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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Fig. 2 includes reference character “180” without a description in the specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 53 is objected to because of the following informalities:
Claim 53 should be amended to recite wherein the risk factor is further determined based on the first image data and the second image data, since claim 51 already recites determining a risk factor…based at least in part on the first image data.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 51-53, 55, 59, 62, 64, 66-69, 71-72, 76-78, 80, 87-88, and 91 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 51 recites “a control system ..configured to…generate first image data” which is then used to “determine the risk factor…” Meanwhile claim 51 also recites “an electronic interface configured to generate data associated with the individual, receive data associated with the individual, or both”. Hence, it is unclear whether and how the data generated or received by the electronic interface is separate and different from the first image data that is used for the risk factor determination.
Claims 52-53, 55, 59, 62, 64, 66-69, 71-72, 76-78, 80, 87-88, and 91 are rejected based on their respective dependence on claim 51.
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 51-53, 55, 59, 62, 64, 66-69, 71-72, 76-78, and 87-88 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: YES – Claim recites “A system for determining a risk factor for an individual that is associated with a condition” and, therefore, is a device.
Step 2A, Prong 1, Judicial Exception: YS - The claim recites the following limitations:
“determine the risk factor for the individual associated with the condition based at least in part on the first image data”.
This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a control system including one or more processors configured to execute the machine- readable instructions”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “a control system including one or more processors configured to execute the machine- readable instructions” language, the claim encompasses a user simply examining the image data based on some criteria, as it appears to be performed in the claims, and then determine a risk factor for the individual associated with the condition in her/his mind. The mere nominal recitation of a generic network appliance does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
Step 2A, Prong 2, Integrated into Practical Application: No - The claim recites additional elements: an electronic interface configured to generate data associated with the individual, receive data associated with the individual, or both; a memory storing machine-readable instructions; generate first image data of an interior of a mouth of an individual, an interior of a throat of the individual, or both, the first image data associated with one or more internal physical features of the individual. The data generation and/or receiving step and the first image data generation step are recited at a high level of generality (i.e., as a general means of collecting and storing data), and amounts to mere data gathering, which is a form of insignificant pre extra-solution activity. The electronic interface, memory storing machine-readable instructions, and the control system including one or more processors configured to execute the machine-readable instructions, that perform the data gathering steps are also recited at a high level of generality, as generic sensors or detectors and generic computer components, and merely automate the data gathering step. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the control system including one or more processors configured to execute the machine-readable instructions).
The combination of these additional elements is no more than mere instructions to apply the exception using a generic sensors (electronic interface) and computer components (memory and control system). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B, Inventive Concept: No - As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the data generation and/or receiving step and the first image data generation step were considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the example does not provide any indication that the electronic interface is anything more than a generic sensor or detector, and the memory and control system is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the data generation and/or receiving step and the first image data generation step are well-understood, routine, conventional activity is supported under Berkheimer Option 2. For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
Claim 52 recites “wherein determining the risk factor includes determining that the individual currently has the condition” which merely further recites specificities of the risk factor determining step and hence fails to integrate the abstract idea into a practical application.
Claim 53 recites “wherein the one or more processors are further configured to execute the machine-readable instructions to generate second image data of a head of the individual, a neck of the individual, or both, the second image data associated with one or more external physical features of the individual, and wherein the risk factor is determined based on the first image data and the second image data” which comprise further data gathering steps of acquiring second image data and further mental steps of determining risk factor, and hence fail to incorporate the abstract idea into practical application.
Claim 55 recites “wherein the risk factor determined based at least in part on the first image data is an initial risk factor, and wherein the one or more processors are further configured to execute the machine-readable instructions to update the initial risk factor based at least in part on the second image data” which merely amending the output of the determining step based on newly acquired data, and hence fails to integrate the mental step into a practical application.
Claim 59 recites “wherein the one or more processors are further configured to execute the machine-readable instructions to determine a contribution degree of (i) the one or more internal physical features, (ii) the one or more external physical features, or (iii) both (i) and (ii), the contribution degree of a respective physical feature being an estimate of an impact of the respective physical feature on a presence of the condition” which merely specifies conditions for the determining step which fail to integrate the mental step into a practical application.
