DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-7 are drawn to a method, which is within the four statutory categories (i.e. process). Claims 8-14 are drawn to a system, which is within the four statutory categories (i.e. apparatus). Claims 15-20 are drawn to a computer-readable medium, which is within the four statutory categories (i.e. manufacture).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, claim 1 (and substantially similar with independent claims 8, 15) recites:
A computer implemented method executed on a computing system, the method comprising:
receiving, from a plurality of devices, respective pieces of input data regarding a first status of a target individual, each piece of input data is received from a respective device in the plurality of devices that is remote from the computing system,
wherein multiple devices in the plurality of devices are associated with respective people in a circle of individuals that each has a respective specified relationship with the target individual, and
wherein at least a piece of input data is received in response to an interaction of a person with a respective device in the plurality of devices, the person being in the circle of individuals;
pre-processing the pieces of input data to obtain input for a machine learning model, wherein the pre-processing includes:
extracting from a piece of input data multiple features associated with a particular input module in one or more input modules of the machine learning model, each input module being associated with a respective marker, and
transforming each extracted feature to a respective input feature that has a respective format associated with the input module, wherein at least two input modules are associated with different formats, and wherein at least one extracted feature is transformed to the different formats associated with the at least two input modules;
inputting the pre-processed pieces of input to respective input modules of the machine learning model, wherein each input module has an input layer including the input features obtained through the pre-processing, wherein an input layer of a first module is different from an input layer of a second module;
receiving, from the machine learning model, an output prediction for the target individual;
analyzing the output to determine an urgency associated with the prediction, wherein the urgency is determined in response to determining that a probability of the prediction is more than a pre-specified threshold value; and
generating and sending an alert to at least one other individual, notifying them about the urgency, wherein the alert includes an action recommendation.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the computing system, processor, computer memory with instructions, plurality of devices, respective device, machine learning model, the limitations in the context of this claim encompass analyzing input data to make predictions about the target individual and alert other individuals and recommend action. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-7, 9-14, and 16-20 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 1 (and substantially similar with independent claims 8, 15) recites:
A computer implemented method executed on a computing system, the method comprising: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
receiving, from a plurality of devices, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) respective pieces of input data regarding a first status of a target individual, each piece of input data is received from a respective device in the plurality of devices that is remote from the computing system, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
wherein multiple devices in the plurality of devices are associated with respective people in a circle of individuals that each has a respective specified relationship with the target individual, and
wherein at least a piece of input data is received in response to an interaction of a person with a respective device in the plurality of devices, the person being in the circle of individuals;
pre-processing the pieces of input data to obtain input for a machine learning model, wherein the pre-processing includes:
extracting from a piece of input data multiple features associated with a particular input module in one or more input modules of the machine learning model, each input module being associated with a respective marker, and
transforming each extracted feature to a respective input feature that has a respective format associated with the input module, wherein at least two input modules are associated with different formats, and wherein at least one extracted feature is transformed to the different formats associated with the at least two input modules;
inputting the pre-processed pieces of input to respective input modules of the machine learning model, wherein each input module has an input layer including the input features obtained through the pre-processing, wherein an input layer of a first module is different from an input layer of a second module;
receiving, from the machine learning model, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) an output prediction for the target individual;
analyzing the output to determine an urgency associated with the prediction, wherein the urgency is determined in response to determining that a probability of the prediction is more than a pre-specified threshold value; and
generating and sending an alert to at least one other individual, notifying them about the urgency, wherein the alert includes an action recommendation.
The 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 no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the computing system, processor, computer memory with instructions, plurality of devices, respective device, machine learning model, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0050], [0054], [00103], [00109]-[00110], [00129]-[00130], [00138], see MPEP 2106.05(f))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-4, 7, 9-12, 14, and 16-18 recite additional limitations which further the abstract idea; claims 5-6, 12-13, 19-20 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea, and claims 2-7, 9-14, and 16-20 recite additional limitations which generally link the abstract idea to a particular technological environment or field of use). 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 of the Alice/Mayo Test for Claims
The claims 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. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the computing system, processor, computer memory with instructions, plurality of devices, respective device, machine learning model, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0050], [0054], [00103], [00109]-[00110], [00129]-[00130], [00138]); using a computing system, processor, computer memory with instructions, machine learning model, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
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 and are generally linking the abstract idea to a particular field of environment. 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. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
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 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over LaBorde (US 2023/0138557) in view of Brunner (US2017/0249434).
