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 .
Status of Claims
This office action for the 18/990399 application is in response to the communications filed December 20, 2024.
Claims 1-20 were initially submitted December 20, 2024.
Claims 1-20 are currently pending and considered below.
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.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method for providing personalized treatment recommendations for Meibomian Gland Dysfunction (MGD), the method comprising: collecting sensor data, supplying the sensor data to a model; and adjusting one or more treatment parameters for treatment of the user. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, 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. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“at a user device comprising one or more processors”, “at the user device”, “machine learning”, “trained on training sensor data and training user outcome data”, “at the user device and based on an output from the machine learning model” and “with the MGD treatment device” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0005] of the as-filed specification describes that the hardware that implements the steps of the abstract idea amount to nothing more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“from one or more sensors on a MGD treatment device being used for treatment of a user” which corresponds to mere data gathering and/or output.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, 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. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“from one or more sensors on a MGD treatment device being used for treatment of a user” which corresponds to receiving or transmitting data over a network.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 2 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein collecting sensor data comprises collecting additional sensor data” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“from one or more user device sensors on the user device, and wherein supplying the sensor data to the machine learning model comprises supplying the additional sensor data.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“comprising refining one or more treatment suggestions based on accumulated user data.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“comprising adjusting the one or more treatment parameters over time while continuing collecting sensor data.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“comprising collecting, …, user feedback from the user” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“supplying the user feedback to the machine learning model.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
“using a graphical user interface of the user device” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to mere data gathering and/or output and receiving or transmitting data over a network.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 5 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“comprising adjusting, …, the one or more treatment parameters based on an additional output from the machine learning model based on the user feedback.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“the user device” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 5 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein collecting user feedback comprises … collecting feedback, answering user questions, and guiding treatment.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“providing a chatbot configured for” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“comprising providing sensor data or user feedback or both” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“to a remote computer system for remote monitoring or telemedicine or both” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to mere data gathering and/or output and receiving or transmitting data over a network.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“comprising monitoring the MGD treatment device and, in response to the monitoring, initiating one or more safety measures in response to detecting one or more anomalies or unsafe usage patterns.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 10 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“comprising analyzing supplementary health data of the user in conjunction with the sensor data or user feedback or both and, in response, adjusting the one or more treatment parameters.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Claim 11 is substantially similar to claim 1. Accordingly, claim 11 is rejected for the same reason as claim 1.
As per claim 12,
Claim 12 is substantially similar to claim 2. Accordingly, claim 12 is rejected for the same reason as claim 2.
As per claim 13,
Claim 13 is substantially similar to claim 3. Accordingly, claim 13 is rejected for the same reason as claim 3.
As per claim 14,
Claim 14 is substantially similar to claim 4. Accordingly, claim 14 is rejected for the same reason as claim 4.
As per claim 15,
Claim 15 is substantially similar to claim 5. Accordingly, claim 15 is rejected for the same reason as claim 5.
As per claim 16,
Claim 16 is substantially similar to claim 6. Accordingly, claim 16 is rejected for the same reason as claim 6.
As per claim 17,
Claim 17 is substantially similar to claim 7. Accordingly, claim 17 is rejected for the same reason as claim 7.
As per claim 18,
Claim 18 is substantially similar to claim 8. Accordingly, claim 18 is rejected for the same reason as claim 8.
As per claim 19,
Claim 19 is substantially similar to claim 9. Accordingly, claim 19 is rejected for the same reason as claim 9.
As per claim 20,
Claim 20 is substantially similar to claim 10. Accordingly, claim 20 is rejected for the same reason as claim 10.
Claim Rejections - 35 USC § 102
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 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)(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.
Claims 1, 2, 4, 9, 11, 12, 14 and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang (US 2022/0047449).
