DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the Application and claims filed 09/26/2025.
Claims 1-20 have been examined and are pending.
Information Disclosure Statement (IDS)
Acknowledgement is hereby made of receipt of Information Disclosure Statements filed by applicant on 10/02/2025; 10/15/2025; and 02/12/2026.
(AIA ) Examiner Note
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.
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 at the time any inventions covered therein were effectively filed 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 at the time a later invention was effectively filed 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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/ patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/ patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,554,610 B2 (hereinafter ‘610 patent reference). Although the claims at issue are not identical, they are not patentably distinct from each other because the only difference is that in the instant application the claims are broader than as recited in the ‘610 patent, except for an obvious variation recited in the instant application (i.e. of prompting a user to input information) and which is merely as being recited as being received by the system of the ‘610 patent which needs some initial mechanism by which to initiate request for the information which it receives. Prompting a user to input information as is recited in the instant application but absent from the ‘610 patent is within the level of skill of a person of ordinary skill in the art before the effective filing date of the claimed invention. Therefore, these additional steps are found to be obvious variations of the ‘610 patent in view of the knowledge of one of ordinary skill in the art, and from the nature of the problem to be solved. Therefore, it would have been obvious to implement such variations by a person of ordinary skill in the art who has read the -610 patent because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649. Furthermore, KSR forecloses the argument that a specific teaching, suggestion, or motivation is require to support a finding of obviousness. See the Board decision Ex parte Smith, -- USPQ2d --, slip op. at 20, (Bd. Pat. App. & Interf. June 25, 2007) (citing KSR, 82 USPQ2d at 1396). Examiner notes that "Section 103 forbids issuance of a patent when 'the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains.'" KSR Int'l Co. v. Teleflexlnc., 127 S.Ct. 1727, 1734 (2007). If a person of ordinary skill in the art can implement a predictable variation, and would see the benefit of doing so, §103 likely bars its patentability. Moreover, if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar devices in the same way, using the technique is obvious unless its actual application is beyond that person's skill. KSR Int'l Co. v. Teleflex, Inc., No 04-1350 (U.S. Apr. 30, 2007). Also, KSR states that "the combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results." KSR, 127 S.Ct. at 1739 and 1741, 82 USPQ2d at 1396.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting
as being unpatentable over claims 1 & 6 of co-pending Application No. 18/665,103 (hereinafter reference
application ‘103) and claims 1 & 6 of co-pending Application No. 18/907,085 (hereinafter reference application ‘085). Although the claims at issue are not identical, they are not patentably distinct
from each other because the claims of the instant application are taught by the intervening
claims associated with application ('103) and also by the intervening claims associated with application ('085). This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
For example, reference application '103 teaches the following limitations of claim 1 as follows:
A computing device for monitoring and predicting a lifetime of one or more appliances, the computing device comprising at least one processor and at least one memory device, the at least one processor configured to:
('103 - [claim 1] "A computing device for predicting a lifetime of one or more appliances, the computing device comprising at least one processor and at least one memory device, the at least one processor configured to:")
[…]
retrieve appliance data relating to the first appliance;
('103 - [claim 1] "receive appliance data relating to a first appliance;")
compute, using an artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the retrieved appliance data of the first appliance,
('103 - [claim 1] "compute, using the artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the received appliance data")
wherein the artificial intelligence model is trained based upon historical appliance data including data associated with historical lifetimes of similar appliances
('103 - [claim 1] "receive historical appliance data including historical lifetimes of a plurality of appliances, at least some of the historical appliance data generated by sensors configured to monitor one or more parameters of at least one of the plurality of appliances train an artificial intelligence model using the historical appliance data including the historical lifetimes of the plurality of appliances")
generate a recommendation to repair or replace the first appliance based upon the predicted remaining lifetime; and
('103 - [claim 6] "The computing device of Claim 1, wherein the at least one processor is further configured to: generate, using the artificial intelligence model, a recommendation for increasing a lifetime of the first appliance; and transmit recommendation data to the user device that, when received by the user device, causes the user device to generate the user interface to include the recommendation.")
cause the user interface to present at least the predicted remaining lifetime of the first appliance ('103 - [claim 1] "transmit content data to a user device that, when received by the user device, causes the user device to generate a user interface including at least the predicted remaining lifetime.") and the generated recommendation. ('103 - [claim 6] "The computing device of Claim 1, wherein the at least one processor is further configured to: generate, using the artificial intelligence model, a recommendation for increasing a lifetime of the first appliance; and transmit recommendation data to the user device that, when received by the user device, causes the user device to generate the user interface to include the recommendation.")
Although the ‘103 reference does not explicitly recite “cause a user device to present a user interface prompting a selection of an appliance; receive, from the user device, a selection of a first appliance;” Examiner notes that these features are within the level of skill of a person of ordinary skill in the art and because the ‘103 reference requires some mechanism by which to initiate the '103 - [claim 1] "receive appliance data relating to a first appliance;", there is motivation for a person of ordinary skill in the art to use the knowledge and skills within their possession to initiate this action including prompting a user, e.g. via the user interface of ‘103 for selection of a first appliance for which the user would like to receive a recommendation regarding repair and/or cost for replacement and then receiving a selection of such a first appliance so that the system of ‘103 knows which appliance data to retrieve – i.e. knows "receive appliance data relating to a first appliance;" because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.
