Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Election/Restrictions
Applicant’s election without traverse of Group I, Claims 1-15 in the reply filed on 7/28/2026 is acknowledged. Claims 16-20 are withdrawn from consideration.
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 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); 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 1-15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of U.S. Patent No. 12652357 (the first reference Patent). Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding Claim 1 of the instant Application, Claim 1 of the first reference Patent teaches a method (100) for providing support for one or more multi-function devices (MFD) (270) each networked to a central processor (220) (a “multi-function device (MFD) configured to detect an MFD anomaly and provide support”; “a processor configured to…detect…an anomaly in one or more MFDs”), comprising: monitoring (140) the one or more MFDs, comprising receiving MFD monitoring data, the MFD monitoring data comprising one or more of MFD sensor data and user input data to an MFD (“one or more MFD sensors configured to obtain MFD sensor data”, “(i) receive MFD monitoring data, the MFD monitoring data comprising one or more of the MFD sensor data and user input data received from a user of the MFD”); detecting (150), from the received MFD monitoring data, an anomaly in one or more of the one or more MFDs (“ (ii) detect, from the received MFD monitoring data, an anomaly in one or more MFDs”); identifying (160), by a trained support model analyzing the detected anomaly, a solution to the detected anomaly (“(iii) identify, by a trained support model analyzing the detected anomaly, a solution to the detected anomaly”); transmitting (170) the identified solution to the one or more MFDs experiencing the anomaly (MFD comprising ”a user interface configured to provide the identified solution to the user”; and providing (180), via a user interface of the one or more MFDs experiencing the anomaly, the identified solution (“a user interface configured to provide the identified solution to the user”).
Regarding Claim 2 of the instant Application, Claim 2 of the first reference Patent teaches the method of claim 1, wherein the anomaly is detected by analysis of the received MFD monitoring data by an anomaly detection model.
Regarding Claim 3 of the instant Application, Claim 3 of the first reference Patent teaches further comprising the step of automatically enacting (190) the identified solution at the one or more MFDs experiencing the anomaly.
Regarding Claim 4 of the instant Application, Claim 4 of the first reference Patent teaches wherein automatically enacting the identified solution comprises one or more of ordering a supply for an MFD, order a part for an MFD, and adjusting a setting or parameter of an MFD.
Regarding Claim 5 of the instant Application, Claim 5 of the first reference Patent teaches wherein providing the identified solution via the user interface of the one or more MFDs experiencing the anomaly comprises providing an instruction to a user.
Regarding Claim 6 of the instant Application, Claim 6 of the first reference Patent teaches wherein providing the identified solution via the user interface of the one or more MFDs experiencing the anomaly comprises a visualization of the identified solution.
Regarding Claim 7 of the instant Application, Claim 7 of the first reference Patent teaches wherein providing the identified solution comprises one or more of closing a query from a user received via the user input to the MFD, and dismissing the detected anomaly.
Regarding Claim 8 of the instant Application, Claim 8 of the first reference Patent teaches wherein providing the identified solution further comprises escalating the detected anomaly to an expert.
Regarding Claim 9 of the instant Application, Claim 9 of the first reference Patent teaches wherein the user input data to the MFD comprises a query received from a user of the MFD.
Regarding Claim 10 of the instant Application, Claim 10 of the first reference Patent teaches wherein detecting the anomaly in one or more of the one or more MFDs comprises analyzing the query received from the user of the MFD with a language model.
Regarding Claim 11 of the instant Application, Claim 11 of the first reference Patent teaches wherein detecting the anomaly in one or more of the one or more MFDs comprises analyzing the query received from the user of the MFD with one or more knowledge graphs identifying one or more contextual features in the query, and further wherein the trained support model identifies the solution to the detected anomaly based at least in part on the identified one or more contextual features in the query.
Regarding Claim 12 of the instant Application, Claim 12 of the first reference Patent teaches wherein identifying the solution to the detected anomaly further comprises classifying the detected anomaly into an anomaly category.
Regarding Claim 13 of the instant Application, Claim 13 of the first reference Patent teaches further comprising the step of closing (192), after the identified solution is implemented, the detected anomaly.
Regarding Claim 14 of the instant Application, Claim 14 of the first reference Patent teaches wherein identifying the solution to the detected anomaly comprises generating, with a trained language model, a natural language response.
