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 reply to the application filed on 10/30/2023.
Claims 1-39 are currently pending and have been examined.
Claim Objections
Claim 6 is objected to because of the following informalities: The claim should end with a period(.) . Appropriate correction is required.
Claim 24 is objected to because of the following informalities: The claim should end with a period(.) . Appropriate correction is required.
Claim Rejections – 35 § 112(b)
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 1-39 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, recites in part, “calculating a health value for the user using one more health data parameters based on the received health data, wherein the health data parameters are selected and applied according to the first prioritization for the data”. It is unclear how a health value for the user using one more health data parameters based on the received health data, wherein the health data parameters are selected and applied according to the first prioritization for the data, is being calculated. Is there an algorithm or formula that is being used to calculate calculating a health value for the user using one more health data parameters based on the received health data, wherein the health data parameters are selected and applied according to the first prioritization for the data? Claims 19 and 39 recite similar limitations. Claims 1, 19 and 39 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-18 and 20-38 are rejected for at least the same reason.
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 1-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-39: Step 1
Claims 1-18 are drawn to a method for persistent health data collection and multi-level prioritization, which is within the four statutory categories (i.e. process). Claims 19-38 are drawn to a system for prioritization, which is within the four statutory categories (i.e. machine). Claim 39 is drawn to a non-transitory computer readable medium comprising instructions executed by at least one hardware processor, which is within the four statutory categories (i.e. machine).
Claims 1-39: Step 2A Prong One
Claim 1 recites providing at least one graphical user interface to a user that is configured to receive user input for one or more of a device selection, a device prioritization, and a data prioritization; creating, using application program interfaces (APIs), persistent connections to collect health data from one or more devices, wherein a first prioritization for the connected devices is stored based on user input received through the at least one graphical user interface, the first prioritization for the connected devices defining an order for collecting health data from the connected devices; storing, based on further user input received through the at least one graphical user interface, a first prioritization for the data, the first prioritization for the data defining an order among the health data from the connected devices for selecting and applying one or more health data parameters; receiving, using the persistent connections, health data from the devices in accordance with the first prioritization for the devices; and calculating a health value for the user using one more health data parameters based on the received health data, wherein the health data parameters are selected and applied according to the first prioritization for the data. Claims 19 and 39 recite similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by manually following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. But for the recitation of generic computer components, these limitations encompass a user receiving input for one or more of a device selection, a device prioritization, and a data prioritization; creating persistent connections to collect health data from one or more devices, wherein a first prioritization for the connected devices is stored based on user input received, the first prioritization for the connected devices defining an order for collecting health data from the connected devices; storing, based on further user input received, a first prioritization for the data, the first prioritization for the data defining an order among the health data from the connected devices for selecting and applying one or more health data parameters; receiving, using the persistent connections, health data from the devices in accordance with the first prioritization for the devices; and calculating a health value for the user using one more health data parameters based on the received health data, wherein the health data parameters are selected and applied according to the first prioritization for the data. These steps could be carried out manually by a user following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Claims 19 and 39 recite similar limitations.
Claims 2-18 and 20-38 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, Claims 2 and 20 further define periodically collecting the health data. Claims 3, 4, 21 and 22 further define collecting health data in response to a triggering event. Claims 5, 6, 23 and 24 further define the triggering event. Claims 7, 9, 25 and 28 further define receiving user input. Claims 8 and 26 further define the first prioritization for the data. Claims 10 and 27 further define the health data includes user data and health data parameters. Claims 11 and 29 further define the first prioritization. Claims 12, 13, 30 and 31 further define storing a second prioritization. Claims 14, 15, 32 and 33 further define parsing the received health data. Claims 16, 17, 34 and 35 further define generating a health or wellness recommendation. Claims 18 and 36 further define the health or wellness recommendation. Claim 37 further defines the health value. Claim 38 further defines configuring a predictive model. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-39: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with insignificant, extra-solution data gathering activity, and adding limitations similar to adding the words “apply it” to the abstract idea. Claim 1 recites the additional elements that the computer-implemented method steps are performed by at least one processor. Claim 19 recites additional elements of a computer-implemented system comprising at least one processor. Claim 39 recites additional elements of non-transitory computer readable medium comprising instructions executed by at least one hardware processor.