A similar Provisional non-statutory double patenting rejection over co-pending Application ‘085 (i.e. 18/907,085) is also applicable.
Claim Rejections - 35 USC § 112
The following is a quotation 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..
Claims 1-20 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 claims contain subject matter which is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 13, 20 recite limitations directed towards the following:
“computing, using an artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the retrieved appliance data of the first appliance, wherein the artificial intelligence model is trained based upon historical appliance data including data associated with historical lifetimes of similar appliances; generating a recommendation to repair or replace the first appliance based upon the predicted remaining lifetime…”
These limitations recite functionality attributed to a generic “artificial intelligence model” and/or a generic processor “using” such generic AI model. However, the broadly claimed functionality (i.e. computing a predicted remaining lifetime of the first appliance based upon the retrieved appliance data of the first appliance) is not a function that any general purpose computer can perform without special programming. Although the claims further recite: “…wherein the artificial intelligence model is trained based upon historical appliance data including data associated with historical lifetimes of similar appliances”, the entire original disclosure (original claims, specs, drawings) are actually completely silent in regards to any particular training which does result in a generic AI capable of the functionality now claimed. Merely stating that the AI model is trained is not the same as providing the necessary steps such that a person of ordinary skill in the art would understand what such training is intended to entail such that the functionality now claimed may be achieved. The Specification simply does not provide sufficient written description support full claim feature – there is no description of special machine learning or AI capable of performing all of applicant’s recited steps without further specific information pertaining to actual training steps needed to achieve the claimed functionality.
This deficiency further indicates that the Specification describes only "a mere wish or plan for" achieving the functionality, as now claimed, by some generic “artificial intelligence model”. See Eli Lilly, 119 F .3d at 1566 (citation omitted). As such, the broadly recited limitation "merely recite[s] a description of the problem to be solved," and leaves to future inventors to "complete an unfinished invention." See Ariad, 598 F.3d at 1353. In this case, without the Specification describing any particular algorithm to achieve the claimed function of generating a de-duplicated advertising campaign, one of ordinary skill in the art would not have reasonably concluded that the inventors invented the claimed invention or that they possessed the claimed subject matter at the time of filing of the application. See Vasudevan, 782 F.3d at 683; see also Regents, 119 F.3d at 1566; § 112 Guidance at 61.
To satisfy the written description requirement of 35 U.S.C. § 112, first paragraph, the Specification must reasonably convey to an artisan of ordinary skill that Appellant had possession of the claimed invention at the time the application was filed. Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 682 (Fed. Cir. 2015) (citing Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en banc)). Functional claim language that merely describes an intended result and fails to support the scope of the claimed invention is insufficient to show possession, even when the claim recitations are found word-for-word in the Specification. Vasudevan, 782 F.3d at 682 ("[t]he written description requirement is not met if the specification merely describes a 'desired result"') (citing Ariad, 598 F.3d at 1349); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968 (Fed. Cir. 2002) ("[t]he appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy" the written description requirement). The Specification must explain, for example, how Appellant intended to achieve the claimed function to satisfy the written description requirement. Vasudevan, 782 F.3d at 683. While "[t]here is no rigid requirement that the disclosure contain 'either examples or an actual reduction to practice,"' due to the written description requirement, the Specification must set forth "an adequate description that 'in a definite way identifies the claimed invention' in sufficient detail such that a person of ordinary skill would understand that the inventor had made the invention at the time of filing." Allergan, Inc. v. Sandoz Inc., 796 F.3d 1293, 1308 (Fed. Cir. 2015) (citing Ariad, 598 F.3d at 1352); see also Examining Computer-Implemented Functional Claim Limitations for Compliance with 35 US.C. 112, 84 F.R. 57, 61- 62 (January 7, 2019) ("112 Guidance").
Here, the Specification does not sufficiently support any particular “Artificial intelligence model ” which is capable of performing the recited functionality as recited in the aforementioned limitations; i.e. as recited as noted per claims 1, 13, and 20.
The specification does not provide a single technique nor method nor algorithm regarding specific training any artificial intelligence model should undergo such that it may be sufficiently trained to achieve the functionality now claimed. Instead, the Specification (e.g. Fig. 4, [0218], [0234], [0288]-[0294]) merely asserts, e.g. per [0234]: “…the AMS computing device 150 computes using an artificial intelligence model 416 (shown in Figure 4), a predicted remaining lifetime of the first appliance 312 based upon the received appliance data 404 of the first appliance 312. …" ; however, #416 of Fig. 4 is a directional arrow and connotes nothing more than data flow. Fig. 4 #406 is identified as “Operational Predictive Model” and #418 is identified as “Model Trainer”. However, there are no details attached to nor associated with these two illustrative flow diagram boxes; they are in effect the proverbial “black box” as no information is given regarding their innerworkings. Indeed, the entire specification is silent as to any specific inner working of these black boxes. Instead, the Specification implies that any conceivable type of artificial intelligence model will suffice (e.g. see Specification at [0290]-[0294], e.g.: “…In some embodiments, at least one of a plurality of ML methods and algorithms may be applied, which may include but are not limited to: linear or logistic regression, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, combined learning, reinforced learning, dimensionality reduction, and support vector machines. In various embodiments, the implemented ML methods and algorithms are directed toward at least one of a plurality of categorizations of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning. [0291] In one embodiment, the ML module employs supervised learning,… [0292] In another embodiment, a ML module may employ unsupervised learning, which involves finding meaningful relationships in unorganized data. Unlike supervised learning, unsupervised learning does not involve user-initiated training based upon example inputs with associated outputs… [0293] In yet another embodiment, a ML module may employ reinforcement learning, which involves optimizing outputs based upon feedback from a reward signal…”
The remainder of the Specification is notably void of any particular discussion or claim to a novel or inventive artificial intelligence model which is capable of performing the steps now attributed to it.