As per claim 1,
Zhang discloses a method for providing personalized treatment recommendations for Meibomian Gland Dysfunction (MGD), the method comprising: collecting, at a user device comprising one or more processors, sensor data from one or more sensors on a MGD treatment device being used for treatment of a user:
(Paragraphs [0002], [0025] and [0071] of Zhang. The teaching describes a dry eye treatment device with a robotic arm, specifically relating to meibomian gland dysfunction. The robotic arm and the associated computer system could be trained with machine learning with a large quantity of images and videos of real-life dry eye treatment by medical professionals, so that the device could be self-administrated [providing personalized treatment recommendations for Meibomian Gland Dysfunction (MGD)] by the subject, yet still could provide professional dry eye treatment. The robotic arm 100 comprises a base 101, and a series of links 103, 105, 107, 109, 112. These links are connected by joints 102, 104, 106, 108, 110. The joints allow rotational and/or translational motion. In FIG. 12, the end effector of the robotic arm is the heated roller 111. Further, the link 112 connects to two cameras, one is a visible camera 113 to help the robotic arm to determine the relative position with the eye of the subject so that the robotic arm could place the heated roller precisely and provide massaging of the closed eyelids by the motion of the roller and the control of the pressure applied [sensor data] to the roller. The other camera is a thermal camera [sensor data] 114, which measures the temperatures of the roller and the closed eyelids during use. In some other embodiments, three cameras are employed. Two visible cameras are used for stereoscopic machine vision to precisely locate the robotic arm end effector and the eye of a subject; and an additional thermal camera is for eye temperature monitoring. Preferably, the end effector of the robotic arm 111 is adjustable, so that convex or concave rollers of different sizes could be used.)
Zhang further discloses supplying, at the user device, the sensor data to a machine learning model trained on training sensor data and training user outcome data and adjusting, at the user device and based on an output from the machine learning model, one or more treatment parameters for treatment of the user with the MGD treatment device:
(Paragraphs [0051], [0071] and [0076] of Zhang. The teaching describes that the robotic arm and the associated computer system are trained with machine learning (i.e., robot learning) with a large quantity of images and videos, including measurements of pressure and temperature, of real-life dry eye treatment by human medical professionals, so that the device could be self-administrated by the subject, yet still could provide professional dry eye treatment, comparable to that in a hospital or an ophthalmic clinic. The machine learning algorithm could be included in the computer software for the robotic arm motion control. Further, with artificial intelligence employed in the robotic arm, human errors could be minimized in the treatment of an eye. A temperature monitoring system could be used to monitor and control the heating process in real time. The temperature control circuit compares the real-time temperature of the roller surface with a predetermined temperature range and adjusts the electric current of the heating element, in order to maintain the roller surface temperature within a predetermined temperature range)
As per claim 2,
Zhang discloses the limitations of claim 1.
Zhang further discloses wherein collecting sensor data comprises collecting additional sensor data from one or more user device sensors on the user device, and wherein supplying the sensor data to the machine learning model comprises supplying the additional sensor data:
(Paragraphs [0002], [0025] and [0071] of Zhang. The teaching describes a dry eye treatment device with a robotic arm, specifically relating to meibomian gland dysfunction. The robotic arm and the associated computer system could be trained with machine learning with a large quantity of images and videos of real-life dry eye treatment by medical professionals, so that the device could be self-administrated [providing personalized treatment recommendations for Meibomian Gland Dysfunction (MGD)] by the subject, yet still could provide professional dry eye treatment. The robotic arm 100 comprises a base 101, and a series of links 103, 105, 107, 109, 112. These links are connected by joints 102, 104, 106, 108, 110. The joints allow rotational and/or translational motion. In FIG. 12, the end effector of the robotic arm is the heated roller 111. Further, the link 112 connects to two cameras, one is a visible camera 113 to help the robotic arm to determine the relative position with the eye of the subject so that the robotic arm could place the heated roller precisely and provide massaging of the closed eyelids by the motion of the roller and the control of the pressure applied [sensor data] to the roller. The other camera is a thermal camera [sensor data] 114, which measures the temperatures of the roller and the closed eyelids during use. In some other embodiments, three cameras are employed. Two visible cameras are used for stereoscopic machine vision to precisely locate the robotic arm end effector and the eye of a subject; and an additional thermal camera is for eye temperature monitoring. Preferably, the end effector of the robotic arm 111 is adjustable, so that convex or concave rollers of different sizes could be used. This sensor data from the pressure and temperature sensors are continuously collected and dynamically adjusted.)