Regarding claim 1, LaBorde discloses a computer implemented method executed on a computing system, the method comprising:
receiving, from a plurality of devices, respective pieces of input data regarding a first status of a target individual, each piece of input data is received from a respective device in the plurality of devices that is remote from the computing system, (LaBorde Fig. 1 and corresponding text; [0051] a system which includes various input data sources, client devices and backend devices. The input data source may include a Data Collection Engine (DCE) and an RFID tag associated, for example, identifications of medical professionals and patients. The backend devices can be one or more server devices [0055] an exemplary operating environment in which the system according to various embodiments can be implemented will be discussed. The environment includes various input data sources such as a DCE 102, social media data server 152, medical claims information system 153, a pharmacy management information system data 154, third party predictive information system data 155, and an electronic medical records system data 156. The system is also capable of utilizing data originating from cameras, video sensors and even closed-circuit television and similar technologies in conjunction with facial recognition technology and facial expression analysis for emotion and behavior prediction as inputs into its predictive models)
wherein multiple devices in the plurality of devices are associated with respective people in a circle of individuals that each has a respective specified relationship with the target individual, and (LaBorde [0005] third party data sources and sensor-based data have the potential to significantly augment insight to providers of healthcare and care coordinators {construed as a circle of individuals that each have relationships with the target individual} [0057] Other data can also come from sources such as 911 call centers, police scanners, law enforcement databases, social media data, human facial imaging data and changes over time and interpretations thereof, location data, attributes describing a patient's living situation, economic data from the patient's location such as the unemployment rate, attributes of the patient such as marital status, race, etc . . . , data collected from care givers, family members and friends of the patient)
wherein at least a piece of input data is received in response to an interaction of a person with a respective device in the plurality of devices, the person being in the circle of individuals; (LaBorde [0195] The input attributes can further be the data derived from natural language processing (NLP) of data from text based narratives stored on a medium and/or audio and/or video content from which text is extracted from the audio signal using natural language processing or other method (e.g., manual transcription, etc.), generated by administrative or clinical healthcare personnel or other individuals (e.g., family members, spouses, care takers, police officers, attorneys, juries, judges, credit reports, collection officials notices etc.) wherein the content provides insight into stressful life events including housing instability, job instability, marital instability, social isolation, food insecurity, relationship problems, justice involvement, alcohol and/or drug use or detox, access to lethal means derived or other insights from a plurality of potential sources with records/narratives that can be matched to the identity of the subject/actor/patient participant in the new event)
pre-processing the pieces of input data to obtain input for a machine learning model, wherein the pre-processing includes: extracting from a piece of input data multiple features associated with a particular input module in one or more input modules of the machine learning model, each input module being associated with a respective marker, and transforming each extracted feature to a respective input feature that has a respective format associated with the input module, (LaBorde [0126] A representation of the process for creating, training and using the trained model is shown in FIG. 14. Raw data 1101 is normalized 1103, and then input into the model 1105. The model 1105 is trained to form the trained model 1107. New data 1109 is normalized 1103 and input into the trained model 1107. The output data of the trained model 1107 is de-normalized 1111 to obtain the output data (predicted raw results) 1113. As shown in FIG. 15, the raw data 1101 and new data 1109 include sets of data [1,2 . . . N] with known outcomes and properties of each of the data. For example, the data can be past patient events with known suicide outcomes. The properties of the data can be suicide attributes. [0130] Neural network models only use numerical double values for training and processing. Thus any nominal categorical data fields that are a part of raw data that will ultimately be used by models in the system are first encoded to numerical values and “raw” numerical data in many cases by a pre-processing such as normalization 1103 before training and processing. While normalization and de-normalization steps may not be explicitly described as being carried out before or after data consumption by any given model, this should not be misconstrued and lead to the assumption that these routine steps are not carried out. [0215] The backend device receives a plurality of input attributes of a new patient event. This data may come from a client device, from the database at the server, or a combination. The data is pre-processed (for example, normalized) to generate an input data set, and the data is input into the trained model 1107 which then generates an output value. The output value is then post-processed (for example, de-normalized). Finally, the output value is classified into a suicide risk category (classification task) or a value such as the probability of a suicide attempt (regression task) to predict the outcome. For example, in the simplest case the de-normalized output value can be a Boolean value (suicide or no suicide). In another case, the output value can be a probability of a suicide occurring. In this case, the server may assign probability ranges which define particular suicide categories)
