in order to bring the predicted health condition to the user’s attention.
Claim(s) 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lafon, Ferenc, and Mei as applied to claims 1 and 14 above, and further in view of Wu et al. US Publication 20220254493 A1.
Claims 2 and 15:
As per claims 2 and 15, Lafon, Ferenc, and Mei teach the method and system of claims 1 and 14 as described above but do not teach wherein generating the plurality of tensors based on applying the CNN to the monitor parameter data comprises:
generating a feature map identifying patterns in the monitored parameter data based on applying the CNN to the monitored parameter data. However, Wu teaches a Chronic Disease Prediction System based on Multi-Task Learning Model and further teaches, “A chronic disease prediction system based on a multi-task learning model comprises a computer memory, a computer processor and a computer program which is stored in the computer memory and executable on the computer processor, wherein a trained chronic disease prediction model is stored in the computer memory, and the chronic disease prediction model is composed of a shared layer convolutional neural network and a plurality of chronic disease branch networks. When executing the computer program, the computer processor implements the following steps:” (paragraph 0045), “a to-be-predicted physical examination record is preprocessed and then is input into the shared layer convolutional neural network of the chronic disease prediction model to perform feature extraction to obtain a feature map; and then the obtained feature map is input into each chronic disease branch network respectively to perform feature extraction and prediction respectively to obtain a chronic disease prediction result.” (paragraph 0046), and “The chronic disease prediction model of the present invention takes a two-dimensional vector as an input, as shown in FIG. 3, firstly, a shared layer convolutional neural network shared by various diseases was designed, and feature extraction was performed on the potential correlations possibly existing among various diseases; and the feature maps after common feature extraction were subjected to feature extraction and prediction respectively through each branch for different chronic diseases.” (paragraph 0057). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include generating a feature map identifying patterns in the monitored parameter data based on applying the CNN to the monitored parameter data as taught by Wu in order to analyze the monitored parameter data received to determine patterns or trends associated with a patient.
Ferenc teaches and dividing, by the processor, the feature map into the plurality of tensors (abstract and paragraphs 0023 and 0036). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include dividing, by the processor, the feature map into the plurality of tensors as taught by Ferenc in order to determine patterns or trends corresponding to a particular time period.
Claim(s) 4-5 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lafon, Ferenc, and Mei as applied to claims 3 and 16 above, and further in view of Kailasam et al. US Patent 11100289 B1.
Claims 4 and 17:
As per claims 4 and 17, Lafon, Ferenc, and Mei teach the method and system of claims 3 and 16 as described above but do not teach wherein the one or more additional embedding vectors comprises a first embedding vector obtained by processing health records for a user via a natural language processing algorithm, wherein the health records correspond to a registered user of the wearable device. However, Kailasam teaches Systems and Methods for Enhancing Natural Language Processing and further teaches, “Embodiments of EHR system 160 include one or more data stores of health-related records, which may be stored on storage 121, and may further include one or more computers or servers that facilitate the storing and retrieval of the health records. In some embodiments, EHR system 160 and/or other records systems may be implemented as a cloud-based platform or may be distributed across multiple physical locations. EHR system 160 may further include record systems that store real-time or near real-time patient (or user) information, such as wearable sensor or monitor, bedside, or in-home patient monitors or sensors, for example. Although FIG. 1A depicts an example EHR system 160, it is contemplated that an embodiment relies on natural language process (NLP) application 140 for storing and retrieving patient record information.” (column 7, lines 17-31), “In this example system, a natural language processing engine receives patient information (such as from one or more patient EHRs or a data stream), which may be provided by a patient sensor or provided each time a patient is assessed by a caregiver. Natural language processing engine may use one or more natural language processing agents to process the received patient information into consumable content, which may be used as discussed in connection to FIG. 3. In some embodiments, the consumable content is de-identified. In some embodiments, the patient information is encoded into one or more clinical concepts, which may be translated or “mapped” to a standard or universal nomenclature, thereby rendering the content consumable by the other decision support services, applications, features, and agents described herein. In some embodiments and contexts, the NLP engine may be referred to herein as medical language processing (MLP) engine. FIG. 2 depicts one example architecture that may be used for implementing the enhanced NLP system, but alternative arrangements may be used.” (column 12, lines 1-20) and “In some embodiments, the objective of using the natural language processing techniques is to find a diagnosis of a clinical condition by identifying a clinical condition within the unstructured data. The clinical condition extracted from the unstructured data may be ambiguous in that the status of the diagnosis may not be immediately clear to the system running the natural language processing techniques without further corroboration. For instance, based on the expression within which the clinical condition is extracted, it may be unclear whether the individual was diagnosed with the clinical condition, is at risk for the clinical condition, asked the clinician about the condition, or experienced a change in the clinical condition. In this way, the expression of the clinical condition may be considered ambiguous. As such, in some embodiments, method 300 further comprises determining the clinical condition is ambiguously expressed within the health-related data, and, if so, the process of verifying the clinical condition continues. In alternative aspects, an extracted expression of the clinical condition is verified regardless of whether or not it is ambiguously expressed.” (column 13, line 30-50). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include wherein the one or more additional embedding vectors comprises a first embedding vector obtained by processing health records for a user via a natural language processing algorithm, wherein the health records correspond to a registered user of the wearable device as taught by Kailasam in order to analyze and process data received to determine patterns or trends.
