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 .
Priority
Acknowledgment is made of Applicant's claim for priority to the following application(s):
* 2302219.7 filed 16 February 2023
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on the following date(s) is/are entered and considered by Examiner:
* 15 February 2024
* 10 February 2024
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
A method of monitoring a person, comprising:
obtaining time series sensor data from one or more sensors of a sock worn by the person, the time series sensor data comprising one or both of i) physiological data from one or more physiological sensors characterizing a physiological state of the person and ii) motion data from one or more motion sensors characterizing motion of the person;
obtaining a sequence of observations of the time series sensor data; and
processing the sequence of observations using a machine learning classifier to generate classification data that predicts, for the sequence of observations, one of a plurality of categories, wherein the categories characterize different respective predicted states of stress of the person.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
It is noted that the non-volatile data carrier of claim 19 is considered to be a form of non-transitory computer readable medium, based on the broadest reasonable interpretation.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the step of processing a sequence of observation to generate classification data is traditionally performed by a human being, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a generic computer recited with a high level of generality to implement the abstract idea in a post hoc manner, the step of processing data may be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 2, 4-11 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people, in particular claims 8-10 recite various mathematical calculations).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
obtaining time series sensor data from one or more sensors of a sock worn by the person, the time series sensor data comprising one or both of i) physiological data from one or more physiological sensors characterizing a physiological state of the person and ii) motion data from one or more motion sensors characterizing motion of the person;
obtaining a sequence of observations of the time series sensor data; and
using a machine learning classifier.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the machine learning classifier, the Specification as originally filed in application 2302219.7 filed on 16 February 2023 (hereafter referred to as “the Priority Specification”) discloses generic types of machine learning classifiers (page 5 paragraph 3), and amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
Regarding the steps of obtaining data, these limitations merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims (such as claim 19 reciting a computer, mere generic computer to implement the abstract idea, i.e. “apply it”; claim(s) 2-3, 20 reciting the sock sensor with a skin sensor and accelerometer, claim 4 reciting an alert, claims 7, 10 reciting a neural network and CNN layers, additional limitation(s) which add(s) insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, for example).
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.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does 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 elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
The steps of obtaining data amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim(s) 2-3, 20 reciting the sock sensor with a skin sensor and accelerometer, Gotlieb (20240032836) discloses that a sock comprising skin sensors and accelerometer is WURC (page 4 paragraph 0035); claim 4 reciting an alert, Gotlieb disclosing that an alert is WURC (page 5-6 paragraph 0046), claims 7, 10 reciting a neural network and CNN layers reciting, Gotlieb disclosing CNN layers is WURC (page 20 paragraph 0171)). MPEP 2106.05(d)(II)(ii))
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.
The claim is not patent eligible.
Claim(s) 12-18 recite(s) substantially similar limitations as those of claim(s) 1-11, 19-20 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 9, 11-13, 15-16, 18-20 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Freckleton (20220304603).
Claim 1: Freckleton discloses:
A method (Abstract illustrating a method) of monitoring a person (Abstract illustrating monitoring a person), comprising:
obtaining time series sensor data (Figure 8 illustrating data plotted over time [considered to be a form of “time series”]) from one or more sensors of a sock worn by the person (page 2 paragraph 0030 illustrating a sock wearable device), the time series sensor data comprising one or both of i) physiological data from one or more physiological sensors characterizing a physiological state of the person (Figure 8, page 10 paragraph 0095 illustrating physiological data) and ii) motion data from one or more motion sensors characterizing motion of the person (page 6 paragraph 0063 illustrating motion data);
obtaining a sequence of observations of the time series sensor data (Figure 8, page 8 paragraph 0078 illustrating generating a plurality of trend data); and
processing the sequence of observations using a machine learning classifier to generate classification data (page 1 paragraph 0017 illustrating a machine learning classifier) that predicts, for the sequence of observations, one of a plurality of categories, wherein the categories characterize different respective predicted states of stress of the person (page 7 paragraph 0073 illustrating determining the level of stress of the person).
