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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 3, 2025, has been entered.
Response to Amendment
Claims 1, 4, 5, 10, 11, 14-16, 19, and 20 have been amended. Claims 3, 7-9, 17, and 18 have been canceled. Claim 22 is new. Claims 1, 2, 4-6, 10-16, and 19-22 and pending and are provided to be examined upon their merits.
Response to Arguments
Applicant's arguments filed September 3, 2025 have been fully considered but they are not persuasive. A response is provided below in bold where appropriate.
Applicant argues 35 USC §101 Rejection, starting pg. 13 of Remarks:
REJECTIONS UNDER 35 U.S.C. § 101
It was alleged at pages 5, 12, and 13 of the Office Action that ““Feedback’” is not in the independent claims. Further, the above is using computers to do what computers do. Re-train is claimed at a high level of generality ... [c]laims 1, 2, 4-6, 10-16, and 18- 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more ... limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity.” (emphasis added).
Regarding Prong One of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance (Step 2A-Prong 1): the features of amended independent claim 1 recites “control, based on the selected first health template, one or more sensors associated with the electronic device to determine a second set of activities of the user: determine at least one feedback based on the determined deviation, wherein the determined at least one feedback is associated with one of the selected first health template or the received set of health templates; augment a first training dataset associated with the stored first Al model, based on the selected first health template, the received set of health templates, and the received user profile information; augment a second training dataset associated with the stored first Al model, based on the selected first health template, the determined at least one feedback, and the received user profile information; update a plurality of weights and a plurality of regularization parameters of the stored first Al model based on a plurality of combinations of the augmented first training dataset and the augmented second training dataset; re-train the stored first Al model based on the updated plurality of weights and the updated plurality of regularization parameters; apply the re-trained first Al model on the first set of activities and the set of health recommendations of the selected first health template.” (emphasis added).
Accordingly, the Applicant respectfully submits that the claimed subject matter inextricably requires physical object such as “circuitry” to augment multiple training datasets of an artificial intelligence (Al) model (stored in a memory), update the weights and parameters of the Al model based on a plurality of combinations of the augmented training data sets, and further to re-train the Al model based on the updated parameters and weights which are also not interpreted as a method for organizing human activity or mental process. Therefore, the features of amended independent claim 1 do not describe an abstract concept, or a concept similar to those found by the Courts to be Abstract, such as a method for organizing human activity or mental process.
The above “control, based on the selected first health template, one or more sensors associated with the electronic device to determine a second set of activities of the use…” could not be found in the specification. Regarding using AI models, the AI models are recited at a high level of generality.
Regarding Prong Two of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance, even if one were to arrive at a conclusion satisfying the Prong One of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits the alleged abstract idea is integrated into a practical implementation.
The Applicant's Specification describes, for example, that “[a]dvancements in technology have led to a proliferation of various electronic devices (such as, smart phones, smartwatches, activity trackers, and the like) with embedded sensors for fitness tracking ... [f]urther, the first electronic device 102 may control rendering of health information (such as, a health dashboard) including statistical information associated with the health of the first user 118. The statistical information may include the recommendations, the determined set of activities, and/or the determined health parameters of the first user 118. The health information may serve as a consolidated cross-functional dashboard including insights that may help to track the health of the first user 118 and also to ascertain a progress of the first user 118 with respect to the desired health goals of the first user 118. Also, the first user 118 may be spared from the effort associated with search of several different software applications and health-related devices for health tracking, as the first electronic device 102 may provide a holistic health tracking experience to the first user 118 ... [g]enerally, a user may employ a dedicated software application associated with such electronic devices for a particular health goal. For example, a first software application may be employed to manage diet and workout plans, a second software application may be employed to track a sleep cycle, and a third software application may be employed to track water intake and other activities. In such a scenario, the user may have to spend some time to search for all such different software applications for different