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 .
DETAILED ACTION
Response to Amendment
In the Amendment dated 11 December 2025, the following occurred:
Claims 1, 4, 5, 8, 13, and 16 were amended.
Claims 1-20 are pending.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, line 7, “identifying caregiving action” should read “identifying a caregiving action”.
Appropriate corrections are required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 5, and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite methods and a computer-readable medium for improved machine learning models for classifying motion data based on the underlying action(s) being performed, and therefore meet step 1.
Step 2A1
The limitations of (Claim 1) receiving motion data collected during a first time by… a user, wherein…, the motion data comprises accelerometer data collected…, and the accelerometer data indicates orientation and movement of the one or more parts of the user; identifying caregiving action performed by the user to assist a patient during the first time by evaluating one or more event records indicating one or more prior actions performed by one or more users; labeling the motion data based on the caregiving action; and training…, based on the labeled motion data, to identify caregiving actions, comprising: generating a predicted caregiving action by processing the motion data…; generating a loss between the predicted caregiving action and the identified caregiving action; and updating one or more parameters… based on the loss, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
The limitations of (Claim 5 being representative) receiving motion data collected during a first time by… a user, wherein…, the motion data comprises accelerometer data collected…, and the accelerometer data indicates orientation and movement of the one or more parts of the user; identifying a patient associated with the motion data; generating a predicted caregiving action performed by the user to assist the patient by processing the motion data…; and generating an event record indicating the action, the patient, and the user, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting methods and a computer-readable medium implemented by one or more processors (general-purpose computing devices), the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that the Applicant has not described what the particular training of the machine learning model entails and thus it is interpreted to be simple enough for a human to accomplish (i.e., linear or logistic regression) and has been included in the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a non-transitory computer-readable storage medium (claim 13) and one or more processors (claim 13) that implement the identified abstract idea. The computing elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., the computing device includes a CPU, memory, storage, a network interface, and one or more I/O interfaces, see, e.g., Para. 0197) such that it amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, receiving data is considered insignificant extra solution activity such as pre-solution activity e.g., data gathering (performed by receiving/ transmitting/ etc.) See MPEP 2106.05(g).
The claims recite the additional elements of (1) one or more wearable sensors comprising accelerometers and (2) a machine learning model/system. The (1) wearable sensors represent locations from which data is received and merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application.
The Examiner notes that the (2) machine learning system is described in the Specification at Para. 0049, 0117, 0139 as encompassing a neural network algorithm, various loss algorithms, and one or more smoothing algorithms. The implementation of the trained data is interpreted to represent “apply it” on a computer. MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Step 2B
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 integration of the abstract idea into a practical application, the additional element of using a processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of (1) one or more wearable sensors was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more.
Regarding (2), as discussed above with respect to integration of the abstract idea into a practical application, the additional element of a machine learning model was determined to represent “apply it” on a generic computer. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(I)(A) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such the claim is not patent eligible.
Claims 2-4, 6-12, and 14-20 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.
Claims 2, 6, and 14 merely describe the one or more wearable sensors, which further defines the abstract idea.
Claims 3, 7, and 15 merely describe the motion data, which further defines the abstract idea.
Claims 4, 8, and 16 merely describe the machine learning model, which further defines the abstract idea.
Claims 4, 8, and 16 also include the additional element of “a second machine learning model” which is analyzed the same as the “machine learning model” and does not provide a practical application or significantly more for the same reasons.
Claims 9 and 17 merely describe identifying the patient, which further defines the abstract idea.
Claims 10, 11, 18, and 19 merely describe determining the location of the user, which further defines the abstract idea.
Claims 12 and 20 merely describe identifying one or more other users, which further defines the abstract idea.
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 1 is rejected under 35 U.S.C. 103 as being unpatentable over Shelton, IV et al. (U.S. 2023/0025827) in view of Shelton, IV et al. (U.S. 2022/0233253) and Chow et al. (U.S. 10758309), referred to hereinafter as Shelton, Shelton ‘253, and Chow, respectively.
