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 action is in response to applicant arguments filled on 11/24/2025 for application 18/342208.
Claims 1, 2, 9, 15, 17, 18, and 20 have been amended.
Claims 3, 16, and 19 have been canceled.
Claims 1-2, 4-15, 17-18, and 20 are currently pending and have been examined.
Detailed Action
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 1-2, 4-15, 17-18, and 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.
Step 1:
Claims 1-2, 4-15, 17-18, and 20 are drawn to a method and system, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One:
Independent claims 1, 17, and 20 recite analyzing the collected data; generating a measure of the user’s sleep; determining a demographic of the user; and determining a correlation between the measure of the user’s data sleep measures of user sleep generated for other users associated with the demographic
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity by identifying and reporting events preceding a pattern in a set of user data. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea (Step 2A Prong One: YES).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including a computer, AI, and a device, which are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The claims recite the additional element of collecting data and altering how at least one of applications operates for the user, which is considered limitations directed to insignificant extra-solution activity that does not amount to an inventive concept because the limitation does not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, the claimed receiving limitations are incidental to the performance of the recited abstract idea of identifying and reporting events preceding a pattern in a set of user data. See: MPEP 2106.05(g); and the altering step is an extra solution activity.
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See: MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, and
Paragraph 18, where “For the purposes of this disclosure a non-transitory computer readable medium (or computer-readable storage medium/media) stores computer data, which data can include computer program code (or computer-executable instructions) that is executable by a computer, in machine readable form. By way of example, and not limitation, a computer readable medium may include computer readable storage media, for tangible or fixed storage of data, or communication media for transient interpretation of code-containing signals. Computer readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and non-volatile, removable and non-removable media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data. Computer readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, optical storage, cloud storage, magnetic storage devices, or any other physical or material medium which can be used to tangibly store the desired information or data or instructions and which can be accessed by a computer or processor.”
Paragraph 20, where “For the purposes of this disclosure a “network” should be understood to refer to a network that may couple devices so that communications may be exchanged, such as between a server and a client device or other types of devices, including between wireless devices coupled via a wireless network, for example. A network may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), a content delivery network (CDN) or other forms of computer or machine-readable media, for example. A network may include the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), wire‐line type connections, wireless type connections, cellular or any combination thereof. Likewise, sub‐networks, which may employ differing architectures or may be compliant or compatible with differing protocols, may interoperate within a larger network.”
Paragraph 24, where “For purposes of this disclosure, a client (or user, entity, subscriber or customer) device may include a computing device capable of sending or receiving signals, such as via a wired or a wireless network. A client device may, for example, include a desktop computer or a portable device, such as a cellular telephone, a smart phone, a display pager, a radio frequency (RF) device, an infrared (IR) device a Near Field Communication (NFC) device, a Personal Digital Assistant (PDA), a handheld computer, a tablet computer, a phablet, a laptop computer, a set top box, a wearable computer, smart watch, a smart ring, an integrated or distributed device combining various features, such as features of the forgoing devices, or the like.”
Paragraph 57, wherein “In some embodiments, sleep engine 200 may include a specific trained artificial intelligence / machine learning model (AI/ML), a particular machine learning model architecture, a particular machine learning model type (e.g., convolutional neural network (CNN), recurrent neural network (RNN), autoencoder, support vector machine (SVM), and the like), or any other suitable definition of a machine learning model or any suitable combination thereof.”
Paragraph 71, where “In Step 312, sleep engine 200 can output a correlation, determined for a user in Step 308, to the user (e.g., by communicating the correlation to the user over a network such as network 104). The correlation may be output to the user in a variety of ways. For example, the correlation can be displayed on a device of the user (e.g., in the form of an application notification, electronic message, and the like). In one such example, sleep engine 200 can generate a user interface display that includes the correlation and/or can trigger a user device of the user (e.g., operating within UE 102) to present the correlation (e.g., via the user interface display) via a display element of the user device. In some examples, the user interface display can be configured to present the correlation to the user as part of an explanation of how the user compares with other users who share a particular demographic with the user.”
Paragraph 26, wherein “For example, access points, devices (e.g., televisions, smart phones, and the like), lights and/or any other smart home feature can be engaged and/or toggled to different and/or modified modes upon the determination of a user’s sleep, which can assist the user from engaging in activity that may be detrimental to their known sleep habits.”
The claims recite the additional element of collecting data and altering applications, which amounts to extra-solution activity concerning mere data gathering and displaying. The specification (e.g., as excerpted above) does not provide any indication that the additional elements are anything other than well‐understood, routine, and conventional functions when claimed in a merely generic manner (as they are here). See: MPEP 2106.05(g).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim 7 teaches additional element of a training model and claim 13 teaches sensors for tracking patients which are additional elements that are recited at a high level of generality such that it amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f). Dependent claims 2, 4-6, 8-12, 14-15, and 18-19 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shouldice (US 2023/0173221 A1).