Claim 62 recites “wherein the one or more processors are further configured to execute the machine-readable instructions to: identify at least one threshold physical feature having a contribution degree above a threshold value; and determine whether each threshold physical feature is associated with (i) a tongue of the individual, a jaw of the individual, or both, or (ii) an airway of the individual” which includes thresholding conditions for the determining step and hence fail to integrate the mental step into a practical application.
Claim 64 recites “wherein the one or more processors are further configured to execute the machine-readable instructions to identify one or more modifiable physical features from the internal physical features and the external physical features, each of the one or more modifiable physical features being modifiable in response to (i) a change in a physical activity regimen of the individual, (ii) a change in a diet of the individual, (iii) a change in a medication regimen of the individual, or (iv) any combination of (i)-(iii)” which comprise further mental steps of identifying features of regions of interest in the acquired images and hence fail to incorporate the mental step into a practical application.
Claim 66 recites “wherein the one or more modifiable physical features of the individual include a circumference of a neck of the individual, an amount of body fat in the head and neck area of the individual, a location of the body fat in the head and neck area of the individual, an amount of muscle in the head and neck area of the individual, a location of the muscle in the head and neck area of the individual, or any combination thereof” which merely specifies the region of interest for the data gathering steps and hence fails to incorporate the mental step into a practical application.
Claim 67 recites “wherein the one or more processors are further configured to execute the machine-readable instructions to identify one or more non-modifiable physical features from the internal physical features and the external physical features that are not modifiable in response to (i) a change in a physical activity regimen of the individual, (ii) a change in a diet of the individual, (iii) a change in a medication regimen of the individual, or (iv) any combination of (i)-(iii)” which also indicates which regions of interest are to be imaged in the data gathering step and hence fails to integrate the mental step into a practical application.
Claim 68 recites “wherein the one or more non-modifiable physical features of the individual include a position of a jaw of the individual, a width of the jaw of the individual, a height of a tongue of the individual, a distance between a tongue of the individual and a roof of the mouth of the individual, a relative position between upper teeth of the individual and lower teeth of the individual, or any combination thereof” which also indicates which regions of interest are to be imaged in the data gathering step and hence fails to integrate the mental step into a practical application.
Claim 69 recites “wherein the one or more processors are further configured to execute the machine-readable instructions to determine a treatment plan for the individual based at least in part on the identified at least one physical feature having the contribution degree above the threshold value” which is a further mental step of providing a plan for prophylaxis but not actually implementing the prophylaxis and hence fails to integrate the mental step identified in claim 51 into a practical application.
Claim 71 recites “wherein: in response to the at least one threshold physical feature including one or more modifiable physical features, the determined treatment plan is a first treatment plan; and in response to the at least one threshold physical feature including no modifiable physical features, the determined treatment plan is a second treatment plan that is different than the first treatment plan” which is a further mental step of providing a plan for prophylaxis but not actually implementing the prophylaxis and hence fails to integrate the mental step identified in claim 51 into a practical application.
Claim 72 recites “wherein the one or more processors are further configured to execute the machine-readable instructions to determine a position of a body of the individual when the first image data is generated, and wherein the risk factor is based at least in part on the position of the body of the individual” which merely specifies an orientation or pose of the region of interest during data gathering and hence fails to integrate the mental step into a practical application.
Claim 76 recites “wherein at least a portion of the first image data is generated during one or more sleep sessions of the individual, and wherein the treatment plan includes (i) a recommended type of pillow to use during one or more subsequent sleep sessions, (ii) a recommended body position to be in during the one or more subsequent sleep sessions, or (iii) both (i) and (ii)” which only recites the conditions of the individual during the data gathering step and hence fails to integrate the mental step into a practical application.
Claim 77 recites “wherein the first image data is generated at a first time, and wherein the one or more processors are further configured to execute the machine-readable instructions to: generate additional first image data at a second time after the first time;[[ and]] determine a change in the one or more external physical features of the individual based at least in part on the first image data and the additional first image data; and determine a change in the risk factor based at least in part on the change in the one or more external physical features of the individual” which describes changes in variables and output of the risk factoring determining step identified above as a mental step, and hence fails to integrate the mental step into a practical application.
