DETAILED ACTION
The present office action represents a final action on the merits.
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 .
Priority
This application claims the priority date of provisional application 63/558,890 of February 28, 2024.
Status of Claims
Claims 1-2, 6, 8, 12-15, 17-20 are amended, claims 21-22 are new, and claims 1-22 are pending.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-11 and 21-22 are drawn to method of notifying caretakers of patient's risk of falling or incurring a wound, which is within the four statutory categories (i.e., process). Claims 12-16 are drawn to a system, which is within the four statutory categories (i.e., machine). Claims 17-20 are drawn to a non-transitory computer-readable storage medium storing instructions, which is within the four statutory categories (i.e., machine).
Claims 1-11 and 21-22 recite a method of notifying caretakers of patient's risk of falling or incurring a wound, performed at a computer system having one or more processors and memory storing one or more programs that are executable by the computer system, the method comprising:
obtaining historical patient data via one or more databases communicatively coupled with the computer system;
generating a training set including a subset of the historical patient data, the training set including one or more features for training an injury detection system;
training the injury detection system using the training set, wherein the injury detection system is configured to detect at least a fall risk and/or a wound risk for a patient;
receiving at least partially unstructured new patient data, wherein the new patient data includes vital statistics of the patient, the vital statistics including at least one of blood pressure, oxygen saturation, and pulse information;
transforming the at least partially unstructured new patient data into structured new patient data;
inputting the structured new patient data into the injury detection system that was previously trained using the training set;
determining, based on the structured new patient data provided to the injury detection system, a fall risk and/or wound risk of the patient;
generating, based on the patient's fall risk and/or wound risk, a patient report; and
providing a caretaker with remote access to the patient report, wherein the patient report presents the fall risk and/or wound risk of the patient and includes one or more user interface elements.
Claims 12-16 recite a system, comprising:
one or more processors;
a memory coupled to the one or more processors, the memory storing one or more programs configured for execution by the one or more processors, the one or more programs including instructions for:
obtaining historical patient data via one or more databases communicatively coupled with the system;
generating a training set including at least a subset of the historical patient data, the training set including one or more features for training an injury detection system;
training the injury detection system using the training set, wherein the injury detection system is configured to detect at least a fall risk and/or a wound risk for a patient;
receiving at least partially unstructured new patient data, wherein the new patient data includes vital statistics of the patient, the vital statistics including at least one of blood pressure, oxygen saturation, and pulse information; transforming the at least partially unstructured new patient data into structured new patient data;
inputting the structured new patient data into the injury detection system that was previously trained using the training set;
determining, based on the structured new patient data provided to the injury detection system, a fall risk and/or wound risk of the patient;
generating, based on the patient's fall risk and/or wound risk, a patient report; and
providing a caretaker with remote access to the patient report, wherein the patient report
presents the fall risk and/or wound risk of the patient and includes one or more user interface elements.
Claims 17-20 recite a non-transitory computer-readable storage medium storing one or more programs comprising instructions that when executed by a computer system having a processor and memory, cause the computer system to:
obtain historical patient data via one or more databases communicatively coupled with a computer system;
generate a training set including at least a subset of the historical patient data, the training set including one or more features for training an injury detection system;
train the injury detection system using the training set, wherein the injury detection system is configured to detect at least a fall risk and/or a wound risk for a patient;
receive at least partially unstructured new patient data, wherein the new patient data includes vital statistics of the patient, the vital statistics including at least one of blood pressure, oxygen saturation, and pulse information;
transform the at least partially unstructured new patient data into structured new patient data; input the structured new patient data into the injury detection system that was previously trained using the training set;
determine, based on the structured new patient data provided to the injury detection system, a fall risk and/or wound risk of the patient;
generate, based on the patient's fall risk and/or wound risk, a patient report; and
provide a caretaker with remote access to the patient report, wherein the patient report presents the fall risk and/or wound risk of the patient and includes one or more user interface elements.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and/or mathematical concepts, but for the recitation of generic computer components (e.g., a computer system, one or more processors, memory, one or more databases, the memory resource, a system, an injury detection system, etc.). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity or mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-11, 13-16, and 18-22 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. These limitations only serve to further limit the abstract idea (or contain the same additional elements found in the independent claim), and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 12, and 17.