Regarding Claim 15 of the instant Application, Claim 15 of the first reference Patent teaches further comprising training (120/500) the support model to analyze the detected anomaly in order to identify a solution, comprising: generating (520) support model training data, comprising the steps of: receiving (522) a corpus of historical multimodal MFD data, the historical multimodal MFD data comprising at least MFD sensor data and historical ticketing data, wherein at least some of the corpus of historical multimodal MFD data comprises historical MFD anomalies and associated historical anomaly solutions; transforming (524), using a natural language process model, the received corpus of historical multimodal MFD data to support model training data of a single mode; and training (540) a support model with the generated support model training data, wherein the support model is trained to analyze an identified MFD anomaly to identify a solution to the identified MFD anomaly.
Claims 1-13 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of U.S. Patent No. 12659411 (the second reference Patent). Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding Claim 1 of the instant Application, Claim 1 of the second reference Patent teaches a method (100) for providing support for one or more multi-function devices (MFD) (270) each networked to a central processor (220) (a “multi-function device (MFD) configured to provide real-time help to a user”; “a processor configured to…detect…an anomaly in one or more MFDs”), comprising: monitoring (140) the one or more MFDs, comprising receiving MFD monitoring data, the MFD monitoring data comprising one or more of MFD sensor data and user input data to an MFD (“one or more MFD sensors configured to obtain MFD sensor data”, (i) automatically receive MFD monitoring data, the MFD monitoring data comprising MFD sensor data and user input data to an MFD, the user input data comprising a request for assistance with a component of the MFD”); detecting (150), from the received MFD monitoring data, an anomaly in one or more of the one or more MFDs (“ (ii) automatically detect, from the received MFD monitoring data, an anomaly in one or more MFDs”); identifying (160), by a trained support model analyzing the detected anomaly, a solution to the detected anomaly (“(iii) automatically identify, by a trained support model analyzing the detected anomaly, a solution to the detected anomaly”); transmitting (170) the identified solution to the one or more MFDs experiencing the anomaly (MFD comprising “a user interface configured to automatically provide the identified solution to the user, wherein the identified solution is provided to the user in real-time in response to the provided request for assistance”; and providing (180), via a user interface of the one or more MFDs experiencing the anomaly, the identified solution (“a user interface configured to automatically provide the identified solution to the user, wherein the identified solution is provided to the user in real-time in response to the provided request for assistance”).
Regarding Claim 2 of the instant Application, Claim 8 of the second reference Patent teaches wherein the anomaly is detected by analysis of the received MFD monitoring data by an anomaly detection model.
Regarding Claim 3 of the instant Application, Claim 2 of the second reference Patent teaches further comprising the step of automatically enacting (190) the identified solution at the one or more MFDs experiencing the anomaly.
Regarding Claim 4 of the instant Application, Claim 3 of the second reference Patent teaches wherein automatically enacting the identified solution comprises one or more of ordering a supply for an MFD, order a part for an MFD, and adjusting a setting or parameter of an MFD.
Regarding Claim 5 of the instant Application, Claim 4 of the second reference Patent teaches wherein providing the identified solution via the user interface of the one or more MFDs experiencing the anomaly comprises providing an instruction to a user.
Regarding Claim 6 of the instant Application, Claim 5 of the second reference Patent teaches wherein providing the identified solution via the user interface of the one or more MFDs experiencing the anomaly comprises a visualization of the identified solution.
Regarding Claim 7 of the instant Application, Claim 6 of the second reference Patent teaches wherein providing the identified solution comprises one or more of closing a query from a user received via the user input to the MFD, and dismissing the detected anomaly.
Regarding Claim 8 of the instant Application, Claim 7 of the second reference Patent teaches wherein providing the identified solution further comprises escalating the detected anomaly to an expert.
Regarding Claim 9 of the instant Application, Claim 9 of the second reference Patent teaches wherein the user input data to the MFD comprises a query received from a user of the MFD.
Regarding Claim 10 of the instant Application, Claim 9 of the second reference Patent teaches wherein detecting the anomaly in one or more of the one or more MFDs comprises analyzing the query received from the user of the MFD with a language model.
Regarding Claim 11 of the instant Application, Claim 10 of the second reference Patent teaches wherein detecting the anomaly in one or more of the one or more MFDs comprises analyzing the query received from the user of the MFD with one or more knowledge graphs identifying one or more contextual features in the query, and further wherein the trained support model identifies the solution to the detected anomaly based at least in part on the identified one or more contextual features in the query.
Regarding Claim 12 of the instant Application, Claim 11 of the second reference Patent teaches wherein identifying the solution to the detected anomaly further comprises classifying the detected anomaly into an anomaly category.
Regarding Claim 13 of the instant Application, Claim 6 of the second reference Patent teaches further comprising the step of closing (192), after the identified solution is implemented, the detected anomaly.