wherein at least two input modules are associated with different formats, (LaBorde [0195] The input attributes can further be the data derived from natural language processing (NLP) of data from text based narratives stored on a medium and/or audio and/or video content from which text is extracted from the audio signal using natural language processing or other method (e.g., manual transcription, etc.), generated by administrative or clinical healthcare personnel or other individuals (e.g., family members, spouses, care takers, police officers, attorneys, juries, judges, credit reports, collection officials notices etc.) wherein the content provides insight into stressful life events including housing instability, job instability, marital instability, social isolation, food insecurity, relationship problems, justice involvement, alcohol and/or drug use or detox, access to lethal means derived or other insights from a plurality of potential sources with records/narratives that can be matched to the identity of the subject/actor/patient participant in the new event)
inputting the pre-processed pieces of input to respective input modules of the machine learning model, wherein each input module has an input layer including the input features obtained through the pre-processing, (LaBorde [0159] Referring to FIG. 22, the backend devices can use a neural network model (NNM) 1400. The NNM 1400 includes an input layer 1401, a hidden layer 1404 and an output layer 1406)
receiving, from the machine learning model, an output prediction for the target individual; (LaBorde [0013] a controller operatively coupled to the transceiver; and one or more memory sources operatively coupled to the controller, the one or more memory sources storing a trained neural network model (NNM) for generating an output value corresponding to a present event based upon one or more of the identification information and position information, wherein the output value corresponds to a suicide risk. [0077] The controller 2004 is configured to predict a suicide risk associated with a patient event based upon inputting attributes of the patient event into the trained model such as a neural network model or self-organizing map network. [0114] As mentioned above, the server device (or DCE) can utilize machine learning algorithms to predict events related to, risk of suicide or changes therein. A trained model can be used to determine whether a patient that has been flagged to be at risk for suicide (to address one use case of the system described herein) has had a change (for example a worsening) in the predicted risk of suicide)
analyzing the output to determine an urgency associated with the prediction, wherein the urgency is determined in response to determining that a probability of the prediction is more than a pre-specified threshold value; and (LaBorde [0111] Entities deploying the system for this purpose can then use the system's notification micro service to obtain real time situational awareness and alerts about individuals that may be at risk for suicide and potentially in need of intervention, outreach and/or healthcare and mental health services. [0287] One demonstrative approach the server might take would be to recommend the deployment of an available resource if the probability weighted reduction in the risk of suicide exceeded a particular threshold)
generating and sending an alert to at least one other individual, notifying them about the urgency, wherein the alert includes an action recommendation. (LaBorde [0111] if the individuals being assessed for potential risk of committing suicide have had opted into potential notification of particular authorized individuals, for example family members, spouses, healthcare providers, case managers, social workers, or any other individuals, the system can be configured to request specific actions by a plurality of said individuals)
LaBorde does not appear to explicitly disclose the following, however, Brunner teaches it is old and well known in the art of healthcare data processing:
wherein at least one extracted feature is transformed to the different formats associated with the at least two input modules; ([0047] If data is missing, an Imputation Algorithm 7 may supply the appropriate data using one of two modules, the Feature Domain Knowledge 8 and the Disease Domain Knowledge 9. Once the dataset is complete, it is stored in a Database Complete 10 for future analysis. [0082] As used herein, “Data Formatting” refers to modules which provide processes used to adjust, manipulate, complete, or transform the incoming data. [0184] In a Data Formatting 4 step, it may be determined if the dataset is complete or has missing values according to an analysis performed by a Missing Data Algorithm 6 (FIGS. 1 and 4) that combs the data and returns a flag for each data cell that remains empty after data entry. If data is missing, an Imputation Algorithm 7 (FIG. 4) can supply the appropriate data using Feature Domain Knowledge 8 and or Disease Domain Knowledge 9 as appropriate, or other suitable algorithms such as replacement by the group average, by a predictive model trained using available data against the variable to impute)