Under these circumstances, the Examiner has determined the Specification merely states a wish that AI could be used via this generically recited “using an artificial intelligence model” to perform the steps as attributed to it per the claims, but the Specification does not demonstrate the Applicant was in possession of such a capable artificial intelligence model nor how applicant intended to create such an AI model capable of performing the steps as now claimed. The Specification does not describe the claimed invention in sufficient detail such that an ordinarily skilled artisan would understand that the inventor had made the invention at the time of filing. Thus, the Examiner rejects claims 1, 13, and 20 under 35 U.S.C. § 112(a) for a lack of written description support.
Dependent claims 2-12, 14-19 inherit the deficiencies of their parent claim and are also rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the Subject Matter Eligibility Guidance outlined in the MPEP 2106, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the MPEP 2106, as follows:
Per Independent claims 1, 13, 20:
compute, …, a predicted remaining lifetime of the first appliance based upon the retrieved appliance data of the first appliance,…;
generate a recommendation to repair or replace the first appliance based upon the predicted remaining lifetime.
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106. Specifically, these limitations fall within the group Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
That is, the steps, as drafted, are a business decision to target advertising (e.g. the generated recommendation to repair or replace an appliance) based on targeting criteria (i.e. an estimate of the remaining life of an appliance estimated [computed] from appliance data) and thus falling into Certain Methods of Organizing Human Activity. There is no technical problem being solved and there is no technical solution presented nor claimed for solving a technical problem. Instead, this idea has a direct analog to targeted marketing and sales and business in general. For example, a merchant (e.g. Kenmore, MayTag, GE, Sears, Samsung, etc…) who wishes to sell a new appliance and/or sell their services of repairing appliances is known to prefer to have some information upon which to make a business decision regarding whether to advise a client to repair or buy new. Such merchant requests data from their client regarding current appliance (e.g. age of appliance, current service conditions, etc…) and then makes a decision using this information about when the appliance might fail (i.e. the remaining life of appliance) so that they may have a basis upon which to logically make a recommendation / offer to their client regarding whether the client should repair or replace. For example, a water heater may be known to be corroding through and will soon fail and therefore it may not make monetary sense to repair a part on such water heater because the ROI (return on investment) will be low or negative as opposed to buying a new water heater where the initial investment cost may be high but its expected life will be long and the ROI is expected to be high. This idea is a normal business practice and is common across industries including the appliance industry.
Furthermore, the mere nominal recitation of a generic computing components executing generic software intended to perform this abstract idea does not take the claim limitation out of the enumerated grouping. Note that the generic reference to “a computing device” and “processor” which execute or otherwise perform the method by using software (i.e. “using an artificial intelligence model”) is at such a high level of generality that these features themselves cannot be taken as technical solutions but instead are understood to be generic references to “use” of generic hardware and software to generally attempt to automate the abstract idea. Applicant has not purported any advancement to AI techniques themselves or to AI modeling. Applicant’s entire disclosure fails to put forth any particular AI model but instead mentions that any known such technique may be used (e.g. Specification [0218], Fig. 4 model 416 and/or 406, and Specification [0232]-[0263] and [0288]-[0294]). Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts or, link them to a field of use (i.e. in this targeted advertising “using” generic AI models) or, serve as insignificant extra-solution activity. The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea.
These additional limitations, exemplified in limitations of method claim 13, are as follows: “A computer-implemented method for predicting a lifetime of one or more appliances, the computer-implemented method performed by a computing device including at least one processor and at least one memory device, the computer implemented method comprising: causing a user device to present a user interface prompting a selection of an appliance; receiving, from the user device, a selection of a first appliance; retrieving appliance data relating to the first appliance; … using an artificial intelligence model,… wherein the artificial intelligence model is trained based upon historical appliance data including data associated with historical lifetimes of similar appliances;… causing the user interface to present at least the predicted remaining lifetime of the first appliance and the generated recommendation.”
