DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 30, 2026 has been entered.
Notice to Applicant
The following is a Non-Final Office Action for Application Serial Number: 17/863,046, filed on June 16, 2025. In response to Examiner's Final Office Action dated March 13, 2026, Applicant on June 30, 2026, amended claim 1. Claims 1-4, 7-10, 12 and 13 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
The 35 U.S.C. § 102 rejections of claims 1-4, 9, 10 and 12 are hereby maintained in light of Applicant’s amendments and arguments regarding claim 1.
The 35 U.S.C. § 103 rejections of claims 2-8, 10, 12 and 13 are hereby maintained in light of Applicants amendments to claim 1.
Response to Arguments
Applicant's Arguments/Remarks filed June 30, 2026 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks regarding the pending rejections will be addressed herein below in the order in which they appear in the response filed June 30, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states on page 5 of the Office Action, the Office asserts claim 1 to be directed to an abstract idea of (i) certain methods of organizing human activity and (ii) mental processes. Applicants respectfully disagree.
A proper identification of the "directed to" idea is essential to the Step 2A analysis. In Thales Visionix, the Federal Circuit stated that "[a]t step one, 'it is not enough to merely identify a patent- ineligible concept underlying the claim; we must determine whether that patent-ineligible concept is what the claim is "directed to."' (Thales Visionix Inc. v. U.S., No. 2015-5150, 2017 WL 914618, at *5 (Fed. Cir. Mar. 8, 2017) (citing Rapid Litig., 827 F.3d at 1050)). For at least the above-noted reasons, under Prong One of Revised Step 2A, independent claim 1 does not recite (and thus is not directed to) any judicial exception such as certain methods of organizing human activity or mental process as concepts performed in the human mind as alleged by the Office on at least pages 5-6 of the Office Action.
Applicants note that with respect to at least certain methods of organizing human activities, according to MPEP § 2106.04(a)(2), "not all methods of organizing human activity are abstract ideas." (MPEP § 2106.04(a)(2)). The 2019 PEG provides that the methods of organizing human activity "is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, managing personal behavior, and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in Section III(C) of the 2019 PEG." (2019 PEG, pp. 4-5). "The courts have used the phrases 'fundamental economic practices' or 'fundamental economic principles' to describe concepts relating to the economy and commerce. Fundamental economic principles or practices include hedging, insurance, and mitigating risks." (MPEP 2106.04(a)(2)(II)(A)).
In response, Examiner respectfully disagrees. Examiner notes the explicitly language on pg. 5 of the Final Office Action states “Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. The analysis then outlined the specific limitations of claim 1 that recite abstract ideas that constitutes methods based on managing personal behavior, as well as, observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. Thus, Examiner maintains the analysis under Step 2A-Prong One is proper.
Additionally, Examiner finds the advancements in Core Wireless were found eligible because the claims are directed to a particular manner of summarizing and presenting information in electronic devices, as well as, the improvements to the user interface for electronic devices. In contrast, the present claim is directed towards the data analysis of the environmental impact of predefined activities. Examiner finds Applicants claim is not analogous to the improvements regarding the user interfaces of Core Wireless.
Regarding the 35 U.S.C. 101 rejection, Applicants note that with respect to at least certain methods of organizing human activities, according to MPEP § 2106.04(a)(2), "not all methods of organizing human activity are abstract ideas." (MPEP § 2106.04(a)(2)). The 2019 PEG provides that the methods of organizing human activity "is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, managing personal behavior, and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in Section III(C) of the 2019 PEG." (2019 PEG, pp. 4-5). "The courts have used the phrases 'fundamental economic practices' or 'fundamental economic principles' to describe concepts relating to the economy and commerce. Fundamental economic principles or practices include hedging, insurance, and mitigating risks." (MPEP 2106.04(a)(2)(II)(A)).
Present independent claim 1 is directed to implementation of an artificially intelligent, automated control strategy to "assist the user in achieving the at least one change that would achieve the diminished environmental impact, responsive to acceptance of the at least one change that would achieve the diminished environmental impact," where artificial intelligence is used to "suggest the at least one change via machine learning to avoid making suggestions that have a probability of user rejection that is over a rejection probability threshold" and that can "override the selected setting [by a user as a setting for the device], wherein the override automatically causes the selected setting to be changed to a different setting that is in accordance with the at least one change that would achieve the diminished environmental impact."
Such features directed to diminished environment impact by a device with changeable settings via automation and artificial intelligence are not reasonably equivalent to and do not recite any enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, managing personal behavior, and relationships or interactions between people and thus would not be abstract as a certain method of organizing human activity. Further, even assuming for the sake of argument such a certain method of organizing human activity would be involved, the present claims do not recite (and are not directed to) the certain method of organizing human activity and are thus still patent eligible.