Claims 1-39, directly or indirectly, recite the following generic computer components: “a processor,” “computer-implemented system comprising at least one processor,” and “non-transitory computer readable medium comprising instructions executed by at least one hardware processor” which are similar to adding the words “apply it” to the abstract idea. The written description discloses that the recited computer components encompass generic components including “Some embodiments may involve at least one processor. A processor may be any physical device or group of devices having electric circuitry that performs a logic operation on input or inputs. For example, the at least one processor may include one or more integrated circuits (IC), including application-specific integrated circuit (ASIC), microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field- programmable gate array (FPGA), server, virtual server, or other circuits suitable for executing instructions or performing logic operations“ (see at least Paragraph [0047]). Although the additional element “machine learning model” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning), and thus fails to add an inventive concept to the claims. See MPEP 2106.05 (h). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-39: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (for example, machine learning) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). As explained above, the generic computer components and machine learning are at best the equivalent of merely adding the words “apply it” to the judicial exception.
Receiving and transmitting data over a network (i.e. receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Therefore, the high-level recitation of an output of results also fails to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
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 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)(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.
(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.
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.
Claims 1-3, 6-15, 19-21, 24-33 and 39 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Keen et al. (US Patent Application Publication US 2015/0347690 A1).
Claim 1:
Keen discloses the following limitations as shown below:
providing at least one graphical user interface to a user that is configured to receive user input for one or more of a device selection, a device prioritization, and a data prioritization (at least Paragraph 4, The computer system may receive priority information identifying a priority of the first source and the second source; Paragraph 29, However, as noted, health information of a user may be considered extremely personal and/or confidential; thus, the user may be given the ability to protect or otherwise not share some of the heath information with some, any, or all of the third-party applications (even the ones that provided the health information to the first-party process initially); Paragraph 33, In this case, the user may be enabled to provide priority information to the process such that the process is able to aggregate the step information from the two sources in a meaningful way. In some cases, the priority information may identify which data (e.g., from which source) to use when there are multiple data entries for the same point in time. In some examples, the source with the highest priority may be rendered when there is overlap in cumulative data (e.g., steps walked are cumulative because they can be summed to a total); Paragraph 36, the user may be able to enter additional data, update the data, include incorrect data, or otherwise configure the information; Paragraph 45, The user device 102 may also include I/O device(s) 330, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, a display, speakers, etc.);
creating, using application program interfaces (APIs), persistent connections to collect health data from one or more devices, wherein a first prioritization for the connected devices is stored based on user input received through the at least one graphical user interface, the first prioritization for the connected devices defining an order for collecting health data from the connected devices (see at least Paragraph 3, Embodiments of the present disclosure can provide systems, methods, and computer-readable medium for managing user information (e.g., personal information collected by one or more external devices). In some examples, a data interchange may be managed that enables and/or allows third-party applications to provide user information to the data interchange and potentially retrieve user information provided by other third-party applications. While managed, the data interchange may be configured to provide access to particular data types of the user data based at least in part on authorization from the user associated with the data; Paragraph 4, a method may be executed by a computer system to at least identify a particular data type of a plurality of data types to manage. The method may also cause the computer system to receive health data corresponding to the particular data type from at least a first source and a second source of a plurality of data sources. The computer system may receive priority information identifying a priority of the first source and the second source. The method may also cause the computer system to identify a time interval for partitioning the health data by the plurality of data sources. In some examples, the computer system may identify a data entry for the particular data type with a highest identified priority when the data entry exists in a data store configured to maintain the received health data corresponding to the particular data type during each identified time interval over an amount of time. The computer system may also aggregate each identified data entry to form an aggregated record for the particular data type over the amount of time; Paragraph 28, Additionally, in some non-limiting examples, the information being collected and/or managed may be health, fitness, and/or activity information of the user (e.g., blood glucose levels, weight, height, calories burned, heart rate, etc.). The user information may be categorized or otherwise identified by one or more data types (or categories); Paragraph 32, In some examples, the plug-ins may be implemented as code that can read application programming interface (API) method calls with identifiers and/or strings associated with the data types);