wherein an input layer of a first module is different from an input layer of a second module; ([0173] The Data Formatting 4 module (FIGS. 1 and 2) comprises several aspects. An Unstructured Digital Dataset 21 is exemplified as being comprised of 3 different data streams: stream “@” with binary data from the GPS, stream & with binary data from an eye tracking device, and stream “#” with numerical data from EKG—where each symbol represents a different data stream. Algorithms are used to detect and identify events and states as defined above. For example, A=101′ may be identified in DataStream “@” from the GPS, as an event, such as the onset of walking, which may be called event A. In like manner, B='011′ is another event in “@.” Events and states are stored in a Semi-structured Dataset 22. [0184] In a Data Formatting 4 step, it may be determined if the dataset is complete or has missing values according to an analysis performed by a Missing Data Algorithm 6 (FIGS. 1 and 4) that combs the data and returns a flag for each data cell that remains empty after data entry. If data is missing, an Imputation Algorithm 7 (FIG. 4) can supply the appropriate data using Feature Domain Knowledge 8 and or Disease Domain Knowledge 9 as appropriate, or other suitable algorithms such as replacement by the group average, by a predictive model trained using available data against the variable to impute)
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify LaBorde to incorporate wherein at least one extracted feature is transformed to the different formats associated with the at least two input modules; and wherein an input layer of a first module is different from an input layer of a second module, as taught by Brunner, in order to adjust the data to account for missing data and complete the dataset. See Brunner [0047], [0082].
Regarding claim 2, LaBorde-Brunner teaches the method of claim 1, and LaBorde further discloses wherein the output prediction of the machine learning model includes respective probabilities that multiple events happen to the target individual. (LaBorde [0195] provides insight into stressful life events including housing instability, job instability, marital instability, social isolation, food insecurity, relationship problems, justice involvement, alcohol and/or drug use or detox, access to lethal means derived or other insights from a plurality of potential sources).
Regarding claim 3, LaBorde-Brunner teaches the method of claim 2, and LaBorde further discloses wherein an input module in the one or more input modules provides outputs including respective values that each indicates a probability of a respective event in the multiple events for the target individual. (LaBorde [0111] Entities deploying the system for this purpose can then use the system's notification micro service to obtain real time situational awareness and alerts about individuals that may be at risk for suicide and potentially in need of intervention, outreach and/or healthcare and mental health services. [0287] One demonstrative approach the server might take would be to recommend the deployment of an available resource if the probability weighted reduction in the risk of suicide exceeded a particular threshold).
Regarding claim 4, LaBorde-Brunner teaches the method of claim 2, and LaBorde further discloses wherein each of the input modules provides respective outputs for the same events as the multiple events for which the machine learning model provides the output. (LaBorde [0111] Entities deploying the system for this purpose can then use the system's notification micro service to obtain real time situational awareness and alerts about individuals that may be at risk for suicide and potentially in need of intervention, outreach and/or healthcare and mental health services. [0195] The input attributes can further be the data derived from natural language processing (NLP) of data from text based narratives stored on a medium and/or audio and/or video content from which text is extracted from the audio signal using natural language processing or other method (e.g., manual transcription, etc.), generated by administrative or clinical healthcare personnel or other individuals (e.g., family members, spouses, care takers, police officers, attorneys, juries, judges, credit reports, collection officials notices etc.) wherein the content provides insight into stressful life events including housing instability, job instability, marital instability, social isolation, food insecurity, relationship problems, justice involvement, alcohol and/or drug use or detox, access to lethal means derived or other insights from a plurality of potential sources with records/narratives that can be matched to the identity of the subject/actor/patient participant in the new event). [0287] One demonstrative approach the server might take would be to recommend the deployment of an available resource if the probability weighted reduction in the risk of suicide exceeded a particular threshold)
Regarding claim 5, LaBorde-Brunner teaches the method of claim 1, and LaBorde further discloses wherein the interaction of the person with the respective device includes entering information on an application running on the respective device. (LaBorde [0205] The input attributes can further be a chat/Instant Message (i.e., a standalone IM service or that embedded in a webpage such as that at veteranscrisisline.net).