In response, Examiner respectfully disagrees and notes Applicant’s arguments regarding the artificially intelligence and machine learning elements are moot because these elements are not considered abstract and therefore were not analyzed under Step 2A-Prong One. Examiner asserts these features recited in the pending claim are considered additional elements and therefore were analyzed under Step 2A-Prong Two of the Two-Part Analysis. However, Examiner finds the recitation of the artificial intelligence and machine learning does not take the claim out of the mental processes and certain methods of organizing human activity groupings.
Regarding the 35 U.S.C. 101 rejection, Applicants argues the August 4th Memorandum (see p. 8-9, Applicant Remarks) and states indeed, the features of claim 1 directed to diminished environment impact by a device with changeable settings via automation and artificial intelligence are not practically performed entirely within a human mind such that the features are not directed to mental processes. The claims are patent eligible, and not directed to a mental process, as a skilled artisan would understand that a human mind is not equipped to practically perform entirely recited features of at least (as set forth in the present claims) to at least apply artificial intelligence and machine learning to suggest at least one change to a changeable setting of a device that would achieve a diminished environmental impact, let alone to suggest the at least one change via machine learning to avoid making and exclude suggestions that a user is likely to reject. See MPEP 2106.04(a)(2)(III) (citing SRIInt'l, Inc. v. Cisco Sys., Inc., 930 F. 3d 1295, 1304 (Fed. Cir. 2019) ("the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited in the claims" thus finding the claims non-abstract and patent eligible)). Such features "could not, as a practical matter, be performed entirely in a human's mind" and thus the claims are patent eligible. See MPEP 2106.04(a)(2)(III) (citing Cyberfone Sys., L.L.C. v. CNN Interactive Grp., 558 Fed.Appx. 988, 992 (Fed. Cir. 2014)(quoting SiRF Tech. v. Int'l Trade Comm'n, 601 F.3d 1319, 1332 (Fed. Cir. 2010)).
In response, Examiner respectfully disagrees. Claims can recite a mental process even if they are claimed as being performed on a computer; see MPEP 2106.04(a)(2)(III)(C). Examiner finds the pending claims recite similar limitations to claims the courts have indicated may not be sufficient in showing an improvement in computer-functionality, such as accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; see MPEP 2106.05(a)(I) and MPEP 2106.05(a)(II). Examiner finds the present claims are directed to the data analysis of implementing a control strategy to achieve a diminished environmental impact. Examiner respectfully reminds Applicant, regardless of complexity and/or granularity, data analysis without meaningful limitations within the claims that amount to significantly more than the abstract idea itself is a judicial exception (i.e. abstract idea). Even in a computer environment, these limitations are still considered abstract by reciting limitations that mimic human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind. Examiner maintains the claims recite an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicants argues the Example 40 (see p. 9, Applicant Remarks) and states under Prong Two of Revised Step 2A, the recited features of claim 1 and the present claims are eligible as they clearly integrate any alleged judicial exception into a practical application. Similar to EXAMPLE 40 of the 2019 PEG that found a judicial exception integrated into a practical application through recitation of a meaningful limitation of at least the practical application of collecting additional Netflow protocol data relating to traffic based on a meaningful limitation of when the collected network delay, packet loss, or jitter is greater than the predefined threshold, the independent claims integrate any alleged judicial exception into a practical application of at least "override the selected setting, wherein the override automatically causes the selected setting to be changed to a different setting that is in accordance with the at least one change that would achieve the diminished environmental impact" based on a meaningful limitation of at least "following a determination that the selected setting is not in accordance with the at least one change that would achieve the diminished environmental impact.".
In response, Examiner respectfully disagrees and finds the above cited features, are not comparable to the eligible features of Example 40. Example 40 recites a specific manner of collecting additional NetFlow protocol data whenever the initially collected data reflects an abnormal condition, thus optimizing network performance. Example 40 provides a specific improvement over prior systems, resulting in improved network monitoring. However, Examiner finds the present claims do not recite an improvement to a technology, technological field or computer-related technology. Specifically the claim uses additional elements as tools to manage the personal behavior of users. Examiner maintains the claims are directed to the abstract idea of implementing a control strategy to achieve a diminished environmental impact without reciting limitations indicative of integrating the abstract idea into a practical application.
Regarding the 35 U.S.C. 101 rejection, Applicants Further, under Step 2B, the claims are patent eligible and recite "significantly more" than an abstract idea as they affect an improvement in the technology and/or technical field of automated and intelligent device setting control to achieve a diminished environment impact while excluding undesired suggestions to the device settings via machine learning for improved processing and a smarter automated control strategy, and supposed by at least paragraphs [0063] and [0101] of the present application.