The dependent claims include additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 12, and 17.
The additional elements from the claims include:
a computer system (apply it, MPEP 2106.05(f)).
one or more processors (apply it, MPEP 2106.05(f)).
memory storing one or more programs that are executable (apply it, MPEP 2106.05(f)).
one or more databases (apply it, MPEP 2106.05(f)).
an injury detection system (apply it, MPEP 2106.05(f)).
one or more user interface elements (apply it, MPEP 2106.05(f)).
a system (apply it, MPEP 2106.05(f)).
a memory storing instructions (apply it, MPEP 2106.05(f)).
the memory resource (apply it, MPEP 2106.05(f)).
the one or more processors being configured to execute the instructions (apply it, MPEP 2106.05(f)).
a non-transitory computer-readable storage medium storing instructions that when executed by a processor, causes the processor to (apply it, MPEP 2106.05(f)).
These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception – for example, the recitation of a computer system, one or more processors, memory storing one or more programs that are executable, one or more databases, an injury detection system, one or more user interface elements, a system, a memory storing instructions, the memory resource, the one or more processors being configured to execute the instructions, a non-transitory computer-readable storage medium storing instructions, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g., see Specification Paragraphs [0018]-[0022], [0029]-[0030], [0055], and [0080]-[0085] (See MPEP 2106.05(f)).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Paragraphs [0018]-[0022], [0029]-[0030], [0055], and [0080]-[0085] disclose that the additional elements (i.e., a computer system, one or more processors, memory storing one or more programs that are executable, one or more databases, an injury detection system, one or more user interface elements, a system, a memory storing instructions, the memory resource, the one or more processors being configured to execute the instructions, a non-transitory computer-readable storage medium storing instructions) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., a computer);
Relevant court decisions: The following example of court decision demonstrating well understood, routine and conventional activities, e.g., see MPEP 2106.05(d)(II): Receiving patient data, e.g., see Intellectual Ventures v. Symantec – similarly, the current invention obtains patient data.
Dependent claims 2-11, 13-16, and 18-22 include other limitations, but none of these functions are deemed significantly more than the abstract idea. Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity or mathematical concepts, for detecting patients susceptible to falls and wounds and providing notification for preventing or mitigating falls and wounds incurred by the susceptible patients, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-22 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 1-4, 6, 9-15, and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Main (U.S. Pub. No. 2022/0087617 A1) in view of Amarasingham (U.S. Pub. No. 2015/0213225 A1).
Regarding claim 1, Main discloses a method of notifying caretakers of patient's risk of falling or incurring a wound, performed at a computer system having one or more processors and memory storing one or more programs that are executable by the computer system, the method comprising (Paragraphs [0006]-[0009] and [0061] discuss notification generated based on patient pressure injury outcome or fall outcome to healthcare professionals and the computing server that executes code instructions to cause one or more processors to perform various processes.):
obtaining historical patient data via one or more databases communicatively coupled with the computer system (Paragraphs [0006], [0008], and [0082]-[0083] discuss a database of patient health data and a data store that stores continuous collection of historical patient data.);
generating a training set including at least a subset of the historical patient data, the training set including one or more features for training an injury detection system (Paragraph [0083] discusses the historical patient data may be utilized by one or more machine learning models to train the models to determine pressure injury outcomes and/or fall outcomes for current or future patients.);
