DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections Claims 3-5 are objected to because of the following informalities: “ communicatable with ” should be “ in communica tion with .” Appropriate correction is required. Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a) an operation information acquisition section that acquires operation information of the work machine , b) an inspection information acquisition section that acquires inspection information of the work machine , c) a part replacement/repair information acquisition section that acquires part replacement/repair information of the work machine , and d) a malfunction prediction section that predicts a malfunction probability of each part of the work machine , in claim 1 ; and a learning section that learns the deviation information between the malfunction probability predicted by the malfunction prediction section and the inspection performance of the work machine , in claim 2 . Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. 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 -8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al (US 20180005132) in view of Trinh et al (US 20200379454). As to claim 1, Singh discloses a malfunction prediction system that predicts a malfunction of a work machine (FIGS. 1-2), the malfunction prediction system comprising: an operation information acquisition section that acquires operation information of the work machine (FIG. 2, apparatus 200 acquires alert data 228; see [0027], the alert data is associated with operating parameters of the work vehicle); an inspection information acquisition section that acquires inspection information of the work machine (FIG. 2, apparatus 200; see [0025], the warranty database 202 includes maintenance logs and failure information associated with a work vehicle (e.g., an identifier of a work vehicle) and the parts and associated maintenance database 203 includes Technician Assistance Center (DTAC) data including part lists and/or technical documentation used when performing maintenance and/or repairs on the respective work vehicles); a part replacement/repair information acquisition section that acquires part replacement/repair information of the work machine (FIG. 2, apparatus 200; see [0025], the parts and associated maintenance database 203 includes Technician Assistance Center (DTAC) data including part lists and/or technical documentation used when performing maintenance and/or repairs on the respective work vehicles); and a malfunction prediction section that predicts a malfunction probability of each part of the work machine, based on the operation information acquired by the operation information acquisition section, the inspection information acquired by the inspection information acquisition section, the part replacement/repair information acquired by the part replacement/repair information acquisition section (FIG. 2, apparatus 200 ; see FIG. 3, [0036] and [0044], t he probability of the machine failure occurring in the work machine is determined based on the model and the identified alert sequence (block 316) by, for example, the identifier 225 identifying the machine failure and alert sequence mapped and/or linked with the corresponding probabilit y; see FIG. 7) . Singh fails to explicitly disclose that the malfunction prediction section that predicts the malfunction probability of each part of the work machine, based on the deviation information between the malfunction probability of each part of the work machine and inspection performance of the work machine stored in a storage section. However, Trinh teaches predict ing a malfunction probability of each part of the work machine, based on the deviation information between the malfunction probability of each part of the work machine and inspection performance of the work machine stored in a storage section (see Abstract, The server may assign an anomaly score based on the differences between the predicted values and the measured values ; see [0051], The failure classification and prediction model store 260 stores machine learning models that are used to identify specific components or aspects of a piece of equipment 150 that may need inspection and/or repair … The models that are trained to classify failures may estimate failure probabilities of the equipment 150 or of a particular component of the equipment 150; see [0057], [0073] , [0077] ; FIG. 2, data store 220 ) . At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Singh using Trinh’s teachings to include predicting a malfunction probability of each part of the work machine, based on the deviation information between the malfunction probability of each part of the work machine and inspection performance of the work machine stored in a storage section in order to reduce maintenance cost and prevent permanently damage components of the equipment by detecting anomalies of the equipment often before the equipment show signs of failure (Trinh; [0032]-[0033]). As to claim 2, modified Singh fails to explicitly disclose further comprising a learning section that learns the deviation information between the malfunction probability predicted by the malfunction prediction section and the inspection performance of the work machine, wherein the malfunction prediction section predicts the malfunction probability of each part of the work machine, based on the deviation information learned by the learning section. However, Trinh teaches a learning section that learns the deviation information between the malfunction probability predicted by the malfunction prediction section and the inspection performance of the work machine, wherein the malfunction prediction section predicts the malfunction probability of each part of the work machine, based on the deviation information learned by the learning section (see [0044], The predictive maintenance server 110 may train one or more machine learning models that assign anomaly scores to a piece of equipment 150; see [0050]- [0051], The anomaly detection model store 250 may store a plurality of trained machine learning models that are used to determine the anomaly scores of one or more pieces of equipment 150 … The failure classification and prediction model store 260 stores machine learning models that are used to identify specific components or aspects of a piece of equipment 150 that may need inspection and/or repair … The models that are trained to classify failures may estimate failure probabilities of the equipment 150 or of a particular component of the equipment 150; see [0077], the trained machine learning model's predicted values show a large deviation from the actual values ). At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Singh using Trinh’s teachings to include a learning section that learns the deviation information between the malfunction probability predicted by the malfunction prediction section and the inspection performance of the work machine, wherein the malfunction prediction section predicts the malfunction probability of each part of the work machine, based on the deviation information learned by the learning section in order to reduce maintenance cost and prevent permanently damage components of the equipment by detecting anomalies of the equipment often before the equipment show signs of failure (Trinh; [0032]-[0033]). As to claim 3, modified Singh further discloses further comprising a server communicatable with the work machine, wherein the operation information acquisition section, the inspection information acquisition section, the part replacement/repair information acquisition section, the malfunction prediction section, and the learning section are provided in the server (FIG. 8 and [0053]) . As to claim 4, modified Singh further discloses further comprising a portable terminal communicatable with the server, wherein the portable terminal is provided with a display section that displays the malfunction probability predicted by the malfunction prediction section (see [005 8 ]) . As to claim 5, modified Singh further discloses further comprising a portable terminal communicatable with the work machine, wherein the operation information acquisition section, the inspection information section, the part replacement/repair information acquisition section, the malfunction prediction section, and the learning section are provided in the portable terminal (FIGS. 1-2, central data processing center 102/200; FIG. 8 and [0053]) . As to claim 6, modified Singh further discloses wherein the portable terminal is provided with a display section that displays the malfunction probability predicted by the malfunction prediction section ( see FIG. 7, [0052] - [ 0053] and [0058]) . As to claim 7 , modified Singh further discloses wherein the operation information acquisition section, the inspection information acquisition section, the part replacement/repair information acquisition section, the malfunction prediction section, and the learning section are provided in the work machine (FIG. 8 and [0053], platform 800 which can be provided in the work vehicles) . As to claim 8 , modified Singh further discloses wherein the work machine is provided with a display section that displays the malfunction probability predicted by the malfunction prediction section ( see FIG. 7, [0052] - [ 0053] and [0058]). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT BOUBACAR ABDOU TCHOUSSOU whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-7625 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 8am-4pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT Chris Kelley can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 5712727331 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BOUBACAR ABDOU TCHOUSSOU/ Primary Examiner, Art Unit 2482