However, these elements do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a technique nor technical solution for “receiving” data such as user requests, nor “retrieving” data such as data related to a user request, nor is it a technique nor technical solution for causing an interface to present or otherwise display targeting advertising information (e.g. an estimate of a predicted remaining lifetime of an appliance and the advertised recommendation regarding whether to replace or repair such appliance). Indeed, at this high level of generality, these are nothing more than insignificant extra-solution and insignificant pre-solution activities as relate to the identified abstract idea. Furthermore, the idea of “using an artificial intelligence model” is nothing more than asserting a computer can perform the step automatically “using” undisclosed software described as this generic “AI model” – note, applicant has not invented an AI model nor does applicant’s entire original disclosure purport that the applicant has invented any particular model. Intead, applicant merely implies any such known model is believed to be able to be used to achieve the desired functionality if properly trained but Applicant does not offer the details necessary to train any such known AI model framework to achieve his claimed functionality. Therefore, the additional elements do not recite a specific manner of performing any of the steps core to the already identified abstract idea. Instead, these features merely serve to generally “apply” the aforementioned concepts (e.g. “using” a generic “Artificial intelligence model” and using generic computer device with processor, etc…) and/or, link them to a field of use (i.e. targeting advertising to persons seeking advice regarding replacing or repairing an appliance) or, are insignificant extra-solution activity (e.g. data gathering, collection, storage, transmittal, or presentation/display) as relates to the already identified abstract idea and do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components (e.g. “using an artificial intelligence model” executed by a generic processor of a generic computing device) and “link” them to a field of use (i.e. targeted advertising/marketing), or as insignificant extra-solution activity (e.g. as noted supra). For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (i.e. a server) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claim 2 recites the following: “The computing device of Claim 1, wherein the appliance data includes a repair cost associated with the first appliance.” However, a description of data upon which the abstract idea is intended to operate is not significantly more than the abstract idea itself. For example, making a business decision about whether to repair an appliance or replace an appliance according to well-established business principles should evaluate the cost of repair; e.g. if the cost is very low such as flipping a switch or plugging in the appliance so that it works again is far better than buying new and replacing the otherwise working appliance. Stating this obvious fact is not significantly more than the already identified abstract idea.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and therefore the claims are not found to be patent eligible.
Please see the MPEP 2106 and the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 103 (AIA )
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as obvious over Gibson et al. (U.S. 2023/0342868 A1; hereinafter, "Gibson") in view of Jones (U.S. 10,013,677 B2; hereinafter, "Jones").
Claims 1, 13, 20
Pertaining to claims 1, 13, 20 exemplified in the limitations of claim 1, Gibson as shown teaches the following:
A computing device for monitoring and predicting a lifetime of one or more appliances, the computing device comprising at least one processor and at least one memory device, the at least one processor configured to:
cause a user device to present a user interface prompting a selection of an appliance; and receive, from the user device, a selection of a first appliance (Gibson, see at least Fig. 1 user mobile device 112 with display 160 [user interface], and per at least [0047]-[0048], e.g.: “…unit 174 may be configured to prompt a user to… input information at the display 160 [user interface]. The mobile device 112 may then generate a communication that may include the home telematics data [e.g. selection of an appliance]…, and may transmit the communication 192 to the request server 140 via communications interface 152…”; Gibson, see also at least [0041]-[0044] in view of at least [0050]-[0052] and [0112], teaching, e.g.: “…Depending on the embodiment, the user telematics data and/or the home telematics data may include information input by the user at a computing device… the home telematics data may only be collected or otherwise generated after receiving a confirmation [e.g. a selection of a first appliance] from the user [received, from the user device],… telematics data may include image data of the property 116, as well as internal diagnostic data on functionality of particular devices or components of the property 116… the home telematics data may be provided by a user who opts in to allow the smart device 110 to provide such information…”; Examiner notes Gibson’s “home telematics data” as noted supra and per [0040]-[0041] appears to include identifying information pertaining to a specific smart appliance, e.g. “particular devices” and this telematics data is noted as being either input by the user e.g. via user’s device’s display, after prompting user for such input, and/or after receiving a confirmation from the user, and then this information is transmitted to server per teachings at [0047]-[0048]. Also, the aforementioned “confirmation” [a selection by the user] discussed by Gibson appears to be in regards to which particular smart appliance(s) the user wishes data to be collected by the system; see again [0044] and [0112]. Therefore, whether explicitly stated, Examiner finds there to be motivation given by Gibson to perform Gibson’s prompt for input of telematics data to include a specific prompt for the user to input a selection of a smart appliance and then receive from his user, via the user’s device, a selection of such smart appliance, e.g. as a mechanism by which to provide Gibson’s “confirmation” regarding from which smart appliance(s) the system is allowed to collect Gibson’s home telematics information, e.g. such as noted per [0040]-[0041]: “…home telematics data regarding the usage and/or occupancy of the property… data such as security camera data, electrical system data, plumbing data, appliance data, energy data, maintenance data, guest data, homeshare data, and any other suitable data representative of property 116 occupancy and/or usage…. the home telematics data may include electricity usage data, water usage data, HVAC usage data (e.g., how often the furnace or air conditioner unit is on), and smart appliance data (e.g., how often the stove, oven, dish washer, or clothes washer is operated). The home telematics data may also include home occupant mobile device data or home guest mobile device data, such as GPS or other location data…”. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have performed the aforementioned limitations as a means by which to communicate Gibson’s confirmation of which smart appliance home telematics data the user authorizes the system to collect and because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.);