Indeed, such recitations reflect an improvement in the recited technology to reduce system noise from information that is undesired, which further avoids using additional computing processing resources and provides an improved user interface and more efficient processing technology. And as set forth in a September 26, 2025, Appeals Review Panel (ARP) decision issued by USPTO Director Squires in Ex Parte Desjardins et al., Appeal 2024-000567, in which the USPTO vacated a Patent Trial and Appeal Board's rejection under 35 U.S.C. 101 of an AI- related patent application, the ARP decision agree with Appellant's assertion that the claims recite additional features reflecting an improvement to computer functioning or other technology or technical via certain limitations asserted to "'provide technical improvements over conventional systems by addresses challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training."' Ex Parte Desjardins et al., at page 7. Similarly, as set forth above, the present claim recitations reflect an improvement in the recited technology to reduce system noise from information that is undesired, avoiding using additional computing processing resources while reducing storage requirements and provides an improved user interface and more efficient processing technology.
Thus, for at least the reasons set forth above with respect to amended independent claim 1, the present claims are patent eligible. Withdrawal and reconsideration of the rejections is thus respectfully requested.
Examiner respectfully disagrees. Examiner notes in Desjardins the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and the claims reflected the improvement identified in the specification. The improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Examiner finds no similar improvement in the present claims. Applicant is describing the use of the additional elements as tools to improve the automated control strategy used to achieve diminished environmental impact without any improvement to how the artificial intelligence and machine learning functions, reflecting and/or submitting that the technology used is being improved or there was a technical problem with the technology that the claimed invention solves. Examiner maintains the additional elements recited in the claim function as intended with no improvement to the technology. Examiner asserts the claim is directed to an abstract idea.
Additionally, Examiner respectfully reminds Applicant claims are evaluated to ensure the claim itself reflects the disclosed improvement; MPEP 21060.04(d)(1). Examiner finds the pending claims do not reflect how the arrangement of additional elements provides technical improvements in reducing system noise from information that is undesired, avoiding using additional computing processing resources while reducing storage requirements and providing an improved user interface and more efficient processing technology as Applicant describes. Examiner finds the additional elements involved here have no substantial practical application except in connection with a digital computer and thus maintains the claim is directed to an abstract idea. For at least these reasons, the pending claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Regarding the 35 U.S.C. 102/103 rejection, Applicants states Vega does not anticipate or reasonably suggest “a system comprising one or more processors configured to, inter alia, "determine at least one change to the predefined activity that would achieve a diminished environmental impact; suggest the at least one change that would achieve the diminished environmental impact to the user via a device interface via application of artificial intelligence to suggest the at least one change via machine learning to avoid making suggestions that have a probability of user rejection that is over a rejection probability threshold; ... following a selection by the user of a setting for the device with the one or more changeable settings, receive an indication of the selected setting ... following a determination that the selected setting is not in accordance with the at least one change that would achieve the diminished environmental impact, override the selected setting, wherein the override automatically causes the selected setting to be changed to a different setting that is in accordance with the at least one change that would achieve the diminished environmental impact." (see p. 11-13, Applicant Remarks).
In response, Examiner respectfully disagrees. Examiner has considered Applicant’s arguments and found Applicant’s arguments are moot because they are directed to newly amended subject matter that fail to comply with the written description requirement under 35 U.S.C. 112(a). Additionally, Examiner finds Veg teaches an analytics and optimization engine that identifies energy consumption and associated environmental footprint optimization for each customer based on their behavior and usage patterns, lifestyle, preferences and energy aspirations. Energy optimization opportunity, actuation, and service or product provider selection is based on statistical usage patterns, machine learning descriptive and prescriptive analytics, behavior on premises, occupant lifestyle, consumer conscious and unconscious preferences (e.g., quantifiable and non-quantifiable). Veg further provides people and businesses access to actionable insight about their own energy habits with easy to activate or implement optimization recommendations, alerts and reports that highlight unusual consumption, deviation from customer set targets, and visibility when options better aligned with consumer preferences and objectives become available (see par. 0076) and therefore is sufficient in teaching the amended claim language. For at least these reasons, the pending claims remain rejected under 35 U.S.C. § 102/103 as being unpatentable over the prior art of record. Please refer to the 35 U.S.C. 102/103 rejection for further explanation and rationale.