As per claim 4,
Zhang discloses the limitations of claim 1.
Zhang further discloses comprising adjusting the one or more treatment parameters over time while continuing collecting sensor data:
(Paragraphs [0002], [0025] and [0071] of Zhang. The teaching describes a dry eye treatment device with a robotic arm, specifically relating to meibomian gland dysfunction. The robotic arm and the associated computer system could be trained with machine learning with a large quantity of images and videos of real-life dry eye treatment by medical professionals, so that the device could be self-administrated [providing personalized treatment recommendations for Meibomian Gland Dysfunction (MGD)] by the subject, yet still could provide professional dry eye treatment. The robotic arm 100 comprises a base 101, and a series of links 103, 105, 107, 109, 112. These links are connected by joints 102, 104, 106, 108, 110. The joints allow rotational and/or translational motion. In FIG. 12, the end effector of the robotic arm is the heated roller 111. Further, the link 112 connects to two cameras, one is a visible camera 113 to help the robotic arm to determine the relative position with the eye of the subject so that the robotic arm could place the heated roller precisely and provide massaging of the closed eyelids by the motion of the roller and the control of the pressure applied [sensor data] to the roller. The other camera is a thermal camera [sensor data] 114, which measures the temperatures of the roller and the closed eyelids during use. In some other embodiments, three cameras are employed. Two visible cameras are used for stereoscopic machine vision to precisely locate the robotic arm end effector and the eye of a subject; and an additional thermal camera is for eye temperature monitoring. Preferably, the end effector of the robotic arm 111 is adjustable, so that convex or concave rollers of different sizes could be used. This sensor data from the pressure and temperature sensors are continuously collected and dynamically adjusted.)
As per claim 9,
Zhang discloses the limitations of claim 1.
Zhang further discloses comprising monitoring the MGD treatment device and, in response to the monitoring, initiating one or more safety measures in response to detecting one or more anomalies or unsafe usage patterns:
(Paragraphs [0051], [0071], [0076] and [0079] of Zhang. The teaching describes that the robotic arm and the associated computer system are trained with machine learning (i.e., robot learning) with a large quantity of images and videos, including measurements of pressure and temperature, of real-life dry eye treatment by human medical professionals, so that the device could be self-administrated by the subject, yet still could provide professional dry eye treatment, comparable to that in a hospital or an ophthalmic clinic. The machine learning algorithm could be included in the computer software for the robotic arm motion control. Further, with artificial intelligence employed in the robotic arm, human errors could be minimized in the treatment of an eye. A temperature monitoring system could be used to monitor and control the heating process in real time. The temperature control circuit compares the real-time temperature of the roller surface with a predetermined temperature range and adjusts the electric current of the heating element, in order to maintain the roller surface temperature within a predetermined temperature range. When the treatment exceeds this temperature range, the system adjusts the temperature back to a safe range. A typical temperature increment could be 0.5° C., 1° C. or 2° C. The upper limit of the roller surface temperature should be less than 60° C. to avoid eyelid burning or discomfort. For most subjects, extra care has to be taken when the roller surface temperature is above 45° C. to minimize discomfort. Therefore, a small increment (for example, less than 2° C.) in temperature increase is preferred when the roller surface temperature is above 45° C.)
As per claim 11,
Claim 11 is substantially similar to claim 1. Accordingly, claim 11 is rejected for the same reason as claim 1.
As per claim 12,
Claim 12 is substantially similar to claim 2. Accordingly, claim 12 is rejected for the same reason as claim 2.
As per claim 14,
Claim 14 is substantially similar to claim 4. Accordingly, claim 14 is rejected for the same reason as claim 4.
As per claim 19,
Claim 19 is substantially similar to claim 9. Accordingly, claim 19 is rejected for the same reason as claim 9.