training the injury detection system using the training set, wherein the injury detection system is configured to detect at least a fall risk and/or a wound risk for a patient (Paragraphs [0009]-[0012] and [0083] discuss historical patient data may be utilized by one or more machine learning models to train the models to determine pressure injury outcomes and/or fall outcomes for current or future patients and the computer predicts a pressure injury outcome and/or a fall outcome based on the pressure data. The pressure injury outcome includes a prediction of risk of the patient developing a pressure injury. The fall outcome includes a prediction of risk of the patient experiencing a fall.);
receiving at least partially unstructured (Examiner is interpreting “unstructured” based on Specification [0040] as raw patient data.) new patient data, wherein the new patient data includes vital statistics of the patient, the vital statistics including at least one of blood pressure, oxygen saturation, and pulse information (Paragraphs [0049] discuss for the pressure injury outcome detection, a computer may receive raw pressure data (e.g., generated from an embedded capacitive sensor layer), raw surface moisture data (e.g., generated from an embedded capacitive sensor layer), raw surface temperature data (e.g., generated from the thermistors), a health record of an individual, or any combination thereof, or the person's vitals such as heart rate, respiration rate, body temperature, blood pressure, blood sugar level, etc.);
determining, based on the new patient data provided to the injury detection system, a fall risk and/or wound risk of the patient (Paragraph [0057] discusses the weight support device may generate sensor signals and be in communication with a computer to automatically detect a pressure injury outcome and/or a fall outcome of the person, the pressure readings and other sensor readings (e.g., surface moisture readings), may be provided to a computer with an artificial intelligence system to identify the pressure injury outcome (e.g., a risk of the person developing a pressure injury) or the fall outcome (e.g., a risk of the person falling off of the weight support device).);
generating, based on the patient's fall risk and/or wound risk, a patient report (Paragraphs [0093]-[0094], [0145]-[0147], [0169]-[0170] and FIGS. 1B, 6A, 7A, 9B discuss notify/alert nurse of risk of fall or pressure injury and compile compliance reports related to pressure injury outcomes and/or fall outcomes of patient(s).); and
providing a caretaker with remote access to the patient report, wherein the patient report presents the fall risk and/or wound risk of the patient and includes one or more user interface elements (Paragraphs [0147], [0151], [0153], [0159], [0170], [0176], and FIGS. 6A, 9B discuss computer (e.g., the local computer, the user device, and/or the management device) can display the graphical user interface (GUI) inform the healthcare professional and/or other caregiver via an alert or notification about patient risk of developing pressure injury and/or fall injury.).
Main does not explicitly disclose:
transforming the at least partially unstructured new patient data into structured new patient data;
inputting the structured new patient data into the injury detection system that was previously trained using the training set; and
the structured new patient data provided to the injury detection system.
Amarasingham teaches:
transforming the at least partially unstructured new patient data into structured new patient data (Paragraph [0054] discusses the data cleansing process "cleans" or pre-processes the data, putting structured data in a standardized format and preparing unstructured text.);
inputting the structured new patient data into the injury detection system that was previously trained using the training set (Paragraphs [0054] and [0058] discuss the data cleansing process "cleans" or pre-processes the data, putting structured data in a standardized format and preparing unstructured text for natural language processing (NLP) to be performed in the disease/risk logic module.).
the structured new patient data provided to the injury detection system (Paragraphs [0054]-[0056] discusses the data cleansing process "cleans" or pre-processes the data, putting structured data in a standardized format and preparing unstructured text and the data is passed to the disease/risk logic module for assessment.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Main to include, transforming the at least partially unstructured new patient data into structured new patient data, inputting the structured new patient data into the injury detection system that was previously trained using the training set, and the structured new patient data provided to the injury detection system, as taught by Amarasingham, in order to provide a much more realistic and accurate depiction of the patient's overall health status and holistic healthcare environment. (Amarasingham Paragraph [0036]).
