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 .
Notice to Applicant
Claims 1-9 are pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 9, 2023 and September 1, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 7 objected to because of the following informalities:
Claim 7 recites “the training model being a model trained on a relationship between a change in a value of an examination item of a medical examination, a healthy action, basic information.” This claim limitation should be amended to read “the training model being a model trained on a relationship between a change in a value of an examination item of a medical examination, a healthy action, and basic information.”
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 2, 6, and 7 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.
The term “similar” in claim 2 is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 6 and 7 introduce “a training model”. However, it is indefinite as to if following limitation of “the training model being a model trained on a relationship…” refers to the first training model recited in claim 1 or each of the second training models introduced in claims 6 and 7.
Claim 7 is further indefinite as the claim recites the limitation “basic information”. There is insufficient antecedent basis for this limitation in the claim. Furthermore, the claim recites “acquir[ing] biometric information about the user, the biometric information being measured by a measurement instrument installed in a store;”. For the purposes of examination, Examiner will interpret the “basic information” in claim 7 to instead refer to “biometric information”.
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 1-9 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine
(claims 1-9). Accordingly, claims 1-9 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified system Claim 1 as the claim that represents the claimed invention for analysis and is similar to method claim 8 and product claim 9.
Claim 1:
A system for health management comprising:
a user terminal having a display and configured to receive display signal and to provide a predetermined display based on the display signal, the predetermined display including an input form for receiving input of effort information;
one or more memories storing instructions; and
one or more processors in communication with the user terminal and a server of an institution having medical examination information, the processors configured to execute the instructions to:
acquire the medical examination information about a result of a medical examination of a user from the server and the effort information about a healthy action from the user terminal, the healthy action being an action engaged by the user;
calculate an estimation value of an examination item of a medical examination of the user using a training model, the medical examination information and the effort information, the training model being a model trained on a relationship between a change in a value of an examination item of a medical examination and a healthy action by machine learning;
generate advice on a healthy action based on a difference between a value of the examination item indicated in the medical examination information and the estimation value;
convert the calculated estimation value and the advice into the display signal; and
transmit the display signal to the user terminal.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity under managing personal behaviors of people. The claim elements are directed towards “acquir[ing] the medical examination information about a result of a medical examination of a user… and the effort information about a healthy action”, “calculat[ing] an estimation value of an examination item of a medical examination of the user”, and “generat[ing] advice on a healthy action based on a difference between a value of the examination item indicated in the medical examination information and the estimation value”, which are typical human activities performed by medical providers managing their patient’s health and providing guidance, teaching, or instruction to the patient.
Accordingly, the claim recites at least one abstract idea.
Claims 8 and 9 are abstract for the same reasons as above.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
user terminal (1,8-9); display (1); one or more memories storing instructions (1); one or more processors (1-3,5-7); server (1,8-9); training model (1,6-9); machine learning (1,8-9); display signal (1,8-9); body composition meter (6); measurement instrument (7); non-transitory computer readable recording medium (9); program (9); computer (9)
Any computing devices that would be able to perform the method (user terminal, computer, one or more processors, server) and their associated elements (one or more memories storing instructions, non-transitory computer readable recording medium, program) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. Pg. 4 of Applicant specification recites: “The user terminal 200 is a terminal used by the user. The user terminal 200 may be a smartphone, a tablet terminal, a personal computer, or the like.” No specific, technical improvements are being made to computing devices as generic computing devices are applied to perform the abstract idea of predicting patient health and providing advice and an insignificant extra-solution activity of gathering data; MPEP 2106.05(g).
Machine learning (training model, machine learning) is also taught at a high level of generality. Pg. 26 recites: “The training model may be, for example, a linear model such as a generalized estimation equation, or a model trained by deep learning or the like using a neural network. The example of the training model is not limited to this example.” No specific, technical improvements are being made to machine learning as generically trained machine learning models are applied to perform the abstract idea of predicting patient health and providing advice.
