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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/09/2025 is being considered by the examiner.
Claim Objections
Claims 1-2 and 11-12 are objected to because of the following informalities:
In claims 1 and 11, “as a function of the interaction data” should be “as a function of interaction data.” Appropriate correction is required.
In claims 2 and 12, “receiving interaction data” should be “receiving the interaction data.” Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2023/0229459 to Kenneth Neumann (“Neumann1”) in view of U.S. Patent Application No. 2021/0374826 to Kenneth Neumann (“Neumann2”).
Examiner’s Note: This application is a continuation-in-part (CIP) of Application No. 18/113878 (the ’818 application”), which is a CIP of Application No. 17/087,713 (the ’713 application”), and recites subject matter “nourishment source,” which is not supported by the specification of the ’878 application or the ’713 application. Accordingly, the claims are given the filing date of the present Application, which is November 21, 2024. MPEP §2152.01. Because Neumann1 published on July 20, 2023, it qualifies as prior art to the present application.
Regarding claim 1:
A system for a configurable device environment (Neumann1 at par. [0002] and claim 1.),
the system comprising a computing device (Neumann1 discloses a computing device 104. Neumann1 at pars. [0004] and [0019], Fig. 1, and claim 1.), wherein the computing device is configured to:
receive remote data corresponding to a subject (Neumann1 at pars. [0004] and [0019], Fig. 1, and claim 1.),
wherein the remote data further comprises biological extraction data (Newman1 discloses that the remote data includes biological extraction data. Neumann1 at pars. [0004], [0030], and [0076], and claim 1.);
retrieve a biometric profile of the subject as a function of a plurality of signals (Newmann discloses retrieving a biometric profile 112. Neumann1 at pars. [0004], [0035]-[0038], Figs. 1 and 4 and claim 1.),
wherein the plurality of signals comprises simulated data comprising a three dimensional portrayal of the subject tracking user nourishment source over time (Neumann1 discloses the claimed simulated data and portraying a subject in three-dimensions using nourishment (e.g., optimal nourishment) as a criteria over a time period such as 3, 6, 9 or 12 months (“over time”). Neumann1 at pars. [0030]-[0031] and [0033].);
identify a pattern of accessory device states for the plurality of accessory devices (Neumann1 discloses the claimed identification. Neumann1 at pars. [0004] and [0039], Fig. 1 and claim 1.),
wherein identifying the pattern further comprises: determining, by the computing device, a coordinated state change for a group of accessory devices of the plurality of accessory devices as a function of the interaction data and the biometric profile (Neumann1 discloses the claimed determining. Neumann1 at pars. [0004] and [0040], Fig. 1 and claim 1.); and
identifying the pattern of accessory device states as a function of the coordinated state change (Neumann1 discloses the claimed identifying. Neumann1 at pars. [0004] and [0043]-[0044], Fig. 1 and claim 1.); and
control the function of the plurality of accessory devices as a function of the pattern of accessory device states (Neumann1 discloses the claimed controlling. Neumann1 at pars. [0004] and [0063], Fig. 1 and claim 1.),
In view of Neumann2:
wherein the plurality of signals comprises simulated data comprising a three dimensional portrayal of the subject tracking user nourishment source over time (As discussed above, Neumann1 discloses portraying a subject in three-dimensions using nourishment (e.g., “optimal nourishment”) as a criteria that is tracked over time, but Neumann does not disclose “tracking user nourishment source over time.” That is, there is no disclosure or suggestion that the source of the nourishment is used as part of the nourishment criteria.
In the same field of endeavor, configuring computing devices based on user preferences (and thus analogous art), Neumann2 discloses that a user’s behavior with respect to nourishment can include an indication of whether “the user consumed all organic ingredients and all products were vegan and did not contain any animal products” (“tracking user nourishment source”). Neumann2 at par. [0028]. In addition, Neumann2 indicates that a “behavior goal may relate to a food source, such as a desire to only go out to eat no more than three days each week …[and to] food option such as to only consume foods that do not contain genetically modified organisms, or to only consume foods that do not contain high fructose corn syrup (“user nourishment source”). Neumann2 at par. [0029]; see also par. [0025] (taking into account user’s preference for food “sourced from organically grown ingredients.”). Neumann2 further discloses that a user’s “behavior goal may be calculated into a user's nourishment score 140.” Neumann2 at par. [0029]. Thus, Neumann2’s nourishment score incorporates the nourishment source as part of its calculations. As disclosed in Neumann2, the nourishment score can be “graded on a continuum, where a score of zero may indicate a user who is in extremely poor nutritional health while a score of 100 may indicate a user who is in excellent nutritional health.” Neumann2 at par. [0028]. The score of 100 is analogous to the “optimal nourishment” of Neumann1.
It would have been obvious and one skilled in the art would have been motivated to use Neumann2’s nourishment score, which considers the nourishment source, in the nourishment tracking of Neumann1 because the source of the nourishment will have an impact on the user’s health. Neumann2 at par. [0047]. Because both references relate to user nourishment, there is a reasonable chance of success. MPEP §2143.I.G.).
Regarding claim 2: The system of claim 1,
wherein receiving the remote data comprises receiving interaction data from a plurality of accessory devices (Neumann1 at par. [0039] and claim 2.).
Regarding claim 3: The system of claim 2,
wherein the interaction data corresponds to an interaction with a subject (Neumann1 at par. [0081] and claim 3.).