Claim 78 recites “wherein the first image data and the second image data are generated at a first time, and wherein the one or more processors are further configured to execute the machine-readable instructions to: generate additional first image data and second image data at a second time after the first time;[[ and]] (i) determine a change in the one or more external physical features of the individual based at least in part on the first image data and the additional first image data, (ii) determining a change in the one or more internal physical features of the individual based at least in part on the second image data and the additional second image data, or (iii) both (i) and (ii); and determine a change in the risk factor based at least in part on (i) the change in the one or more external physical features of the individual, (ii) the change in the one or more internal physical features of the individual, or (iii) both (i) and (ii)” which describes changes in variables and output of the risk factoring determining step identified above as a mental step, and hence fails to integrate the mental step into a practical application.
Claim 87 recites “wherein determining the risk factor for the individual associated with the condition includes inputting the first image data, the second image data, or both into a trained machine learning model, the machine learning model being configured to output the risk factor” which includes mere use of a machine learning model applied to the mental step and hence fails to integrate the mental step into a practical application.
Claim 88 recites “wherein the one or more processors are further configured to execute the machine-readable instructions to generate acoustic data representative of one or more sounds produced by the individual and to analyze the acoustic data to identify the one or more external physical features of the individual, the one or more internal physical features of the individual, or both, wherein the risk factor is based at least in part on the acoustic data” which merely specifies the imaging modality for the data gathering step and hence fails to integrate the mental step into a practical application.
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.
Claims 51-53, 59, 62, 64, 66-69, 71, 77-78, 80, and 87 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stern, J.C., US 20190117151 A1.
Regarding claim 51, Stern teaches a system for determining a risk factor for an individual that is associated with a condition (abstract states “An automated method and system for diagnosing sleep disorders and predicting treatment effectiveness using data collected on the user using an automated application including questionnaire, facial recognition technology, and historical user and patient data. The data can be used by a diagnostic and treatment prediction algorithm to diagnose sleep disorders, for example sleep apnea, and predict treatment effectiveness. The system is an automated, self-learning algorithm capable of assessing the risk for sleep disorders and predicting treatment effectiveness”), the system comprising:
an electronic interface configured to generate data associated with the individual, receive data associated with the individual, or both ([0031] states “Device 140 for example can be a smartphone, tablet computer, laptop computer, or desktop computer equipped with camera 142 and microphone 144”);
a memory storing machine-readable instructions ([0093] states “Those having ordinary skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs”); and
a control system including one or more processors configured to execute the machine-readable instructions ([0093] states “Those having ordinary skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs”) to:
generate first image data of an interior of a mouth of an individual, an interior of a throat of the individual, or both, the first image data associated with one or more internal physical features of the individual ([0041]-[0048] state “The method for collection of photograph input can include the following steps: [0042] 1. Take a photo of your face straight ahead [0043] a. Measures length and width of face [0044] 2. Take a profile picture in app 11 [0045] a. measures angle between neck and chin in app 11 [0046] 3. Say ahh and stick your tongue out and take a photo [0047] a. Measures Mallampati score in app 11 [0048]”); and
determine the risk factor for the individual associated with the condition based at least in part on the first image data ([0052] states “[0052] Historical patient data 55 included in relational database 52 can be compared against the current user's measurements determined from input source 12 to calculate OSA Likelihood score 20 using OSA Likelihood algorithm 21 and Treatment Effectiveness score 30 using Treatment Effectiveness algorithm 31. OSA Likelihood algorithm 21 automates the interpretation of data from relational database 52 and a rules engine to automatically create OSA Likelihood score 20. Treatment Effectiveness algorithm 31 automates the interpretation of data from relational database 52 and a rules engine to automatically create Treatment Effectiveness score 30”).
Regarding claim 52, Stern further teaches wherein determining the risk factor includes determining that the individual currently has the condition ([0052] states historical patient data 55 included in relational database 52 can be compared against the current user's measurements determined from input source 12 to calculate OSA Likelihood score 20 using OSA Likelihood algorithm 21 and Treatment Effectiveness score 30 using Treatment Effectiveness algorithm 31, at least suggesting that the user is currently with the condition).