Measurement devices (body composition meter, measurement instrument) are also taught at a high level of generality. Pg. 26 recites: “In the third example embodiment, the health management device 101 acquires further information in addition to the medical examination information and the effort information. Examples of the further information include biometric information measured by various measurement instruments. The measurement instrument is, for example, a body composition meter and a sphygmomanometer. That is, a specific example of the further information is basic information about the user. The basic information is information about the body composition of the user. For example, the basic information includes a height, a weight, a body fat percentage, a BMI, a muscle mass, a basal metabolic rate, and the like of the user. The basic information may be information measured by a body composition meter. A specific example of the information is blood pressure information. The blood pressure information indicates the blood pressure of the user measured by the sphygmomanometer. An example of the information is genetic information about the user. The genetic information may be, for example, information encoded as a base sequence of deoxyribonucleic acid (DNA), information indicating information about an amino acid sequence of a protein, and the like. The present disclosure is not limited to this example, and the genetic information may be information indicating a disease of a relative of the user.” No specific, technical improvements are being made to measurement instruments as generic instruments are applied to perform an insignificant extra-solution activity of gathering data; MPEP 2106.05(g).
Displays (display, display signal) are also taught at a high level of generality. Pg. 8 recites: “The information including the estimation value is displayed, for example, on a display included in the user terminal 200. In other words, the calculated estimation value is converted into the display signal and transmitted the display signal to user terminal. In this manner, the presentation unit 130 presents the calculated estimation value. Presentation unit 130 is an example of a presentation means.” Pg. 16 further recites: “That is, the presentation unit 130 may present the advice on the healthy action based on the difference between the value of the examination item indicated in the medical examination information and the estimation value. In other words, the advice is converted into the display signal and transmitted the display signal to user terminal.” No specific, technical improvements are being made to displays and display signals as generic output displays are applied to perform an insignificant extra-solution activity of outputting data; MPEP 2106.05(g).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 2: This claim recites wherein the one or more processors are configured to execute the instruction to present, when there is another user whose medical examination result is similar to a medical examination result of the user, a healthy action being performed by the another user; which teaches an abstract idea of certain methods of organizing human activities under managing personal behaviors by presenting similar results from other users.
Claim 3: This claim recites wherein the one or more processors are configured to execute the instructions to: acquire medical examination information and effort information associated with each of a plurality of users; calculate the estimation value with respect to each of the plurality of users; calculate a difference between a value of an examination item in the medical examination information and the estimation value for each of two or more users having similar medical examination results; generate a list in which the two or more users are ranked using the difference; and present the generated list; which teaches an abstract idea of mathematical processes by calculating difference values and ranking users based on the difference value, and certain methods of organizing human activity by ranking people compared to each other.
Claim 4: This claim recites wherein the advice is information about a medium indicating at least one of a method of exercise as a healthy action and content of a meal as a healthy action for assisting the user in decision making; which only serves to further limit the abstract idea of the advice.
Claim 5: This claim recites wherein the one or more processors are configured to execute the instructions to: acquire information about a place of employment of the user; and generate the advice including information about a benefit and welfare system related to a healthy action, the benefit and welfare system being a benefit and welfare system of the place of employment of the user; which teaches an abstract idea of certain methods of organizing activity by providing advice based on benefits and welfare available to a user.
Claim 6: This claim recites wherein the one or more processors are configured to execute the instructions to: acquire basic information, the basic information being information about a body composition of the user measured by a body composition meter; and calculate the estimation value using a training model, the medical examination information, the effort information, and the basic information, the training model being a model trained on a relationship between a change in a value of an examination item of a medical examination, a healthy action, and basic information; which teaches an abstract idea of certain methods of organizing activity by predicting a user’s health parameters. This claim teaches a body composition meter and a training model at a high level of generality, such that the body composition meter only performs an insignificant extra-solution activity of gathering data and the training model is merely applied to perform the abstract idea.