storing, based on further user input received through the at least one graphical user interface, a first prioritization for the data, the first prioritization for the data defining an order among the health data from the connected devices for selecting and applying one or more health data parameters (see at least Paragraph 3, Embodiments of the present disclosure can provide systems, methods, and computer-readable medium for managing user information (e.g., personal information collected by one or more external devices). In some examples, a data interchange may be managed that enables and/or allows third-party applications to provide user information to the data interchange and potentially retrieve user information provided by other third-party applications. While managed, the data interchange may be configured to provide access to particular data types of the user data based at least in part on authorization from the user associated with the data; Paragraph 4, a method may be executed by a computer system to at least identify a particular data type of a plurality of data types to manage. The method may also cause the computer system to receive health data corresponding to the particular data type from at least a first source and a second source of a plurality of data sources. The computer system may receive priority information identifying a priority of the first source and the second source. The method may also cause the computer system to identify a time interval for partitioning the health data by the plurality of data sources. In some examples, the computer system may identify a data entry for the particular data type with a highest identified priority when the data entry exists in a data store configured to maintain the received health data corresponding to the particular data type during each identified time interval over an amount of time. The computer system may also aggregate each identified data entry to form an aggregated record for the particular data type over the amount of time; Paragraph 28, Additionally, in some non-limiting examples, the information being collected and/or managed may be health, fitness, and/or activity information of the user (e.g., blood glucose levels, weight, height, calories burned, heart rate, etc.). The user information may be categorized or otherwise identified by one or more data types (or categories); Paragraph 33, In this case, the user may be enabled to provide priority information to the process such that the process is able to aggregate the step information from the two sources in a meaningful way. In some cases, the priority information may identify which data (e.g., from which source) to use when there are multiple data entries for the same point in time. In some examples, the source with the highest priority may be rendered when there is overlap in cumulative data (e.g., steps walked are cumulative because they can be summed to a total));
receiving, using the persistent connections, health data from the devices in accordance with the first prioritization for the devices (see at least Paragraph 3, Embodiments of the present disclosure can provide systems, methods, and computer-readable medium for managing user information (e.g., personal information collected by one or more external devices). In some examples, a data interchange may be managed that enables and/or allows third-party applications to provide user information to the data interchange and potentially retrieve user information provided by other third-party applications. While managed, the data interchange may be configured to provide access to particular data types of the user data based at least in part on authorization from the user associated with the data; Paragraph 28, Additionally, in some non-limiting examples, the information being collected and/or managed may be health, fitness, and/or activity information of the user (e.g., blood glucose levels, weight, height, calories burned, heart rate, etc.). The user information may be categorized or otherwise identified by one or more data types (or categories)); and
calculating a health value for the user using one more health data parameters based on the received health data, wherein the health data parameters are selected and applied according to the first prioritization for the data (see at least Paragraph 28, Additionally, in some non-limiting examples, the information being collected and/or managed may be health, fitness, and/or activity information of the user (e.g., blood glucose levels, weight, height, calories burned, heart rate, etc.). The user information may be categorized or otherwise identified by one or more data types (or categories); Paragraph 33, In this case, the user may be enabled to provide priority information to the process such that the process is able to aggregate the step information from the two sources in a meaningful way. The data may be aggregated from a statistical perspective (e.g., to determine the actual number of steps walked by the user) and/or the data may be aggregated to provide a user interface (UI) that helps the user visualize the number of steps they walked. In some cases, the priority information may identify which data (e.g., from which source) to use when there are multiple data entries for the same point in time. In some examples, the source with the highest priority may be rendered when there is overlap in cumulative data (e.g., steps walked are cumulative because they can be summed to a total)).
Claims 19 and 39 recite substantially similar system and non-transitory computer readable medium limitations to those of method claim 1 and, as such, are rejected for similar reasons as given above.
Claim 2:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
periodically collecting the health data from the one or more connected devices in the order defined by the first prioritization for the connected devices (see at least Paragraph 5, The aggregated record may only include each identified data entry corresponding to the highest identified priority when the identified type is cumulative. The aggregated record may include each identified data entry corresponding to the highest identified priority and other identified data entries for the identified time period when the identified type is discrete; Paragraph 31, In other examples, third-party applications may be able to subscribe to certain data types, and the first-party process may be configured to automatically wake up the third-party application (e.g., in the background) and ensure that the third-party application is able to process the data. For example, a third-party application of the user may subscribe to a blood pressure data type and indicate an associated subscription frequency).
Claim 20 recites substantially similar system limitations to those of method claim 2 and, as such, is rejected for similar reasons as given above.
Claim 3:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
collecting health data in response to a triggering event, the triggering event causing the at least one processor to automatically collect the health data from the one or more connected devices in the order defined by the first prioritization for the connected devices (see at least Paragraph 31, In other examples, third-party applications may be able to subscribe to certain data types, and the first-party process may be configured to automatically wake up the third-party application (e.g., in the background) and ensure that the third-party application is able to process the data. For example, a third-party application of the user may subscribe to a blood pressure data type and indicate an associated subscription frequency. Based at least in part on that frequency, when a new blood pressure reading is received by the first-party process, the process may wake up the appropriate third-party application background, provided with the new data and provide it with the updated data).
Claim 21 recites substantially similar system limitations to those of method claim 3 and, as such, is rejected for similar reasons as given above.
