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 .
Status of Claims
The office action is being examined in response to the application filed by the Applicant on November 3, 2025.
Claims 1-15 were cancelled, while claims 16 - 28 were added.
Claims 16 - 28 are pending and have been examined.
This action is made NON-FINAL.
The Examiner would like to note that this application is now being handled by examiner Ivonnemary Rivera González.
Foreign Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE102023204026.3, filed on May, 2, 2023.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 3, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The references cited in the PCT international search reports by the WIPO on the issue dates of November 3, 2025 and November 12, 2025 and as listed in the DO/EO acceptance file received in July 8, 2026 have been considered.
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 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 (based on the 3-prong analysis; see MPEP 2181(I)). Specifically, claim 26 and their respective limitation(s) recite(s) the generic placeholders of “units” (at Prong one): “locating unit”, “image capture unit”, “an identification unit”. These claim limitations are reciting the terms “for” and/or “configured to” as a “means plus function” or recitation of modified functional language wherein such claim limitations are (at Prong two): “a locating unit for locating a component to be replaced of a technical system…”, “an image capture unit for capturing image data from a located component to be replaced” and “an identification unit for automated identification of the component to be replaced…and said identification unit configured to: determine whether the component to be replaced is uniquely identifiable…and determine a specific prompt to a maintenance person for appropriate interaction with the mobile electronic terminal to identify the component to be replaced…”
However, the structure of these placeholders is not modified by sufficient structure, material, or acts for performing the claimed functions and thus, this structure is not disclosed in the claims (at Prong three).
Because these claims limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 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.
This application includes one or more claim limitations that use the word “means” or “for” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Specifically, claim 16 and its respective limitation(s) recite(s) the generic placeholder(s) of a “unit” (at Prong one): “an image capture unit”. This claim limitations are reciting the term “for”, but because there is an absence of the word “means” (or “step”) in the claims, this 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. Such rebuttal of this presumption occurs when the claim limitations recite function(s) without reciting sufficient structure, material or acts to entirely perform the recited functions, which is not the case. Thus, there is no “means plus function” or recitation of modified functional language in the claims at Prong two. At Prong three, this unit is reciting sufficient structure, material or acts that modifies as it recites that the unit is “optically acquiring image data” which deems the recitation of structure that modifies the placeholder requiring an optical capture of the image and one of ordinary skilled in the art would consider it as being a “camera” which is a hardware structure and such unit is also defined in Applicant disclosure as the same (see L25 – 26 in p. 21 and Fig 8 from Applicant disclosure).
Because these claim limitation(s) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends 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 remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 24 and 26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Because claim 26 recites in part the limitation of "determine a specific prompt to a maintenance person for appropriate interaction with the mobile electronic terminal to identify the component to be replaced…", (emphasis added as underlined). There is insufficient antecedent basis for this limitation in the claim. Because this claim contains no earlier recitation or limitation for the element(s) of “the mobile electronic terminal” which is unclear as to what element the limitation was referring since it was not initially claimed. Therefore, the scope of what is claimed is not clear for these reasons and is considered to be indefinite. Refer to MPEP 2173.05(e) for more details. For purposes of examination, the Examiner interprets this element as being first introduced. The Examiner suggests amending the respective element of “the mobile electronic terminal” so it can be recited earlier in the limitations as “a mobile electronic terminal”, that is the hardware component that executes the determination of the “specific prompt” as well as to establish the relationship or linking its deriving “units” and database claimed (i.e. “locating unit”, “image capture unit”, “an electronic replacement parts database”, “an identification unit”) that such mobile electronic terminal comprises thereof, when in view of independent claim 16 as well as L21 – 28 in p. 10; L18 – 22 in p. 30; L3 – 4 in p. 31 and Fig 8 from Applicant disclosure.
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 16 - 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of this claimed invention recited in the claims begins in view of independent claim 26, the most representative claim of the independent claims set 16 and 26, as follows:
At Step 1: Claims 16 – 25 falls under statutory category of a process and claim 26 is considered a machine and claims 27 – 28 are directed to an article of manufacture as non-transitory computer-readable medium(s) or computer program product, when considered as being executed by a “computer system/unit” as claimed.
At Step 2A Prong 1: Claim 26 (representative of claim 16) recites an abstract idea in the following limitations:
…locating a component to be replaced of a technical system on a basis of a previously performed fault analysis of the technical system;
…capturing image data from a located component to be replaced;
…
…automated identification of the component to be replaced on a basis of the image data and on a basis of the replacement part data…and…automatically identifying a required replacement part on a basis of an identified component to be replaced and the replacement part data, the component to be replaced being identified by applying…and…configured to:
determine whether the component to be replaced is uniquely identifiable, on a basis of a comparison of the image data with the replacement part data…; and
determine a specific prompt to a maintenance person for appropriate interaction with…to identify the component to be replaced on the basis of the image data and the replacement part data…if the component to be replaced is not uniquely identifiable, the specific prompt containing an automated prompt to mark and/or segment identification codes relating to the component to be replaced that are shown in the image data.
Generally, and as disclosed in the specification in L6 – 9 in p. 1 and L19 – 22 L in p. 1, this claimed invention provides a system and “a method for automated identification of a replacement part for a technical system” such as a “rail vehicle” for efficient and correct identification of replacement/spare parts. However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) are/is recited in claim 26 in the form of “commercial or legal interactions”. Specifically, the abstract idea is recited in the step(s) of “…automated identification of the component to be replaced on a basis of the image data and on a basis of the replacement part data…automatically identifying a required replacement part on a basis of an identified component to be replaced and the replacement part data…”, “determine whether the component to be replaced is uniquely identifiable, on a basis of a comparison of the image data…” and when the “component is not uniquely identifiable”, “determine a specific prompt to a maintenance person for appropriate interaction with the mobile electronic terminal to identify the component to be replaced…” wherein such prompt is to “mark and/or segment identification codes relating to the component to be replaced” in the image data. Because such claimed steps are directly involved with commercial interactions directed to repair/maintenance services when identifying, determining and comparing components to their respective image data and the replacement part data that further encompasses marketing or sales activities or behaviors.