REGARDING CLAIM 1
Shelton teaches the claimed method of training machine learning models, comprising:
receiving motion data collected during a first time by one or more wearable sensors of a user, wherein the one or more wearable sensors are worn on one or more parts of the user, the motion data comprises accelerometer data collected by one or more accelerometers of the one or more wearable sensors, and the accelerometer data indicates […] movement of the one or more parts of the user; [Para. 0085 teaches a sensing system worn on a surgeon’s wrist (wearable sensor) using an accelerometer to detect hand motion (motion data).]
labeling the motion data based on the caregiving action; and [Para. 0154 teaches determining whether the surgeon is deviating from the expected course of action, and indicating that an unexpected action is being performed. Para. 0214 teaches actions are determined based on the motion information.]
training a machine learning model, based on the labeled motion data, to identify caregiving actions, comprising: [Para. 0144 teaches training a machine learning system, based on data from healthcare provider monitoring devices, to derive contextual information regarding a surgical procedure. Para. 0143 teaches contextual information inferred from the received data can include the particular step of the surgical procedure being performed (e.g., user actions).]
generating a predicted caregiving action by processing the motion data using the machine learning model; [Para. 0154 teaches determining the type of surgical procedure being performed (by processing the motion data using the machine learning model), and retrieving the corresponding list of steps. The list of steps includes predicted caregiving actions.]
Shelton may not explicitly teach
…orientation and…
However, Shelton ‘253 teaches the following:
…orientation and… [Para. 0493 teaches determining positions of one or more body parts in comparison to the orientation pertaining to one or more surgical instruments.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton to receive motion data indicating orientation as taught by Shelton ‘253 with the motivation of improving outcomes (see Shelton ‘253 at Para. 0060, 0275).
Shelton in view of Shelton ‘253 may not explicitly teach
identifying caregiving action performed by the user to assist a patient during the first time by evaluating one or more event records indicating one or more prior actions performed by one or more users;
generating a loss between the predicted caregiving action and the identified caregiving action; and updating one or more parameters of the machine learning model based on the loss.
However, Chow teaches the following:
identifying caregiving action performed by the user to assist a patient during the first time by evaluating one or more event records indicating one or more prior actions performed by one or more users; [Col. 12, Line 59-62 teaches identifying a surgical procedure by processing video streams from a previous surgical procedure.]
generating a loss between the predicted caregiving action and the identified caregiving action; and updating one or more parameters of the machine learning model based on the loss. [Col. 11, Line 17-20 teaches configuring a machine learning training system to define the set of parameters to minimize or maximize a loss function. The Examiner notes that these limitations are merely describing the definition of machine learning training.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253 to identify an action by processing previous actions, generate a loss, and update parameters of the machine learning model as taught by Chow, with the motivation of enhancing patient safety (see Chow at Col. 8, Line 30-33).
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton in view of Shelton ‘253, Chow, and Knickerbocker et al. (U.S. 2022/0199235), referred to hereinafter as Knickerbocker.
REGARDING CLAIM 2
Shelton in view of Shelton ‘253 and Chow teaches the claimed method of claim 1.
Shelton in view of Shelton ‘253 and Chow may not explicitly teach
wherein the one or more wearable sensors comprise a respective wrist-mounted sensor on each respective wrist of the user.
However, Knickerbocker teaches the following:
wherein the one or more wearable sensors comprise a respective wrist-mounted sensor on each respective wrist of the user. [Para. 0056 teaches a multi-sensor health monitoring platform that uses wristwatch sensors.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253 and Chow to include sensors on each wrist as taught by Knickerbocker, with the motivation of improving care of patients (see Knickerbocker at Para. 0076).
REGARDING CLAIM 3
Shelton in view of Shelton ‘253, Chow, and Knickerbocker teaches the claimed method of claim 2.
Shelton further teaches
wherein the motion data comprises, for each respective wrist, respective accelerometer data indicating movement of the respective wrist and orientation of the respective wrist. [Para. 0085 teaches a sensing system worn on a surgeon’s wrist that uses an accelerometer to detect hand motion. Para. 0226 teaches gauging the orientation of a surgical instrument in a surgeon’s hand.]
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Shelton in view of Shelton ‘253, Chow, and Makrinich et al. (U.S. 2021/0313052), referred to hereinafter as Makrinich.
REGARDING CLAIM 4
Shelton in view of Shelton ‘253 and Chow teaches the claimed method of claim 1.
Shelton in view of Shelton ‘253 and Chow may not explicitly teach
wherein the machine learning model is trained based on motion data corresponding to a right hand of the user, the method further comprising training a second machine learning model based on motion data corresponding to a left hand of the user.
However, Makrinich teaches the following:
wherein the machine learning model is trained based on motion data corresponding to a right hand of the user, the method further comprising training a second machine learning model based on motion data corresponding to a left hand of the user. [Para. 0257 teaches assessing a subject’s economy of motion (an efficiency of the subject’s movements or an indication of the subject’s dexterity during a surgical procedure) via identifying various actions taken by the subject. As an example, if the subject uses only one hand, without coordination with the less dominant hand, subject may receive a relatively lower economy of motion assessment. A machine learning model is trained using training examples to assess economy of motion from surgical footage. An example of such training example may include surgical footage from a particular prior surgical procedure. Para. 0058 teaches using one or more artificial neural networks.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253 and Chow to train machine learning models based on motion data of each hand as taught by Makrinich, with the motivation of improving accuracy (see Makrinich at Para. 0053).