Claim 6:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
wherein the triggering event is defined based on at least one of: a period of time since the last collection of health data, a request for a health value, a request for a health or wellness recommendation, an authentication of the user, a connection or reconnection of a device, a data synchronization event or schedule, or an expiry of one or more health data parameters (see at least Paragraph 31, In other examples, third-party applications may be able to subscribe to certain data types, and the first-party process may be configured to automatically wake up the third-party application (e.g., in the background) and ensure that the third-party application is able to process the data. For example, a third-party application of the user may subscribe to a blood pressure data type and indicate an associated subscription frequency. Based at least in part on that frequency, when a new blood pressure reading is received by the first-party process, the process may wake up the appropriate third-party application background, provided with the new data and provide it with the updated data; Paragraph 64, In some cases, the frequency may be automatically determined based at least in part on the type of data and/or a historical frequency associated with the data. For example, weight data doesn't generally change that much within a day, so the frequency may be automatically set at daily. Alternatively, blood glucose level can change drastically within a few minutes and can be life threatening. As such, the frequency for a subscription to blood glucose level may be automatically set at “immediate” or every “minute.”)).
Claim 24 recites substantially similar system limitations to those of method claim 6 and, as such, is rejected for similar reasons as given above.
Claim 7:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
wherein the user input received through the at least one graphical user interface further includes manually entered health data for the user, the manually entered health data including one or more health data parameters (see at least Paragraph 4, The computer system may receive priority information identifying a priority of the first source and the second source; Paragraph 29, However, as noted, health information of a user may be considered extremely personal and/or confidential; thus, the user may be given the ability to protect or otherwise not share some of the heath information with some, any, or all of the third-party applications (even the ones that provided the health information to the first-party process initially); Paragraph 33, In this case, the user may be enabled to provide priority information to the process such that the process is able to aggregate the step information from the two sources in a meaningful way. In some cases, the priority information may identify which data (e.g., from which source) to use when there are multiple data entries for the same point in time. In some examples, the source with the highest priority may be rendered when there is overlap in cumulative data (e.g., steps walked are cumulative because they can be summed to a total); Paragraph 36, the user may be able to enter additional data, update the data, include incorrect data, or otherwise configure the information; Paragraph 45, The user device 102 may also include I/O device(s) 330, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, a display, speakers, etc.).
Claim 25 recites substantially similar system limitations to those of method claim 7 and, as such, is rejected for similar reasons as given above.
Claim 8:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
wherein the first prioritization for the data defines an order for selecting and applying health data parameters among the health data received from the connected devices and manually entered health data received from the user through the at least one graphical user interface (see at least Paragraph 4, The computer system may receive priority information identifying a priority of the first source and the second source; Paragraph 29, However, as noted, health information of a user may be considered extremely personal and/or confidential; thus, the user may be given the ability to protect or otherwise not share some of the heath information with some, any, or all of the third-party applications (even the ones that provided the health information to the first-party process initially); Paragraph 33, In this case, the user may be enabled to provide priority information to the process such that the process is able to aggregate the step information from the two sources in a meaningful way. In some cases, the priority information may identify which data (e.g., from which source) to use when there are multiple data entries for the same point in time. In some examples, the source with the highest priority may be rendered when there is overlap in cumulative data (e.g., steps walked are cumulative because they can be summed to a total); Paragraph 36, the user may be able to enter additional data, update the data, include incorrect data, or otherwise configure the information; Paragraph 45, The user device 102 may also include I/O device(s) 330, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, a display, speakers, etc.; Paragraph 82, The priority information may be configured via the UI 1414, such that the user may slide, drag, or otherwise virtually move the sources (e.g., the data collection devices 1406, 1408) up and down to identify a preferred priority. As desired, the priority information and the received data entries may correspond to any number of data collection devices and/or applications).
Claim 26 recites substantially similar system limitations to those of method claim 8 and, as such, is rejected for similar reasons as given above.
Claim 9:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
wherein the user input received through the at least one graphical user interface further includes a selection to prioritize one or more health data parameters manually entered by the user (see at least Paragraph 82, The priority information may be configured via the UI 1414, such that the user may slide, drag, or otherwise virtually move the sources (e.g., the data collection devices 1406, 1408) up and down to identify a preferred priority. As desired, the priority information and the received data entries may correspond to any number of data collection devices and/or applications).
Claim 27 recites substantially similar system limitations to those of method claim 9 and, as such, is rejected for similar reasons as given above.