At Step 2A Prong 2: For independent claims 16 and 26 as well as claims 27 - 28, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of a locating unit (from claim 26); a technical system, a mobile electronic terminal, an image capture unit, an electronic replacement parts database, an identification unit, applying an Al-based algorithm (from claims 16 and 26); A non-transitory computer program product containing a computer program, a memory unit of a computing system (from claim 27) and A non-transitory computer-readable medium, a computer unit (from claim 28). These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). Specifically, these steps are recited as being performed by the computer (i.e. the mobile electronic terminal) that further applies “an Al-based algorithm”. The computer and algorithm used are recited at a high level of generality that is being used as a tool to perform the generic computer functions for automatically identifying, determining and comparing components that need to be replaced based on their respective image data and the replacement part data and further order such replacement components. Furthermore, the application of the “Al-based algorithm” lacks technological discussion in the Applicant disclosure and is not distinguishable from other general AI tools/algorithms (i.e. even if further specified as the “so-called backpropagation algorithm” disclosed in L1 – 11 in p. 7 and L30 – 32 and L1-3 in pp. 16 - 17 from Applicant disclosure), rather this algorithm is merely being invoked (i.e. being applied) by the computer that is using it as a tool to execute the “determining” functions (see MPEP 2106.05 (f)). Similarly, the application of such algorithm by the computer is considered a link to the field of use of artificial intelligence or machine learning technology to apply the abstract idea identified (see MPEP 2106.05 (h)) Thus, these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language the performing limitations from functions that generic computer components can perform.
Step 2B: For independent claims 16 and 26 as well as claims 27 - 28, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: a locating unit (from claim 26); a technical system, a mobile electronic terminal, an image capture unit, an electronic replacement parts database, an identification unit, applying an Al-based algorithm (from claims 16 and 26), including the step of “applying an AI-based algorithm” to determine identifying component and prompt data and A non-transitory computer program product containing a computer program, a memory unit of a computing system (from claim 27) and A non-transitory computer-readable medium, a computer unit (from claim 28). These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 17 - 25, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of a method of organizing human activity. They describe additional limitations steps of:
Claims 17 – 20 and 22 – 25: further describes the abstract idea of the method for automated identification of a required replacement part for a technical system and merely recites further embellishments of the abstract idea for: determining a “specific prompt” to generate multiple “automated prompt(s)” for “identification code” and “component name” inputs for a component based on a “displayed electrical or pneumatic circuit diagram” and automatically identifying the component to be replaced by “presenting provisional identification data” and providing prompt for confirmation inputs of correct component identification. Further, in claim 20 recites the conditional steps: for an “automated process” to prompt a user to “manually identify/select” a component and “display” their replacement part data as well as to “ask” for detailed image data acquisition to “generate enhanced replacement part data” and an “automated training” of the “automated identification” step based on the enhanced replacement part data (i.e. considered just refining/repeating this identification step to refine results); after replacement part is delivered, different component data are used to “check “ if the delivered replacement part “matches” the identified replacement part as well as to “display” manual instructions for replacing the component. Finally, claims 24 – 25 recite generating “labeled training data” that includes multiple types of identification/image data and “tailoring an automated method” that is broadly claimed. Thus, these limitations do not claim anything that amounts to significantly more than the abstract idea itself. Therefore, these steps are further directed to the abstract idea group of “commercial or legal interactions” encompassing marketing or sales activities or behaviors.
Claim 21: further describes the abstract idea of the method for automated identification of a required replacement part for a technical system and merely recites optional “automated additional steps” for “displaying” more prompts to “order a suitable replacement part for the identified component” and “accept” inputs related to fulfill/complete the order while displaying “recommended tools/technical accessories” and a message for “confirmation and summary” of the order which further encompass advertising (i.e. recommending products during product checkout).
Step 2A Prong 2 and Step 2B: For dependent claims 17 - 25, these claims do not include additional elements. Rather, what is claimed simply further defines the same abstract idea that was set forth in independent claims 16 and 26. Nothing additional is claimed that is not part of the abstract idea. Accordingly, for the same reasons stated above, these additional element(s) claimed cannot provide an inventive concept at Step 2B.
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.
Claims 16 - 20 and 24 - 28 are rejected under 35 U.S.C. 103 as being unpatentable over Utke (U.S. Pub No. 20190073641 A1) in view of Nelson (U.S. Patent No. 10949814 B1).
Regarding claims 16 and 26:
This independent claim set is represented by claim 26
Utke discloses a system for “analyzing images of vehicles for damage and determining damaged parts of vehicles” (see ¶0001; Utke). Thus, Utke teaches:
an image capture unit for capturing image data from a located component to be replaced; (In ¶0032; Figs. 3A – 3B and 7; Fig. 8 (805): teaches at step 203, “the mobile estimate application 153 may prompt a user to point a camera at the vehicle and walk around it while recording a video. The mobile estimate application 153 may provide user interfaces with instructions indicating how to capture the video”. Further, in ¶0039 that the application can “store and execute quality assurance algorithms that detect whether the captured videos include images of a vehicle (e.g., using object recognition models such as neural networks)”. Finally, the “mobile estimate application 153 may convert each analyzed frame of video to an input in a format usable by object recognition model 155 (e.g., an input array or matrix of floating point numbers, each representing one or more pixels of the image)” (¶0041).)