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.
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.
Claims 3, 5-8, 10, 13, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Mason et al. (US 2021/0350901; herein referred to as Mason).
As per claim 3,
Zhang discloses the limitations of claim 1.
Zhang does not explicitly teach comprising refining one or more treatment suggestions based on accumulated user data.
However Mason teaches a medical treatment device that refines one or more treatment suggestions based on accumulated user data:
(Paragraphs [0038] and [0039] of Mason. The teaching describes that a patient may change as the new patient uses the treatment apparatus to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now-changed characteristics as the new patient. In some embodiments, the treatment plans may be presented, during a telemedicine or telehealth session, to a medical professional. The medical professional may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment apparatus. In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional. Here the treatment data is filtered or refined based on what treatments are included or excluded.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the machine learning-based medical treatment device of Zhang, the techniques used with the machine learning-based medical treatment device of Mason. Paragraph [0033] of Mason teaches that the present disclosure pertain to using artificial intelligence and/or machine learning to dynamically control a treatment apparatus based on the assignment during an adaptive telemedical session. The results of this treatment apparatus can lead to improved user outcomes when using the device with the disclosed methods. One of ordinary skill in the art in possession of Zhang would have looked to Mason to achieve this improved patient outcome advantage. One of ordinary skill in the art would have added to the teaching of Zhang, the teaching of Mason based on this incentive without yielding unexpected results.
As per claim 5,
Zhang discloses the limitations of claim 1.
Zhang does not explicitly teach comprising collecting, using a graphical user interface of the user device, user feedback from the user and supplying the user feedback to the machine learning model.
However, Mason teaches collecting, using a graphical user interface of the user device, user feedback from the user and supplying the user feedback to the machine learning model:
(Paragraphs [0058], [0059] and [0092] of Mason. The teaching describes one or more machine learning models 13 may be trained to match patterns of a first set of parameters (e.g., information pertaining to a medical condition, treatment plans for patients having a medical condition, a set of monetary value amounts associated with the treatment plans, patient outcomes, instructions for the patient to follow in a treatment plan, a set of billing procedures associated with the instructions, and/or a set of constraints) with a second set of parameters associated with a billing sequence and/or optimal treatment plan. Different machine learning models 13 may be trained to recommend different treatment plans tailored for different parameters. For example, one machine learning model may be trained to recommend treatment plans for a maximum monetary value amount generated, while another machine learning model may be trained to recommend treatment plans based on patient outcome, or based on any combination of monetary value amount and patient outcome, or based on those and/or additional goals. A help data display 140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 50 and/or the treatment apparatus 70. The help data display 140 may also include research data or best practices. In some embodiments, the help data display 140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient's problem. In some embodiments, the assistant interface 94 may present two or more help data displays 140, which may be the same or different, for simultaneous presentation of help data for use by the assistant. for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the machine learning-based medical treatment device of Zhang, the techniques used with the machine learning-based medical treatment device of Mason. Paragraph [0033] of Mason teaches that the present disclosure pertain to using artificial intelligence and/or machine learning to dynamically control a treatment apparatus based on the assignment during an adaptive telemedical session. The results of this treatment apparatus can lead to improved user outcomes when using the device with the disclosed methods. One of ordinary skill in the art in possession of Zhang would have looked to Mason to achieve this improved patient outcome advantage. One of ordinary skill in the art would have added to the teaching of Zhang, the teaching of Mason based on this incentive without yielding unexpected results.
As per claim 6,
The combined teaching of Zhang and Mason teaches the limitations of claim 5.