Regarding claim 53, Stern further teaches wherein the one or more processors are further configured to execute the machine-readable instructions to generate second image data of a head of the individual, a neck of the individual, or both, the second image data associated with one or more external physical features of the individual, and wherein the risk factor is determined based on the first image data and the second image data ([0041]-[0048] state “The method for collection of photograph input can include the following steps: [0042] 1. Take a photo of your face straight ahead [0043] a. Measures length and width of face [0044] 2. Take a profile picture in app 11 [0045] a. measures angle between neck and chin in app 11 [0046] 3. Say ahh and stick your tongue out and take a photo [0047] a. Measures Mallampati score in app 11 [0048]”).
Regarding claim 59, wherein the one or more processors are further configured to execute the machine-readable instructions to determine a contribution degree of (i) the one or more internal physical features, (ii) the one or more external physical features, or (iii) both (i) and (ii), the contribution degree of a respective physical feature being an estimate of an impact of the respective physical feature on a presence of the condition ([0072] states that “The qualitative responses are used to: [0073] 1) Pinpoint non-quantifiable behavioral and/or environmental flaws that could potentially contribute to the user's sleep problems [0074] 2) Support the results of the quantitative analysis [0075] 3) Prepare the user's on-going sleep diary (i.e. aspects to improve) for Sleep Coaching”).
Regarding claim 62, Stern further teaches wherein the one or more processors are further configured to execute the machine-readable instructions to:
identify at least one threshold physical feature having a contribution degree above a threshold value (see qualitative and quantitative analyses performed in [0071] and [0076]); and
determine whether each threshold physical feature is associated with (i) a tongue of the individual, a jaw of the individual, or both, or (ii) an airway of the individual ([0047] describes measuring Mallampati score based on the tongue photo).
Regarding claim 64, Stern further teaches wherein the one or more processors are further configured to execute the machine-readable instructions to identify one or more modifiable physical features from the internal physical features and the external physical features, each of the one or more modifiable physical features being modifiable in response to (i) a change in a physical activity regimen of the individual, (ii) a change in a diet of the individual, (iii) a change in a medication regimen of the individual, or (iv) any combination of (i)-(iii) ([0046] 3. Say ahh and stick your tongue out and take a photo [0047] a. Measures Mallampati score in app 11 [0048] 4. Look up and take a picture of your neck [0049] a. Measures neck circumference in app 11, where the circumference of the neck is modifiable. NB: the limitation as recited requires what the physical features are and not limited by the recitation of “each of the one or more modifiable physical features being modifiable in response to (i) a change in a physical activity regimen of the individual, (ii) a change in a diet of the individual, (iii) a change in a medication regimen of the individual, or (iv) any combination of (i)-(iii)” ).
Regarding claim 66, Stern further teaches wherein the one or more modifiable physical features of the individual include a circumference of a neck of the individual, an amount of body fat in the head and neck area of the individual, a location of the body fat in the head and neck area of the individual, an amount of muscle in the head and neck area of the individual, a location of the muscle in the head and neck area of the individual, or any combination thereof ([0046] 3. Say ahh and stick your tongue out and take a photo [0047] a. Measures Mallampati score in app 11 [0048] 4. Look up and take a picture of your neck [0049] a. Measures neck circumference in app 11, where the circumference of the neck is modifiable).
Regarding claim 67, Stern further teaches wherein the one or more processors are further configured to execute the machine-readable instructions to identify one or more non-modifiable physical features from the internal physical features and the external physical features that are not modifiable in response to (i) a change in a physical activity regimen of the individual, (ii) a change in a diet of the individual, (iii) a change in a medication regimen of the individual, or (iv) any combination of (i)-(iii) ([0046] 3. Say ahh and stick your tongue out and take a photo [0047] a. Measures Mallampati score in app 11 [0048] 4. Look up and take a picture of your neck [0049] a. Measures neck circumference in app 11, where the circumference of the neck is modifiable. NB: the limitation as recited requires what the physical features are and not limited by the recitation of “each of the one or more modifiable physical features being modifiable in response to (i) a change in a physical activity regimen of the individual, (ii) a change in a diet of the individual, (iii) a change in a medication regimen of the individual, or (iv) any combination of (i)-(iii)” ).