an electronic replacement parts database that includes replacement part data about replaceable components of the technical system; (In ¶0022; Fig. 1B (115): teaches that “mappings of vehicle-specific parts to parts clusters may be stored in database 115 of damage detection computing platform 110, and the damage detection computing platform 110 may query the database using the associated parts cluster and vehicle identification information to determine the list of vehicle-specific parts”. See ¶0022 for more database details.)
an identification unit for automated identification of the component to be replaced on a basis of the image data and on a basis of the replacement part data of the electronic replacement parts database (In ¶0084 – 85; Fig. 2D (215); Fig. 8 (820): teaches that “based on the parts cluster damage information” outputted by “the parts cluster damage model 116E” that includes “asymmetry data (e.g., asymmetry arrays or matrices and/or magnitude of the asymmetry), vehicle damage information, and/or representative images of damage (which may be converted into an input format usable by the model)” inputted in the model, the “damage detection computing platform 110 may determine a repair/replace decision. For example, “if one or more parts clusters are associated with a “severe” damage indication, the damage detection computing platform 110 may determine that the corresponding parts should be replaced, whereas parts associated with a “mild” damage indication may be associated with a repair decision”. See ¶0088 for details of training “repair/replace models 116F”.)
and for automatically identifying a required replacement part on a basis of an identified component to be replaced and the replacement part data, the component to be replaced being identified by applying an Al-based algorithm and said identification unit configured to: (In ¶0088 – 89; Fig. 1B (116F); Fig. 2E (216): teaches “at step 216” wherein “based on the parts cluster damage information, the damage detection computing platform 110 may determine a repair/replace decision” (see ¶0085), the “damage detection computing platform 110 may input the asymmetry data (e.g., asymmetry arrays or matrices and/or magnitude of the asymmetry), vehicle damage information, and/or representative images of damage (which may be converted into an input format usable by the model) to the relevant repair/replace model 116F in order to determine whether the corresponding parts cluster should be repaired or replaced”. Further, the repair/replace model can be “trained to map the input data to an output variable indicating whether the relevant parts cluster needs to be replaced or not” (see ¶0088). Also, refer to ¶0087 wherein for more details of the training of the repair/replace model that is further used by the system and is interpreted as the application of the AI-based algorithm to identify the component to be replaced. The Examiner notes that this prior art reference does not expressly discloses that the “repair/replace model 116F” is based of AI (i.e. the model being “a neural network or some other machine-learning or statistical model”, as it is the case of others models used by the system such as the “object recognition model 155”, “damage model 116B” and “angle model 116C”; see ¶0040, ¶0051 and ¶0064, respectively as well as Fig. 1B).)
determine whether the component to be replaced is uniquely identifiable, on a basis of a comparison of the image data with the replacement part data of the electronic replacement parts database; and (In ¶0050 – 51; Fig. 2B (206); Figs. 3A – 3B: teaches at “step 206, the damage detection computing platform 110 may use image analysis techniques to determine which frames of the video show damage to the vehicle, which may beneficially allow the damage detection computing platform 110 and/or claims adjuster system 120 to focus further analysis on the frames showing damage” and “the damage detection computing platform 110 may generate tags that indicate which portions of video contain damage” including “starting and ending timestamps that indicate which segments of the video contain damage” as well as tags for “indicating which frames and/or segments of the walk-around video(s) contain damage” (see ¶0052), in accordance with the example of identifying uniquely identifiable components given in L12 – 23 in p. 13 from Applicant disclosure. Further, the “damage model 116B may be trained using a training data set comprising images of both damaged and undamaged vehicle parts or components, damaged and undamaged vehicle materials” to indicate “the presence of damage or no damage in each image”.)
Utke teaches that before prompting the user to upload photos, an “incident reporting process” can begin in order for the user to “select an incident reporting feature to report that an accident or other damage incident has occurred” and the mobile application may “interact with the user to determine information about the incident such as a number of vehicles involved, whether immediate medical assistance is needed, information about the vehicles such as make/model/year and/or VIN, and other such relevant information about the incident” (see ¶0029 – 30 and Fig. 2A (201 – 202); Utke). But Utke does not explicitly teach the abilities of applying an algorithm, specifically AI-based to identify components to be replaced, locating (i.e. identifying) a component for replacement based on previously performed fault analysis of the technical system and determining a specific/automated prompt to a maintenance person via the mobile electronic terminal to identify the component to be replaced by specifically marking and/or segmenting identification codes in the image data. However, Nelson which is directed to “an intelligent vehicle repair estimation system that processes images of a damaged vehicle to intelligently generate an estimate for repairing the vehicle” (see C1; L6 – 9; Nelson) teaches:
…the component to be replaced being identified by applying an Al-based algorithm and said identification unit configured to: (In C9; L4 – 21: teaches the application of an algorithm based on AI which is directed to “the parts prediction model 30 may utilize one or more machine learning algorithms, statistical algorithms, rule-based logic techniques, and/or historical claim analyses in furtherance of analyzing the image attribute data. Based on the inputs (e.g., the independent variables of the model 30), the parts prediction model 30 determines one or more outputs (e.g., dependent variables of the model 30), including the predicted, identified, determined, generated, and/or created initial set of parts 28 corresponding to the damage indicated by the image attributes (and optionally, the other data that was input into the model 30)”. Further in C9; L25 – 40, “the parts prediction model 30 may have been generated a priori (or, in some cases, may be generated in real-time or on-demand upon reception of the images 25 a-25 n) based on one or more data analysis techniques such as principal components analyses, cluster analyses, linear regression analyses, multivariate regression analyses such as the Ordinary Least Squares algorithm, a logistic regression analysis, a K-th nearest neighbor (k-NN) analysis, a K-means analysis, a Naïve Bayes analysis, another suitable or desired predictive data analysis, statistical algorithms, one or more machine learning algorithms, or some combination thereof performed on historical data 32 related to repairing damaged vehicles to thereby determine which image attributes are more closely correlated with which various parts that were actually needed to repair vehicle damage, and whether those parts were replaced or repaired.”)