Claims 5 and 18:
As per claims 5 and 18, Lafon, Ferenc, Mei, and Kailasam teach the method and system of claims 4 and 17 as described above and Kailasam further teaches wherein the health records are stored in a database and comprise at least one of:
claims records received from a health care provider (column 6, lines 17-34 and column 7, lines 17-31). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include claims records received from a health care provider as taught by Kailasam in order to gain insight regarding the health condition of the user by analyzing claims record.
prescription records received from a pharmacy;
or laboratory results received from a laboratory or other health care provider.
Claim(s) 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Lafon, Ferenc, Mei, and Kailasam as applied to claim 4 above, and further in view of Shaw et al. US Publication 20210296000 A1.
Claim 6:
As per claim 6, Lafon, Ferenc, Mei, and Kailasam teach the method of claim 4 as described above but do not teach wherein the one or more additional embedding vectors further comprises a second embedding vector related to social determinants data comprises one or more of:
economic information;
neighborhood information;
education information. However, Shaw teaches Clinical Risk Model and further teaches, “…Machine learning system 400 may extract one or more features (or feature vectors) from the records and apply training algorithm 420 to determine correlations between the features and the subsequent medical visits. These features may be extracted from structured and/or unstructured data as described above with respect to FIG. 2. In some embodiments, the features may include demographic features, such as a patient's gender, race/ethnicity, sexual orientation, age, social indicators (e.g. income level, education level, food insecurity, access to housing and utility services, proximity to facility, access to caregiver) or other demographic information that may be included in a patient's medical data.” (paragraph 0043). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include education information as taught by Shaw in order to determine the impact of the user’s education on health condition or health outcomes.
nutritional information;
or other environmental information.
Claim 7:
As per claim 7, Lafon, Ferenc, Mei, Kailasam, and Shaw teach the method of claim 6 as described above and Shaw further teaches wherein the second embedding vector is further related to demographic data comprising one or more of:
age information (paragraph 0043). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include age information as taught by Shaw in order to determine the impact of a user’s age on health condition(s) or health outcomes.
gender information;
neighborhood type information;
family size information;
or employment indicator information.
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Lafon, Ferenc, and Mei as applied to claim 12 above, and further in view of Aranke et al. US Patent 11830623 B1.
Claim 13:
As per claim 13, Lafon, Ferenc, and Mei teach the method of claim 12 as described above but do not teach wherein the suggested action comprises information to facilitate scheduling an appointment with a healthcare provider. However, Aranke teaches Health Care Management using Total Health Index and further teaches, “The condition management engine 208 performs co-morbidity analysis to identify patient members who are at risk of developing a new disease or health condition given their current and/or existing diagnosis of another health condition. The condition management engine 208 determines that the health risk is a likelihood of a patient developing one or more co-morbidities and determines an actionable intervention for managing the health risk. Example input data analyzed by the condition management engine 208 for predicting a risk of developing co-morbidities may include, but are not limited to, diagnoses, individual social determinant data, problem list, laboratory tests and medication data, demographic data, and census and other public data. The condition management engine 208 facilitates a data analyst to segment the data to understand most frequently occurring multi-morbidity occurrences. The goal is to identify which diagnoses tend to appear together in a higher frequency than expected by chance. By analyzing association of each diagnosis with the rest, a co-morbidity network of associations between diagnoses can be built. In some implementations, the condition management engine 208 uses a temporal exponential-family random graph model (TERGM) for analyzing the data from co-morbidity network to identify co-occurring patterns. For example, the condition management engine 208 analyzes patient procedures and medications, CMS-HCC risk score, co-morbidity indices, social determinants, and zip-code level variables to detect a likelihood of co-morbidities developing in a patient. The condition management engine 208 creates and conveys personalized risk propensities for the patient. The condition management engine 208 generates a notification for a health care provider of the patient to open discussion on multi-morbidities with their patients identified being at risk. The condition management engine 208 automatically generates a suggestion of scheduling an appointment for medically testing for a disease associated with the co-morbidity that appears highly correlated with the patient's problem list. Once the suggestion is accepted by the patient and/or the health care provider, the condition management engine 208 automatically schedules and creates the appointment.” (column 21, lines 1-40). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Lafon to include wherein the suggested action comprises information to facilitate scheduling an appointment with a healthcare provider as taught by Aranke in order to facilitate scheduling an appointment to address the health condition.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW L HAMILTON whose telephone number is (571)270-1837. The examiner can normally be reached Monday-Thursday 9:30-5:30 pm EST.
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/MATTHEW L HAMILTON/Primary Examiner, Art Unit 3681