Regarding claim 68, Stern further teaches wherein the one or more non-modifiable physical features of the individual include a position of a jaw of the individual, a width of the jaw of the individual, a height of a tongue of the individual, a distance between a tongue of the individual and a roof of the mouth of the individual, a relative position between upper teeth of the individual and lower teeth of the individual, or any combination thereof ([0046] 3. Say ahh and stick your tongue out and take a photo [0047] a. Measures Mallampati score in app 11 Mallampati score measures a height of a tongue of the individual, a distance between a tongue of the individual and a roof of the mouth of the individual).
Regarding claim 69, Stern further teaches wherein the one or more processors are further configured to execute the machine-readable instructions to determine a treatment plan for the individual based at least in part on the identified at least one physical feature having the contribution degree above the threshold value ([0031] discloses that the sleep test can be a home monitoring sleep test. An example sleep monitoring device 120 is ApneaLink™ air home sleep testing device manufactured by Resmed Corp, San Diego, Calif. Data 125 from sleep monitoring device 120 and result data 65 can be analyzed by a physician using app 11 at device 140. Device 140 for example can be a smartphone, tablet computer, laptop computer, or desktop computer equipped with camera 142 and microphone 144. The physician can provide a medical prescription through app 11 at device 140 to treat the user's sleep disorder).
Regarding claim 71, wherein: in response to the at least one threshold physical feature including one or more modifiable physical features, the determined treatment plan is a first treatment plan ([0059] Available treatments include: [0060] Oral appliance therapy (OAT); Continuous positive airway pressure (CPAP); Surgery and the like); and
in response to the at least one threshold physical feature including no modifiable physical features, the determined treatment plan is a second treatment plan that is different than the first treatment plan ([0059] Available treatments include: [0060] Oral appliance therapy (OAT); Continuous positive airway pressure (CPAP); Surgery and the like).
Regarding claim 77, Stern further teaches wherein the first image data is generated at a first time, and wherein the one or more processors are further configured to execute the machine-readable instructions to: generate additional first image data at a second time after the first time ([0053]-[0054] disclose historical patient data including the measurements of the neck and tongue as performed for a current patient in [0052]);
determine a change in the one or more external physical features of the individual based at least in part on the first image data and the additional first image data; and determine a change in the risk factor based at least in part on the change in the one or more external physical features of the individual ([0055] OSA Likelihood algorithm 21 can be used to calculate OSA Likelihood score 20 for a user. The components of OSA Likelihood algorithm 21 can include responses from questionnaire 13, facial recognition measurements from photograph input 15, and relative similarity to historical patient data 55. The relative similarity to historical patient data offers assessment of changes to the physical features and used to determine the similarity of changes in the likelihood score).
Regarding claim 78, Stern further teaches wherein the first image data and the second image data are generated at a first time ([0053]-[0054] disclose historical patient data including the measurements of the neck and tongue as performed for a current patient in [0052]), and wherein the one or more processors are further configured to execute the machine-readable instructions to:
generate additional first image data and second image data at a second time after the first time([0053]-[0054] disclose historical patient data including the measurements of the neck and tongue as performed for a current patient in [0052]. Here the first image data corresponds to the historical patient data and the second image data comprise the current measurements);
(i) determine a change in the one or more external physical features of the individual based at least in part on the first image data and the additional first image data, (ii) determining a change in the one or more internal physical features of the individual based at least in part on the second image data and the additional second image data, or (iii) both (i) and (ii); and determine a change in the risk factor based at least in part on (i) the change in the one or more external physical features of the individual, (ii) the change in the one or more internal physical features of the individual, or (iii) both (i) and (ii) ([0055] OSA Likelihood algorithm 21 can be used to calculate OSA Likelihood score 20 for a user. The components of OSA Likelihood algorithm 21 can include responses from questionnaire 13, facial recognition measurements from photograph input 15, and relative similarity to historical patient data 55. The relative similarity to historical patient data offers assessment of changes to the physical features which is then used to determine similarity or changes to the likelihood score between the historical patient data and the current measurements).