Claim 7: This claim recites wherein the one or more processors are configured to execute the instructions to: acquire biometric information about the user, the biometric information being measured by a measurement instrument installed in a store; and calculate the estimation value using a training model, the medical examination information, the effort information, and the basic information, the training model being a model trained on a relationship between a change in a value of an examination item of a medical examination, a healthy action, basic information; which teaches an abstract idea of certain methods of organizing activity by predicting a user’s health parameters. This claim teaches a measurement instrument and a training model at a high level of generality, such that any generic measurement instrument only performs an insignificant extra-solution activity of gathering data and the training model is merely applied to perform the abstract idea.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to 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 generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as known activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Usage of body composition meters/measurement instruments to assess determine patient health for use in patient monitoring and clinical assessment is known, as evidenced by:
Khalil; Sami, The Theory and Fundamentals of Bioimpedance Analysis in Clinical Status Monitoring and Diagnosis of Diseases, 19 Jun 2014, Sensors, 14(6), 10895-10928: pg. 1, “Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status.”
Mialich; Mirele Savegnago, Analysis of Body Composition: A Critical Review of the Use of Bioelectrical Impedance Analysis, 08 Jan 2014, International Journal of Clinical Nutrition, Vol. 2, No. 1, 1-10: pg. 1, “Bioelectrical impedance analysis (BIA) is a method extensively used in studies assessing body composition, especially in view of the high speed of information processing, as a noninvasive method for generating information through portable, easy to use and relatively inexpensive equipment that estimates the distribution of body fluids in the intra- and intercellular spaces in addition to the body components... In this respect, the objective of the present report is to review the main concepts involved in the BIA technique, to describe the types of BIA available, their limitations and applications to clinical practice, especially the monitoring of chronic diseases. After this review, we conclude that BIA is an important instrument for health professionals and that its use can provide safe data about body composition, in addition to complementary data about the clinical course of patients followed up on a medium- and long-term basis.”
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-7 additional limitations which amount to elements that have been recognized as known activities in particular fields, claims 2-7, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2-7, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-9 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 1, 4, 6, and 8-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Crepp (US 20180240358).
Regarding claim 1, Crepp teaches a system for health management comprising:
a user terminal having a display and configured to receive display signal and to provide a predetermined display based on the display signal, the predetermined display including an input form for receiving input of effort information ([0075], “FIG. 8, the first user device 110 is a smartphone (shown in greater detail in FIG. 9) that operates software to present the user with a GUI such as the example GUI shown in FIGS. 5A-5D. Through the GUI, the user provides information to the system 100 and interacts with the features and functions of the system 100.” Fig. 5B). See dietary information, which is encompassed by effort information according to pg. 6 of Applicant specification (“The acquisition unit 110 acquires effort information. The effort information is information indicating a healthy action that the user was engaged in. The healthy action is an action for the purpose of maintaining, recovering, improving, and the like of health. Examples of the healthy action include exercise, diet, and sleep.”), below.
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one or more memories storing instructions ([0014], “a memory coupled to the controller, wherein the memory is configured to store program instructions executable by the controller”); and
one or more processors in communication with the user terminal and a server of an institution having medical examination information ([0067], “the one or more controllers may be a PC based implementation of a control processing system utilizing a central processing unit (CPU), memory and an interconnect bus. The CPU may contain a single microprocessor, or it may contain a plurality of microprocessors for configuring the CPU as a multi-processor system including cloud implementations.” [0074], “The first user device 110 is further in communication with a server 130 which communicates with a database 140. The server 130 is also in communication with a plurality of other user devices 150a, 150b, 150c, each of which is in communication with their associated other user fitness trackers 160a, 160b, 160c.” [0077], “the server 130 and the database 140 collect the user data from each of the first user device 110 and the plurality of other user devices 150a, 150b, 150c to build the data sets from which the algorithms described herein are trained.”). Examiner interprets the place where the server is physically located to encompass the institution, as it stores medical information, as described above.