retrieve appliance data relating to the first appliance (Gibson, see citations noted supra, including also at least [0041] e.g.: “…the home telematics data [appliance data] may include electricity usage data, water usage data, HVAC usage data (e.g., how often the furnace or air conditioner unit is on), and smart appliance data (e.g., how often the stove, oven, dish washer, or clothes washer is operated)…”; in view of at least [0050]-[0051], e.g.: “…The computing device 117 associated with the property 116 may obtain the home telematics data [appliance data]… through interfacing with a mobile device 112… home telematics data [appliance data] may be used to determine that the property 116 and/or components of the property 116 are likely to require repair and/or replacement, and may lead to a potential risk or claim associated with the property 116. For instance, make/model data, usage data, and age data may be collected from smart appliances [therefore, telematics data is appliance data] and analyzed by a processor to determine that a smart appliance is approaching or likely approaching end-of-life, and needs replacement or maintenance…”);
compute, using an artificial intelligence model, a predicted remaining lifetime of the first appliance based upon the retrieved appliance data of the first appliance, (Gibson, see citations noted supra, further in view of at least [0051] “…make/model data, usage data [historical appliance data], and age data [historical appliance data] may be collected from smart appliances [i.e. based upon the retrieved appliance data] and analyzed by a processor [e.g. using an AI model] to determine [to compute] that a smart appliance is approaching or likely approaching end-of-life [predicted remaining lifetime], and needs replacement or maintenance…”; Note also [0071]-[0080] directed towards use of exemplary machine learning techniques noting: “…Although the methods described elsewhere herein may not directly mention machine learning techniques [AI models], such methods may be read to include such machine learning for any determination or processing of data that may be accomplished using such techniques, etc…”; see also [0113]. In view of these teachings, Examiner finds Gibson provides ample motivation to a person of ordinary skill in the art before the effective filing date of the claimed invention to compute, using his disclosed machine learning model [AI models], his determined [predicted] “likely approaching end-of life” [remaining lifetime] of a smart appliance [of the first appliance] based on his retrieved smart appliance data and to recognize this information is an indication of the remaining lifetime of the appliance relative to the time represented by the smart appliance data which was collected. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have performed the feature in question as it would also be useful for planning regarding Gibson’s disclosed determination of “needs replacement or maintenance” and/or assessing need for smart appliance repair and/or replacement, etc… and because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.)
wherein the artificial intelligence model is trained based upon historical appliance data including data associated with historical lifetimes of similar appliances (Gibson, see citations noted supra, e.g. [0116], e.g.: “…the system 100 may use the likelihood of loss and/or claims data pulled from an insurance database to anticipate the likelihood of a claim using the machine learning model. In particular, the system 100 may use either or both of individual claim data related to a particular property 116, or anonymized and/or historical claim data for a broader class of properties.);
generate a recommendation to repair or replace the first appliance based upon the predicted remaining lifetime (Gibson, see at least [0051]: “…For instance, make/model data, usage data, and age data may be collected from smart appliances [therefore, telematics data is appliance data] and analyzed by a processor to determine that a smart appliance is approaching or likely approaching end-of-life, and needs replacement or maintenance [recommendation to repair or replace]…”); and
Although Gibson teaches the above limitations, and provides motivation to cause the display of his user’s device to present the information which he teaches his system generates, i.e. his determination that a smart appliance is approaching or likely approaching end-of-life, and needs replacement or maintenance, he may not explicitly teach that he does present this information to his user via the display [interface] of the user’s device. Nonetheless, Gibson, in view of Jones teaches the following:
cause the user interface to present at least the predicted remaining lifetime of the first appliance and the generated recommendation (Jones, see at least [3:35-50], e.g.: “…means for receiving contact information for a personal communication device (PCD) associated with a user of the appliance or appliance part; means for generating a lifespan expectancy corresponding to when the appliance or appliance part is expected to exhibit a maintenance issue, based upon an occurrence of maintenance issues in other like appliances or appliance parts; means for determining a length of time that the appliance or appliance part has been in existence or operation; means for, when the length of time approaches the lifespan expectancy, engaging in a communication session with the PCD of the user; and means for, during the communication session, communicating one or more suggestions on where to order, purchase, or seek repair for the appliance or appliance part and price information pertaining to each of the suggestions…”).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Jones (directed towards engaging in a communication session with the (PCD) personal computing device [interface of user device] of the user; and during the communication session, communicating [presenting] one or more suggestions on where to order, purchase, or seek repair for the appliance or appliance part and price information pertaining to each of the suggestions) which is applicable to a known base device/method of Gibson to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Jones to the device/method of Gibson in order to likewise cause Gibson’s user’s display to present the information which Gibson determines, similar to the information presented by Jones, which is Gibson’s determination that a smart appliance is approaching or likely approaching end-of-life, and needs [a recommendation] replacement or maintenance, because Gibson and Jones are analogous art in the same field of endeavor (at least G08B 21/18; or H04L 12/2803; and/or H04L 12/281) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 2, 14
Gibson/Jones teaches the limitations upon which these claims depend. Furthermore, as shown, Gibson teaches the following: …wherein the appliance data includes a repair cost associated with the first appliance (Gibson, see citations noted supra, in view of at least [0066], teaching “repair costs” are received.).