Regarding claim 80, Stern further teaches wherein the one or more processors are further configured to execute the machine-readable instructions to: determine an initial treatment plan for the individual based at least in part on first image data, the second image data, or both ([0059] Available treatments include: [0060] Oral appliance therapy (OAT); Continuous positive airway pressure (CPAP); Surgery and the like); determine an updated treatment plan based at least in part on (i) the change in the one or more external physical features of the individual, (ii) the change in the one or more internal physical features of the individual, or (iii) both (i) and (ii) ([0030] Camera app 19 can be part of app 11 or separate from app 11, Data 16 from input source 12 including results of questionnaire 13 and photograph input 15 can be used in algorithms for determining a user's OSA Likelihood score 20 and Treatment Effectiveness score 30, where the score 30 assesses treatment over time based on the changes in image data from the camera),
wherein the initial treatment plan includes use of a respiratory therapy system with a first therapy pressure, use of the respiratory therapy system with a first type of user interface, or both ([0059] Available treatments include: [0060] Oral appliance therapy (OAT); Continuous positive airway pressure (CPAP); Surgery and the like).
wherein the updated treatment plan includes use of the respiratory therapy system with a second therapy pressure that is less than the first therapy pressure, use of the respiratory therapy system with a second type of user interface different than the first type of user interface, use of a positional adjustment device configured to aid in causing the individual to sleep in a desired position, or any combination thereof ([0059] Available treatments include: [0060] Oral appliance therapy (OAT); Continuous positive airway pressure (CPAP); Surgery and the like).
Regarding claim 87, Stern further teaches wherein determining the risk factor for the individual associated with the condition includes inputting the first image data, the second image data, or both into a trained machine learning model, the machine learning model being configured to output the risk factor ([0058] OSA Likelihood score 20 using OSA Likelihood algorithm 21 and Treatment Effectiveness score 30 using Treatment Effectiveness algorithm 31 using App 11 content information and matching the app information obtained from the lookup is then classified. The classification may include statistical machine-learning to analyze the app information for its content. Any classification method that uses supervised learning, unsupervised learning, reinforcement learning, direct string matching, etc. on a processor may be used to classify the app content according to predefined parameters).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 55 is rejected under 35 U.S.C. 103 as being unpatentable over Stern in view of Kimishima, et al., US 20140058703 A1.
Regarding claim 55, Stern teaches all the limitations of claim 53 above.
Stern fails to teach wherein the risk factor determined based at least in part on the first image data is an initial risk factor, and wherein the one or more processors are further configured to execute the machine-readable instructions to update the initial risk factor based at least in part on the second image data.
However, within the same field of endeavor, Kimishima teaches an information processing device, an information processing method, and a program which can improve sleep efficiency. The information processing device according to one aspect of the present technique has: an acquisition unit which acquires information which indicates an action schedule of a user; and a first determination unit which determines whether the user needs to wake up or sleep, and determines the degree of necessity of waking up when determining that the user needs to wake up and the degree of necessity of sleeping when determining the user needs to sleep, according to the action schedule of the user. The present technique is applicable to a mobile device such as a mobile telephone, a PDA and a digital camera. See abstract. Kimishima teaches wherein the risk factor determined based at least in part on the first image data ([0126] The captured image acquisition unit 73 acquires an image of a face of the user captured by the camera 13, and outputs the image to the face recognition unit 64) is an initial risk factor ([0009] The first determination unit can calculate a first score which indicates the degree of necessity of waking up or the degree of necessity of sleeping when determining the degree of necessity of waking up or the degree of necessity of sleeping), and wherein the one or more processors are further configured to execute the machine-readable instructions to update the initial risk factor based at least in part on the second image data ([0213] In step S77, the sleepiness determination unit 23 calculates again a sleepiness score of the user based on sensor data detected by the sensor unit 12 after the music is played back, and updates the sleepiness score. Generally, the updated sleepiness score becomes close to a sleep onset score or a wakefulness score from the sleepiness score before the music is played back. The updated sleepiness score is also supplied to the information management unit 83 and the detection unit 84).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Stern wherein the risk factor determined based at least in part on the first image data is an initial risk factor, and wherein the one or more processors are further configured to execute the machine-readable instructions to update the initial risk factor based at least in part on the second image data, as taught by Kimishima, to provide an accurate way of assessing the patient’s risks ([0253]).
Claims 72 and 76 are rejected under 35 U.S.C. 103 as being unpatentable over Stern in view of Nagai, et al., US 20060173257 A1.
Regarding claim 72, Stern teaches all the limitations of claim 51.