a locating unit for locating a component to be replaced of a technical system on a basis of a previously performed fault analysis of the technical system; (In C21; L43 – 47; Fig. 2A (201); Fig. 3 (330): teaches that “at least some of the other obtained data that is related to the damaged vehicle may be utilized along with the image attributes to predict the initial set of globally-identified parts (block 330)” wherein the other obtained data includes data corresponding to at least a vehicle’s “incident facts” and it “diagnostics data, accident reports, agent and/or law enforcement notes” which is directed to previously performed fault analysis of the technical system, etc., in accordance with examples given in L17 – 25 in p. 9, L19 – 28 in p. 22 and L22 – 30 in p. 30 from Applicant disclosure. Further, in C22; L13 – 26, “the method 300 may operate on 05′ include obtaining diagnostics data generated by diagnostic scans of various modules and components of the damaged vehicle, processing the diagnostics data to determine attributes indicative of damages to the vehicle, and predicting the initial set of globally-identified parts based on the determined diagnostic attributes”.)
determine a specific prompt to a maintenance person for appropriate interaction with the mobile electronic terminal to identify the component to be replaced on the basis of the image data and the replacement part data of the electronic replacement parts database if the component to be replaced is not uniquely identifiable, the specific prompt containing an automated prompt to mark and/or segment identification codes relating to the component to be replaced that are shown in the image data. (In C28; L59 – 67: teaches this conditional limitation as “the intelligent interceptor engine 405 may determine that human input is required to specify which value of a plurality of a limited set of possible values is to populate a particular field. As such, the intelligent interceptor engine 405 annotates or otherwise associates the particular field of the current draft estimate with a set of guidances, and provides the current draft estimate and the associated set of guidance annotations pertaining to the particular field to the user interface 405 (e.g., as denoted by the arrow (d))” wherein such value(s) can include “enterprise-specific terms, codes, acronyms, etc.” , for the including parts in a “draft estimate” (see C30; L22 – 44), in accordance with the examples of prompts of marking/segment identification codes uniquely identifiable components given in L14 – 25 in p. 9 and L7 – 29 in p. 14 from Applicant disclosure. Further, “the user interface 405, a user control may be presented next to or proximate to the particular field, thereby indicating the presence of the guidance annotations pertaining to the particular field. Upon the user activating the user control at the user interface 405, a drop-down menu including the limited set of possible values for the particular field may be presented, and the user may make the modification (f) by selecting one of the menu items.” But the “user may choose to modify some other aspect of the current draft estimate other than those indicated by guidance annotations.” See C29; L1 – 14 for details of tagging/labelling/annotating images.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Utke to provide the abilities of applying an algorithm, specifically AI-based to identify components to be replaced, locating (i.e. identifying) a component for replacement based on previously performed fault analysis of the technical system and determining a specific/automated prompt to a maintenance person via the mobile electronic terminal to identify the component to be replaced by specifically marking and/or segmenting identification codes in the image data, as taught by Nelson in order to achieve “maximum improvements in accuracy, completeness, and processing speed in electronic vehicle repair estimating” (C2; L7 – 9; Nelson) as well as minimizing the suffering of the “resulting estimates” from “contextual inaccuracies, omissions, inconsistencies, and/or other types of errors which may or may not be detected by the adjuster and, when detected, must be corrected” (C1; L50 – 54; Nelson), see also MPEP 2143.I.G.
Regarding claim 17:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Utke teaches that before prompting the user to upload photos, an “incident reporting process” can begin in order for the user to “select an incident reporting feature to report that an accident or other damage incident has occurred” and the mobile application may “interact with the user to determine information about the incident such as a number of vehicles involved, whether immediate medical assistance is needed, information about the vehicles such as make/model/year and/or VIN, and other such relevant information about the incident” (see ¶0029 – 30 and Fig. 2A (201 – 202); Utke). But Utke does not explicitly teach the abilities of displaying specific/automated prompt(s) to generate other automated prompt to input identification codes when the maintenance person has not carried out marking and/or segmentation and when the component to be replaced is an electric or pneumatic component, have another prompt to input component’s name in an image (i.e. electrical or pneumatic circuit diagram). However, Nelson further teaches:
wherein the specific prompt contains a prompt to perform one of the following specific interactions: generating another automated prompt to input an identification code relating to the component to be replaced if the maintenance person has not carried out marking and/or segmentation; and (In C28; L59 – 67 and C29; L1 - 21: teaches these conditional limitations as in the example of “(a), the intelligent interceptor engine 405 may determine that human input is required to specify which value of a plurality of a limited set of possible values is to populate a particular field” and that “upon the user activating the user control at the user interface 405, a drop-down menu including the limited set of possible values for the particular field may be presented, and the user may make the modification (f) by selecting one of the menu items. Indeed, the subsequent user input (f) entered via the user interface 405 may be based on one or more of the guidance annotations that are presented in conjunction with the current draft estimate, however, this is not a requirement, as the user may choose to modify some other aspect of the current draft estimate other than those indicated by guidance annotations” wherein these “guidance annotations” may be displayed in any useful form, such as via fields into the user can enter free-form text, pop-up windows, visual effects to bring the user's attention to or direct the user's attention away from various fields and/or values, and the like” which is directed to input the required identification codes and/or names of the component to be replaced when the maintenance person has not marked/segmented the component information.)