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 7-10, 12 and 13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites the limitation “suggest the at least one change that would achieve the diminished environmental impact to the user via a device interface via application of artificial intelligence to suggest the at least one change via machine learning to avoid making suggestions that have a probability of user rejection that is over a rejection probability threshold”. Applicant relies on paragraphs [0063] and [0101] of the original discloser to provide support for the assertion that the current application suggest changes to a user that would diminish environmental impact via a application of artificial intelligence where machine learning is used to avoid making suggestions that have a probability of user rejection that is over a rejection probability threshold as described. However there is no actual description given and the specification fails to provide support for the concept, because the specification does not provide disclosure in sufficient detail to demonstrate to one of ordinary skill in the art that the claimed invention achieves such a function. For the purpose of examination, Examiner will interpret accordingly.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-4, 7-10, 12 and 13 are directed towards a system, which is among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-4, 7-10, 12 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite implementing a control strategy to achieve a diminished environmental impact.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, determine an environmental impact value of the predefined activity based on environmental impact caused by one or more aspects of the predefined activity; determine at least one change to the predefined activity that would achieve a diminished environmental impact; and implement a control strategy to assist the user in achieving the at least one change that would achieve the diminished environmental impact, responsive to acceptance of the at least one change that would achieve the diminished environmental impact, wherein, to implement the control strategy to assist the user in achieving the at least one change that would achieve the diminished environmental impact: following a selection by the user of a setting for the device with the one or more changeable settings, receive an indication of the selected setting; determine whether the selected setting is in accordance with the at least one change that would achieve the diminished environmental impact; and following a determination that the selected setting is not in accordance with the at least one change that would achieve the diminished environmental impact, override the selected setting, wherein the override automatically causes the selected setting to be changed to a different setting that is in accordance with the at least one change that would achieve the diminished environmental impact constitutes methods based on managing personal behavior, as well as, observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of processors, devices, a device interface via application of artificial intelligence and machine learning does not take the claim out of the mental processes and certain methods of organizing human activity groupings. Thus the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites collect ongoing user activity information relating to a predefined activity, using one or more devices having a predefined relationship to a user and suggest the at least one change that would achieve the diminished environmental impact to the user via a device interface, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Claim 1 further recites processors, devices and a device interface at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f) and wherein the predefined activity is associated with a device with one or more changeable settings, which merely confines the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Additionally, claim 1 recites a device interface via application of artificial intelligence to suggest the at least one change via machine learning to avoid making suggestions that have a probability of user rejection that is over a rejection probability threshold. The general use of machine learning techniques does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the a device interface via application of artificial intelligence and machine learning disclosed in the claims are solely used as tools to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including processors, devices and device interface amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); see MPEP 2106.05(d)(II). The machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0062]-[0063]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim amounts to significantly more than the abstract idea itself, therefore, the claim is rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 2, 4, and 7 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claim 12 recites deliver a reminder to the mobile personal device to change an aspect of the predefined activity in accordance with the at least one change which is considered an insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g) and does not integrate the abstract idea into practical application. Additionally, claims 3, and 8-10, 12 and 13 recite steps that further narrow the abstract idea. Therefore claims 2-4, 7-10, 12 and 13 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 9, 10 and 12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vega et al., U.S. Publication 2021/0123771 [hereinafter Vega].
Referring to Claim 1, Vega teaches:
A system comprising:
one or more processors configured to (Vega, [0077]; [0172]):
collect ongoing user activity information relating to a predefined activity, using one or more devices having a predefined relationship to a user, wherein the predefined activity is associated with a device with one or more changeable settings (Vega, [0198]), “resulting intermediate fingerprint data is stored in database 1390 and is used as the primary input to the calculations needed to be performed to track, analyze and determine energy usage that is somehow not as expected, as well as operating appliances and adjusting set points, if so programmed by a customer…”; (Vega, [0125]), “receiving, using the communication device, at least one building mode from the at least one electronic device. Further, the at least one building mode may include indication of the at least one appliance corresponding to the at least one actuation data; (Vega, [0122]), “the at least one configuration may include one or more operational settings associated with the at least one appliance. Further, the at least one appliance may include an integration hub and a plurality of smart devices communicatively coupled to the integration hub”; (Vega, [0135]), “receiving at least one environmental information from at least one environmental information source. Further, the at least one environmental information may be associated with the at least one premises. Further, the at least one premises information may include a premises identifier associated with a premises of the at least one premises”; (Vega, [0094]; [0096]; [0110]; [0113]; [0186]),