the processors configured to execute the instructions to:
acquire the medical examination information about a result of a medical examination of a user from the server ([0074], “The first user device 110 is further in communication with a server 130 which communicates with a database 140.” [0041], “Any input data associated with the user and user profile can be stored along with the time stamp of the data/measurement into the database.” [0047], “the system can fit user data to weight patterns of the specific user (e.g., based on the historical data of the user) and/or weight patterns of an ensemble (plurality) of users with similar demographics or profile to that of the user.”). Under the broadest reasonable interpretation of Crepp, Examiner interprets acquiring of the medical examination result (weight data) to be from the server, as historical weight data, which may be previously input data derived from the server database, is used to perform further data processing.
and the effort information about a healthy action from the user terminal, the healthy action being an action engaged by the user ([0031], “User exercise can be tracked based on one or more data platforms available from smart devices, such as phones, watches, and other wearables including sensors and/or tracking functionalities. The activity data can be uploaded and/or recorded into a data set, along with dietary information and weight measurements.”);
calculate an estimation value of an examination item of a medical examination of the user using a training model, the medical examination information and the effort information ([0045], “Accordingly, the system is trained using nutrients and exercise level to model and predict a user's weight as a function of time.” [0048], “One or more machine-learning and other techniques including, but not limited to, Levenberg-Marquardt, least squares, Markov Chain Monte Carlo, Bayesian analysis, back-propagation, deep learning, and other routines (both static and dynamic algorithms) may be implemented to minimize residuals between the users' predicted weight, based on diet and exercise, and their actual weight.”),
the training model being a model trained on a relationship between a change in a value of an examination item of a medical examination and a healthy action by machine learning ([0033], “The present system uses smart devices to track pulse or heart rate (e.g., phones, watches, and other wearables) and works in mass units instead of energy units. In some embodiments, the system uses empirical data to train artificial neural networks using data from individual users to quantify how each user's body responds to diet and exercise.”);
generate advice on a healthy action based on a difference between a value of the examination item indicated in the medical examination information and the estimation value ([0015], “An advantage of the present subject matter is that it can be used to provide quantitative advice regarding the suggested consumption of sugar, protein, carbohydrates, fiber, water, and fat to reach the user's weight goals.” [0035], “based on the amount of user exercise monitored by the system, the system provides the user with a maximum amount of macronutrients the user may consume in a given time frame (e.g., week, day, meal, etc.) to meet the user's weight loss goal.” [0049], “The present system can empirically track a user's progress towards the user's weight gain/loss/maintenance goals with realistic uncertainties in nutrient intake (15%) and weight errors of +/−0.1 lbs.”);
convert the calculated estimation value and the advice into the display signal; and transmit the display signal to the user terminal ([0045], “the system is trained using nutrients and exercise level to model and predict a user's weight as a function of time.” [0050], “The present system can empirically track a user's progress towards the user's weight gain/loss/maintenance goals with realistic uncertainties in nutrient intake (15%) and weight errors of +/−0.1 lbs. when training the neural network.” [0075], “Through the GUI, the user provides information to the system 100 and interacts with the features and functions of the system 100” [0048], “Calculations may be done remotely or locally on the user's smart device or through a cloud server implementation using any variety of languages such as Octave, Matlab, Python, Java, C, C++, or other software packages.”).
Regarding claim 4, Crepp teaches the system of claim 1. Crepp further teaches wherein the advice is information about a medium indicating at least one of a method of exercise as a healthy action and content of a meal as a healthy action for assisting the user in decision making ([0052], “The system can modify the exercise and diet plan based on the diet and exercise data entered… The system can modify the next meal's, next snack's, next day's, next week's maximum carbohydrate and sugar (and/or insulin index or insulin load) consumption to reach the identified weight goals of the user.” [0054], “Exercise activity (e.g., exercise duration and/or intensity level) can be automatically incorporated into the user data set by syncing the program to devices that measure steps and/or heart-rate.”). Examiner interprets modifying the exercise plan, based on exercise activity including steps taken, to encompass a method of exercise that involves taking steps such as walking or running.