health goals and may have to individually configure each software application for fitness tracking ... [i]n a re-training phase, the first electronic device 102 may re-train the first Al model 108 by use of training data including different combinations of the augmented first training dataset and the augmented second training dataset. The re-training of the first Al model 108 may include an update of a set of parameters of the first Al model 108 based on whether an output of the final layer for a given input (from the training data) matches a correct result based on a loss function for the first Al model 108. For example, the set of parameters may include weights, regularization parameters, and the like ... [f]or example, based on the re-training of the first Al model 108, one or more activities or health-related recommendations in the selected first health template 512A may be updated based on the one or more feedbacks 512B (i.e. deviations indicated in Tables 1-5). At 514, the second Al model 110 may be re-trained at the server 104. In an embodiment, the server 104 may be configured to re-train the second Al model 110. In an embodiment, from the first electronic device 102, the server 104 may receive the selection of the first health template 512A. Further, the first electronic device 102 may be configured to transmit the second user input corresponding to the one or more feedbacks 512B to the server 104 which is trained on the second Al model 110 configured to determine the set of health templates (i.e. described at 506). In other words, from the first electronic device 102, the server 104 may receive information indicative of the one or more feedbacks 512B associated with the determined set of health templates or associated with the selected first health template. The server 104 may re-train the second Al model 110 at least based on the received information indicative of the one or more feedbacks 512B or based on the selection of the first health template 512A. For example, the server 104 may augment a training dataset (for example a third training dataset) associated with the second Al model 110 based on at least one of the received selection of the first health template 512A, the set of health templates, and the user profile information associated with the first user 118. Further, the server 104 may augment a fourth training dataset associated with the second Al model 110 based on at least one of the received one or more feedbacks 512B, the selected first health template, and the user profile information associated with thefirst user118. In a _ re-training phase, the server 104 may re-train the second Al model 110 by use of training data including different combinations of the third training dataset and the fourth training dataset. The re- training of the second Al model 110 may be similar to the re-training of the first Al model 108, described further, for example, at 512 ... [tlhe real-time generation and customization of the plurality of health templates based on the usage of the personalized health templates by large number users and based on their corresponding feedbacks, Page 17 of may indicate a real-time course correction performed by the server 104 (or by the first electronic device 102) in the plurality of health templates.” See at Jf] [0003] and [0084- 86] of the Specification, as originally filed (emphasis added).
Therefore, the claimed features provide technological improvements in a functionality of an electronic device that implements personalized health agent. Accordingly, the Applicant has shown teachings in the Specification that describes a practical implementation and how functionality of “an electronic device that implements personalized health agent” is improved by implementing functionality of several different software and dynamically updating weights and parameters artificial intelligence model that results in dynamic update of health templates for the user. Therefore, the Applicant has established a clear nexus between the claim language and the practical implementation of the alleged judicial exception, and improvements to the technology.
Respectfully, using existing technology for a health assistant is applying such technology to an abstract idea of managing personal behavior.
Therefore, the Applicant respectfully submits that amended independent claim 1 recite patent eligible subject matter.
Further, the Applicant respectfully submits that amended independent claims 16 and 20 recite features similar to amended independent claim 1 and, are therefore, patent eligible for reasons similar to those presented above with respect to amended independent claim 1. Further, dependent claims 2, 4-6, 10-15, 19, and 21 recite patent eligible subject matter based at least on the dependence on amended independent claims 1 or 16.
Therefore, the Applicant respectfully requests that the rejections of claims 1, 2, 4- 6, 10-16, and 19-21 under 35 U.S.C. § 101 be withdrawn.
The rejection is respectfully maintained but modified for the claim amendments.
Applicant argues 35 USC §112 Rejection, pg. 19 of Remarks:
REJECTIONS UNDER 35 U.S.C. § 112, SECOND PARAGRAPH
Claims 1 and 16 have been amended, as set forth above. In view of the amendments, the Applicant respectfully requests that the rejection of the claims 1, 2, 4- 6, 10-16, and 19-21 under 35 U.S.C. § 112, second paragraph, be withdrawn.
The rejection is modified for control sensors based on a template. The rejection to claim 16 regarding scope is withdrawn based on the amendment.