Regarding claims 2, 13, and 18 Main discloses wherein:
the new patient data includes respective patient data for a plurality of patients (Paragraph [0006] discusses continuous patient monitoring and continuous collection of patient data and a high-resolution visualization of areas of high pressure, the patient monitoring solution helps clinicians improve patient safety, clinicians and patients are provided visual, easy-to-understand pressure images that identify areas that are experiencing elevated pressures.); and
the method further comprises (Paragraph [0010] discusses method for determining a risk of a patient developing a pressure injury.):
determining, for each patient of the plurality of patients, the patient's fall risk and/or wound risk using the injury detection system and based on the respective patient data for the plurality of patients (Paragraphs [0010], [0083] and [0089] discuss predicting, based on the pressure data, a pressure injury outcome of the patient supported by the weight support device, the data store may store historical patient data that includes health record data, sensors data, and analysis results for patients to be utilized by one or more machine learning models to train the models to determine pressure injury outcomes and/or fall outcomes for current or future patients, then the system monitors and tracks the pressure injury outcomes regarding risk of pressure injury.); and
generating a respective patient report for each patient of the plurality of patients (Paragraphs [0153], [0169]-[0170], and FIG. 7C discuss the GUI may present a visual representation of the person with certain body parts labeled with an amount of time until a pressure injury may occur at that particular body part, determine a risk of fall outcome that may be communicated to a user (e.g., a healthcare professional, a caregiver, the person, etc.) via a notification displayed in a graphical user interface.).).
Regarding claim 3, Main discloses further comprising:
determining, using the injury detection system, a human-readable explanation for the patient's fall risk (Paragraph [0086] discusses interface may include various visualizations and graphical elements to display notifications and/or information to users, a GUI which includes graphical elements to display a digital heatmap, other pressure injury-related information, or other fall-related information.);
determining, using the injury detection system, a human-readable explanation for the patient's wound risk (Paragraph [0147] discusses the GUI may display information related to the positioning (e.g., related to a pose) of the person, such as a side label, display one or more symbols (e.g., circles of different sizes, colors, etc.) that are used to represent specific areas of the heatmap, the specific areas may include body parts (e.g., joint locations), areas of surface moisture, areas of high surface temperature, areas of the person at risk of developing pressure injury, or any combination thereof, detected by the computer.); and
wherein the patient report includes the human-readable explanation for the patient's fall risk and the human-readable explanation for the patient's wound risk (Paragraphs [0086], [0147], [0151], [0153], and [0164] discuss interface may include various visualizations and graphical elements to display notifications and/or information to users, a GUI which includes graphical elements to display a digital heatmap, other pressure injury-related information, or other fall-related information, for example, the GUI includes a heatmap that depict pressure data for the person, surface moisture data of the weight support device, surface temperature data of the person and/or weight support device, area(s) of the person at risk of a pressure injury, or any combination thereof to the healthcare provider or caregiver; notification may include an alert or message to a user (e.g., a nurse) that the person supported by the weight support device is at risk of falling off of the weight support device, which side(s) of the weight support device the person is at risk of falling off of and/or a recommendation for how best to adjust a positioning of the person to avoid the fall..).
Regarding claim 4, Main discloses further comprising:
determining, using the injury detection system, a first plurality of factors contributing to the patient's fall risk (Paragraphs [0162] and [0165] discuss computer predicts fall risk, based on the pressure data recommendations regarding position adjustments of the person may be based on tracked sensor data, for example, if the person consistently lays on a particular edge of the weight support device.);
determining, using the injury detection system, a second plurality of factors contributing to the patient's wound risk (Paragraph [0147] discusses the GUI includes a heatmap corresponding to a person supported by a weight support device and depict pressure data for the person, surface moisture data of the weight support device, surface temperature data of the person and/or weight support device, area(s) of the person at risk of a pressure injury, or any combination thereof.); and
wherein the patient report includes the first plurality of factors and the second plurality of factors (Paragraphs [0147]-[0149] and [0164] discuss the GUI includes circles that represent the areas of the person at risk of developing pressure injury, for example, a circle may be in a first color (e.g., yellow) for a certain amount of pressure and may turn to a second color (e.g., red) if the amount of pressure detected at the body part exceeds a threshold pressure amount; the computer displays a notification, alert or message to a user (e.g., a nurse) that the person supported by the weight support device is at risk of falling off of the weight support device, which side(s) and/or a recommendation for how best to adjust a positioning of the person to avoid the fall.).