generating a further automated prompt for the maintenance person to input a component name on a basis of a displayed electrical or pneumatic circuit diagram of the technical system, if the component to be replaced is an electric or pneumatic component. (In C30; L47 – 52: teaches that “the interactive display presents a current draft estimate which is able to be directly edited by the user via the user interface 405. Via sets of guidance annotations, the interactive display may intelligently prompt the user to enter and/or change data in an intelligently sequenced set of actions” which can include prompt specifically directed to input the name of the descriptive subject matter of an electric or pneumatic component. Examiner notes that the descriptive matter further defining the type of component to the replaced does not hold patentable weight. See C30; L19 – 25 wherein “the intelligent interceptor engine 410 may determine at least some of the guidance annotations based on a configuration that has been defined a priori, e.g., by system user. For example, the configuration may specify a particular order in which entries, fields, and/or are to be populated; the configuration may present suggestions using enterprise-specific terms, codes, acronyms, etc.; and the like”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Utke to provide the abilities of displaying specific/automated prompt(s) to generate other automated prompt to input identification codes when the maintenance person has not carried out marking and/or segmentation and when the component to be replaced is an electric or pneumatic component, have another prompt to input component’s name in an image (i.e. electrical or pneumatic circuit diagram), as taught by Nelson in order to achieve “maximum improvements in accuracy, completeness, and processing speed in electronic vehicle repair estimating” (C2; L7 – 9; Nelson) as well as minimizing the suffering of the “resulting estimates” from “contextual inaccuracies, omissions, inconsistencies, and/or other types of errors which may or may not be detected by the adjuster and, when detected, must be corrected” (C1; L50 – 54; Nelson), see also MPEP 2143.I.G.
Regarding claim 18:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Utke further teaches:
wherein the step of automatically identifying the component to be replaced on the basis of the image data further comprises the sub-steps of: presenting provisional identification data relating to the identified component to be replaced on the mobile electronic terminal of the maintenance person; and (In ¶0072; Figs. 6 – 7 : teaches that the “damage detection computing platform 110 may transmit the selected representative images to the claims adjuster system 120. A claims adjuster may use the representative images to manually determine a settlement cost, which may be used in addition to or as an alternative to an automatic estimate. Additionally or alternatively, the claims adjuster may review the representative images to provide feedback on the automatic analyses performed by damage detection computing platform 110”. Further, in ¶0095 “claims adjuster system 120 may display information about the estimate and highlight certain discrepancies between any aspect of the automatic estimate and any manual estimate generated by the claims adjuster”, for example “the automatic estimate information may display indications of each damaged parts cluster and an associated repair/replace decision” which further includes “a parts list and associated cost for each part, as well as an indication of whether the part is available used. The automatic estimate information may further include a projected labor time, a local labor cost, and a total cost based on the parts costs, labor rate, and labor time” as shown in Fig. 6. See ¶0099 wherein “claims adjuster system 120 may present an example graphical user interface 700 for providing feedback on the most relevant photos” and “user interface 700 may allow the claims adjuster to select which images were actually relevant and/or manually adjust a relevance score for each photo”.)
providing a prompt for the maintenance person to input confirmation of a correct identification of the component to be replaced. (In ¶0096 – 97; Figs. 6 – 7: teaches that the “the claims adjuster system 120 may provide an alert of the discrepancy by highlighting the manual estimate amount. In some examples, an alert may be sent to a system associated with a reviewer or other supervisory role, which may cause display of the user interface 600 to the reviewer”. Further, the “mobile estimate application 153, upon receipt of the estimate information, may cause the mobile system 150 to display a total amount of the proposed settlement. The mobile estimate application 153 may allow a user to accept the settlement and/or request a review of the settlement amount. The mobile estimate application 153 may cause the mobile system 150 to communicate a user's acceptance of the settlement or request for review back to the damage detection computing platform 110 and/or claims adjuster system 120, which may review the settlement and/or cause payment to the user.”)
Regarding claim 19:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Utke further teaches:
wherein if unique or correct automated identification of the component to be replaced has not occurred, an automated process prompts the maintenance person to take the replacement part data of the electronic replacement parts database as a basis for manually performing the identification of the component to be replaced by identifying and selecting the component to be replaced in the electronic replacement parts database, and the replacement part data of the electronic replacement parts database are displayed to the maintenance person. (In ¶0099 – 100; Figs. 6 – 7: teaches that the “user interface 700 may allow the claims adjuster to select which images were actually relevant and/or manually adjust a relevance score for each photo. The user interface 700 may further provide a function for reviewing the associated video so that the claims adjuster can manually select other frames of the video as being relevant and assign manual relevance scores. The claims adjuster system 120 may transmit any such feedback, including additional frames of video along with any manual relevance scores, back to damage detection computing platform. In similar fashion, the claims adjuster system 120 may provide other user interfaces for providing feedback on any output of any of models 116A-F, and such feedback may be transmitted to the damage detection computing platform 110.” Further, “if the claims adjuster provided feedback on whether a particular frame contained damage or not (and/or the extent of damage, such as mild, medium, or severe), the particular frame and the indication of damage and/or the extent of damage may be added to the training data set for the damage model 116B” and as another example, “if the claims adjuster provided feedback on a particular frame and which parts cluster therein contains damage, the particular frame and the information indicating the parts cluster containing damage may be added to the training data set for the parts cluster damage model 116E.”)