Stern fails to teach wherein the one or more processors are further configured to execute the machine-readable instructions to determine a position of a body of the individual when the first image data is generated, and wherein the risk factor is based at least in part on the position of the body of the individual.
However, within the same field of endeavor, Nagai teaches Measurement of evaluation parameter of a subject, the parameter being variable due to sleep apnea of the subject. A body position of the subject is detected in terms of angle information. The parameter measurement and the body angle detection are executed at a predetermined sampling frequency. See abstract. Nagai teaches wherein the one or more processors ([0047] a system controller 14, and an analyzer 15 serving as a processor) are further configured to execute the machine-readable instructions ([0169] In general, the routines executed to implement the embodiment of the invention, whether implemented as part of an operating system or a specific application, component, program, object, module or sequence of instructions will be referred to as "programs". The program comprises one or more instructions that are resident at various times in various memory and storage devices in a computer, and that cause the computer to perform the steps necessary to execute steps or elements embodying the various aspects of the invention) to determine a position of a body of the individual when the first image data is generated, and wherein the risk factor is based at least in part on the position of the body of the individual ([0132] The PC display controller 34 is a functioning part for displaying the various data calculated in the main calculator 33 on the display unit 37 in the form of a certain image or for outputting the various data to an output unit 38. For instance, the PC display controller 34 generates the composite data image as shown in FIG. 16 created by the data synthesizer 331, and displays the image on the display unit 37. The PC display controller 34 includes an ODI display data generator 341, an AHI display data generator 342, and a body position related display data generator 343).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Stern wherein the one or more processors are further configured to execute the machine-readable instructions to determine a position of a body of the individual when the first image data is generated, and wherein the risk factor is based at least in part on the position of the body of the individual, as taught by Nagai, as such modification would promote accurately determining an optimal approach for treating individual subjects e.g. suggesting a recommended body position in which the subject should lie in sleep ([0008]).
Regarding claim 76, Stern in view of Nagai teaches all the limitations of claim 51.
Stern fails to teach wherein at least a portion of the first image data, the second image data, or both is generated during one or more sleep sessions of the individual, and wherein the treatment plan includes (i) a recommended type of pillow to use during one or more subsequent sleep sessions, (ii) a recommended body position to be in during the one or more subsequent sleep sessions, or (iii) both (i) and (ii).
However, Nagai further teaches wherein at least a portion of the first image data, the second image data, or both is generated during one or more sleep sessions of the individual, and wherein the treatment plan includes (i) a recommended type of pillow to use during one or more subsequent sleep sessions, (ii) a recommended body position to be in during the one or more subsequent sleep sessions, or (iii) both (i) and (ii) ([0130] The recommended angle calculator 335 calculates a body angle with less or no likelihood of occurrence of Dip in the subject i.e. a body angle having a frequency of occurrence of Dip less than a predetermined number of times based on a computation result of the angle-based ODI calculator 333 or the angle-based AHI calculator 334, and defines the body angle as the recommended body angle with less or no likelihood of apnea. Providing the recommended angle calculator 335 enables to provide the data that readily notifies the subject of the body angle effective in suppressing apnea. Also see [0123]-[0124] for the pillow recommendations).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Stern wherein the one or more processors are further configured to execute the machine-readable instructions to determine a position of a body of the individual when the first image data is generated, and wherein the risk factor is based at least in part on the position of the body of the individual, as taught by Nagai, as such modification would promote accurately determining an optimal approach for treating individual subjects e.g. suggesting a recommended body position in which the subject should lie in sleep ([0008]).
Claims 88 and 91 are rejected under 35 U.S.C. 103 as being unpatentable over Stern in view of Zigel, et al., US 20190298271 A1.
Regarding claim 88, Stern teaches all the limitations of claim 1 above.
Stern fails to teach wherein the one or more processors are further configured to execute the machine-readable instructions to generate acoustic data representative of one or more sounds produced by the individual and to analyze the acoustic data to identify the one or more external physical features of the individual, the one or more internal physical features of the individual, or both, wherein the risk factor is based at least in part on the acoustic data.