Regarding claim 6, Main discloses further comprising:
determining, using the injury detection system, one or more first mitigating suggestions for ameliorating the patient's fall risk (Paragraphs [0162] and [0165] discuss computer predicts, based on the pressure data recommendations regarding position adjustments of the person may be based on tracked sensor data, the computer may recommend via the notification that one or more pillows or bolsters be placed on that edge to prevent the person from falling off that edge of the weight support device.);
determining, using the injury detection system, one or more second mitigating suggestions for ameliorating the patient's wound risk (Paragraphs [0093] and [0129] discuss the cloud actionable insight system provides notifications and recommendations to the patient or other caregiver to position the patient in a pose to allow the patient's body parts that were at risk of developing pressure injury to recover.); and
wherein the patient report includes the one or more first mitigating suggestions and the one or more second mitigating suggestions for the patient's fall risk and the patient’s wound risk, respectively (Paragraphs [0164]-[0165] and FIG. 7D discuss the computer displays a notification, alert or message to a user (e.g., a nurse) that the person supported by the weight support device is at risk of falling off of the weight support device, which side(s) and/or a recommendation for how best to adjust a positioning of the person to avoid the fall, recommend one or more pillows be placed on that edge to prevent the person from falling and determining a risk of a patient developing a pressure injury recommend a turn sequence.).
Regarding claim 9, Main discloses wherein the patient report includes a patient information indicating the patient's respective fall risk and/or wound risk (Paragraphs [0147], [0151], [0176] discuss computer (e.g., the local computer, the user device, and/or the management device) can display the graphical user interface (GUI) inform the healthcare professional and/or other caregiver via an alert or notification about patient risk of developing pressure injury and/or fall injury.).
Main does not explicitly disclose:
a patient rank indicating the patient's respective fall risk and/or wound risk in relation to other patients.
Amarasingham teaches:
a patient rank indicating the patient's respective fall risk and/or wound risk in relation to other patients (Paragraph [0062] discusses the module may rank the patients according to the risk scores, and provide a sortable list.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Main to include, a patient rank indicating the patient's respective fall risk and/or wound risk in relation to other patients, as taught by Amarasingham, in order to provide timely identification of disease and appropriate engagement of patients and families required to offer patients appropriate care and treatment in order to avoid the progression of existing disease as well as the occurrence of a new adverse event, as well as to ensure that appropriate interventions and resources are available and deployed according to patients' needs. (Amarasingham Paragraph [0011]).
Regarding claim 10, Main discloses further comprising:
in response to detection of a triggering event associated with a respective patient, providing another notification to the caretakers, the other notification providing the caretakers remote access to a respective patient report (Paragraphs [0006], [0147], [0151], [0176] discuss clinicians and patients are provided visual, easy-to-understand pressure images that identify areas that are experiencing elevated pressures so that body position adjustments can be made efficiently and effectively, the computer (e.g., the local computer, the user device, and/or the management device) can display the graphical user interface (GUI) inform the healthcare professional and/or other caregiver via an alert or notification about patient risk of developing pressure injury and/or fall injury.).
Regarding claim 11, Main discloses wherein the triggering event includes a fall risk above a predetermined fall risk threshold, a wound risk above a predetermined wound risk threshold, and an update to a patient report (Paragraphs [0071] discuss if the pressure sensing points in the contact area above a minimum pressure threshold show little variation over a period of time, then the person can be considered motionless. A variation threshold of 10% to 100% of the measured pressure can be used to determine if there is movement on a particular sensing point or group of sensing points.).
Regarding claim 12, Main discloses a system, comprising:
one or more processors (Paragraph [0203] discusses a computer system includes one or more processors.);
a memory coupled to the one or more processors, the memory storing one or more programs configured for execution by the one or more processors, the one or more programs including instructions for (Paragraph [0204] discusses instructions reside in the memory, a main memory or processor, the storage unit includes a computer-readable medium on which is stored instructions.).
Claim 12 discloses substantially the same limitations as Claim 1 and is rejected for similar reasons.