Claims 5, 9, 11-13, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton in view of Shelton ‘253, Andrews et al. (U.S. 2014/0278545), and Attaluri et al. (U.S. 2013/0275148), referred to hereinafter as Andrews and Attaluri, respectively.
REGARDING CLAIM 5
Shelton teaches the claimed method of classifying motion using machine learning, comprising:
receiving motion data collected during a first time by one or more wearable sensors of a user, wherein the one or more wearable sensors are worn on one or more parts of the user, the motion data comprises accelerometer data collected by one or more accelerometers of the one or more wearable sensors, and the accelerometer data indicates orientation and movement of the one or more parts of the user; [Para. 0085 teaches a sensing system worn on a surgeon’s wrist (wearable sensor) using an accelerometer to detect hand motion and/or shakes (motion data).]
generating a predicted caregiving action performed by the user to assist the patient by processing the motion data using a machine learning model trained to predict caregiving actions based on accelerometer data; and [Para. 0154 teaches determining the type of surgical procedure being performed (by processing the motion data using the machine learning model), and retrieving the corresponding list of steps. The list of steps includes predicted caregiving actions.]
Shelton may not explicitly teach
…orientation and…
However, Shelton ‘253 teaches the following:
…orientation and… [Para. 0493 teaches determining positions of one or more body parts in comparison to the orientation pertaining to one or more surgical instruments.]
Motivation to combine the teaching of Shelton ‘253 with the teaching of Shelton is the same as that used with respect to claim 1 and is therefore reiterated here.
Shelton in view of Shelton ‘253 may not explicitly teach
identifying a patient associated with the motion data;
However, Andrews teaches the following:
identifying a patient associated with the motion data; [Para. 0007 teaches providing one or more signature screens through which a patient may enter their electronic signature.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253 to identify a patient as taught by Andrews, with the motivation of reducing the amount of time required to manually verify patient visits (see Andrews at Para. 0037).
Shelton in view of Shelton ‘253 and Andrews may not explicitly teach
generating an event record indicating the action, the patient, and the user.
However, Attaluri teaches the following:
generating an event record indicating the action, the patient, and the user. [Para. 0019 teaches an event record representing information regarding an event relevant to care(giving) of each patient. The event record comprises Event Identification, Patient Identification, and Nurse (user) Identification.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253 and Andrews to generate an event record as taught by Attaluri, with the motivation of improving quality of care and reducing chance for errors (see Attaluri at Para. 0001).
REGARDING CLAIM 9
Shelton in view of Shelton ‘253, Andrews, and Attaluri teaches the claimed method of claim 5.
Andrews further teaches
wherein identifying the patient comprises: determining a location of the user when the motion data was collected; and determining that the location is associated with the patient. [Para. 0006 teaches determining the location of the clinician. Para. 0005 teaches determining a location of the patient.]
REGARDING CLAIM 11
Shelton in view of Shelton ‘253, Andrews, and Attaluri teaches the claimed method of claim 9.
Andrews further teaches
wherein the location of the user is determined using a proximity sensor. [Para. 0038 teaches providing accuracy data pertaining to the location of the clinician. Status messages such as “location within 300 meters” may be provided.]
REGARDING CLAIM 12
Shelton in view of Shelton ‘253, Andrews, and Attaluri teaches the claimed method of claim 5.
Attaluri further teaches
identifying one or more other users that assisted with the action; and indicating the one or more other users in the event record. [Para. 0019 teaches an event record representing information regarding an event relevant to care(giving) of each patient. The event record comprises Nurse Identification and Doctor Identification.]
REGARDING CLAIMS 13, 17, 19, AND 20
Claims 13, 17, 19, and 20 are analogous to Claims 5, 9, 11, and 12, respectively, thus Claims 13, 17, 19, and 20 are similarly analyzed and rejected in a manner consistent with the rejections of Claims 5, 9, 11, and 12.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton in view of Shelton ‘253, Andrews, Attaluri, and Makrinich.
REGARDING CLAIM 8
Shelton in view of Shelton ‘253, Andrews, and Attaluri teaches the claimed method of claim 5.