Claim 2: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
wherein the time series sensor data comprises a series of physiological data from one or more physiological sensors (as discussed above and incorporated herein), and wherein the sock (as discussed above and incorporated herein) is used to bring one or more of the physiological sensors into direct contact with the skin of the person to collect the physiological data (page 2 paragraph 0030 illustrating skin contact sensor).
Claim 3: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
wherein the one or more sensors comprise a sensor to sense electrodermal activity of the skin of the person (page 3 paragraph 0032 illustrating an EDA sensor) and an accelerometer (page 3 paragraph 0032 illustrating an accelerometer).
Claim 4: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
further comprising providing an alert in response to the generated classification data indicating a change in the predicted state of stress (page 5 paragraph 0054 illustrating generating an alert based on the user’s stress level).
Claim 5: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
further comprising treating the person to reduce stress in response to the generated classification data indicating that the person has a heightened predicted state of stress (page 8 paragraph 0079 illustrating providing various types of treatment for a stressed person).
Claim 6: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
wherein obtaining the sequence of observation comprises applying a rolling window to the time series sensor data, wherein the rolling window has a duration of at least 30 seconds (Figure 9 illustrating analyzing at least 30 seconds of data).
Claim 9: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
wherein processing the sequence of observations using the machine learning classifier comprises:
processing the sequence of observations to generate a set of features or metrics representing the sequence of observations, wherein the metrics comprise one or more of mean, median, standard deviation (page 7-8 paragraph 0074 illustrating a deviation), and interquartile range (the remaining elements are rendered optional by the limitation “one or more” and therefore need not be disclosed by the applied art); and
processing the set of features or metrics using a decision tree model to generate the classification data page 7-8 paragraph 0074 illustrating a decision tree).
Claim 11: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
comprising:
obtaining training data from a particular person (page 8-9 paragraph 0081 illustrating obtaining an individual’s data); and
fine-tuning the machine learning classifier using the training data (page 8-9 paragraph 0081 illustrating fine-tuning); and
using the fine-tuned machine learning classifier to monitor the particular person (page 8-9 paragraph 0081 illustrating using the training machine learning model to process the individual’s data).
Claim(s) 12, 13 recite(s) substantially similar limitations as those of claim(s) 1, 3 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Claim 15: Freckleton discloses:
The computer-implemented method of claim 12, as discussed above and incorporated herein.
Freckleton further discloses:
the method further comprising determining whether the machine-learning model has previously generated output data indicating that the person is exhibiting a heightened physiological response in excess of a threshold number of times within a period of time, and wherein the output data is indicative that the person is exhibiting a heightened physiological response; wherein the period of time is 3 to 5 minutes (Figure 9 illustrating processing data for 5 minutes or more).
Claim 16: Freckleton discloses:
The computer-implemented method of claim 12, as discussed above and incorporated herein.
Freckleton further discloses:
further comprising:
receiving, from a user, a sensitivity parameter indicating the sensitivity of the person to the heightened physiological response (page 8 paragraph 0077 illustrating adjusting sensitivity); and
wherein generating the input data is further based upon the sensitivity parameter (page 8 paragraph 0077 illustrating sensitivity used to process the individual’s data).
Claim 18: Freckleton discloses:
The computer-implemented method of claim 12, as discussed above and incorporated herein.
Freckleton further discloses:
further comprising:
receiving, from the person or one or more observers of the person, response data indicating whether the person is exhibiting the heightened physiological response (page 10 paragraph 0093 illustrating receiving the individual’s data);
generating a trained machine-learning model by training the machine-learning model using the physiological data and the response data (page 10 paragraph 0093 illustrating training the machine learning model); and
storing the trained machine-learning model (page 10 paragraph 0093 illustrating storing the model for use in processing the individual’s data); further comprising:
retraining machine-learning model periodically (page 10 paragraph 0093 illustrating further training the model based on the individual user’s preference and reactions [considered to be a form of “periodically”]).