Regarding claim 14, Main discloses wherein the one or more programs further include instructions for (Paragraph [0204] discusses instructions within the main memory or within the processor during execution thereof by the computer system.).
Claim 14 discloses substantially the same limitations as Claim 3 and are rejected for similar reasons.
Regarding claim 15, Main discloses wherein the one or more programs further include instructions for, (Paragraph [0204] discusses instructions within the main memory or within the processor during execution thereof by the computer system.):
Claim 15 disclose substantially the same limitations as Claim 4 and are rejected for similar reasons.
Regarding claim 17, Main discloses a non-transitory computer-readable storage medium storing one or more programs comprising instructions that when executed by a computer system having a processor and memory, cause the computer system to (Paragraphs [0081] and [0204] discuss the storage unit includes a non-transitory computer-readable medium on which is stored instructions, includes a computer-readable medium on which is stored instructions.):
Claim 17 discloses substantially the same limitations as Claim 1 and is rejected for similar reasons.
Regarding claim 19, Main discloses wherein the one or more programs further comprise instructions that, when executed by the computer system cause the computer system to (Paragraph [0204] discusses instructions within the main memory or within the processor during execution thereof by the computer system.).
Claim 19 discloses substantially the same limitations as Claim 3 and are rejected for similar reasons.
Regarding claim 20, Main discloses wherein the one or more programs further comprise instructions that, when executed by the computer system, cause the computer system to (Paragraph [0204] discusses instructions within the main memory or within the processor during execution thereof by the computer system.):
Claim 20 disclose substantially the same limitations as Claim 4 and are rejected for similar reasons.
Regarding claim 21, Main discloses further comprising:
receiving, via a user interface element of the one or more user interface elements, additional information about the patient (Paragraphs [0006]-[0007] discuss continuous patient monitoring, a touchscreen monitor displays visual representation of the data.);
creating updated patient data, the updated patient data including the new patient data and the additional information (Paragraphs [0006]-[0007] discuss continuous patient monitoring, a touchscreen monitor displays visual representation of the data.); and
determining, by the injury detection system according to the updated patient, an update fall risk and/or updated wound risk of the patient (Paragraph [0007] discusses sensors (e.g., thousands of sensors) that continuously measure the patient's body surface pressures. A touch screen monitor displays visual representations of pressures with markers that clearly identify areas of the body (e.g., areas experiencing sustained pressure). A patient turn clock can track the time since a last position adjustment, visually notifying the clinician that it's time to adjust the patient's body position.).
Main does not explicitly disclose:
converting the additional information into a format that is compatible with the new patient data;
Amarasingham teaches:
converting the additional information into a format that is compatible with the new patient data (Paragraph [0054] discusses the data cleansing process "cleans" or pre-processes the data, putting structured data in a standardized format and preparing unstructured text.);
Therefore, it would have been obvious to one of ordinary skill in the art to modify Main to include, converting the additional information into a format that is compatible with the new patient data, as taught by Amarasingham, in order to provide a much more realistic and accurate depiction of the patient's overall health status and holistic healthcare environment. (Amarasingham Paragraph [0036]).
Claims 5, 7, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Main in view of Amarasingham and in further view of Bly (U.S. Pub. No. 2022/0071551 A1).
Regarding claims 5 and 16, Main does not explicitly disclose wherein factors of the first plurality of factors and the second plurality of factors are ranked from highest contributing factor to least contributing factor.
Bly teaches:
wherein factors of the first plurality of factors and the second plurality of factors are ranked from highest contributing factor to least contributing factor (Paragraphs [0094]-[0100] discuss factors can be considered in determining the POP Box Score, for example, one factor can be nutritional status and the presence of a feeding tube can be a rating of 1 with the absence of a feeding tube being a rating of 0, the factor can be weighted differently in determining the score.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Main to include, wherein factors of the first plurality of factors and the second plurality of factors are ranked from highest contributing factor to least contributing factor, as taught by Bly, in order to provide a more extensive system than currently used and that is inclusive of interrelated variables that can effect integrity of skin or rather the impairment of skin integrity. (Bly Paragraph [0007]).