Regarding Claim 15 of the instant Application, Claim 12 of the second reference Patent teaches further comprising training (120/500) the support model to analyze the detected anomaly in order to identify a solution, comprising: generating (520) support model training data, comprising the steps of: receiving (522) a corpus of historical multimodal MFD data, the historical multimodal MFD data comprising at least MFD sensor data and historical ticketing data, wherein at least some of the corpus of historical multimodal MFD data comprises historical MFD anomalies and associated historical anomaly solutions; transforming (524), using a natural language process model, the received corpus of historical multimodal MFD data to support model training data of a single mode; and training (540) a support model with the generated support model training data, wherein the support model is trained to analyze an identified MFD anomaly to identify a solution to the identified MFD anomaly.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method (100) for providing support for one or more multi-function devices (MFD) (270) each networked to a central processor (220), comprising:
monitoring (140) the one or more MFDs, comprising receiving MFD monitoring data, the MFD monitoring data comprising one or more of MFD sensor data and user input data to an MFD;
detecting (150), from the received MFD monitoring data, an anomaly in one or more of the one or more MFDs;
identifying (160), by a trained support model analyzing the detected anomaly, a solution to the detected anomaly;
transmitting (170) the identified solution to the one or more MFDs experiencing the anomaly; and
providing (180), via a user interface of the one or more MFDs experiencing the anomaly, the identified solution.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Under Step 1 of the analysis, Claim 1 belongs to a statutory category, namely it is a method claim.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, Claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a mental process. This can be seen in the claim limitations of detecting and identifying, which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to identify an anomaly and a solution to the anomaly and is capable of being performed mentally and/or with the aid of pen and paper.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including “one or more multi-function devices (MFD) (270) each networked to a central processor (220)”; monitoring (140) the one or more MFDs, comprising receiving MFD monitoring data, the MFD monitoring data comprising one or more of MFD sensor data and user input data to an MFD”; “transmitting (170) the identified solution to the one or more MFDs experiencing the anomaly”; and “providing (180), via a user interface of the one or more MFDs experiencing the anomaly, the identified solution”. However the monitoring, transmitting, and providing steps are merely data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity. Further, the “one or more multi-function devices (MFD) (270) each networked to a central processor (220)” are broadly claimed, generic hardware that are tangentially involved in the abstract idea, and the “trained support model analyzing the detected anomaly” merely recites generic computer hardware for implementing the abstract idea.
The generic data gathering, processing, and output steps, are recited at such a high level of generality (e.g. using a “central processor”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed method. For instance, nothing is done with the identified solution.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 1 amounts to significantly more than the abstract idea.
With regards to the dependent claims, Claims 2-15, merely further expand upon the abstract idea, and the outputting of the result of the abstract idea, and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for parent claims
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claim(s) 1-7, 12, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mishra et al (U.S. Pat. No. 11017321, hereinafter “Mishra”) in view of St. Jaques, JR. et al (U.S. Pub. No. 2011/0134451, hereinafter “Jaques”).
Regarding Claim 1, Mishra teaches a method (100) (Fig. 5) for providing support for a device (Fig. 1, equipment asset 150), comprising: monitoring (140) the device, comprising receiving device monitoring data (Fig. 5, block 502; Fig. 1, monitoring device 102 and operating characteristics data 136), the device monitoring data comprising one or more of device sensor data (Fig. 1, sensor(s) 152) and user input data to an device (optional due to “one or more of”); detecting (150), from the received device monitoring data, an anomaly in one or more of the device (Fig. 5, blocks 504 and 506; Fig. 1, priority events 112; categorization engine 124 and machine learning models 126; inference engine 128 and machine learning models 130); identifying (160), by a trained support model analyzing the detected anomaly, a solution to the detected anomaly (Fig. 5, block 508; Fig. 1, recommendation engine 132 and machine learning models 134); transmitting (170) the identified solution to the device experiencing the anomaly (Fig. 1, output 138, maintenance actions 116, and user device 162; column 20, line 37-column 21, line 11, some maintenance actions may be performed automatically); and providing (180), via a user interface of the device experiencing the anomaly, the identified solution (Fig. 5, block 510; column 41, lines 13-16; Fig. 1, output 138, maintenance actions 116, user device 162, and display device 140; column 20, line 37-column 21, line 11).