EXAMINER REQUEST It was alleged in the Office action that: The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1 paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
See the Office Action at page 18.
In response to the above request, the Applicant respectfully submits that support for amendments to the claims, filed on April 30, 2025, can be found at least, for example at ¶¶ [0029], [0049], [0077-0085], and [0095] of the Specification, as originally filed
Noted. However, “control, based on the selected first health template, one or more sensors…” where sensors are controlled by a template could not be found in the specification or above cited paragraphs.
From the above paragraphs:
[0029] The server 104 may include suitable logic, circuitry, and interfaces, and/or code that may be configured to store a plurality of health templates and a trained artificial
intelligence (Al) model (such as, the second Al model 110). The server 104 may be
configured to determine the set of health templates from the stored plurality of health
templates based on an application of the second Al model 110 on the user profile
information received from the first electronic device 102. The server 104 may further
transmit the determined set of health templates to the first electronic device 102. Each of the determined set of health templates may indicate the set of activities and the set of
health recommendations for the first user 118 of the first electronic device 102. In certain embodiments, the server 104 may receive the selected first health template and
periodically determined values of the set of health parameters and/or the set of activities
of the first user 118 from the first electronic device 102. The server 104 may apply the
second Al model 110 on the received first health template and at least one of the received set of health parameters and/or the set of activities of the first user 118 to determine health recommendations for the first user 118. The server 104 may further transmit the determined health recommendations to the first electronic device 102, for display on the display device 116.
The above paragraph does not teach control or sensor.
[0049] FIG. 2 is a block diagram that illustrates an exemplary first electronic device of
FIG. 1, in accordance with an embodiment of the disclosure. FIG. 2 is explained in
conjunction with elements from FIG. 1. With reference to FIG. 2, there is shown the first
electronic device 102. The first electronic device 102 may include circuitry 202, a memory 204, an input/output (I/0) device 206, a network interface 208, and the one or more sensors 114. The 1/0 device 206 may include the display device 116. The memory 204 may include the first artificial intelligence (Al) model 108. The network interface 208 may connect the first electronic device 102 with the server 104 and the database 106, via the communication network 112.
The above paragraph does not teach control a sensor using a template.
[0077] In certain embodiments, the first electronic device 102 may be configured to
determine the one or more feedbacks 512B based on a determination of a set of activities and a set of health parameters associated with the first user 118, and the selected first health template. The determination of the set of activities and the set of health parameters associated with the first user 118, is described, for example, at 522 in FIG. 5A. The first electronic device 102 may determine a deviation of the determined set of activities or the determined set of health parameters from a certain health goal in the selected first health template. The one or more feedbacks 512B may be determined based on the determined deviation of the determined set of activities or the determined set of health parameters from the particular health goals in the selected first health template. An example of a planned calorie consumption (based on the selected first health template) versus an actual calorie consumption of the first user 118 is provided in Table 1, as follows:
PNG
media_image1.png
186
642
media_image1.png
Greyscale
[0078] In another example, planned exercise and water intake activities (based on the
selected first health template) versus actual exercise and water intake activities of the first user 118 are provided in Table 2, as follows:
PNG
media_image2.png
184
640
media_image2.png
Greyscale
[0079] In yet another example, a planned eye break activity (based on the selected first
health template) versus an actual eye break activity of the first user 118 is provided in
Table 3, as follows:
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media_image3.png
452
646
media_image3.png
Greyscale
[0080] In yet another example, a planned stretch break activity (based on the selected
first health template) versus an actual stretch break activity of the first user 118 is provided in Table 4, as follows:
PNG
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192
646
media_image4.png
Greyscale
[0081] An example of the one or more feedbacks 512B determined as a deviation of
the monitored activities of the first user 118 from the recommended (i.e., planned)
activities for the first user 118 (as per the selected first health template) is presented in
Table 5, as follows:
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778
654
media_image5.png
Greyscale
[0082] With reference to the table 5, the one or more feedbacks 512B may indicate that
the time for exercise for the first user 118 may require reduction by 20 minutes from 60
minutes to 40 minutes and the calorie consumption may require increasing by 500 kcal
from 2100 kcal to 2600 kcal. Further, the one or more feedbacks 512B may indicate that
the eye break may require reduction by 2 minutes, the stretch break may require
increasing by 2 minutes, and the water intake may require decreasing by 200 ml. It should be noted that data provided in the Tables 1, 2, 3, 4, and 5 may merely be taken as experimental data for exemplary purpose and may not be construed as limiting the
present disclosure.