Regarding claim 4: The system of claim 1,
wherein receiving the remote data comprises receiving a plurality of signals from at least a sensor proximate to the subject, wherein a signal of the plurality of signals comprises skin data (Neumann1 at par. [0021] and claim 4.).
Regarding claim 5: The system of claim 4,
wherein a signal of the plurality of signals comprises simulated data generated at an online platform (Neumann1 at par. [0030] and claim 5.).
Regarding claim 6: The system of claim 1,
wherein controlling the function of the plurality of accessory devices comprises determining an automation rule for the group of accessory devices as a function of the pattern of accessory device states (Neumann1 at pars. [0051]-[0052] and claim 6.).
Regarding claim 7: The system of claim 6,
wherein controlling the function of the plurality of accessory devices further comprises transmitting, to the group of accessory devices, the automation rule (Neumann1 at par. [0063] and claim 7.).
Regarding claim 8: The system of claim 1,
wherein retrieving the biometric profile further comprises: training a biometric machine-learning model as a function of training data that includes a plurality of entries wherein each entry models sensor signals to physiological data related to biometric state metrics data; and determining, as a function of the biometric machine-learning model and the plurality of signals, the biometric profile (Neumann1 at pars. [0035], [0041]-[0042], and [0082] and claim 8.).
Regarding claim 9: The system of claim 1,
wherein: the biometric profile includes environmental parameters associated with the plurality of accessory devices; and identifying the pattern of accessory device states that should be applied to the group of accessory devices further comprises identifying a pattern of changes in the group of accessory devices, wherein the pattern of changes is from a first state described in the interaction data to a second state related to the optimum environmental parameters (Neumann1 at pars. [0083]-[0084] and claim 9.).
Regarding claim 10: The system of claim 1,
wherein retrieving the biometric profile further comprises identifying a malady as a function of the plurality of signals (Neumann1 at pars. [0083]-[0084] and claim 10.).
Regarding claim 11:
A method for a configurable device environment (Neumann1 at par.[0002]. and claim 11.), the method:
receiving, by at least a computing device, remote data corresponding to a subject, wherein the remote data further comprises biological extraction data; retrieving, by the at least computing device, a biometric profile of the subject as a function of the plurality of signals wherein the plurality of signals comprises simulated data comprising a three dimensional portrayal of the subject tracking user nourishment source over time; identifying, by the at least computing device, a pattern of accessory device states for the plurality of accessory devices, wherein identifying the pattern further comprises: determining, by the computing device, a coordinated state change for a group of accessory devices of the plurality of accessory devices as a function of the interaction data and the biometric profile; and identifying the pattern of accessory device states as a function of the coordinated state change; and controlling, by the at least computing device, the function of the plurality of accessory devices as a function of the pattern of accessory device states (Please see analysis in claim 1 and Neumann1 at claim 11.).
Regarding claim 12: The method of claim 11,
wherein receiving the remote data comprises receiving interaction data from a plurality of accessory devices (Please see analysis in claim 2 and Neumann1 at claim 12.).
Regarding claim 13: The method of claim 12,
wherein the interaction data corresponds to an interaction with a subject (Please see analysis in claim 3 and Neumann1 at claim 13.).
Regarding claim 14: The method of claim 11,
wherein receiving the remote data comprises receiving a plurality of signals from at least a sensor proximate to the subject, wherein a signal of the plurality of signals comprises skin data (Please see analysis in claim 4 and Neuman1 at claim 14.).
Regarding claim 15: The method of claim 14,
wherein a signal of the plurality of signals comprises simulated data generated at an online platform (Please see analysis in claim 4 and Neuman1 at claim 15.).
Regarding claim 16: The method of claim 11,
wherein controlling the function of the plurality of accessory devices comprises determining an automation rule for the group of accessory devices as a function of the pattern of accessory device states (Please see analysis in claim 6 and Neuman1 at claim 16.).
Regarding claim 17: The method of claim 16,
wherein controlling the function of the plurality of accessory devices further comprises transmitting, to the group of accessory devices, the automation rule (Please see analysis in claim 7 and Neuman1 at claim 17.).
Regarding claim 18: The method of claim 11,
wherein retrieving the biometric profile further comprises: training a biometric machine-learning model as a function of training data that includes a plurality of entries wherein each entry models sensor signals to physiological data related to biometric state metrics data; and determining, as a function of the biometric machine-learning model and the plurality of signals, the biometric profile (Please see analysis in claim 8 and Neuman1 at claim 18.).
Regarding claim 19: The method of claim 11,
wherein: the biometric profile includes environmental parameters associated with the plurality of accessory devices; and identifying the pattern of accessory device states that should be applied to the group of accessory devices further comprises identifying a pattern of changes in the group of accessory devices, wherein the pattern of changes is from a first state described in the interaction data to a second state related to the optimum environmental parameters (Please see analysis in claim 9 and Neuman1 at claim 19.).
Regarding claim 20: The method of claim 11,
wherein retrieving the biometric profile further comprises identifying a malady as a function of the plurality of signals (Please see analysis in claim 10 and Neuman1 at claim 20.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent No. 12,217,194 to Kateliju Vleugels discloses food intake tracking, including identifying the type and brand of the food being consumed.
U.S. Patent Application Publication No. 2023/0315443 to Kenneth Neumann discloses user food preference categories that include cuisine type.
Vu, Tri, et al. "Wearable food intake monitoring technologies: A comprehensive review." Computers 6.1 (2017): 4 discloses recognizing food types.
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/B.K./Examiner, Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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