Shelton in view of Shelton ‘253, Andrews, and Attaluri may not explicitly teach
wherein the machine learning model corresponds to a right hand of the user, the method further comprising generating a second predicted caregiving action by processing the motion data using a second machine learning model corresponding to a left hand of the user.
However, Makrinich teaches the following:
wherein the machine learning model corresponds to a right hand of the user, the method further comprising generating a second predicted caregiving action by processing the motion data using a second machine learning model corresponding to a left hand of the user. [Para. 0257 teaches assessing a subject’s economy of motion (an efficiency of the subject’s movements or an indication of the subject’s dexterity during a surgical procedure) via identifying various actions taken by the subject. As an example, if the subject uses only one hand, without coordination with the less dominant hand, subject may receive a relatively lower economy of motion assessment. A machine learning model is trained using training examples to assess economy of motion from surgical footage. An example of such training example may include surgical footage from a particular prior surgical procedure. Para. 0058 teaches using one or more artificial neural networks.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253, Andrews, and Attaluri to train machine learning models based on motion data of each hand as taught by Makrinich, with the motivation of improving accuracy (see Makrinich at Para. 0053).
REGARDING CLAIM 16
Claim 16 is analogous to Claim 8, thus Claim 16 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 8.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton in view of Shelton ‘253, Andrews, Attaluri, and Knickerbocker.
REGARDING CLAIM 6
Shelton in view of Shelton ‘253, Andrews, and Attaluri teaches the claimed method of claim 5.
Shelton in view of Shelton ‘253, Andrews, and Attaluri may not explicitly teach
wherein the one or more wearable sensors comprise a respective wrist-mounted sensor on each respective wrist of the user.
However, Knickerbocker teaches the following:
wherein the one or more wearable sensors comprise a respective wrist-mounted sensor on each respective wrist of the user. [Para. 0056 teaches a multi-sensor health monitoring platform that uses wristwatch sensors.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253, Andrews, and Attaluri to include sensors on each wrist as taught by Knickerbocker, with the motivation of improving care of patients (see Knickerbocker at Para. 0076).
REGARDING CLAIM 14
Claim 14 is analogous to Claim 6, thus Claim 14 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 6.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton in view of Shelton ‘253, Andrews, Attaluri, Knickerbocker, and Shelton, IV et al. (U.S. 2023/0028677), referred to hereinafter as Shelton ‘677.
REGARDING CLAIM 7
Shelton in view of Shelton ‘253, Andrews, Attaluri, and Knickerbocker teaches the claimed method of claim 6.
Shelton in view of Shelton ‘253, Andrews, Attaluri, and Knickerbocker may not explicitly teach
wherein the motion data comprises, for each respective wrist, respective accelerometer data indicating movement of the respective wrist and orientation of the respective wrist.
However, Shelton ‘677 teaches the following:
wherein the motion data comprises, for each respective wrist, respective accelerometer data indicating movement of the respective wrist and orientation of the respective wrist. [Para. 0085 teaches a sensing system worn on a surgeon’s wrist that uses an accelerometer to detect hand motion. Para. 0226 teaches gauging the orientation of a surgical instrument in a surgeon’s hand.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253, Andrews, Attaluri, and Knickerbocker to include accelerometer data as taught by Shelton ‘677, with the motivation of improving patient outcomes (see Shelton ‘677 at Para. 0031).
REGARDING CLAIM 15
Claim 15 is analogous to Claim 7, thus Claim 15 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shelton in view of Shelton ‘253, Andrews, Attaluri, and Heeter (U.S. 2019/0258814).
REGARDING CLAIM 10
Shelton in view of Shelton ‘253, Andrews, and Attaluri teaches the claimed method of claim 9.
Shelton in view of Shelton ‘253, Andrews, and Attaluri may not explicitly teach
wherein the location of the user is determined based on a check- in scan performed by the user.
However, Heeter teaches the following:
wherein the location of the user is determined based on a check- in scan performed by the user. [Para. 0107 teaches a person performing a check-in. The device reports back the user’s location when scanning.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Shelton in view of Shelton ‘253, Andrews, and Attaluri to determine the location of the user based on a check-in scan as taught by Heeter, with the motivation of improving internal control (see Heeter at Para. 0110).
REGARDING CLAIM 18
Claim 18 is analogous to Claim 10, thus Claim 18 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 10.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-20, the Examiner has considered the arguments but they are not persuasive. Applicant argues:
…the present claims, which relate to training and using machine learning to automatically identify and label physical actions, are not abstract…First, not all methods of organizing human activity are abstract ideas...Second, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings…
Regarding (a), the Examiner respectfully disagrees. The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to identify and label physical actions. The Examiner notes that Applicant’s Introduction describes charting (see Spec. Para. 0001) as a human task. Furthermore, the Examiner submits that healthcare itself inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. The claimed invention merely uses machine learning as a tool to automate an existing process. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to identify and label physical actions, the claimed invention is directed to an abstract idea.