[0083] In an embodiment, the first electronic device 102 may augment a first training
dataset associated with the first Al model 108 based on at least one of the selection of
the first health template (for example, a selection of the first health template 512A
determined based on the first user input 51 0A), the received set of health templates, and the user profile information associated with the first user 118. The first electronic device 102 may augment a second training dataset associated with the first Al model 108 based on at least one of the selection of the first health template 512A, the one or more feedbacks 512B (as per Table 5), and the user profile information associated with the first user 118.
[0084] In a re-training phase, the first electronic device 102 may re-train the first Al
model 108 by use of training data including different combinations of the augmented first training dataset and the augmented second training dataset. The re-training of the first Al model 108 may include an update of a set of parameters of the first Al model 108 based on whether an output of the final layer for a given input (from the training data) matches a correct result based on a loss function for the first Al model 108. For example, the set of parameters may include weights, regularization parameters, and the like. The above process may be repeated for the same or a different input from the training data till a minima of loss function may be achieved and a training error may be minimized. Several methods for training are known in art, for example, gradient descent, stochastic gradient descent, batch gradient descent, gradient boost, meta-heuristics, and the like. For example, based on the re-training of the first Al model 108, one or more activities or health-related recommendations in the selected first health template 512A may be updated based on the one or more feedbacks 512B (i.e. deviations indicated in Tables 1-5).
[0085] At 514, the second Al model 110 may be re-trained at the server 104. In an
embodiment, the server 104 may be configured to re-train the second Al model 110. In
an embodiment, from the first electronic device 102, the server 104 may receive the
selection of the first health template 512A. Further, the first electronic device 102 may be configured to transmit the second user input corresponding to the one or more feedbacks 512B to the server 104 which is trained on the second Al model 110 configured to determine the set of health templates (i.e. described at 506). In other words, from the first electronic device 102, the server 104 may receive information indicative of the one or more feedbacks 512B associated with the determined set of health templates or associated with the selected first health template. The server 104 may re-train the second Al model 110 at least based on the received information indicative of the one or more feedbacks 512B or based on the selection of the first health template 512A. For example, the server 104 may augment a training dataset (for example a third training dataset) associated with the second Al model 110 based on at least one of the received selection of the first health template 512A, the set of health templates, and the user profile information associated with the first user 118. Further, the server 104 may augment a fourth training dataset associated with the second Al model 110 based on at least one of the received one or more feedbacks 512B, the selected first health template, and the user profile information associated with the first user 118. In a re-training phase, the server 104 may re-train the second Al model 110 by use of training data including different combinations of the third training dataset and the fourth training dataset. The re-training of the second Al model 110 may be similar to the re-training of the first Al model 108, described further, for example, at 512.
[0095] In certain embodiments, the server 104 may be configured to determine the one or more feedbacks 512B based on the received information about the determined set of activities and/or the set of health parameters associated with the first user 118, and based on the selected first health template. The determination of the set of activities and the set of health parameters associated with the first user 118, is described, for example, at 522 in FIG. 5A. The server 104 may determine the deviations of the determined set of activities or the determined set of health parameters from a certain health goal in the selected first health template. The one or more feedbacks 512B may be determined based on the determined deviations of the determined set of activities or the determined set of health parameters from the particular health goals indicated in the selected first health template. Examples of the one or more feedbacks 512B are provided, for example, at 512 in FIG. 5A. The second Al model 110 in the server 104 may be re-trained based on the one or more feedbacks 512B, as described, for example, at 514 in FIG. 5A.
The above paragraphs [0077]-[0085] and [0095] do not teach control or sensor.