determine an environmental impact value of the predefined activity based on environmental impact caused by one or more aspects of the predefined activity (Vega, [0281]), “gathering their historical energy usage data and other data that impacts energy usage. Data integration of smart meter and other advisor database information 1320 is the next step followed by a “time period” extraction of historical energy usage or consumption data 1330. FIGS. 26-27 illustrate the process steps unique to the advisor implementation that results once the initial (energy fingerprint) intermediary method steps have been completed”; (Vega, [0111]), “the at least one lifestyle information may include a first lifestyle information associated with the first time period and a second lifestyle information associated with the second time period. Further, the at least one premises information may include at least one efficiency indicator associated with the at least one utility consuming appliance deployed in the at least one premises. Further, the at least one efficiency indicator may include a first efficiency indicator corresponding to a first time period and a second efficiency indicator corresponding to a second time period. Further, the analyzing may include determining a lifestyle variation based on comparing the first lifestyle information and the second lifestyle information. Further, the analyzing may include determining an efficiency variation based on comparing the first efficiency indicator and the second efficiency indicator”; (Vega, [0202]), “energy leakage calculation of the present disclosure directly links customer historical usage, lifestyle schedules, preferences, and settings through analysis, comparisons and simplified pragmatic methods to identify non-intrusive ways to save energy without requiring efforts by the customer to change any regular activities in which electricity is actively consumed in the household. This integration of a plurality of customer inputs, data and behavioral science brings visibility to previously unknown wasted electricity, quantify its associated cost and environmental impact”; (Vega, [0322]; [0079]; [0210]; [0233]-[0234]; [0285]);
determine at least one change to the predefined activity that would achieve a diminished environmental impact (Vega, [0089]), “displaying and alerting an end-user of variances in energy use based on one or more of selected set points, excessive usage, variances from baseline, and unintentional usage, and (vii) recommending possible remediation(s) to eliminate or mitigate usage increases”; (Vega, [0296]), “Providing actionable insights for energy usage variations using a personalized context is a powerful tool that can help educate and guide consumers to make adjustments and decisions that can reduce consumption, costs and environmental impact”; (Vega, [0180]), “generate calculations, comparisons and recommendations 148 for a customer…energy analytics engine (optimization advisory engine) may generate energy consumption statistics and/or recommendations 148 to “save energy”…”; (Vega, [0190]), “The information from a GUI like that of FIG. 10 is needed for more detailed analysis of historical usage data and for analysis and presentations for potential recommendations to decrease energy consumption”; (Vega, [0085]; [0105]-[0106]);
suggest the at least one change that would achieve the diminished environmental impact to the user via a device interface via application of artificial intelligence to suggest the at least one change via machine learning to avoid making suggestions that have a probability of user rejection that is over a rejection probability threshold (Veg, [0076]), “The analytics and optimization engine … identify the energy consumption and associated environmental footprint optimization for each customer based on their behavior and usage patterns, lifestyle, preferences and energy aspirations. Energy optimization opportunity, actuation, and service or product provider selection is based on statistical usage patterns, machine learning descriptive and prescriptive analytics, behavior on premises, occupant lifestyle, consumer conscious and unconscious preferences (e.g., quantifiable and non-quantifiable). The present disclosure provides people and businesses access to actionable insight about their own energy habits… with easy to activate or implement optimization recommendations, alerts and reports that highlight unusual consumption, deviation from customer set targets, and visibility when options better aligned with consumer preferences and objectives become available”; (Veg, [0084]), “The present disclosure provides a system for end-user energy analytics and optimization that is useful for obtaining and analyzing power consumption to establish a baseline for energy consumption and monitoring actual consumption for variances from the baseline and the determination of the cause for and correction of a variance… machine learning and employing artificial intelligence models to identify data clustering, outliers and other data driven insights and incorporating ongoing feedback to selected portions of the analysis…. monitoring actual consumption for variances from the baseline… receiving end-user goals, lifestyle behaviors, and premises information and occupation data, (ii) displaying synchronized time slice data in one or more pre-selected formats, (iii) displaying alternative representations of energy usage data associated with a source of energy for said premises, (iv) displaying recommendations for available energy reduction choices, (v) displaying energy consumption for said energy devices associated with said premises, (vi) displaying and alerting an end-user of variances in energy use based on one or more of selected set points, excessive usage, variances from baseline, and unintentional usage, and (vii) recommending possible remediation(s) to eliminate or mitigate usage increases”; (Veg, [0188]), “the Cloud or web based platform 110 of the present