The Examiner also notes that the Applicant has misrepresented the Office Action where it is argued that “[t]he Office states that the claims are ‘interpreted to be simple enough for a human to accomplish.’” This statement in the prior and current Office Action was/is very clearly in reference to the (undefined) training of the machine learning model, not the entire abstraction. To further clarify, this is not an indication that the abstract idea is interpreted to be a mental process, but an indication that the (undefined) training is not complex such that it is removed from be something that a human can perform; humans routinely perform linear and/or logistic regression to create models. As such, this feature is interpreted to be part of the rules or instructions.
…the present claims clearly recite elements that enable various improvements. For example, as explained in the specification, "by training and using the machine learning model(s) to classify motion data, the system may be able to automatically record or chart user actions in order to significantly reduce manual effort, increase the accuracy of the records, and improve the overall system through creation of better data and improved results for the patients and users." [0028] … Applicant respectfully submits that the amended claims as a whole include an improvement to a computer, specifically to the technical field of motion detection and classification using machine learning.
Regarding (b), the Examiner respectfully disagrees. There is no improvement to the computer nor is there an improvement to another technology, such as the one or more wearable sensors or the machine learning system. Because neither type of improvement is present in the claims, an improvement to technology is not present and there is no practical application.
Moreover, the entire field of motion detection and classification using machine learning is not reasonably understood to be a problem arising in technology, as it is instead a problem arising in medical (and non-medical) settings. The claimed invention is using a computer as a tool and any improvement present is an improvement to the abstract idea of, to paraphrase, identifying and labeling physical actions. Finally, where Applicant’s line of reasoning correct, the invention in Alice Corp. would have been subject matter eligible because it was an improvement to the technology of settlement risk mitigation.
…"any downstream systems or models that rely on this event data can be significantly improved" due to the "more accurate and voluminous action records." Id…the present claims enable "improved and more efficient training of the machine learning model" by evaluating "action context" to "automatically label the motion data based on the action that was performed," which results in "a vast amount of training data" to improve the model performance. [0031].
Regarding (c), the Examiner respectfully disagrees. Training using voluminous data is not reflected in the claim, and even if it were, that is using a computer and machine learning for its intended task. Every effective machine learning model utilizes voluminous training data; that is how machine learning training works. Applicant is using machine learning as a tool in the way it was meant to be used.
Desjardins…. Applicant submits that the present claims similarly reflect improvements to automatically classifying detected motion by training and using machine learning models.
Regarding (d), the Examiner respectfully disagrees. The Examiner respectfully submits that there is no improvement to the claim machine learning as there is in Desjardins. As found by the Desjardins Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application- “improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Applicant’s claims do not provide such an improvement. There is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained. Improving the accuracy of a machine learning model by supplying it with specific data is not an improvement to how the model is trained within the meaning of Desjardins (see quotations from Recentive, infra). This is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning model of applicant’s invention uses the data to train itself is not improved, which is the holding of Desjardins. Applicant is merely improving the accuracy of the model by optimizing the data selected/used by the model. Improving the accuracy of a model is not an improvement by any measure in MPEP 2106.
Examiner’s position is also supported by the decision in Recentive Analytics, Inc. v. Fox Corp. Recentive held that non-specifically claimed training of an ML algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
Rejection under 35 U.S.C. § 103
Regarding the prior art rejection of Claims 1-20, the Examiner has considered the arguments but they are not persuasive. Applicant argues:
…Applicant submits that detecting tremors of a surgeon does not teach or suggest identifying a “caregiving action” using machine learning.
Regarding (a), the Examiner respectfully disagrees. Shelton at Para. 0085 teaches a sensing system worn on a surgeon’s (caregiver’s) wrist (wearable sensor) uses an accelerometer to detect hand motion (motion data). Hand motion is not limited to tremors. Para. 0154 goes on to teach determining whether the surgeon is deviating from the expected course of action, and indicating that an unexpected action is being performed. Para. 0214 teaches actions are determined based on the motion information, which includes the aforementioned hand motion data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jagannathan et al. (U.S. 11950901) which discloses systems and methods for assessing gait, stability, and/or balance of a user.
Kopeinigg et al. (U.S. 2020/0312011) which discloses methods and systems for applying machine learning to volumetric capture of a body in a real-world scene.
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/CAMRYN B LEWIS/
Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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