A rejection is provided on control sensors based on a template as this feature cannot be found in the specification.
Applicant has amended Claim 16 to remove the scope issue with selects therefore that rejection is withdrawn.
Applicant argues 35 USC §103 Rejection, pg. 19 of Remarks:
Based on the claim amendments and further search and consideration, the prior art rejection is withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful proc ess, 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, 2, 4-6, 10-16, and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 2, 4-6, 10-16, and 19-22 are directed to a system or method, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified system Claim 16 and method Claim 20 as the claims that represents the claimed invention for analysis and are similar to system claim 1.
Claim 16 recites the limitations of:
A server, comprising:
a memory configured to store a first plurality of health templates and a trained artificial intelligence (Al) model; and
circuitry configured to:
receive user profile information associated with a user from an electronic device via a network:
apply the stored Al model on the received user profile information associated with the user;
determine a set of health templates from the stored first plurality of health templates, based on the application of the stored Al model on the received user profile information associated with the user;
transmit the determined set of health templates to the electronic device associated with the user;
receive, from the electronic device, information about a set of health parameters of the user and a first set of activities of the user, wherein
the information is transmitted by the electronic device based on a first health template from the set of health templates selected by the electronic device, and
the information about the set of health parameters of the user and the first set of activities of the user is obtained from one or more sensors associated with the electronic device;
receive the first health template from the electronic device, wherein the received first health template indicates a second set of activities and a set of health recommendations;
determine a deviation of the first set of activities from the second set of activities;
determine at least one feedback based on the determined deviation, wherein the determined at least one feedback is associated with one of the transmitted set of health templates or the first health template selected by the electronic device;
augment a first training dataset associated with the stored Al model, based on the first health template, the transmitted set of health templates, and the received user profile information;
augment a second training dataset associated with the stored Al model, based on the first health template, the determined at least one feedback, and the received user profile information;
update a plurality of weights and a plurality of regularization parameters of the stored Al model based on a plurality of combinations of the augmented first training dataset and the augmented second training dataset;
re-train the stored Al model based on the updated plurality of weights and the updated plurality of regularization parameters; and
generate a second plurality of health templates based on an application of the re-trained Al model.
Claim 20 recites the limitations of:
A method, comprising:
in an electronic device that includes an Artificial Intelligence (Al) model:
receiving user profile information associated with a user of the electronic device;
transmitting the received user profile information to a server via a network;
receiving, from the server, a set of health templates based on the transmitted user profile information associated with the user;
selecting a first health template from the received set of health templates, wherein the selected first health template indicates a first set of activities and a set of health recommendations;
controlling, based on the selected first health template, one or more sensors associated with the electronic device to determine a second set of activities of the user;
determining a deviation of the determined second set of activities from the first set of activities;
determining at least one feedback based on the determined deviation, wherein the determined at least one feedback is associated with one of the received set of health templates or the selected first health template;
augmenting a first training dataset associated with the Al model, based on the selected first health template, the received set of health templates, and the received user profile information;
augmenting a second training dataset associated with the Al model, based on the selected first health template, the determined at least one feedback, and the received user profile information;
updating a plurality of weights and a plurality of regularization parameters of the Al model based on a plurality of combinations of the augmented first training dataset and the augmented second training dataset;
re-training the Al model based on the updated plurality of weights and the updated plurality of regularization parameters;
applying the re-trained Al model on the first set of activities and the set of health recommendations of the selected first health template;
updating the first set of activities and the set of health recommendations of the selected first health template based on the application of the re-trained Al model; and
controlling a display device associated with the electronic device to render health information indicating the updated first set of activities and the updated set of health recommendations.