Regarding claim 20:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Utke further teaches:
wherein if unique or correct automated identification has not occurred: an automated process transmits to the maintenance person a prompt message asking him to use the mobile electronic terminal to acquire detailed image data from different perspectives from the component to be replaced; (In ¶0043 – 44; Fig. 4: teaches this conditional limitation as the “mobile estimate application 153 may perform the object recognition and display of warnings in real-time or near-real-time as the user is capturing the walk-around video(s), so that the user may correct any problems before concluding the recording of a walk-around video” and thus, the user can recapture the “walk-around video”. See ¶0099 wherein “user interface 700 may further provide a function for reviewing the associated video so that the claims adjuster can manually select other frames of the video as being relevant and assign manual relevance scores.”)
a further automated process generates enhanced replacement part data, the detailed image data and information about a manually identified and selected component to be replaced being added to the replacement part data of the electronic replacement parts database; and automated training of the automated identification is carried out on a basis of the enhanced replacement part data of the electronic replacement parts database. (In ¶0100: teaches the automated process of generating enhanced replacement part data as the “damage detection computing platform 110 may retrain the one or more models 116A-F based on the feedback received from the claims adjuster system 120. Data associated with the current settlement may be added into training sets along with the feedback on the output of the models 116A-F provided by the claims adjuster.” For example, “if the claims adjuster provided feedback on a particular frame and whether the parts cluster therein should be repaired or replaced, the particular frame and the information indicating the repair/replace decision may be added to the training data set for the relevant repair/replace damage model 116F”. See ¶0102 wherein “systems and methods described herein allow manual review and feedback on the automatic analyses and determinations of the systems, which beneficially allows the systems and methods to improve accuracy over time by collecting additional training data and retraining models.”)
Regarding claim 24:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Nelson additionally teaches that “the parts prediction model 30 is re-trained or updated to account for additional data (e.g., additional image attribute data and other data related to repairing damaged vehicles) that has been added to the historical data store 32” and “additionally or alternatively, at least some of the other characteristics of the damaged vehicle may be automatically determined, based on the images 25 a-25 n, by other models or modules of the parts prediction engine 12” (see C11; L5 – 9 and C11; L25 – 28; Nelson). But Utke further teaches:
A method for training the step for automatically identifying the component to be replaced on the basis of the image data and on the basis of replacement part data of the electronic replacement parts database of the method for automated identification of the required replacement part for the technical system according to claim 16, which comprises the steps of: generating labeled training data, which include: the replacement part data of the electronic replacement parts database for technical components of the technical system; (In ¶0040: teaches that the “object recognition model 155 may be trained using a training data set comprising images of vehicles (e.g., in various lighting conditions and at various angles and orientations). The object recognition model 155 may be trained to map a representation of an input image to an output variable indicating whether the image contains a vehicle and/or a confidence score”. See ¶0050 – 51 wherein “the damage detection computing platform 110 may use image analysis techniques to determine which frames of the video show damage to the vehicle” and in such analysis, it may include the generation of “tags that indicate which portions of video contain damage” which is directed to labeled training data generated.)
image data relating to the technical components of the technical system, acquired by the mobile electronic terminal; (In ¶0032: teaches at step 203, “the mobile estimate application 153 may prompt a user to point a camera at the vehicle and walk around it while recording a video. The mobile estimate application 153 may provide user interfaces with instructions indicating how to capture the video”. Further, in ¶0036 the “in addition to capturing a 360° walk-around video, the mobile estimate application 153 may calculate and determine that a user should capture a walk-around video focusing on specific portions of the vehicle 140”. See ¶0037 – 38 for more technical image capturing as instructed by the application.)
correct identification data for the technical components concerning the image data acquired; (In ¶0043 – 44; Fig. 4: teaches this conditional limitation as the “mobile estimate application 153 may perform the object recognition and display of warnings in real-time or near-real-time as the user is capturing the walk-around video(s), so that the user may correct any problems before concluding the recording of a walk-around video” and thus, the user can recapture the “walk-around video”. See ¶0099 wherein “user interface 700 may further provide a function for reviewing the associated video so that the claims adjuster can manually select other frames of the video as being relevant and assign manual relevance scores.”)
performing automated identification of the replacement part on the basis of the image data acquired, the identification data being determined for the technical components; and (In ¶0084 – 85; Fig. 2D (215); Fig. 8 (820): teaches that “based on the parts cluster damage information” outputted by “the parts cluster damage model 116E” that includes “asymmetry data (e.g., asymmetry arrays or matrices and/or magnitude of the asymmetry), vehicle damage information, and/or representative images of damage (which may be converted into an input format usable by the model)” inputted in the model, the “damage detection computing platform 110 may determine a repair/replace decision. For example, “if one or more parts clusters are associated with a “severe” damage indication, the damage detection computing platform 110 may determine that the corresponding parts should be replaced, whereas parts associated with a “mild” damage indication may be associated with a repair decision”. See ¶0088 for details of training “repair/replace models 116F”.)
tailoring an automated method on a basis of the identification data determined by an automated process and a correct identification data relating to the technical components of the replacement parts. (In ¶0100: teaches such tailoring of automated method using the identification data determined and the correct identification data relating to the technical components as “the damage detection computing platform 110 may retrain the one or more models 116A-F based on the feedback received from the claims adjuster system 120” and for example, “if the claims adjuster provided feedback on a particular frame and whether the parts cluster therein should be repaired or replaced, the particular frame and the information indicating the repair/replace decision may be added to the training data set for the relevant repair/replace damage model 116F” and thus, “the accuracy of the models may improve over time”, in accordance to the example given wherein the automated method or automated process is also referred as a “supervising training” in L3 – 7 in p.30 from Applicant disclosure. Further in ¶0102 wherein “systems and methods described herein allow manual review and feedback on the automatic analyses and determinations of the systems, which beneficially allows the systems and methods to improve accuracy over time by collecting additional training data and retraining models”. See ¶0039 and ¶0045 wherein the “mobile estimate application 153 may store and execute quality assurance algorithms that detect whether the captured videos include images of a vehicle (e.g., using object recognition models such as neural networks), have acceptable quality lighting, as well as other quality assurance analyses” and the system “may store and/or execute an object recognition model 116A (as referred to in FIG. 1B) and/or other algorithms used for quality assurance”, respectively, which contributes to the claimed tailoring the automated method based on identification data determined and the correct identification data relating to the technical components.)