Claims 5, 17
Gibson/Jones teaches the limitations upon which these claims depend. Furthermore, as shown, Gibson teaches the following: …wherein the user interface includes one or more data fields prompting input of a respective one of at least a model identifier, an appliance type, an appliance manufacturer, an installation date, or a photograph (Gibson, see citations noted supra, including again at least [0048], e.g.: “…mobile device 112 may obtain the home telematics data and/or user telematics data via a user interaction with a display 160 of the mobile device 112. For example, a user may take a photograph… and/or input information regarding a characteristics indicative of potential hazards or other such home score factors associated with the property 116 at the display 160…”)
Claim 11
Gibson/Jones teaches the limitations upon which these claims depend. Furthermore, as shown, Gibson teaches the following: The computing device of Claim 1, wherein the at least one processor is further configured to receive at least some of the appliance data of the first appliance as (i) a natural language input by a user via the user device, or (ii) a data signal from the first appliance (Gibson, see citations noted supra, e.g. [0075]-[0080] regarding: “…The machine learning programs may
include Bayesian program learning (BPL ), voice recognition [receive natural language input]… The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or machine learning…”; per [0096]: “…The user-input device (not shown) may also include a microphone capable of receiving user voice input…”).
Claim 12
Gibson/Jones teaches the limitations upon which these claims depend. Furthermore, as shown, Gibson teaches the following: The computing device of Claim 1, wherein the at least one processor is further configured to: retrieve additional appliance data relating to the first appliance from one or more external data sources; and compute the predicted remaining lifetime of the first appliance based at least in part upon the retrieved additional appliance data (Gibson, see citations noted supra, including also at least [0095]: “…(0095] Furthermore, the communication unit 220 may communicate with databases, other smart devices and/or mobile devices, or other external sources of information to transmit and receive information relating to… home telematics data…”; i.e. the “home telematics data” may be received from “external sources” and as noted per at least [0051] it is the “home telematics data” which is used “…to determine that a smart appliance is approaching or likely approaching end-of-life, and needs replacement or maintenance…”).
Claims 3, 8, 9, 10, 15 are rejected under 35 U.S.C. 103 as obvious over Gibson in view of Jones and Official Notice.
Claims 3, 15
Gibson/Jones teaches the limitations upon which these claims depend including predicted remaining lifetime, repair cost, and generating a recommendation regarding whether to repair or replace. However, they may not delve into the minutia of the well-known techniques as recited below. However, regarding these features, Gibson/Jones in view of Official Notice teaches the following:
…wherein the at least one processor is further configured to: compute a current predicted value of the first appliance based at least in part upon the predicted remaining lifetime (Examiner takes Official Notice of the following facts: It was old and well-known before the effective filing date of the claimed invention that Appliance market value is often calculated using a Straight-Line Depreciation Method. The remaining value [a current predicted value] is determined by taking the initial cost, dividing it by the appliance's expected lifespan to find the yearly cost, and multiplying that by the remaining years of expected life [based on predicted remaining lifetime]); and generate the recommendation based upon a comparison between the repair cost and the current predicted value (Examiner takes Official Notice of the following facts: It was old and well-known before the effective filing date of the claimed invention that appliance repair technicians often use what’s called the 50/50 rule to evaluate an appliance's market value against its repair cost, to assess whether an appliance should be repaired or replaced; i.e. if a repair costs over 50% of a new comparable unit, and the appliance is past half its expected lifespan, replacement is usually the smarter investment. Expected lifespan is often estimated by the manufacturer and may also be estimated based on the lifespan of similar appliances of similar build quality. If a repair is under 30% of replacement cost, then repair is almost always worth it, regardless of age. If repair cost is 30%–50% of replacement cost & under half-life, then repair is usually a smart choice, as the machine still has years of useful life remaining. However, if the repair cost is over 50% of replacement cost, then replacing typically provides better long-term value, as aging components may soon cause additional breakdowns. These facts are all within the knowledge of a person of ordinary skill in the art.)
Therefore, the Examiner understands that the limitations in question are merely applying known techniques per Official Noticed Facts which are applicable to a known base device/method of Gibson/Jones to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply these techniques to the device/method of Gibson/Jones in order to perform the limitations in question because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 8
Gibson/Jones teaches the limitations upon which these claims depend including predicted remaining lifetime, repair cost, and generating a recommendation regarding whether to repair or replace. However, they may not delve into the minutia of the well-known techniques as recited below. However, regarding these features, Gibson/Jones in view of Official Notice teaches the following:
The computing device of Claim 1, wherein the at least one processor is further configured to: generate, using the artificial intelligence model, at least one second recommendation for increasing a lifetime of the first appliance; determine a cost to perform the at least one second recommendation for increasing the lifetime of the first appliance; and transmit recommendation data to the user device that, when received by the user device, causes the user device to present the second recommendation and the cost to perform the at least one second recommendation (Examiner takes Official Notice of the following facts: It was old and well-known before the effective filing date of the claimed invention that repairing an appliance, such as replacing a broken component such as a dead battery, provides added useful life to the otherwise in-operable appliance. Recommending such a repair, e.g. battery replacement for an appliance needing a battery, is within the level of skill of a person of ordinary skill in the art such as routinely done by appliance repair technicians. Furthermore, providing an estimate to perform a repair service is old and well-known as is communicating such recommendation to a client who has asked for an assessment of whether to repair or replace an appliance, which is often accomplished by transmitting a phone call or an e-mail or text message to a user where the user receives such communication via the user’s phone [user device]. Furthermore, generic artificial intelligence models, as is claimed here, are old and well-known and known to be useful in performing well-known and understood tasks if trained properly. As applicant possesses no particular technique of training an AI model to perform the step as claimed, this step is wholly generic and may be accomplished in any well-known manner within the level of skill of a person of ordinary skill in the art.)