Claim 10:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
wherein the health data includes user data and health data parameters including one or more of age, gender, ethnicity, location, quality of life measures, dietary intake and preferences, weight, height, arm circumference, waist circumference, systolic blood pressure, diastolic blood pressure, total blood pressure, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, oxidized LDL (oxLDL) levels, triglyceride levels, plasma glucose levels, plasma insulin or c-peptide levels, non-esterified fatty acids (NEFA) levels, blood pressure, activity level, moderate muscle strength, vigorous muscle strength, walking distance per time unit, body fat percentage, squat strength, pushup strength, knee pushup strength, plank strength, balance tests, Short Physical Performance Battery (SPPB) score, sleep patterns, blood oxygen saturation (SpO2) level, maximum rate of oxygen (VO2), physical activity patterns, calorie consumption, resting heart rate, breathing rate, respiratory rate, health diseases, family history of health diseases, pregnancy, breast feeding patterns, pregnancy complications, contraceptive practices, perceived stress level, immune markers, vitamin and mineral levels, food intolerance, allergies or allergen markers, microbiome composition and markers, and genotypic or epigenetic information (see at least Paragraph 28, Additionally, in some non-limiting examples, the information being collected and/or managed may be health, fitness, and/or activity information of the user (e.g., blood glucose levels, weight, height, calories burned, heart rate, etc.). The user information may be categorized or otherwise identified by one or more data types (or categories)).
Claim 28 recites substantially similar system limitations to those of method claim 10 and, as such, is rejected for similar reasons as given above.
Claim 11:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
wherein the first prioritization for the connected devices is defined based on an order in which the connections to the devices are established (see at least Paragraph 4, The computer system may receive priority information identifying a priority of the first source and the second source; Paragraph 90, Illustrative methods and systems for managing user device connections are described above. Some or all of these systems and methods may, but need not, be implemented at least partially by architectures such as those shown at least in FIGS. 1-17 above. While many of the embodiments are described above with reference to personal and/or health-related information, it should be understood any time of user information or non-user information (e.g., data of any type) may be managed using these techniques. Further, in the foregoing description, various non-limiting examples were described. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the examples. However, it should also be apparent to one skilled in the art that the examples may be practiced without the specific details. Furthermore, well-known features were sometimes omitted or simplified in order not to obscure the example being described).
Claim 29 recites substantially similar system limitations to those of method claim 11 and, as such, is rejected for similar reasons as given above.
Claim 12:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
storing a second prioritization for the connected devices in response to further user input received through the at least one graphical user interface, the second prioritization for the connected devices being different from the first prioritization for the connected devices that defines an order for collecting health data from the connected devices (see at least Paragraph 4, a method may be executed by a computer system to at least identify a particular data type of a plurality of data types to manage. The method may also cause the computer system to receive health data corresponding to the particular data type from at least a first source and a second source of a plurality of data sources. The computer system may receive priority information identifying a priority of the first source and the second source. The method may also cause the computer system to identify a time interval for partitioning the health data by the plurality of data sources. In some examples, the computer system may identify a data entry for the particular data type with a highest identified priority when the data entry exists in a data store configured to maintain the received health data corresponding to the particular data type during each identified time interval over an amount of time. The computer system may also aggregate each identified data entry to form an aggregated record for the particular data type over the amount of time; Paragraph 6, The processor may be configured to execute instructions stored on the memory to configure the memory to receive data of a particular data type from a plurality of sources, the data including at least respective time stamps; Paragraph 82, The priority information may be configured via the UI 1414, such that the user may slide, drag, or otherwise virtually move the sources (e.g., the data collection devices 1406, 1408) up and down to identify a preferred priority. As desired, the priority information and the received data entries may correspond to any number of data collection devices and/or applications).
Claim 30 recites substantially similar system limitations to those of method claim 12 and, as such, is rejected for similar reasons as given above.
Claim 13:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
storing a second prioritization for the data in response to further user input received through the at least one graphical user interface, the second prioritization for the data being different from the first prioritization for the data that defines an order among the data from the connected devices or manually entered data for selecting and applying one or more health data parameters (see at least Paragraph 4, a method may be executed by a computer system to at least identify a particular data type of a plurality of data types to manage. The method may also cause the computer system to receive health data corresponding to the particular data type from at least a first source and a second source of a plurality of data sources. The computer system may receive priority information identifying a priority of the first source and the second source. The method may also cause the computer system to identify a time interval for partitioning the health data by the plurality of data sources. In some examples, the computer system may identify a data entry for the particular data type with a highest identified priority when the data entry exists in a data store configured to maintain the received health data corresponding to the particular data type during each identified time interval over an amount of time. The computer system may also aggregate each identified data entry to form an aggregated record for the particular data type over the amount of time; Paragraph 6, The processor may be configured to execute instructions stored on the memory to configure the memory to receive data of a particular data type from a plurality of sources, the data including at least respective time stamps; Paragraph 82, The priority information may be configured via the UI 1414, such that the user may slide, drag, or otherwise virtually move the sources (e.g., the data collection devices 1406, 1408) up and down to identify a preferred priority. As desired, the priority information and the received data entries may correspond to any number of data collection devices and/or applications).