However, within the same field of endeavor, Zigel teaches a method and system for the estimation of apnea-hypopnea index (AHI), as an indicator for Obstructive sleep apnea (OSA) severity, by combining speech descriptors from three separate and distinct speech signal domains (abstract), wherein the one or more processors are further configured to execute the machine-readable instructions ([0040]-[0041] disclose a processor and memory) to generate acoustic data representative of one or more sounds produced by the individual ([0042] obtaining an audio recorded phonogram signal comprising a speech segment and/or an audio recorded phonogram signal comprising a sustained-vowel segment) and to analyze the acoustic data to identify the one or more external physical features of the individual, the one or more internal physical features of the individual, or both, ([0004] The ability to assess OSA during wakefulness was explored using tracheal breath sounds analysis, as it was established that OSA is associated with anatomical and functional abnormalities of the upper airway, leading to alterations of vocal tract physiology and structural changes of soft tissues, collectively affecting the acoustic characteristics of speech) wherein the risk factor is based at least in part on the acoustic data ([0007] Also provided herein a method of estimation of AHI, as an indicator for OSA severity, comprising obtaining acoustic data from a subject, extracting descriptors of the acoustic short-term features (STF) of continuous speech, the long-term features (LTF) of continuous speech, and features of sustained vowels (SVF), obtaining AHI estimates from each set of descriptor features, and combining said AHI estimates to furnish an improved fused AHI estimate).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Stern, wherein the one or more processors are further configured to execute the machine-readable instructions to generate acoustic data representative of one or more sounds produced by the individual and to analyze the acoustic data to identify the one or more external physical features of the individual, the one or more internal physical features of the individual, or both, wherein the risk factor is based at least in part on the acoustic data, as taught by Zigel, as such modification would provide a simple cost-effective approach to sleep disorder screening, diagnosis and estimation ([0005]).
Regarding claim 91, Stern in view of Zigel teaches all the limitations of claim 88 above.
Stern does not teach wherein: the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a pronunciation by the individual of at least one of the one or more sounds, the pronunciation being indicative of the risk factor; or the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a change in a pronunciation by the individual of at least one of the one or more sounds, the change in the pronunciation being indicative of the risk factor; the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a tiredness level of the individual, the tiredness level of the individual being indicative of the risk factor.
However, Zigel further teaches wherein: the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a pronunciation by the individual of at least one of the one or more sounds, the pronunciation being indicative of the risk factor ([0007] Also provided herein a method of estimation of AHI, as an indicator for OSA severity, comprising obtaining acoustic data from a subject, extracting descriptors of the acoustic short-term features (STF) of continuous speech, the long-term features (LTF) of continuous speech, and features of sustained vowels (SVF), obtaining AHI estimates from each set of descriptor features, and combining said AHI estimates to furnish an improved fused AHI estimate. Combining these speech descriptors may provide the ability to estimate the severity of OSA using statistical learning and speech analysis approaches); or
the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a change in a pronunciation by the individual of at least one of the one or more sounds, the change in the pronunciation being indicative of the risk factor ([0084] indicating extracting several features for the subject’s phonogram including Jitter that measures the regularity of pitch periods, and it is represented as the average absolute difference between two consecutive periods, divided by the average period [0095]),
the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a tiredness level of the individual, the tiredness level of the individual being indicative of the risk factor ([0106] states that “the LTF set suitable for OSA evaluation in female speakers comprises a variance of LPCC.sub.4.sup.(3), a variance of LPCC.sub.4.sup.(1), variance of Φ.sup.(3), and an arithmetic mean of LPCC.sub.1.sup.(2). Without being bound by a theory it is believed that the inclusion of Φ.sup.(3) might reflect the more monotonic speech among OSA patients, apparently correlating negatively with AHI, may result from muscle fatigue and fatigue of the patient in general”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Stern, wherein: the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a pronunciation by the individual of at least one of the one or more sounds, the pronunciation being indicative of the risk factor; or the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a change in a pronunciation by the individual of at least one of the one or more sounds, the change in the pronunciation being indicative of the risk factor; the one or more processors are further configured to execute the machine-readable instructions to analyze the acoustic data to determine a tiredness level of the individual, the tiredness level of the individual being indicative of the risk factor, as taught by Zigel, as such modification would provide a simple cost-effective approach to sleep disorder screening, diagnosis and estimation ([0005]).
Conclusion
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/FAROUK A BRUCE/ Examiner, Art Unit 3797
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