Therefore, the Examiner understands that the limitations in question are merely applying known techniques per Official Noticed Facts which are applicable to a known base device/method of Gibson/Jones to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply these techniques to the device/method of Gibson/Jones in order to perform the limitations in question because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 9
Gibson/Jones/Official Noticed Facts teaches the limitations upon which these claims depend including predicted remaining lifetime, repair cost, and generating a recommendation regarding whether to repair or replace and second recommendation for increasing a lifetime of the first appliance, such as repairing a broken component of the appliance. However, they may not delve into the minutia of the well-known techniques as recited below. However, regarding these features, Gibson/Jones in view of Official Notice teaches the following:
The computing device of Claim 8, wherein the at least one second recommendation includes a list of recommended maintenance actions, and wherein the at least one processor is further configured to determine an item cost for each item on the list of recommended maintenance actions (Examiner takes Official Notice of the following facts: It was old and well-known before the effective filing date of the claimed invention that repairing an appliance, such as replacing a broken component such as a dead battery, provides added useful life to the otherwise in-operable appliance. Recommending such a repair, e.g. battery replacement for an appliance needing a battery, is within the level of skill of a person of ordinary skill in the art such as routinely done by appliance repair technicians. Furthermore, providing an estimate to perform a repair service is old and well-known as is providing an itemized list of components which a technician believes require repair as well as an itemized cost for each component and labor involved in repairing the component. For example, this is very common in refrigerator repairs where a technician may find that a refrigerator requires a new compressor and recommends replacing the compressor rather than replacing the entire refrigerator at much greater cost than merely replacing the compressor and the technician often provides the appliance owner, i.e. the customer, with a repair cost estimate which includes labor costs, estimated time for repair, and estimated cost of the component, i.e. a new compressor for the refrigerator appliance. All such recommendations and actions are within the level of skill of a person of ordinary skill in the art and are notoriously well-known so as to be almost ubiquitous.)
Therefore, the Examiner understands that the limitations in question are merely applying known techniques per Official Noticed Facts which are applicable to a known base device/method of Gibson/Jones to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply these techniques to the device/method of Gibson/Jones in order to perform the limitations in question because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 10
Gibson/Jones/Official Noticed Facts teaches the limitations upon which these claims depend including predicted remaining lifetime, repair cost, and generating a recommendation regarding whether to repair or replace and second recommendation for increasing a lifetime of the first appliance, such as repairing a broken component of the appliance. However, they may not delve into the minutia of the well-known techniques as recited below. However, regarding these features, Gibson/Jones in view of Official Notice teaches the following: The computing device of Claim 1, wherein the at least one processor is further configured to train the artificial intelligence model using the historical appliance data of historical appliances similar in type to the first appliance including a lifetime value for each historical appliance and maintenance data associated with each historical appliance (Gibson, see at least [0075]-[0080]: “…Use of machine learning techniques, as described herein, may begin with training a machine learning program, or such techniques may begin with a previously trained machine learning program…”; and again per [0051]-[0052], e.g.: “…For instance, make/model data, usage data, and age data may be collected from smart appliances and analyzed by a processor to determine that a smart appliance is approaching or likely approaching end-of-life, and needs replacement or maintenance….”; Therefore, there is motivation to train Gibson’s machine learning models to perform the determination a smart appliance is approaching or likely approaching end-of-life and needs replacement or maintenance/repair.
Furthermore, Examiner takes Official Notice of the following facts: it was old and well-known and within the level of ordinary skill of a person of ordinary skill in the art before the effective filing date of the claimed invention to train machine learning models on exemplary data for which a user wishes such model to make a prediction and as was known to a person of ordinary skill in the art before the effective filing date of the claimed invention, historical maintenance data of similar appliances to the appliance which a user wishes to model is the type of data which is exemplary of the appliance and upon which a machine learning model should be trained to make the desired determination of Gibson – i.e. Gibson [0051]: “For instance, make/model data, usage data, and age data may be collected from smart appliances and analyzed by a processor to determine that a smart appliance is approaching or likely approaching end-of-life, and needs replacement or maintenance.”