Regarding claim 6, Crepp teaches the system of claim 1. Crepp further teaches wherein the one or more processors are configured to execute the instructions to:
acquire basic information, the basic information being information about a body composition of the user measured by a body composition meter ([0040], “exercise (either steps or heart rate data) to patterns found in the user's weight as a function of time.” [0036], “activity levels can be tracked using readily available smart devices that employ accelerometers and/or heart rate monitors.”). See pg. 26 of Applicant specification, which recites , “the basic information includes a height, a weight, a body fat percentage, a BMI, a muscle mass, a basal metabolic rate, and the like of the user.”
calculate the estimation value using a training model, the medical examination information, the effort information, and the basic information ([0045], “Over time, as the users enter additional data (e.g., real time diet, exercise, and weight data) the system customizes the weight projection.” [0048], “One or more machine-learning and other techniques including, but not limited to, Levenberg-Marquardt, least squares, Markov Chain Monte Carlo, Bayesian analysis, back-propagation, deep learning, and other routines (both static and dynamic algorithms) may be implemented to minimize residuals between the users' predicted weight, based on diet and exercise, and their actual weight.”);
the training model being a model trained on a relationship between a change in a value of an examination item of a medical examination, a healthy action, and basic information ([0033], “The present system uses smart devices to track pulse or heart rate (e.g., phones, watches, and other wearables) and works in mass units instead of energy units. In some embodiments, the system uses empirical data to train artificial neural networks using data from individual users to quantify how each user's body responds to diet and exercise.” [0045], “Accordingly, the system is trained using nutrients and exercise level to model and predict a user's weight as a function of time… Over time, as the users enter additional data (e.g., real time diet, exercise, and weight data) the system customizes the weight projection.”).
Regarding claims 8 and 9, these claims are rejected for the same reasons as claim 1, as described above. Crepp further teaches a method and a non-transitory computer readable recording medium that records a program for causing a computer, in communication with a user terminal and a server of an institution having medical examination information, to execute the method ([0037], “terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution. Such a medium may take many forms. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s). Volatile storage media include dynamic memory, such as the memory of such a computer platform. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards paper tape, any other physical medium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a controller can read programming code and/or data.”).
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 2 is rejected under 35 U.S.C. 103 as being unpatentable over Crepp (US 20180240358) in view of Ahmad (US 20180108272).
Regarding claim 2, Crepp teaches the system of claim 1. Crepp does not teach wherein the one or more processors are configured to execute the instruction to present, when there is another user whose medical examination result is similar to a medical examination result of the user, a healthy action being performed by the another user.
However, Ahmad does teach wherein the one or more processors are configured to execute the instruction to present, when there is another user whose medical examination result is similar to a medical examination result of the user, a healthy action being performed by the another user ([0118], “To select the diet, the system matches Sally to users with similar profiles but who have obtained successful results, and based on the programs successfully followed by these users, generates five diet suggestions for Sally.”).
Crepp in view of Ahmad are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Crepp with Ahmad for the advantage of “identify[ing] groups of similar users that should be following similar protocols” (Ahmad; [0050]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Crepp (US 20180240358) in view of Kim (US 20230381591).