Claim 31 recites substantially similar system limitations to those of method claim 13 and, as such, is rejected for similar reasons as given above.
Claim 14:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
parsing the received health data to obtain the one or more health data parameters (see at least Paragraph 28, in some non-limiting examples, the information being collected and/or managed may be health, fitness, and/or activity information of the user (e.g., blood glucose levels, weight, height, calories burned, heart rate, etc.). The user information may be categorized or otherwise identified by one or more data types (or categories). Weight, number of steps walked, number of calories burned, heart rate, etc., are each an example of a data type; Paragraph 74, As such, code within first-party process 106 may be executed to interpret the data of the asset download 1102 and turn it into actual data types. In some cases, data types may also include analysis of the data values (e.g., whether a data value for a particular data type is good or had for a particular user). This analysis information may also be included in the asset download 1102 and/or published to the network resource for availability to the developers and/or third-party applications. Database 1104 may represent the state of the data types prior to implementation of the asset download 1102 (e.g., with the original data types), while the database 1106 may represent the state of the data types after the implementation of the asset download 1102 (e.g., including the new data types); Paragraph 82, As such, the first-party process 106 may first identify whether the data to be provided to the UI 1414 is discrete or cumulative. The first-party process 106 may also identify if there is priority information provided by the user. If not, the first-party process 106 may determine priority information based at least in part on previous configurations of the user, historical and/or use information from other users of the device 102 or of other devices (e.g., a probability that the user will use the data collection device 1406 at a particular time), and/or information about accuracy of the respective collection devices 1406, 1408. If priority information exists, the first-party process 106 may use that priority information to determine which collected data for a single data type should be included in the data record for each time period or for each time segment of the time period (e.g., each second of the 5 minute window of collected data, etc.)).
Claim 32 recites substantially similar system limitations to those of method claim 14 and, as such, is rejected for similar reasons as given above.
Claim 15:
Keen discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
wherein parsing the received health data comprises applying one or more extract, transform, load (ETL) operations to the received health data to obtain the one or more health data parameters (see at least Paragraph 4, a method may be executed by a computer system to at least identify a particular data type of a plurality of data types to manage. The method may also cause the computer system to receive health data corresponding to the particular data type from at least a first source and a second source of a plurality of data sources. The computer system may receive priority information identifying a priority of the first source and the second source. The method may also cause the computer system to identify a time interval for partitioning the health data by the plurality of data sources. In some examples, the computer system may identify a data entry for the particular data type with a highest identified priority when the data entry exists in a data store configured to maintain the received health data corresponding to the particular data type during each identified time interval over an amount of time. The computer system may also aggregate each identified data entry to form an aggregated record for the particular data type over the amount of time; Paragraph 28, in some non-limiting examples, the information being collected and/or managed may be health, fitness, and/or activity information of the user (e.g., blood glucose levels, weight, height, calories burned, heart rate, etc.). The user information may be categorized or otherwise identified by one or more data types (or categories). Weight, number of steps walked, number of calories burned, heart rate, etc., are each an example of a data type; Paragraph 74, As such, code within first-party process 106 may be executed to interpret the data of the asset download 1102 and turn it into actual data types. In some cases, data types may also include analysis of the data values (e.g., whether a data value for a particular data type is good or had for a particular user). This analysis information may also be included in the asset download 1102 and/or published to the network resource for availability to the developers and/or third-party applications. Database 1104 may represent the state of the data types prior to implementation of the asset download 1102 (e.g., with the original data types), while the database 1106 may represent the state of the data types after the implementation of the asset download 1102 (e.g., including the new data types); Paragraph 82, As such, the first-party process 106 may first identify whether the data to be provided to the UI 1414 is discrete or cumulative. The first-party process 106 may also identify if there is priority information provided by the user. If not, the first-party process 106 may determine priority information based at least in part on previous configurations of the user, historical and/or use information from other users of the device 102 or of other devices (e.g., a probability that the user will use the data collection device 1406 at a particular time), and/or information about accuracy of the respective collection devices 1406, 1408. If priority information exists, the first-party process 106 may use that priority information to determine which collected data for a single data type should be included in the data record for each time period or for each time segment of the time period (e.g., each second of the 5 minute window of collected data, etc.); Paragraph 87, The process 1600 may begin at 1602 by including identification of a particular data type of a plurality of data types. Identification of the particular data type may include identifying the data type itself (e.g., number of steps, weight, heart rate, etc.) or identifying a type or kind of the data type (e.g., cumulative or discrete).).