The above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, in non-bold above, which covers performance of the limitation as managing personal behavior. The claims recite managing personal behavior by receiving user profile information associated with a user, transmitting the received user profile information, receiving, a set of health templates based on the transmitted user profile information associated with the user, selecting a first health template from the received set of health templates, wherein the selected first health template indicates a first set of activities and a set of health recommendations, determine a second set of activities of the user, determining a deviation of the determined second set of activities from the first set of activities, determining at least one feedback based on the determined deviation, wherein the determined at least one feedback is associated with one of the received set of health templates or the selected first health template, augmenting a first training dataset based on the selected first health template, the received set of health templates, and the received user profile information, augmenting a second training dataset based on the selected first health template, the determined at least one feedback, and the received user profile information, updating a plurality of weights and a plurality of regularization parameters based on a plurality of combinations of the augmented first training dataset and the augmented second training dataset; updating the first set of activities and the set of health recommendations of the selected first health template, and render health information indicating the updated first set of activities and the updated set of health recommendations. The above steps are following rules or instructions (receiving user profile information, selecting a first health template, determine a second set of activities of a user, etc.) and teaching (health recommendations). Also, diagnosing or determining a patient’s health status falls under the abstract concept of managing personal behaviors of people. It is important to note that the examples provided by the MPEP such as social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that MPEP 2106.04(a)(2) II stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing personal behavior, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claim 1 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite: electronic device, memory, circuitry, server, network, sensors, display device (Claim 1); server, memory, circuitry, electronic device, sensors (Claim 16); electronic device, server, network, sensors, display device (Claim 20). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The electronic device includes an AI model, which is a generic model applied at a high level of generality. The sensors are also generic devices for receiving information and claimed at a high level of generality (see para. [0003] of the specification). Controlling sensor based on a health template is claimed at a high level of generality. The re-training the trained AI model is using known methods, applied at a high level of generality (see para. [0035] and [0084] where several methods are known in the art). Controlling a display is taught and claimed at a high level of generality. Claim 20 does not provide any details about how retraining the AI model operates or how updating based on application of the re-trained AI model is made. The plain meaning of “updating” encompasses mental observations or evaluations. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 16, and 20 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as receiving and transmitting are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 16, and 20 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2, 4-6, 10-15, 19, 21 and 22 further define the abstract idea that is present in their respective independent claims 1, 16, and 20 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claims 10, 11, and 19 further recite first electronic device, which is just applying the device to abstract concepts. Claim 22 recites control sensors based on a health template which is recited at a high level of generality. Therefore, the claims 2, 4-6, 10-15, 18, 19, 21 and 22 are directed to an abstract idea. Thus, the claims 1, 2, 4-6, 10-16, and 19-22 are not patent-eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 2, 4-6, 10-15, and 20-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 20 recites “controlling, based on the selected first health template, one or more sensors associated with the electronic device to determine a second set of activities of the user” where no teaching of controlling sensors based on a health template can be found in the specification.
The specification teaches:
“Based on the selected first health template, the first electronic device may be configured to determine, by one or more sensors associated with the first electronic device, at least one of a set of health parameters of the first user or a set of activities of the first user. For example, the set of health parameters may include, but is not limited to, a body temperature, a heart rate, a pulse rate, a blood oxygen level, a blood pressure, a blood sugar level, a stress level, a sleep duration, or a depth of sleep. Examples of the set of activities may include, but are not limited to, a water intake, a food intake activity, a sleep, a step count, a meditation activity, a yoga activity, a physical exercise, a breathing exercise, a stretching exercise, a sedentary task, a walk, a run, a jog, a cycling activity, a swimming activity, a work-out activity, or a listening to music activity.” [0019]
Therefore, the electronic device may be configured to determine by sensors health parameters based on the selected first health template. So, for example, a heart rate sensor causes electronic device to determine health parameter (heart rate) based on selected health template (measure heart rate during running exercise). This is not the same as a health template (measure heart rate during running exercise) controlling a heart rate sensor. Claims 1 and 22 have a similar problem.
Claims 2, 4-6, 10-15, 21, and 22 are further rejected as they depend from their respective independent claim.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Prior Art Search
A prior art search was conducted but does not result in a prior art rejection at this time. The closest prior art found to date is Pub. No. US 2022/0384052 to Gnanasambandam et al. Gnanasambandam teaches healthcare and templates. But does not teach augmenting first and second datasets as claimed and regularization.
Conclusion
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684