In claim 1, a computer-implemented method comprising:
Shouldice teaches:
collecting, by an application executing on a device associated with a user, data related to sleep activity of the user (Para. 220 wherein collected patient data to generate sleep reports is taught);
analyzing, by the application executing an artificial intelligence (AI) model, the collected data (Para. 115 and 263-265 wherein AI is used to analyze data);
generating, by the application, based on the AI model-based analysis, a measure of the user’s sleep; (Para. 113 and 266);
further analyzing, by the application a correlation between the measure of the user’s sleep and measures of user sleep generated for other users associated with the demographic (Para. 77 wherein “The system can consider the average rate (or overall rate graph) for other purposes such as comparing to themselves over time, or indeed to someone in a similar demographic”. See also Para. 126); and
communicating, over a network, the correlation to the user via a digital display presented via a display element of a user device (Para. 212); and
causing, over the network, via the application, modification to a digital environment of the user based communicated correlation, the modification altering how at least one applications and devices operate for the user (Para. 11 wherein “Aspects of the embodiment include the step of tracking an outcome of the adjusting of the one or more control parameters to validate an efficacy of the one or more models. Aspects of the embodiment include the step of updating the one or more models based on the outcome of the adjusting of the one or more control parameters to improve the one or more models with respect to optimizing the sleep of the user”… “A sound level could include an alarm, such as a “smart” alarm that is sleep stage- and/or sleep state-based, whereby the optimization is such as to predict a sleep stage during an alarm window and optionally making adjustments such that the user wakes with a reduced sleep inertia; for example, if a user if predicted to be in deep/SWS sleep during the anticipated alarm time, the actual alarm time may be adjusted within a window (e.g., such as a 15 or 30 min flexible alarm period) such that the user is woken from N1, N2, or REM (or if they are already awake, as a reminder to get up). The system could also act to nudge them from deep or REM to N2 prior to activating the alarm (particularly if a flexible alarm period is not desired”).
As per claim 2, Shouldice teaches the method of claim 1, wherein the demographic comprises at least one of: an age; a gender; a weight; a BMI score; an occupation; a level of education; a family status; an ethnicity; a location; or a culture (Para. 73).
As per claim 4, Shouldice teaches the method of claim 1, wherein the measure of the user’s sleep and the measures of user sleep generated for the other users comprise a measure of at least one of sleep latency, total sleep duration, deep sleep duration, light sleep duration, sleep restlessness, or nighttime awakenings (Para. 242).
As per claim 5, Shouldice teaches the method of claim 1, further comprising:
identifying a subset of the users associated with the demographic whose measure of user sleep falls below a threshold value (para. 173, 176, 219, 281 wherein users can be compared to determine if thresholds of sleep data is met);
receiving, as an output from a trained model, a feature associated with the users within the subset of users whose measure of user sleep falls below the threshold value; and
communicating, over the network, the feature to the user via the digital display as part of a suggestion for improving the measure of the user’s sleep (Para. 11 wherein “ Aspects of the embodiment include the step of updating the one or more models based on the outcome of the adjusting of the one or more control parameters to improve the one or more models with respect to optimizing the sleep of the user. Aspects of the embodiment include the step of monitoring the one or more user parameters, the respiratory therapy system, the environment of the user, or a combination thereof to determine whether one or more events occur that satisfy a sleep disturbance threshold”).
As per claim 6, Shouldice teaches the method of claim 5, wherein communicating the feature to the user comprises communicating the feature in response to at least one of:
determining that the measure of the user’s sleep falls below the threshold value (Para. 11); and
determining that the user is associated with the feature (Para. 11).
As per claim 7, Shouldice teaches the method of claim 5, further comprising:
for each particular user within the subset of users whose measure of user sleep falls below the threshold value, identifying one or more devices within a location associated the particular user (Para. 11 and 71);
collecting data from the identified devices (Para. 11 and 71); and
inputting the data collected from the identified devices to the trained model, wherein the feature is generated by the trained model as the output based on the inputted data (Para. 11 and 71).
As per claim 8, Shouldice teaches the method of claim 5, wherein the feature comprises an environmental feature (Para. 76).
As per claim 9, Shouldice teaches the method of claim 8, wherein the environmental feature comprises at least one of: a geographic area associated with the user; an altitude of a geographic area associated with the user; a measure of pollution at a geographic area associated with the user; and a population size of a geographic area associated with the user (Para. 227).
As per claim 10, Shouldice teaches the method of claim 5, wherein the feature comprises a pattern of activity (Para. 241).
As per claim 11, Shouldice teaches the method of claim 10, wherein the pattern of activity comprises at least one of: a pattern of screen usage; and a pattern of movement (Para. 241).
As per claim 12, Shouldice teaches the method of claim 10, wherein the pattern of activity is determined for each particular user within the subset in response to:
identifying a set of devices associated with a location of the particular user (Para. 251);
collecting and analyzing data from each of the set of devices (Para. 251); and
in response to analyzing the data from each of the set of devices, determining the pattern of activity for the particular user (Para. 251).
As per claim 13, Shouldice teaches the method of claim 12, wherein the set of devices corresponds to devices comprising sensor capabilities for tracking movements and information about the particular user at the location (Para. 251).
As per claim 14, Shouldice teaches the method of claim 5, wherein the feature comprises a biometric feature (Para. 72).
As per claim 15, Shouldice teaches the method of claim 14, wherein the biometric feature comprises at least one of: a weight of the user; a blood oxygen level of the user; a temperature of the user; a heartrate of the user; or a blood pressure of the user (Para. 145).
Claims 17-18 and 20 recite substantially similar limitations as seen above and hence are rejected for similar rationale as noted above.
Response to Augments
Applicant's arguments with respect to the art and 101 rejection above have been considered but are moot in view of the new ground(s) of rejection. It is respectfully submitted that the Examiner has applied new passages and citations to amended claims at the present time. The Examiner notes that amended limitations were not in the previously pending claims as such, Applicant's remarks with regard to the application of all applied references to the amended limitations are addressed in the above Office Action.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAROUN P KANAAN whose telephone number is (571)270-1497. The examiner can normally be reached Monday-Friday 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MAROUN P. KANAAN
Primary Examiner
Art Unit 3687
/MAROUN P KANAAN/Primary Examiner, Art Unit 3687