Claim 33 recites substantially similar system limitations to those of method claim 15 and, as such, is rejected for similar reasons as given above.
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 of this title, if the differences between the claimed invention and the prior art axe 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.
Claims 4-5, 16-18, 22-23 and 34-36 are rejected under 35 U.S.C. 103 as being unpatentable over Keen et al., U.S. Patent Application Publication US 2015/0347690 A1 in view of Catani et al., U.S. Patent Application Publication US 2015/0364057 A1.
Claim 4:
Keen discloses the limitations as shown in the rejections above. Keen may not specifically disclose the following limitations, but Catani as shown does:
collecting health data in response to a triggering event, the triggering event causing the at least one processor to prompt the user to manually enter health data through the at least one graphical user interface (see at least Paragraph 7, The processor can be configured to perform functions of displaying on a display a prompt to which a user can provide an input in response thereto, receiving the input, and updating one or more of the variables representing the plurality of characteristics based on the received input; Paragraph 338, The system can be configured to provide 694 reactive, event-based triggers to the user in response to the occurrence of certain predetermined events. The triggers provided 694 can be related to the user's selected activities and can be configured to prompt the user to perform a previously accepted activity. For non-limiting example, a trigger can be provided 694 at a time of day that the user has indicated is a time for exercise; Paragraph 339, The system can be configured to provide 696 time-based outreach to the user in response to it being a certain day and/or a certain time. The outreach can be configured to prompt the user to provide information regarding a previously provided 694 event-based trigger, to provide the user with questions for the user to reply to regarding his/her current emotional wellbeing).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the health data collection system of Keen with the prompt of Catani with the motivation of providing the benefit to “… low a person to manage his/her wellness, health, and lifestyle using a convenient system that can help the person plan strategies for improving and/or maintaining his/her wellness, health, and lifestyle and/or that can help the person track his/her compliance with the strategies” (Catani, see at least the Abstract).
Claim 22 recites substantially similar system limitations to those of method claim 4 and, as such, is rejected for similar reasons as given above.
Claim 5:
The combination of Keen/Catani discloses the limitations as shown in the rejections above. Keen further discloses the following limitations:
wherein the triggering event is defined based on at least one of a condition, event, or schedule (see at least Paragraph 31, In other examples, third-party applications may be able to subscribe to certain data types, and the first-party process may be configured to automatically wake up the third-party application (e.g., in the background) and ensure that the third-party application is able to process the data. For example, a third-party application of the user may subscribe to a blood pressure data type and indicate an associated subscription frequency. Based at least in part on that frequency, when a new blood pressure reading is received by the first-party process, the process may wake up the appropriate third-party application background, provided with the new data and provide it with the updated data; Paragraph 64, In some cases, the frequency may be automatically determined based at least in part on the type of data and/or a historical frequency associated with the data. For example, weight data doesn't generally change that much within a day, so the frequency may be automatically set at daily. Alternatively, blood glucose level can change drastically within a few minutes and can be life threatening. As such, the frequency for a subscription to blood glucose level may be automatically set at “immediate” or every “minute.”).
Claim 23 recites substantially similar system limitations to those of method claim 5 and, as such, is rejected for similar reasons as given above.
Claim 16:
Keen discloses the limitations as shown in the rejections above. Keen may not specifically disclose the following limitations, but Catani as shown does:
applying the one or more health data parameters to generate a health or wellness recommendation for the user (see at least Fig. 7, Your top recommendations, Eat Lean Meats, Go Running, etc.; Paragraph 225, For non-limiting example, the higher the percentage indicated for “Weight,” the more concerned that the user is that his/her goal(s) related to weight will not be achieved. The system 10 can be configured to determine which of a plurality of pre-stored possible activities best match the user's selected percentages based on predetermined characteristics associated with each of the activities, as discussed herein, and be configured to provide those activities as recommendations to the user. As also discussed herein, the recommended activities for the user can change over time as the user interacts with the system 10, e.g., by providing inputs regarding completion of activities, abandonment of activities, interest level in recommended activities, how much they believe they are at risk of not achieving certain goals, etc.; Paragraph 227, FIG. 7 shows an embodiment of a recommendations screen 318 that the system 10 can be configured to display upon user selection of the “Recommendations” 306. User selection of any of the recommendations on the recommendations screen 318 can cause the system 10 to display a detailed recommendations screen with further information about the selected recommendation. FIG. 8 shows an embodiment of a detailed recommendations screen 320, displayed in this embodiment in response to user selection of the “Learn how to eat to better manage your energy” recommendation of the recommendations screen 318).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the health data collection system of Keen with the prompt of Catani for at least the same reasons given for claim 4 above.