Regarding claim 3, Crepp teaches the system of claim 1. Crepp further teaches wherein the one or more processors are configured to execute the instructions to ([0072], “Software may take the form of code or executable instructions for causing a controller or other programmable equipment to perform the relevant steps” [0073], “computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.”):
acquire medical examination information and effort information associated with each of a plurality of users ([0037], “users can input profile information, such as weight, height, age, gender, and other relevant information.” [0077], “The server 130 and the database 140 may also collect and store the real-time data used to update the guidance to the users through the first user device 110 and the plurality of other user devices 150a, 150b, 150c based on what is learned from analysis of the data sets.”);
calculate the estimation value with respect to each of the plurality of users ([0048], “FIG. 4 illustrates an example using several different users' data sets. One or more machine-learning and other techniques including, but not limited to, Levenberg-Marquardt, least squares, Markov Chain Monte Carlo, Bayesian analysis, back-propagation, deep learning, and other routines (both static and dynamic algorithms) may be implemented to minimize residuals between the users' predicted weight, based on diet and exercise, and their actual weight.”);
calculate a difference between a value of an examination item in the medical examination information and the estimation value for each of two or more users having similar medical examination results ([0047], “The system can fit user data by relating diet (macro- and micro-nutrients) and exercise (either steps or heart rate data) to patterns found in the user's weight as a function of time. For example, the system can fit user data to weight patterns of the specific user (e.g., based on the historical data of the user) and/or weight patterns of an ensemble (plurality) of users with similar demographics or profile to that of the user.” [0037], “users can input profile information, such as weight, height, age, gender, and other relevant information.”);
Crepp does not teach wherein the one or more processors are configured to execute the instructions to:
generate a list in which the two or more users are ranked using the difference; and present the generated list.
However, Kim does teach wherein the one or more processors are configured to execute the instructions to ([0131], “the customized exercise information providing apparatus 100 may include a memory 150 for storing one or more instructions and a processor 140 for executing the one or more instructions stored in the memory 150.”):
generate a list in which the two or more users are ranked using the difference; and present the generated list ([0051], “it is possible to provide the user with customized information that may help the user exercise based on a physical condition of the user and a final target desired by the user.” [0093], “the customized exercise information providing apparatus 100 may include a target body shape of the user, such as the amount of body fat and muscle strength desired by the user,” [0101], “the customized exercise information providing apparatus 100 may decide a ranking of each user based on how much a user approaches a target value using information of users in a similar group. Herein, the criterion for deciding the ranking is made based on whether a target value is reached, in other words, the user closest to the target set by the user has the highest priority.”).
Crepp in view of Kim are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Crepp with Kim for the advantage of providing “a higher reward” for “when the first user has a higher ranking than other users in the similar group” (Kim; [0103]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Crepp (US 20180240358) in view of Hayter (US 20110160544).
Regarding claim 5, Crepp teaches the system of claim 1. Crepp does not teach wherein the one or more processors are configured to execute the instructions to: acquire information about a place of employment of the user; and generate the advice including information about a benefit and welfare system related to a healthy action, the benefit and welfare system being a benefit and welfare system of the place of employment of the user.
However, Hayter does teach wherein the one or more processors are configured to execute the instructions to ([0072], “Each module can be implemented as a software program stored on a tangible memory (e.g., random access memory, read only memory, CD-ROM memory, hard disk drive) to be read by a central processing unit to implement the functions of the innovations herein.”):
acquire information about a place of employment of the user ([0035], “The patient care system 100 may also include an employment/benefits management-related entity 180 ("employer 180") that is associated with one or more of the patients 150 and/or the health insurance entity 160, and may also be connected to the data network 130. The employer 180 is also, optionally, connected to the data network and/or software systems associated with or used by the patients 150 and/or health insurance entity 160. Further, the employer 180 is associated with discounts or other incentives and rewards that are provided by employer to a patient 150 consistent with aspects of the innovations set forth herein.”); and
generate the advice including information about a benefit and welfare system related to a healthy action, the benefit and welfare system being a benefit and welfare system of the place of employment of the user ([0068], “FIG. 8, an exemplary method of encouraging management or improvement of patient health may comprise processing first health measurement data 810 …, processing information regarding performance of a health care improvement action or wellness-enhancing activity 830 (e.g., processing instructions regarding providing an improved health measurement, maintaining a favorable health measurement or profile, etc.), and processing instructions or information regarding reward/consideration 840 (e.g., processing instructions regarding any of the various reward and/or consideration features herein). For example, one specific implementation, here, may comprise processing several installments of patient glucose measurement data by the health care provider via a web portal 480 (FIG. 4), processing information to confirm that the patient has improved his/her glucose profile, and notifying the patient via his/her diabetes management device (e.g., CGM meter, etc.) that his/her health insurance premium is being reduced, that other rewards or incentives are available, or providing recommendations for improvements in the patient's health management regimen.”).