Regarding claim 25:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Utke further teaches:
wherein the labeled training data are generated by capturing the image data from the technical components in a state in which the technical components are installed in the technical system and/or by capturing the image data from different viewing angles. (In ¶0040: teaches that the “object recognition model 155 may be trained using a training data set comprising images of vehicles (e.g., in various lighting conditions and at various angles and orientations). The object recognition model 155 may be trained to map a representation of an input image to an output variable indicating whether the image contains a vehicle and/or a confidence score. The object recognition model 155 may be a neural network or some other machine-learning or statistical model.” See ¶0050 – 51 wherein “the damage detection computing platform 110 may use image analysis techniques to determine which frames of the video show damage to the vehicle” and in such analysis, it may include the generation of “tags that indicate which portions of video contain damage” which is directed to labeled training data generated.)
Regarding claims 27:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Utke further teaches:
A non-transitory computer program product containing a computer program that can be loaded directly into a memory unit of a computing system, having program sections to carry out the method according to claim 16 when the computer program is executed in the computing system. (In ¶0103 – 104: teaches that “one or more aspects of the disclosure may be embodied in computer-usable data or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices to perform the operations described herein”. Further, “various aspects described herein may be embodied as a method, an apparatus, or as one or more computer-readable media storing computer-executable instructions. Accordingly, those aspects may take the form of an entirely hardware embodiment, an entirely software embodiment, an entirely firmware embodiment, or an embodiment combining software, hardware, and firmware aspects in any combination”.)
Regarding claim 28:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Utke further teaches:
A non-transitory computer-readable medium storing program sections that can be executed by a computer unit to carry out the method according to claim 16. (In ¶0104: teaches that “various aspects described herein may be embodied as a method, an apparatus, or as one or more computer-readable media storing computer-executable instructions. Accordingly, those aspects may take the form of an entirely hardware embodiment, an entirely software embodiment, an entirely firmware embodiment, or an embodiment combining software, hardware, and firmware aspects in any combination”.)
Claims 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Utke (U.S. Pub No. 20190073641 A1) in view of Nelson (U.S. Patent No. 10949814 B1) in further view of Dagley (U.S. Pub No. 20210342790 A1).
Regarding claim 21:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Nelson at least teaches displaying the input of number of replacement parts to be ordered as the “draft estimate may indicate a physical unitary part indicated by its SKU or individual, per-unit part identifier that is to be replaced (e.g., a new tire)” as well as input entries including “fields corresponding to whether or not the part is to be repaired or replaced, labor, cost(s), time intervals, etc. associated with the identified part” (see C11; L56 – 67; Nelson). However, neither Utke or Nelson explicitly teach the abilities of displaying prompt(s) to select, to input delivery address and order a suitable replacement part, display recommended tools and/or recommended technical accessories and accepting the maintenance person input(s) to order the suitable replacement part/recommended tools or accessories to the delivery address entered and display a confirmation and summary of an order in a message. Thus, Dagley which is directed to “a device having extended reality capabilities (e.g., a virtual reality device, an augmented reality device, a mixed reality device, and/or the like) and/or a part information visualization system that generates content for display using extended reality, where the content provides part information and/or replacement part information to the user” (see ¶0013; Dagley) teaches:
wherein after the component to be replaced has been identified, at least one of the following automated additional steps for a replacement process for the identified component to be replaced is performed: displaying a prompt to order a suitable replacement part for the identified component to be replaced and accepting an input from the maintenance person to order the suitable replacement part; displaying a prompt to input a number of replacement parts to be ordered and accepting the input of the number from the maintenance person; displaying recommended tools and/or recommended technical accessories concerning the identified component to be replaced and displaying a prompt to select and order the technical accessories and accepting an input of the selection and order from the maintenance person; displaying a prompt to input a delivery address for ordered replacement parts and ordered accessories and accepting an input of a delivery address from the maintenance person; and displaying a message containing a confirmation and summary of an order. (In ¶0041 - 42; Fig. 1C: teaches the display prompt to order a part for replacement and accepting such order input as “the content may include a request for input from the user regarding one or more actions to be performed by the extended reality device. For example, the content may include virtual buttons, a menu, and/or the like for selecting an action, such as ordering the one or more replacement parts associated with the part, scheduling delivery of the one or more replacement parts, scheduling a service appointment to replace and/or repair the part, and/or the like”. Further, such accepting maintenance person’s ordering input is taught as “shown in FIG. 1C and by reference number 155, the extended reality device may receive a user selection of the content and may perform the one or more actions based on the user selection of the content”, specifically the actions of “ordering the one or more replacement parts associated with the part, scheduling delivery of the one or more replacement parts, scheduling a service appointment to replace and/or repair the part, and/or the like.” See ¶0092 wherein the display of recommended tools and/or recommended technical accessories is taught as “the device (e.g., using processor 520, memory 530, storage component 540, input component 550, output component 560, communication interface 570, and/or the like) may determine, using one or more second machine learning models, information regarding the part and information regarding one or more replacement parts” including “recommend one or more replacement parts that have historically been used to replace the part”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Utke and Nelson to provide the abilities of displaying prompt(s) to select, to input delivery address and order a suitable replacement part, display recommended tools and/or recommended technical accessories and accepting the maintenance person input(s) to order the suitable replacement part/recommended tools or accessories to the delivery address entered and display a confirmation and summary of an order in a message, as taught by Dagley in order to “conserve computing resources (e.g., processing resources, memory resources, power resources, communication resources, and/or the like) and/or network resources that would otherwise be consumed by the user using a user device to research potential sources of problems, which parts can be used to replace a problematic part, where suitable replacement parts can be purchased, a cost of suitable replacement parts, which repair shops can perform a replacement, scheduling a service with a repair shop, and/or the like” (¶0016; Dagley), see also MPEP 2143.I.G.