disclosure provides a calculation and energy analytics engine (optimization advisory engine) capable of initially generating a unique customer multidimensional energy profile (Energy Fingerprint) that uses as much energy usage data as is available, but preferably at least 12 months of historical energy consumption 210 and in addition integrates household lifestyle activities and user preferences 218 (116) to create a multidimensional envelope providing a more accurate model (e.g., digital twin) of the user's consumption based on a user's premises and its devices, and the user's priorities, behaviors, and activities… energy analytics engine (optimization advisory engine) may be used to calculate an energy optimization score (e.g. Energy IQ) 240 and perform variance analysis for determining a cause, suggest optimization recommendations, and perform device controls and actuations, as more fully described later herein”; (Veg, [0086]; [0196]; [0214]; [0255]; [0235]; [0248]-[0249]; [0399]-[0404]); and
implement a control strategy to assist the user in achieving at least one the change that would achieve the diminished environmental impact, responsive to acceptance of the at least one change that would achieve the diminished environmental impact, responsive to acceptance of the at least one change that would achieve the diminished environmental impact (Vega, [0075]), “Energy advisor helps customers implement energy optimization recommendations, making it easier to make smarter more informed energy decisions that save money, improve efficiency, and advance sustainability with minimized intrusion in energy-consumer's lifestyle”; (Vega, [0112]), “analyzing may include determining an implementation of the at least one utility recommendation based on at least one of the utility consumption variation, the lifestyle variation and the efficiency variation”; (Vega, [0188]), “this energy analytics engine (optimization advisory engine) may be used to calculate an energy optimization score (e.g. Energy IQ) 240 and perform variance analysis for determining a cause, suggest optimization recommendations, and perform device controls and actuations”; (Vega, [0281]), wherein, to implement the control strategy to assist the user in achieving the at least one change that would achieve the diminished environmental impact, the one or more processors are configured to:
following a selection by the user of a setting for the device with the one or more changeable settings, receive an indication of the selected setting; determine whether the selected setting is in accordance with the at least one change that would achieve the diminished environmental impact (Vega, [0322]-[0330]), “Define Optimization is Run based on user settings and goals… impact and alignment are calculated for each optimization opportunity available in the database as weighted scores based on each given's total impact projections and the and alignment with the customer optimization criteria…The customer has the option to set the actuation of the optimization recommendations to manual or automatic, and to define automation settings, notifications and thresholds…Comparison of environmental impact of the current and recommended condition and recommended optimization projected costs based on baseline historical and predicted consumption”; (Vega, [0297]; [0305]), “Once the metrics from advisor are calculated or determined as described herein above, some of the metrics may be continuously monitored and displayed, that for example, but not limited to, include… The Capability to take action (change settings, switch of appliances, etc.)”; (Vega, [0443]; [0213]; [0367]); and
following a determination that the selected setting is not in accordance with the at least one change that would achieve the diminished environmental impact, override the selected setting, wherein the override automatically causes the selected setting to be changed to a different setting that is in accordance with the at least one change that would achieve the diminished environmental impact (Vega, [0344]-[0347]), “the customer may initiate modifications to existing schedules or set points to clear the variance 6062. If not, a query is made regarding changes in occupancy 6064 and if not a query is made regarding changes in appliances and features 6066. For items 6062, 6064, and 6064, if change have been made then the baseline personalized model monitoring step 6030 is changed or modified to reflect these changes. And, if the baseline model is modified then the optimization recommendations and actuations are modified 6070, which in turn results in modifications to the baseline model 6080 and then the energy optimization score (e.g. Energy IQ) 6090. [0345] 10. A user is notified when updated recommendations are available and has the ability to accept, adjust or reject the suggested automation. [0346] 11. Accepted automations are incorporated into the application schedules and building modes. [0347] 12. A user receives periodic notifications, including reports, on the automations performed and the equivalent reduction in energy consumption, cost, and pollution emissions”; (Vega, [0292]).
Referring to Claim 2, Vega teaches the system of claim 1. Vega further teaches:
wherein the devices include a home appliance (Vega, [0190]), “present disclosure also combines household information from a user (dwelling type and size, number of rooms, appliances, number of occupants, etc.), lifestyle behaviors 116 and uses a basic disaggregation algorithm that provides a general split of historical energy consumption into buckets (e.g., A/C, heating, pool, clothes dryers, etc.)…”; (Vega, [0171]), ““premises”, “residence”, “structure”, “building” and “dwelling”, they may be used interchangeably”; (Vega, [0209]; [0228]; [0230]).
Referring to Claim 3, Vega teaches the system of claim 2. Vega further teaches:
wherein the predefined activity includes usage of the home appliance (Vega, [0190]), “present disclosure also combines household information from a user (dwelling type and size, number of rooms, appliances, number of occupants, etc.), lifestyle behaviors 116 and uses a basic disaggregation algorithm that provides a general split of historical energy consumption into buckets (e.g., A/C, heating, pool, clothes dryers, etc.)…”; (Vega, [0171]), ““premises”, “residence”, “structure”, “building” and “dwelling”, they may be used interchangeably”; (Vega, [0209]; [0228]; [0230]).