Regarding claim 7, Main discloses wherein mitigating suggestions of the first mitigating suggestions and the second mitigating suggestions are provided (Paragraphs [0093], [0129], [0162], and [0165] discuss recommendations regarding position adjustments of the person may be based on tracked sensor data, one or more pillows or bolsters be placed on that edge to prevent the person from falling off that edge of the weight support device, recommendations to position the patient in a pose to allow the patient's body parts that were at risk of developing pressure injury to recover.).
Main does not explicitly disclose:
mitigating suggestions are ranked from highest contributing factor to least contributing factor.
Bly teaches:
mitigating suggestions are ranked from highest contributing factor to least contributing factor (Paragraphs [0094]-[0100] discuss factors can be considered in determining the POP Box Score, for example, the presence of a feeding tube can be a rating of 1 with the absence of a feeding tube being a rating of 0, the factor can be weighted differently in determining the score.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Main to include, mitigating suggestions are ranked from highest contributing factor to least contributing factor, as taught by Bly, in order to provide a more extensive system than currently used and that is inclusive of interrelated variables that can effect integrity of skin or rather the impairment of skin integrity. (Bly Paragraph [0007]).
Claims 8 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Main in view of Amarasingham and in further view of Kurfirst (U.S. Pub. No. 2022/0157144 A1).
Regarding claim 8, Main discloses further comprising:
in response to obtaining updated historical patient data via the one or more databases communicatively coupled with the computer system (Paragraphs [0006], [0008], and [0082]-[0083] discuss a database of patient health data and a data store that stores continuous collection of historical patient data.):
generating a training set including a subset of the historical patient data, the training set including the one or more features for training an updated injury detection system (Paragraphs [0082]-[0083] discuss data store may receive data for the computing server to continuously monitor the pressure readings related to the patient and store sensor data received from the hand-held sensors, the data store may store historical patient data that includes health record data, sensors data, and analysis results for patients that have historically been supported by the weight support device. The historical patient data may be utilized by one or more machine learning models to train the models to determine pressure injury outcomes and/or fall outcomes for current or future patients.).
Main does not explicitly disclose:
an updated training set including an updated subset of the historical patient data, the updated training set including the one or more features for training an updated injury detection system;
training the injury detection system using the updated training set to obtain an updated injury detection system; and
replacing the injury detection system with the updated injury detection system.
Kurfirst teaches:
an updated training set including an updated subset of the historical patient data, the updated training set including the one or more features for training an updated injury detection system (Paragraphs [0090] discuss train and update machine learning dataset, the enterprise computing system may obtain data such as the indication of an individual falling, the location (e.g., geographical location) where the fall occurred, one or more medications taken by the individual, and/or a date/time stamp associated with the individual falling.).
training the injury detection system using the updated training set to obtain an updated injury detection system (Paragraph [0090] discusses the enterprise computing system may use any type of machine learning dataset and/or algorithm to determine a causation event/cause of the individual falling and may train and/or update this machine learning dataset and/or algorithm.); and
replacing the injury detection system with the updated injury detection system (Paragraph [0090] discusses the enterprise computing system may use any type of machine learning dataset and/or algorithm to determine a causation event/cause of the individual falling and may train and/or update this machine learning dataset and/or algorithm; after training the dataset, the enterprise computing system may test the trained model using the test data and perform another continuous or discreet analysis and render a decision.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Main to include, an updated training set including an updated subset of the historical patient data, the updated training set including the one or more features for training an updated injury detection system, training the injury detection system using the updated training set to obtain an updated injury detection system, and replacing the injury detection system with the updated injury detection system, as taught by Kurfirst, in order to provide a system that uses devices to detect an occurrence of a fall as well as to improve the response time if/when a fall occurs. (Kurfirst Paragraph [0002]).
Regarding claim 22, Main does not explicitly disclose further comprising:
updating the training set with at least a subset of the new patient data;
retraining the injury detection system using the updated training set to obtain an updated injury detection system; and
applying the updated injury detection system to new patient information to determine an updated fall risk and/or updated wound risk of the patient.