Regarding claim 23:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Neither Utke or Nelson explicitly teaches the ability of displaying to the maintenance person, instructions for replacing the identified component to be replaced with a delivered replacement part based on identification data relating to a delivered replacement part and an electronic maintenance manual. Thus, Dagley further teaches:
wherein after the required replacement part has been delivered, identification data relating to a delivered replacement part and an electronic maintenance manual are taken as a basis for displaying instructions for replacing the identified component to be replaced with the delivered replacement part to the maintenance person. (In ¶0041; Fig. 3C: teaches that “the extended reality device may render content related to the part (e.g., the part associated with the user selection) and information regarding one or more replacement parts associated with the part. The content may include instructions for replacing the part (e.g., with the one or more replacement parts associated with the part)”. The Examiner notes that such rendering can occur after receiving the replacement part delivered since for one of ordinary skilled in art would be able to program the display instructions for the identified component to be replaced while using/considering the delivered replacement part based on identification data and the replacement part’s electronic maintenance manual. See ¶0015 wherein “the extended reality device may render content based on the part information (e.g., text instructing the user to change oil and an oil filter, text instructing the user to change an air filter, text instructing the user to replace a timing belt, and/or the like) and replacement part information (e.g., one or more replacement parts, lifespans of the one or more replacement parts, reliability of the one or more replacement parts, cost of the one or more replacement parts, and/or the like).” Finally, this prior art teaches taking/using electronic maintenance manual in ¶0069, wherein “the resource management component 404 may perform binary rewriting to scan instructions received from a virtual computing system 406 and replace any privileged instructions with safe emulations of those instructions”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Utke and Nelson to provide the ability of displaying to the maintenance person, instructions for replacing the identified component to be replaced with a delivered replacement part based on identification data relating to a delivered replacement part and an electronic maintenance manual, as taught by Dagley in order to “conserve computing resources (e.g., processing resources, memory resources, power resources, communication resources, and/or the like) and/or network resources that would otherwise be consumed by the user using a user device to research potential sources of problems, which parts can be used to replace a problematic part, where suitable replacement parts can be purchased, a cost of suitable replacement parts, which repair shops can perform a replacement, scheduling a service with a repair shop, and/or the like” (¶0016; Dagley), see also MPEP 2143.I.G.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Utke (U.S. Pub No. 20190073641 A1) in view of Nelson (U.S. Patent No. 10949814 B1) in further view of Fyda (U.S. Pub No. 20070156439 A1).
Regarding claim 22:
The combination of Utke and Nelson, as shown in the rejection above, discloses the limitations of claim 16.
Neither Utke or Nelson explicitly teaches the ability of checking whether the delivered replacement part matches an identified replacement part based on the image data captured and the replacement part data stored. Thus, Fyda which is directed to “processing at a revalue center location returned merchandise item purchased from a remote location including one of numerous possible process steps including repackaging, refurbishing, remarketing and or reselling the returned item” (see ¶0004; Fyda) teaches:
wherein after a replacement part has been delivered, further image data are captured from a delivered replacement part and the further image data and the replacement part data of the electronic replacement parts database are taken as a basis for checking whether the delivered replacement part matches an identified replacement part. (In ¶0020 – 21: teaches “When a replacement parts shipment arrives 46 at the revalue center, the contents of the shipment are matched against the associated part purchase orders to ensure all parts requested have been delivered 46. Errors or omissions included with the shipment are reconciled with the supplier of the parts (e.g., OEM, local wholesaler). The correctly delivered parts are entered into the inventory management system and made available for planning, scheduling and processing”. Further “Parts are entered into inventory when received from outside sources/suppliers, from the harvest parts processing step” and these “parts are identified via specific part number bar codes and descriptions then scanned into specific inventory locations for easy retrieval when needed”. See ¶0025 wherein “all pertinent information about the returnee will be recorded and the request is entered into the database for processing and tracking”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Utke and Nelson to provide the ability of displaying to the maintenance person, instructions for replacing the identified component to be replaced with a delivered replacement part based on identification data relating to a delivered replacement part and an electronic maintenance manual, as taught by Fyda in order to “ensure all parts requested have been delivered” and take such correct delivered parts and enter them “into the inventory management system and made available for planning, scheduling and processing” (¶0020; Fyda), see also MPEP 2143.I.G.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hanson (U.S. Pub No. 20230073188 A1) is pertinent because it is about “Systems and methods for automatically determining damage information and publishing said damage information”
Tomlinson (U.S. Pub No. 20220114627 A1) is pertinent because it “generally relates to automatic assessment of vehicle damage and, more particularly, to a network-based system and method for assessing damage of a vehicle based on image data captured by a mobile device.”
Ho (U.S. Pub No. 20240394861 A1) is pertinent because it “relates to a system for assessing a damage condition of a vehicle and a platform for facilitating repairing or maintenance services of a vehicle, and particularly, although not exclusively, to an AI-based car damage assessment report system.”
Knuffman (U.S. Pub No. 20210027390 A1) is pertinent because it “relates to identifying damages to or defects of vehicles via a computerized analysis of one or sets of imaging data captured by a remotely-controlled and/or autonomously operated vehicle, drone, or other inspection device.”
Biggs (U.S. Pub No. 20220358469 A1) is pertinent because it “provide a statement corresponding to a pending repair of a particular vehicle and securely stores that statement in inviolate electronic storage in correlation with a unique vehicle identifier. Parts are ordered to specifically use to repair this vehicle per that statement and again information regarding those ordered parts is securely stored in the inviolate electronic storage (again in correlation with the unique vehicle identifier)”
Dillard (U.S. Pub No. 20150039522 A1) is pertinent because it is “directed to a system and method for updating and displaying a status of a repair process for a vehicle.”
Ranca (CA Pub No. 3163622 A1) is pertinent because it “relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626 /DENNIS W RUHL/Primary Examiner, Art Unit 3626
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