Referring to Claim 4, Vega teaches the system of claim 1. Vega further teaches:
wherein the devices include a mobile personal device (Vega, [0093]), “the at least one lifestyle information source may include a user device (e.g. a desktop computer, a tablet computer, a smartphone, a mobile phone, a wearable computer, etc.) configured to receive the at least one at least one lifestyle information manually entered by a user (e.g. by way of touch inputs, voice commands, gestures etc.) and transmit the at least one lifestyle information over a network (e.g. the Internet). In some embodiments, the at least one lifestyle information source may be an appliance capable of capturing and transmitting the at least one lifestyle information. For instance, the at least the at least one lifestyle information source may be an IoT appliance (e.g. IoT appliance, IoT sensor, IoT camera, microphone etc.) configured to capture and transmit the at least one lifestyle information”; (Vega, [0110]), “the at least one utility consumption information source may include a user device (e.g. a desktop computer, a tablet computer, a smartphone, a mobile phone, a wearable computer, etc.) configured to receive the at least one utility consumption information manually entered by a user (e.g. by way of touch inputs, voice commands, gestures etc.) and transmit the at least one utility consumption information over a network (e.g. the Internet)”.
Referring to Claim 9, Vega teaches the system of claim 1. Vega further teaches:
wherein the impact includes at least a fixed impact associated with one or more devices involved in the predefined activity (Vega, [0290]), “A remediation may include for example, but not be limited to… premises or appliance replacement, premises or appliance upgrade, etc.”; (Vega, [0291]).
Referring to Claim 10, Vega teaches the system of claim 1. Vega further teaches:
wherein the change is determined based at least in part on user-defined parameters exempting specified changes and the change accommodates to the parameters so as not to utilize an exempted change (Vega, [0345]), “A user is notified when updated recommendations are available and has the ability to accept, adjust or reject the suggested automation”; (Vega, [0294.
Referring to Claim 12, Vega teaches the system of claim 1. Vega further teaches:
wherein the control strategy includes delivery of one or more reminders to a user based on engagement in the predefined activity, and wherein the one or more processors are further configured to: determine, based on information collected from at least the mobile personal device, that the user is engaging in the predefined activity; and deliver a reminder to the mobile personal device to change an aspect of the predefined activity in accordance with the at least one change, responsive to determining that the user is engaging in the predefined activity (Vega, [0076]), “identify the energy consumption and associated environmental footprint optimization for each customer based on their behavior and usage patterns, lifestyle, preferences and energy aspirations… provides people and businesses access to actionable insight about their own energy habits… activate or implement optimization recommendations, alerts and reports that highlight unusual consumption, deviation from customer set targets, and visibility when options better aligned with consumer preferences and objectives become available”; (Vega, [0084]), “receiving end-user goals, lifestyle behaviors, and premises information and occupation data, (ii) displaying synchronized time slice data in one or more pre-selected formats, (iii) displaying alternative representations of energy usage data associated with a source of energy for said premises, (iv) displaying recommendations for available energy reduction choices, (v) displaying energy consumption for said energy devices associated with said premises, (vi) displaying and alerting an end-user of variances in energy use based on one or more of selected set points, excessive usage, variances from baseline, and unintentional usage, and (vii) recommending possible remediation(s) to eliminate or mitigate usage increases”; (Vega, [0358]), “the continuous monitoring of the energy consumption comparing it to the baseline and personalized model to detect variances, alert and query customer when predefined threshold is exceeded”; (Vega, [0290]), “advisory recommendations are triggered when the energy advisor system and method associates deviations in energy consumption patterns to situations requiring remediation. Once a predetermined variation threshold in the baseline is exceeded, based on consumer preferences and personalized models, flag and notify customer and prompting for input (including changes in: behavior, lifestyle, household features, appliances, etc.)”; (Vega, [0434]; [0442]; [0093]; [0110]).
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 7, 8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Vega et al., U.S. Publication 2021/0123771 [hereinafter Vega], and further in view of Bellowe, U.S. Publication No. 2017/0351978 [hereinafter Bellowe].
Referring to Claim 7, Vega teaches the system of claim 1. Vega teaches smart devices (e.g., connected to the internet, wireless network, etc.) characterized to identify the energy consumption and associated environmental footprint optimization for each customer based on their behavior and usage patterns, lifestyle, preferences and energy aspirations (see par. 0076), however Vega does not explicitly teach:
wherein the devices include a vehicle.