Mason further teaches comprising adjusting, the user device, the one or more treatment parameters based on an additional output from the machine learning model based on the user feedback:
(Paragraphs [0058], [0059] and [0092] of Mason. The teaching describes one or more machine learning models 13 may be trained to match patterns of a first set of parameters (e.g., information pertaining to a medical condition, treatment plans for patients having a medical condition, a set of monetary value amounts associated with the treatment plans, patient outcomes, instructions for the patient to follow in a treatment plan, a set of billing procedures associated with the instructions, and/or a set of constraints) with a second set of parameters associated with a billing sequence and/or optimal treatment plan. Different machine learning models 13 may be trained to recommend different treatment plans tailored for different parameters. For example, one machine learning model may be trained to recommend treatment plans for a maximum monetary value amount generated, while another machine learning model may be trained to recommend treatment plans based on patient outcome, or based on any combination of monetary value amount and patient outcome, or based on those and/or additional goals. A help data display 140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 50 and/or the treatment apparatus 70. The help data display 140 may also include research data or best practices. In some embodiments, the help data display 140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient's problem. In some embodiments, the assistant interface 94 may present two or more help data displays 140, which may be the same or different, for simultaneous presentation of help data for use by the assistant. for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.)
As per claim 7,
The combined teaching of Zhang and Mason teaches the limitations of claim 5.
Mason further teaches wherein collecting user feedback comprises providing a chatbot configured for collecting feedback, answering user questions, and guiding treatment:
(Paragraphs [0058], [0059] and [0092] of Mason. The teaching describes one or more machine learning models 13 may be trained to match patterns of a first set of parameters (e.g., information pertaining to a medical condition, treatment plans for patients having a medical condition, a set of monetary value amounts associated with the treatment plans, patient outcomes, instructions for the patient to follow in a treatment plan, a set of billing procedures associated with the instructions, and/or a set of constraints) with a second set of parameters associated with a billing sequence and/or optimal treatment plan. Different machine learning models 13 may be trained to recommend different treatment plans tailored for different parameters. For example, one machine learning model may be trained to recommend treatment plans for a maximum monetary value amount generated, while another machine learning model may be trained to recommend treatment plans based on patient outcome, or based on any combination of monetary value amount and patient outcome, or based on those and/or additional goals. A help data display 140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 50 and/or the treatment apparatus 70. The help data display 140 may also include research data or best practices. In some embodiments, the help data display 140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient's problem. In some embodiments, the assistant interface 94 may present two or more help data displays 140, which may be the same or different, for simultaneous presentation of help data for use by the assistant. for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.)
As per claim 8,
Zhang discloses the limitations of claim 1.
Zhang does not explicitly teach comprising providing sensor data or user feedback or both to a remote computer system for remote monitoring or telemedicine or both.
However Mason teaches providing sensor data or user feedback or both to a remote computer system for remote monitoring or telemedicine or both:
(Paragraphs [0058], [0059] and [0092]-[0094] of Mason. The teaching describes one or more machine learning models 13 may be trained to match patterns of a first set of parameters (e.g., information pertaining to a medical condition, treatment plans for patients having a medical condition, a set of monetary value amounts associated with the treatment plans, patient outcomes, instructions for the patient to follow in a treatment plan, a set of billing procedures associated with the instructions, and/or a set of constraints) with a second set of parameters associated with a billing sequence and/or optimal treatment plan. Different machine learning models 13 may be trained to recommend different treatment plans tailored for different parameters. For example, one machine learning model may be trained to recommend treatment plans for a maximum monetary value amount generated, while another machine learning model may be trained to recommend treatment plans based on patient outcome, or based on any combination of monetary value amount and patient outcome, or based on those and/or additional goals. A help data display 140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 50 and/or the treatment apparatus 70. The help data display 140 may also include research data or best practices. In some embodiments, the help data display 140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient's problem. In some embodiments, the assistant interface 94 may present two or more help data displays 140, which may be the same or different, for simultaneous presentation of help data for use by the assistant. for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information. The patient interface setting control 154 may include collaborative browsing or co-browsing capability for the assistant to remotely view and/or control the patient interface 50. For example, the patient interface setting control 154 may enable the assistant to remotely enter text to one or more text entry fields on the patient interface 50 and/or to remotely control a cursor on the patient interface 50 using a mouse or touchscreen of the assistant interface 94.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the machine learning-based medical treatment device of Zhang, the techniques used with the machine learning-based medical treatment device of Mason. Paragraph [0033] of Mason teaches that the present disclosure pertain to using artificial intelligence and/or machine learning to dynamically control a treatment apparatus based on the assignment during an adaptive telemedical session. The results of this treatment apparatus can lead to improved user outcomes when using the device with the disclosed methods. One of ordinary skill in the art in possession of Zhang would have looked to Mason to achieve this improved patient outcome advantage. One of ordinary skill in the art would have added to the teaching of Zhang, the teaching of Mason based on this incentive without yielding unexpected results.