Kurfirst teaches:
updating the training set with at least a subset of the new patient data (Paragraphs [0090] discuss train and update machine learning dataset, the enterprise computing system may obtain data such as the indication of an individual falling, the location (e.g., geographical location) where the fall occurred, one or more medications taken by the individual, and/or a date/time stamp associated with the individual falling.).
retraining the injury detection system using the updated training set to obtain an updated injury detection system (Paragraphs [0003] and [0090] discusses the enterprise computing system may use any type of machine learning dataset and/or algorithm to determine a causation event/cause of the individual falling and may train and/or update this machine learning dataset and/or algorithm, update fall detection models.); and
applying the updated injury detection system to new patient information to determine an updated fall risk and/or updated wound risk of the patient (Paragraphs [0003], [0090] discuss each fall detection device may have their own fall detection model that may be consistently updated and individualized for the particular individual based on the user feedback such that the fall detection model increases in the accuracy after each iteration.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Main to include, updating the training set with at least a subset of the new patient data, retraining the injury detection system using the updated training set to obtain an updated injury detection system, and applying the updated injury detection system to new patient information to determine an updated fall risk and/or updated wound risk of the patient, as taught by Kurfirst, in order to provide a system that uses devices to detect an occurrence of a fall as well as to improve the response time if/when a fall occurs. (Kurfirst Paragraph [0002]).
Response to Arguments
Applicant’s arguments filed 6/9/26 have been fully considered.
Rejections under 35 U.S.C. 101:
With respect to claim 1 and the Prong 1 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 as amended recites an abstract idea, a method of organizing human activity and/or mathematical concepts. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People and See MPEP 2106.04(a)(2)(I) Mathematical Concepts. Applicant states, “each of the independent claims recites a concrete computer-implemented data processing pipeline for operating an injury detection system.” (Remarks, page 12). Examiner respectfully disagrees. The application is not an improvement to the functioning of the hardware and specific software but is an improvement to the abstract idea and does not result in significantly more than the abstract idea. The Application does not contain any benefits improving functionality of the computer or any other additional element. Obtaining historical patient data, generating a training set including features for training the injury detection system, training the injury detection system using that training set, receiving at least partially unstructured new patient data including patient vital statistics, transforming the at least partially unstructured new patient data into structured new patient data, inputting the structured new patient data into the previously trained injury detection system, and determining a fall risk and/or wound risk based on the structured new patient data provided to that injury detection system, is not a technical problem rooted in the technology. The claims as written fail to articulate a technical improvement. The improvement is to the abstract idea.
While practical application is a way to overcome the Prong 2 35 U.S.C. 101 rejection, claim 1 as written fails to result in a practical application. Applicant states, “Because the amended claims recite transformation of data to a different state (i.e., from at least partially unstructured new patient data into structured new patient data), the amended claims "are likely to be significantly more than any recited judicial exception or to integrate any recited judicial exception into a practical application" as indicated in MPEP 2106.05(c).” (Remarks, page 13). Examiner respectfully disagrees. The Application does not contain any benefits improving computer functionality or any other additional element. Here, the application is organizing human activity, directed to the abstract idea of detecting patients susceptible to falls and wounds. There is no improvement to the functioning of the computerized system, the improvement is to the abstract idea. The additional elements in claim 1 do not result in a practical application as they are recited at an apply it level, as stated above.
Rejections under 35 U.S.C. 103:
Applicant argues the amendments overcome the previous rejection. Examiner concedes that the amendments overcome the prior rejection. Applicant’s arguments with respect to amended claim 1 have been considered and the Examiner’s rejection has been amended to address Applicant’s claim 1 amendments.
Applicant’s arguments with respect to claims 1, 12, and 17 have been considered and overcome the previous rejection. Examiner’s rejection has been amended to address Applicant’s claim amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:15PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached on (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAWN T. HAYNES/
Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
Prosecution Insights