Regarding claim 6, LaBorde-Brunner teaches the method of claim 1, and LaBorde further discloses wherein at least a few devices in the multiple devices are respective sensors measuring or monitoring respective parameters of the target individual. (LaBorde [0201] The input attributes can further be data from interaction with a device sensor or data derived from a device sensor (i.e., taps, swipes, clicks, taps, pressure applied over time, velocity, acceleration, global and or indoor positioning systems, wireless beacons and access points, Bluetooth)).
Regarding claim 7, LaBorde-Brunner teaches the method of claim 1, and LaBorde further discloses wherein at least two pieces of input data have different formats, and wherein the formats include one or more of voice, text, selection of an item on a respective device in the multiple devices. (LaBorde [0195] The input attributes can further be the data derived from natural language processing (NLP) of data from text based narratives stored on a medium and/or audio and/or video content from which text is extracted from the audio signal using natural language processing or other method (e.g., manual transcription, etc.), generated by administrative or clinical healthcare personnel or other individuals (e.g., family members, spouses, care takers, police officers, attorneys, juries, judges, credit reports, collection officials notices etc.) wherein the content provides insight into stressful life events including housing instability, job instability, marital instability, social isolation, food insecurity, relationship problems, justice involvement, alcohol and/or drug use or detox, access to lethal means derived or other insights from a plurality of potential sources with records/narratives that can be matched to the identity of the subject/actor/patient participant in the new event. [0213] The input attributes can further be communications initiated by a person).
Regarding claim 8, the claims recite substantially similar limitations as those already recited in the rejection of claim 1, and, as such, are rejected for similar reasons as given above.
Regarding claim 9, the claims recite substantially similar limitations as those already recited in the rejection of claim 2, and, as such, are rejected for similar reasons as given above.
Regarding claim 10, the claims recite substantially similar limitations as those already recited in the rejection of claim 3, and, as such, are rejected for similar reasons as given above.
Regarding claim 11, the claims recite substantially similar limitations as those already recited in the rejection of claim 4, and, as such, are rejected for similar reasons as given above.
Regarding claim 12, the claims recite substantially similar limitations as those already recited in the rejection of claim 5, and, as such, are rejected for similar reasons as given above.
Regarding claim 13, the claims recite substantially similar limitations as those already recited in the rejection of claim 6, and, as such, are rejected for similar reasons as given above.
Regarding claim 14, the claims recite substantially similar limitations as those already recited in the rejection of claim 7, and, as such, are rejected for similar reasons as given above.
Regarding claim 15, the claims recite substantially similar limitations as those already recited in the rejection of claim 1, and, as such, are rejected for similar reasons as given above.
Regarding claim 16, the claims recite substantially similar limitations as those already recited in the rejection of claim 2, and, as such, are rejected for similar reasons as given above.
Regarding claim 17, the claims recite substantially similar limitations as those already recited in the rejection of claim 3, and, as such, are rejected for similar reasons as given above.
Regarding claim 18, the claims recite substantially similar limitations as those already recited in the rejection of claim 4, and, as such, are rejected for similar reasons as given above.
Regarding claim 19, the claims recite substantially similar limitations as those already recited in the rejection of claim 5, and, as such, are rejected for similar reasons as given above.
Regarding claim 20, the claims recite substantially similar limitations as those already recited in the rejection of claim 6, and, as such, are rejected for similar reasons as given above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA R COVINGTON whose telephone number is (303)297-4604. The examiner can normally be reached Monday - Friday, 10 - 5 MT.
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686