Crepp in view of Hayter are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Crepp with Hayter for the advantage of “rewarding the patient for one or both of performing the wellness-enhancing activity and/or achieving health data of an improved value, quantity, quality or profile” (Hayter; [0015]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Crepp (US 20180240358) in view of Kannan (US 20190311814).
Regarding claim 7, Crepp teaches the system of claim 1. Crepp further teaches wherein the one or more processors are configured to execute the instructions to:
acquire biometric information about the user ([0040], “exercise (either steps or heart rate data) to patterns found in the user's weight as a function of time.” [0036], “activity levels can be tracked using readily available smart devices that employ accelerometers and/or heart rate monitors.”); and
calculate the estimation value using a training model, the medical examination information, the effort information, and the basic information ([0045], “Over time, as the users enter additional data (e.g., real time diet, exercise, and weight data) the system customizes the weight projection.” [0048], “One or more machine-learning and other techniques including, but not limited to, Levenberg-Marquardt, least squares, Markov Chain Monte Carlo, Bayesian analysis, back-propagation, deep learning, and other routines (both static and dynamic algorithms) may be implemented to minimize residuals between the users' predicted weight, based on diet and exercise, and their actual weight.”),
the training model being a model trained on a relationship between a change in a value of an examination item of a medical examination, a healthy action, and basic information ([0033], “The present system uses smart devices to track pulse or heart rate (e.g., phones, watches, and other wearables) and works in mass units instead of energy units. In some embodiments, the system uses empirical data to train artificial neural networks using data from individual users to quantify how each user's body responds to diet and exercise.” [0045], “Accordingly, the system is trained using nutrients and exercise level to model and predict a user's weight as a function of time… Over time, as the users enter additional data (e.g., real time diet, exercise, and weight data) the system customizes the weight projection.”).
Crepp does not teach the biometric information being measured by a measurement instrument installed in a store.
However, Kannan does teach the biometric information being measured by a measurement instrument installed in a store ([0056], “the cloud service system 300 may also provide the ability to input data from medical and health devices 308 (e.g., a heart-rate monitor, an at-home blood pressure device, etc.), as well as results from laboratory tests 310 (e.g., from conventional laboratories, at-home or in-pharmacy instruments, etc.).” [0101], “the system 100 may be designed to accept information coming from any medical sensor or monitoring device (e.g., heart rate monitor, blood pressure monitor, etc.).”).
Crepp in view of Kannan are considered analogous to the claimed invention because they are in the field of managing patient wellness. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Crepp with Kannan for the advantage of “accessing patient data from the medical and health devices 308 and the laboratory tests” (Kannan; [0056]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
System And Method For Decision Support Using Lifestyle Factors (US 20170220751) teaches systems and methods for relating to open loop decision-making for management of diabetes. People with diabetes face many problems in controlling their glucose because of the complex interactions between food, insulin, exercise, stress, activity, and other physiological and environmental conditions. Established principles of management of glucose sometimes are not adequate because there is a significant amount of variability in how different conditions impact different individuals and what actions might be effective for them. Accordingly, systems and methods according to present principles minimize the impact of the vagaries of diabetes on individuals, i.e., by looking for patterns and tendencies of an individual and customizing the management to that individual. Consequently, the same reduces the uncertainty that diabetes typically is associated with and improves quality of life.
Patient Data Management Systems And Conversational Interaction Methods (US 20180277246), which teaches infusion devices and related medical devices, patient data management systems, and methods for monitoring a physiological condition of a patient. An exemplary method of querying a database involves receiving an input query from a client device, identifying a logical layer of a plurality of different logical layers of the database for searching based at least in part on the input query, generating a query statement for searching the identified logical layer of the plurality of different logical layers of the database based at least in part on the input query, querying the identified logical layer of the database using the query statement to obtain result data, and providing a search result influenced by the result data.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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