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention with the aforementioned knowledge and motivation to train Gibson’s machine learning model [artificial intelligence model] using “make/model data, usage data, and age data collected from smart appliances” [historical appliance data of historical appliances similar in type to the first appliance] including a other smart appliance “end-of-life” data [lifetime value for each historical appliance] and maintenance data associated with each appliance to make Gibson’s determination that a smart appliance is approaching or likely approaching end-of-life, and needs replacement or maintenance, because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649. Furthermore, in regards to hindsight reasoning, Examiner notes that rigid preventative rules that deny factfinders recourse to common sense are neither necessary under our case law nor consistent with it. KSR Int'l Co. v. Teleflex, Inc., No 04-1350 (U.S. Apr. 30, 2007).)
Claims 4, 16 are rejected under 35 U.S.C. 103 as obvious over Gibson in view of Jones and Buentello (US 12,394,031 B1; hereinafter, “Buentello”).
Claims 4, 16
Although Gibson/Jones teaches the limitations upon which these claims depend including knowing a repair cost as well as collecting information from smart appliances, e.g. Gibson per at least [0054] teaching home telematics data collected via “an evidence oracle… may be devices connected to sensors… as well as other Internet of Things (IiT) devices…”, and teaching generating a recommendation regarding whether to repair or replace, they may not explicitly teach calculating the repair cost which is disclosed as being known from the data which they do collect via sensors. Nonetheless, Gibson in view of Buentello teaches the following:
…wherein the at least one processor is further configured to calculate the repair cost based at least in part on sensor data (Buentello, see at least [], teaching: “…Accordingly, the present embodiments relate to the LIDAR system and associated methods for analyzing LIDAR data (e.g. dimensional data, depth data, volume data, and/or geometric data) collected by one or more LIDAR sensors [based at least in part on sensor data] to determine [calculate] …a recommended repair, and a cost of the recommended repair [repair cost], a time period for initiating the recommended repair, or any combination thereof…”).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Buentello which is applicable to a known base device/method of Gibson to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Buentello to the device/method of Gibson in order to perform the limitation in question because Buentello is pertinent to Gibson and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 6, 18 are rejected under 35 U.S.C. 103 as obvious over Gibson in view of Jones and Main-Reader (US 2023/0092348 A1; hereinafter, “Main-Reader”).
Claims 6, 18
Although Gibson/Jones teaches the limitations upon which these claims depend including a user device which accepts input from a user thereby requiring data-fields for accepting such input, e.g. per [0044], Gibson may not explicitly teach the below nuance. However, regarding this feature as recited, Gibson in view of Main-Reader teaches the following:
…wherein the at least one processor is further configured to, in response to a user input in a first data field of the one or more data fields, prepopulate a second data field of the one or more data fields (Main-Reader, see at least [0122], e.g.: “…The system 102 can prepopulate data fields for the response times of the light curtain and safety relays in section 1804 based on information obtained from the respective sets of specification data for the light curtain and safety relay selected by the user….”).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Main-Reader (directed towards a technique of prepopulating data fields in response to a user input) which is applicable to a known base device/method of Gibson (already directed towards receiving user input, such as home telematics data, implying such input is via a data collection field as provided to the user via the display [user interface] of the user’s device) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Main-Reader to the device/method of Gibson in order to perform the limitation in question because Main-Reader is pertinent to the data collection and user interface interactions of the system of Gibson and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 7, 19 are rejected under 35 U.S.C. 103 as obvious over Gibson in view of Jones and Main-Dejanovic (US 2021/0224936 A1; hereinafter, “Dejanovic”).
Claims 7, 19
Although Gibson/Jones teaches the limitations upon which these claims depend including a user device which accepts input from a user thereby requiring data-fields for accepting such input, e.g. per [0044], Gibson may not explicitly teach the below nuance. However, regarding this feature as recited, Gibson in view of Main-Reader teaches the following:
The computing device of Claim 1, wherein the user interface includes at least one of a dashboard and an audio output, the dashboard including one or more appliances associated with the user device and a respective predicted remaining lifetime associated with each of the one or more appliances, and the audio output in communication with a voice bot, the audio output including an audio speaker for audibly presenting a first appliance identifier of a first appliance of the one or more appliances associated with the user device and the respective predicted remaining lifetime of the first appliance (Dejanovic, see at least [0115], e.g.: “… In one embodiment, the portal, in response to a buyer query, can also include a display of the cost to repair and/or an adjustment in value on the dashboard to bring it into scope to meet buyer criteria. Information related to repair costs may be retrieved directly from information maintained in the service database, or may be provided by service providers identified in response to a buyer query…”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Dejanovic (directed towards a technique of in response to a buyer query e.g. regarding a selected appliance, including a display of the cost to repair such appliance and/or an adjustment in value on the dashboard to bring it, the value, into scope to meet buyer criteria. Information related to repair costs may be retrieved directly from information maintained in the service database, or may be provided by service providers identified in response to a buyer query, etc…) which is applicable to a known base device/method of Gibson (already directed towards receiving user input, such as home telematics data regarding appliances, implying such input is via a data collection field as provided to the user via a display [user interface/dashboard] of the user’s device) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Dejanovic to the device/method of Gibson in order to perform the limitation in question because Dejanovic is pertinent to the data collection and user interface interactions of the system of Gibson and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Sittner/
Primary Examiner, Art Unit 3621
Prosecution Insights