However Bellowe teaches:
wherein the devices include a vehicle (Bellowe, [0096]-[0097]), “member profile can be generated by collecting information about the user from a plurality of devices/sensors associated with the user. The devices and sensors can include… devices coupled to an automobile which can provide information such as diagnostic information… the devices and/or sensors can collect, via sensor signals, information such as characteristics associated with the user such as devices usage information or location. These characteristics can include the status of the device/sensor signals, the geographical user associated with the device, automobile starter such as the average speed of an automobile during a time period, the speed of the automobile, automobile battery use, throttle status, distance driven, the vehicle location, and/or driving habits”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the smart devices in Vega to include the vehicle limitation as taught by Bellowe. The motivation for doing this would have been to improve the method for the generation of an environmental impact component as part of the “Energy Fingerprint” with a matching representation of the environment impact with a calculation of possible actions needed to offset the consumer's consumption impact in Vega (see par. 0233) toto efficiently include the results of generating information which can assist in calculating a carbon footprint associated with the user and/or the user's environment (see Bellowe par. 0096).
Referring to Claim 8, the combination of Vega in view of Bellowe teaches the system of claim 7. Vega teaches smart devices (e.g., connected to the internet, wireless network, etc.) characterized to identify the energy consumption and associated environmental footprint optimization for each customer based on their behavior and usage patterns, lifestyle, preferences and energy aspirations (see par. 0076), however Vega does not explicitly teach:
wherein the predefined activity includes travel using the vehicle.
However Bellowe teaches:
wherein the predefined activity includes travel using the vehicle (Bellowe, [0096]-[0097]), “member profile can be generated by collecting information about the user from a plurality of devices/sensors associated with the user. The devices and sensors can include… devices coupled to an automobile which can provide information such as diagnostic information… the devices and/or sensors can collect, via sensor signals, information such as characteristics associated with the user such as devices usage information or location. These characteristics can include the status of the device/sensor signals, the geographical user associated with the device, automobile starter such as the average speed of an automobile during a time period, the speed of the automobile, automobile battery use, throttle status, distance driven, the vehicle location, and/or driving habits”; (Bellowe, [0058]), “may also be possible to gather more fine grained information about the carbon impact of an automobile trip between point A and point B from the car itself, taking into consideration driving speed, starts and stops, and elevation changes to determine and record a person's location over time, which can include information regarding a path taken, speed, and an amount of time in transit”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the smart devices in Vega to include the activity limitation as taught by Bellowe. The motivation for doing this would have been to improve the method for the generation of an environmental impact component as part of the “Energy Fingerprint” with a matching representation of the environment impact with a calculation of possible actions needed to offset the consumer's consumption impact in Vega (see par. 0233) to efficiently include the results of generating information which can assist in calculating a carbon footprint associated with the user and/or the user's environment (see Bellowe par. 0096).
Referring to Claim 13, Vega teaches the system of claim 12. Vega teaches determining/providing recommendations for available energy reduction choices (see par. 0084), but Vega does not explicitly recite:
wherein the one or more processors are further configured to: determine that the user is engaging in the predefined activity in a manner not in accordance with the at least one change, based on information collected from the at least one mobile personal device, and wherein the delivery of the reminder is further responsive to the determination that the user is engaging in the predefined activity in the manner not in accordance with the at least one change.
However Bellowe teaches:
wherein the one or more processors are further configured to: determine that the user is engaging in the predefined activity in a manner not in accordance with the at least one change, based on information collected from the at least one mobile personal device, and wherein the delivery of the reminder is further responsive to the determination that the user is engaging in the predefined activity in the manner not in accordance with the at least one change (Bellowe, [0118]), “the users activity can be monitored and suggested techniques to lower the carbon footprint can be provided… the user activity of shopping can be added to the predicted schedule and/or dynamically updated based on the user's behavior… the user can be provided with warning messages when the user activity has high carbon impact and no recommendations can be provided to lower it. In at least one embodiment the user can be provided with information about the user's predicted itinerary and/or the items performed. The information can be provided in separate tabs and/or split screen format. In at least one embodiment the information can include the carbon impact of the item and/or the predicted carbon impact of the item”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the recommendations in Vega to include the reminder limitations as taught by Bellowe. The motivation for doing this would have been to improve the method for the generation of an environmental impact component as part of the “Energy Fingerprint” with a matching representation of the environment impact with a calculation of possible actions needed to offset the consumer's consumption impact in Vega (see par. 0233) to efficiently include the results of monitoring user activity to incentivize lowered carbon footprints (see Bellowe par. 0133).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jayan et al. (US 20220344934 A1) – Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning (ML) to forecast energy demand and to generate an energy plan for one or more facilities of an organization. For example, a system may forecast an occupancy of the facilities for use with historical demand data in forecasting the energy demand. The forecasting may be performed by one or more trained ML models. Additional ML models may be trained to select energy resources that satisfy the forecasted energy demand and that prioritize constraint(s). The system may generate an energy plan that indicates information related to the selected energy resources, such as cost, energy type, environmental impact, etc., for use in increasing an amount of renewable energy resources used at the facilities. In some implementations, the system may recommend actions to reduce a negative environmental impact associated with the selected energy resources.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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