Mishra does not specifically teach that the device is one or more multi-function devices (MFD) (270) each networked to a central processor (220). However, Jaques teaches diagnosing problems in one or more multi-function devices (MFD) (270) each networked to a central processor (220) (paragraphs [0002]-[0003], Fig. 2, MFDs 140, 142, and 144, server 235, and distributed data mining module 245; Fig. 4). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Regarding Claim 2, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra further teaches wherein the anomaly is detected by analysis of the received monitoring data by an anomaly detection model (Fig. 5, blocks 504 and 506; Fig. 1, priority events 112; categorization engine 124 and machine learning models 126; inference engine 128 and machine learning models 130).
Mishra does not specifically teach that the monitoring data is MFD monitoring data. However, Jaques teaches MFD monitoring data (Fig. 4, blocks 420 and 440). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Regarding Claim 3, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra further teaches further comprising the step of automatically enacting (190) the identified solution at the device experiencing the anomaly (column 20, line 61-column 21, line 6, some maintenance actions may be performed automatically).
Mishra does not specifically teach that the device is one or more MFDs. However, Jaques teaches one or more MFDs (Fig. 2, MFDs 140, 142, and 144). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Regarding Claim 4, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 3. Mishra further teaches wherein automatically enacting the identified solution comprises one or more of ordering a supply for a device (optional due to “one or more of”), order a part for a device (column 20, line 61-column 21, line 6), and adjusting a setting or parameter of a device (column 20, line 61-column 21, line 6).
Mishra does not specifically teach that the device is an MFD. However, Jaques teaches an MFD (Fig. 2, MFDs 140, 142, and 144). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Regarding Claim 5, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra further teaches wherein providing the identified solution via the user interface of the one or more devices experiencing the anomaly comprises providing an instruction to a user (column 20, lines 27-61).
Mishra does not specifically teach that the device is one or more MFDs. However, Jaques teaches one or more MFDs (Fig. 2, MFDs 140, 142, and 144). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Regarding Claim 6, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra further teaches wherein providing the identified solution via the user interface of the device experiencing the anomaly comprises a visualization of the identified solution (column 20, lines 27-61).
Mishra does not specifically teach that the device is one or more MFDs. However, Jaques teaches one or more MFDs (Fig. 2, MFDs 140, 142, and 144). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Regarding Claim 7, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra further teaches wherein providing the identified solution comprises one or more of closing a query from a user received via the user input to the MFD (optional due to “one or more of”), and dismissing the detected anomaly (column 21, lines 6-11, user may choose whether to accept or dismiss the maintenance action corresponding to the detected anomaly).
Regarding Claim 12, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra further teaches wherein identifying the solution to the detected anomaly further comprises classifying the detected anomaly into an anomaly category (Fig. 1, categorization engine 124 and machine learning models 126; inference engine 128 and machine learning models 130; column 11, line 60-column 12-line 29, classification).
Regarding Claim 14, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra further teaches wherein identifying the solution to the detected anomaly comprises generating, with a trained language model (Fig. 1, recommendation engine 132 and machine learning models 134; see also column 30, lines 4-38, NLP is used), a natural language response (column 20, lines 33-43, text corresponding to the maintenance actions is equated to natural language response).
Claim(s) 8 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mishra in view of Jaques, further in view of Fischman et al (U.S. Pub. No. 2005/0144151, hereinafter “Fischman”).
Regarding Claim 8, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra does not specifically teach wherein providing the identified solution further comprises escalating the detected anomaly to an expert. However, Fischman teaches wherein providing the identified solution further comprises escalating the detected anomaly to an expert (paragraph [0005], if necessary, a ticket, which is equated to the claimed anomaly, is escalated to a qualified individual). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the escalation of Fischman in the system of Mishra, because such escalation is typical (see Fischman, paragraph [0005]).
Regarding Claim 13, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra does not specifically teach further comprising the step of closing (192), after the identified solution is implemented, the detected anomaly. However, Fischman teaches the step of closing (192), after the identified solution is implemented, the detected anomaly (paragraph [0005], after the issue is resolved the ticket, which is equated to the claimed anomaly, is closed). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the closing of Fischman in the system of Mishra, because such closing is typical (see Fischman, paragraph [0005]).
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mishra in view of Jaques, further in view of Monahan et al (U.S. Pub. No. 2017/0302540, hereinafter “Monahan”).
Regarding Claim 9, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra does not specifically teach wherein the user input data to the device comprises a query received from a user of the device. However, Monahan teaches wherein the user input data to the device comprises a query received from a user of the device (paragraph [0024], service request from the user used to determine issues with hardware or software components of a system). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the service requests of Monahan in the system of Mishra, in order to provide automated service request resolution (see Monahan, paragraph [0029]) and reduce the amount of human time required to resolve service requests (see Monahan, paragraph [0001]).