Claim 34 recites substantially similar system limitations to those of method claim 16 and, as such, is rejected for similar reasons as given above.
Claim 17:
The combination of Keen/Catani discloses the limitations as shown in the rejections above. Keen may not specifically disclose the following limitations, but Catani as shown does:
applying one or more health data parameters to a health engine that generates a health or wellness recommendations for the user by using at least one of a programmed model, decision tree, or machine learning model (see at least Fig. 7, Your top recommendations, Eat Lean Meats, Go Running, etc.; Paragraph 225, For non-limiting example, the higher the percentage indicated for “Weight,” the more concerned that the user is that his/her goal(s) related to weight will not be achieved. The system 10 can be configured to determine which of a plurality of pre-stored possible activities best match the user's selected percentages based on predetermined characteristics associated with each of the activities, as discussed herein, and be configured to provide those activities as recommendations to the user. As also discussed herein, the recommended activities for the user can change over time as the user interacts with the system 10, e.g., by providing inputs regarding completion of activities, abandonment of activities, interest level in recommended activities, how much they believe they are at risk of not achieving certain goals, etc.; Paragraph 249, One embodiment of an activities input screen that can be provided by the system 10 is shown in FIG. 5A. The screen of FIG. 5A is a non-limiting example of goal data input in which a user can identify general areas of concern by moving a slide bar for the area of concern from 0% to a value up to 100%. The analysis module 202 can be configured to use the indicated percentage as a factor in determining with one or more algorithms stored in an algorithms database 224 which one or more activities to suggest to the user as activities that the user may want to engage in to help address one or more of his/her areas of concern).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the health data collection system of Keen with the prompt of Catani for at least the same reasons given for claim 4 above.
Claim 35 recites substantially similar system limitations to those of method claim 17 and, as such, is rejected for similar reasons as given above.
Claim 18:
The combination of Keen/Catani discloses the limitations as shown in the rejections above. Keen may not specifically disclose the following limitations, but Catani as shown does:
wherein the health or wellness recommendation for the user includes at least one of a nutrient recommendation, meal composition recommendation, meal timing commendation, physical activity recommendation, mental activity recommendation, sleep recommendation, health supplement recommendation, health article recommendation, or daily task recommendation (see at least Fig. 7, Your top recommendations, Learn how to eat to better manage your energy, Eat Lean Meats, Go Running, etc.; Paragraph 227, FIG. 7 shows an embodiment of a recommendations screen 318 that the system 10 can be configured to display upon user selection of the “Recommendations” 306. User selection of any of the recommendations on the recommendations screen 318 can cause the system 10 to display a detailed recommendations screen with further information about the selected recommendation. FIG. 8 shows an embodiment of a detailed recommendations screen 320, displayed in this embodiment in response to user selection of the “Learn how to eat to better manage your energy” recommendation of the recommendations screen 318).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the health data collection system of Keen with the prompt of Catani for at least the same reasons given for claim 4 above.
Claim 36 recites substantially similar system limitations to those of method claim 18 and, as such, is rejected for similar reasons as given above.
Claims 37 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Keen et al., U.S. Patent Application Publication US 2015/0347690 A1 in view of Neumann, U.S. Patent Application Publication US 2022/0199223 A1.
Claim 37:
Keen discloses the limitations as shown in the rejections above. Keen may not specifically disclose the following limitations, but Neumann as shown does:
wherein the health value comprises a biological age of the user (see at least the Abstract, to calculate a user effective age measurement using a first machine learning process trained with training data correlating a plurality of biological markers to a plurality of effective age measurements).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the health data collection system of Keen with the feature of Neumann with the motivation of providing the benefit “… to determine a food tolerance score as a function of the user effective age, wherein the food tolerance score relates to a user ability to tolerate a food item” (Neumann, see at least the Abstract).
Claim 38:
The combination of Keen/Neumann discloses the limitations as shown in the rejections above. Keen may not specifically disclose the following limitations, but Neumann as shown does:
further comprising at least one predictive model configured to calculate the biological age of the user, wherein the predictive model comprises one or more machine learning models (see at least the Abstract, to calculate a user effective age measurement using a first machine learning process trained with training data correlating a plurality of biological markers to a plurality of effective age measurements; Paragraph 59, Classifying may include predictive modeling that may map an input variable such as a user body data element to a discrete output variable that includes a body dimension).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the health data collection system of Keen with the feature of Neumann for at least the same reasons given for claim 37 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joy Chng whose telephone number is 571.270.7897. The examiner can normally be reached on Monday-Friday, 9:00am-5:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JASON DUNHAM can be reached on 571.272.8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Joy Chng/
Primary Examiner, Art Unit 3686
Prosecution Insights