Claim 19: Freckleton discloses:
Freckleton further discloses:
One or more non-volatile data carriers carrying processor control code that when executed by one or more processors cause the one or more processors to implement the method (Figure 3 illustrating a computer and associated hardware) of claim 1 (as discussed above and incorporated herein).
Claim(s) 20 recite(s) substantially similar limitations as those of claim(s) 1 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 7-8, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Freckleton in view of Wang (20230148879).
Claim 7: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
wherein processing the sequence of observations using the machine learning classifier comprises:
processing the sequence of observations to generate a representation of the sequence of observations as a two-dimensional image that represents relationships between the observations in the sequence (Figure 9 illustrating a 2D graph representing data and relationships with othe types of data); and
processing the two-dimensional image using a neural network comprising one or more
wherein the classification data comprises a score for each category of the plurality of categories, each score representing an estimated likelihood of a respective one of the states of stress (Figure 9-10 illustrating determining the user’s stress level from the physiological data).
Freckleton does not disclose:
convolutional neural network.
Wang discloses:
convolutional neural network (page 3 paragraph 0054 illustrating a deep CNN).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the CNN of Wang within the system of Freckleton with the motivation of improving patient care by leveraging known machine learning techniques to assist with the diagnosis of stressed patients (Wang; page 1 paragraph 0003).
Claim 8: Freckleton discloses:
The method of claim 7, as discussed above and incorporated herein.
Freckleton does not disclose:
wherein processing the sequence of observations to generate the representation of the sequence of observations as the two-dimensional image comprises generating a Markov Transition Field or Gramian Angular Field representation of the sequence of observations.
Wang discloses:
wherein processing the sequence of observations to generate the representation of the sequence of observations as the two-dimensional image comprises generating a Markov Transition Field (page 3 paragraph 0055 illustrating a Markov transition field) or Gramian Angular Field representation of the sequence of observations (page 3 paragraph 0055 illustrating grammian angular summation fields)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the CNN of Wang within the system of Freckleton with the motivation of improving patient care by leveraging known machine learning techniques to assist with the diagnosis of stressed patients (Wang; page 1 paragraph 0003).
Claim(s) 14 recite(s) substantially similar limitations as those of claim(s) 8 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Claim(s) 10, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Freckleton in view of Martinez (20250186015).
Claim 10: Freckleton discloses:
The method of claim 1, as discussed above and incorporated herein.
Freckleton further discloses:
processing the sequence of observations using a neural network to generate classification data comprising a score for each category of the plurality of categories, each score representing an estimated likelihood of a respective one of the states of stress (as discussed above and incorporated herein);
Martinez discloses:
wherein the machine learning classifier comprises a neural network having a plurality of neural layers and an output layer configured to apply a softmax function (page 24 paragraph 0270 illustrating a softmax function), and wherein processing the sequence of observations using the machine learning classifier comprises:
obtaining a control input that specifies a sensitivity of the monitoring (page 23 paragraph 0259, page 24 paragraph 0270 illustrating adjusting sensitivity); and
adjusting a temperature parameter of the softmax function using the control input to adjust the sensitivity of the monitoring (page 24 paragraph 0270 illustrating scaling the temperature of the softmax function).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to include the softmax function of Martinez within the system of Freckleton with the motivation of improving patient care by leveraging known machine learning techniques to assist with the diagnosis of stressed patients in a more accurate and lightweight manner (Martinez; page 24 paragraph 0270).
Claim(s) 17 recite(s) substantially similar limitations as those of claim(s) 10 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cronin (20180008191) discloses using a wearable device to monitor a person’s response to pain (a form of stress) (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Deshpande (20180256029) discloses monitoring a person to diagnose a health condition (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
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/T.N.N./ Examiner, Art Unit 3685 /KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685