Mishra does not specifically teach that the device is an MFD. However, Jaques teaches an MFD (Fig. 2, MFDs 140, 142, and 144). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Regarding Claim 10, Mishra in view of Jaques and Monahan teaches everything that is claimed above with respect to Claim 9. Mishra does not specifically teach wherein detecting the anomaly in one or more of the one or more devices comprises analyzing the query received from the user of the device with a language model. However, Monahan teaches wherein detecting the anomaly in one or more of the one or more devices comprises analyzing the query received from the user of the device with a language model (paragraphs [0024]-[0025], tokenization of natural language service request). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the service request processing of Monahan in the system of Mishra, in order to provide automated service request resolution (see Monahan, paragraph [0029]) and reduce the amount of human time required to resolve service requests (see Monahan, paragraph [0001]).
Mishra does not specifically teach that the device is an MFD. However, Jaques teaches an MFD (Fig. 2, MFDs 140, 142, and 144). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mishra in view of Jaques and Monahan, further in view of Sun (U.S. Pub. No. 2019/0004831).
Regarding Claim 11, Mishra in view of Jaques and Monahan teaches everything that is claimed above with respect to Claim 9. Mishra does not specifically teach wherein detecting the anomaly in one or more of the one or more devices comprises analyzing the query received from the user of the device with one or more knowledge graphs identifying one or more contextual features in the query, and further wherein the trained support model identifies the solution to the detected anomaly based at least in part on the identified one or more contextual features in the query. However, Mishra does teach trained support models (Fig. 1, categorization engine 124 and machine learning models 126; inference engine 128 and machine learning models 130; recommendation engine 132 and machine learning models 134). Further, Sun teaches wherein detecting the anomaly in one or more of the one or more devices comprises analyzing the query received from the user of the device with one or more knowledge graphs identifying one or more contextual features in the query, and identifying the solution to the detected anomaly based at least in part on the identified one or more contextual features in the query (paragraphs [0008]-[0011], [0039], [0045]; device knowledge graph that determines request result). It would have been obvious to one skilled in the art before the effective filing date of the invention to provide an output of a knowledge graph such as is taught in Sun to one or more of the trained support models of Mishra, in order to enable the user to acquire a better customer service experience (see Sun, paragraph [0020]).
Mishra does not specifically teach that the device is an MFD. However, Jaques teaches an MFD (Fig. 2, MFDs 140, 142, and 144). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mishra in view of Jaques, further in view of Mehta et al (U.S. Pub. No. 2020/0184355, hereinafter “Mehta”).
Regarding Claim 15, Mishra in view of Jaques teaches everything that is claimed above with respect to Claim 1. Mishra further teaches further comprising training (120/500) the support model to analyze the detected anomaly in order to identify a solution (column 11, lines 7-59; column 12, line 54-column 13 line 32; column 14, line 35-column 15, line 6), comprising: generating (520) support model training data, comprising the steps of: receiving (522) a corpus of historical multimodal device data, the historical multimodal MFD data comprising at least device sensor data (Fig. 1, training data 118; column 11, lines 7-59), wherein at least some of the corpus of historical multimodal device data comprises historical device anomalies and associated historical anomaly solutions (column 13, lines 7-25); and training (540) a support model with the generated support model training data, wherein the support model is trained to analyze an identified device anomaly to identify a solution to the identified device anomaly (column 11, lines 7-59; column 13, lines 7-25; column 14, line 35-column 15, line 6).
Mishra does not specifically teach the historical multimodal device data comprising historical ticketing data; and transforming (524), using a natural language process model, the received corpus of historical multimodal device data to support model training data of a single mode. However Mehta teaches the historical multimodal device data comprising historical ticketing data; and transforming (524), using a natural language process model, the received corpus of historical multimodal device data to support model training data of a single mode (paragraphs [0056] and [0064], model is trained based on incident tickets that have had NLP applied, see Fig. 5). It would have been obvious to one skilled in the art to include the ticketing data and NLP in the model training of Mishra, in order to ingest historical incident data in the form of incident tickets and extract useful information from incident tickets (see Mehta, paragraphs [0056] and [0064]).
Mishra does not specifically teach that the device is an MFD. However, Jaques teaches an MFD (Fig. 2, MFDs 140, 142, and 144). It would have been obvious to one skilled in the art before the effective filing date of the invention to apply the anomaly detection taught in Mishra to the MFD system of Jaques, in order to diagnose problems and reduce on-site diagnostic time by a service person (see Jaques, paragraph [0038]).
Conclusion
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/CYNTHIA L DAVIS/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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