As per claim 10,
Zhang discloses the limitations of claim 1.
Zhang does not explicitly teach comprising analyzing supplementary health data of the user in conjunction with the sensor data or user feedback or both and, in response, adjusting the one or more treatment parameters.
However Mason teaches analyzing supplementary health data of the user in conjunction with the sensor data or user feedback or both and, in response, adjusting the one or more treatment parameters:
(Paragraphs [0058], [0059] and [0090]-[0094] and Figure 5 of Mason. The teaching describes one or more machine learning models 13 may be trained to match patterns of a first set of parameters (e.g., information pertaining to a medical condition, treatment plans for patients having a medical condition, a set of monetary value amounts associated with the treatment plans, patient outcomes, instructions for the patient to follow in a treatment plan, a set of billing procedures associated with the instructions, and/or a set of constraints) with a second set of parameters associated with a billing sequence and/or optimal treatment plan. Different machine learning models 13 may be trained to recommend different treatment plans tailored for different parameters. For example, one machine learning model may be trained to recommend treatment plans for a maximum monetary value amount generated, while another machine learning model may be trained to recommend treatment plans based on patient outcome, or based on any combination of monetary value amount and patient outcome, or based on those and/or additional goals. A help data display 140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 50 and/or the treatment apparatus 70. The help data display 140 may also include research data or best practices. In some embodiments, the help data display 140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient's problem. In some embodiments, the assistant interface 94 may present two or more help data displays 140, which may be the same or different, for simultaneous presentation of help data for use by the assistant. for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient's problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information. The patient interface setting control 154 may include collaborative browsing or co-browsing capability for the assistant to remotely view and/or control the patient interface 50. For example, the patient interface setting control 154 may enable the assistant to remotely enter text to one or more text entry fields on the patient interface 50 and/or to remotely control a cursor on the patient interface 50 using a mouse or touchscreen of the assistant interface 94. In some embodiments, the patient status display 134 may present other data 138 regarding the patient, such as last reported pain level, or progress within a treatment plan.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the machine learning-based medical treatment device of Zhang, the techniques used with the machine learning-based medical treatment device of Mason. Paragraph [0033] of Mason teaches that the present disclosure pertain to using artificial intelligence and/or machine learning to dynamically control a treatment apparatus based on the assignment during an adaptive telemedical session. The results of this treatment apparatus can lead to improved user outcomes when using the device with the disclosed methods. One of ordinary skill in the art in possession of Zhang would have looked to Mason to achieve this improved patient outcome advantage. One of ordinary skill in the art would have added to the teaching of Zhang, the teaching of Mason based on this incentive without yielding unexpected results.
As per claim 13,
Claim 13 is substantially similar to claim 3. Accordingly, claim 13 is rejected for the same reason as claim 3.
As per claim 15,
Claim 15 is substantially similar to claim 5. Accordingly, claim 15 is rejected for the same reason as claim 5.
As per claim 16,
Claim 16 is substantially similar to claim 6. Accordingly, claim 16 is rejected for the same reason as claim 6.
As per claim 17,
Claim 17 is substantially similar to claim 7. Accordingly, claim 17 is rejected for the same reason as claim 7.
As per claim 18,
Claim 18 is substantially similar to claim 8. Accordingly, claim 18 is rejected for the same reason as claim 8.
As per claim 20,
Claim 20 is substantially similar to claim 10. Accordingly, claim 20 is rejected for the same reason as claim 10.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681