CTNF 18/393,987 CTNF 92914 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to the Application filed on December 22, 2023. Claims 1-20 are pending in the case. Claims 1, 13, and 17 are the independent claims. This action is non-final. Claim Rejections – 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental steps) without significantly more. This judicial exception is not integrated into a practical application because any additional elements amount to implementing the abstract idea on a generic computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding independent claims 1, 13, and 17 , and relying on the evaluation flowchart in MPEP 2106: Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?) : Yes. Claim 1 is a method (process). Claim 13 is a storage medium (article of manufacture). Claim 17 is an apparatus (machine). Step 2a Prong One (Does the claim recite an abstract idea?) : Yes. Claims 1, 13, and 17 recite: predicting labeling information for the at least one device by processing at least a portion of the obtained data (a mental process of observation and evaluation, such as a human mentally determining/predicting labeling information for a device). Under the broadest reasonable interpretation, these steps may be performed mentally, using mental observation and mental determination, including by a human using a physical aid such as pen and paper, including a human mentally performing observations and mentally performing mathematical calculations, and therefore correspond to the Mental Processes grouping. Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?) : No. Claims 1, 13, and 17 additionally recite: obtaining data pertaining to a request from a user for at least one device (insignificant extra-solution activity as discussed in MPEP 2106.05(g)); the predicting is using one or more artificial intelligence techniques (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). generating, based at least in part on the predicted labeling information, at least one image of at least one label to be applied to the at least one device (insignificant extra-solution activity as discussed in MPEP 2106.05(g)) performing one or more automated actions based at least in part on the at least one generated image of the at least one label (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). the method of claim 1 is computer-implemented…wherein the method is performed by at least one processing device comprising a processor coupled to a memory (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the processing device to perform the limitations of claim 13 (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). the apparatus comprising at least one processing device comprising a processor coupled to a memory; the at least processing device being configured to perform the limitations of claim 17 (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components. Step 2b (Does the claim recite additional elements that amount to siqnificantly more than the judicial exception) : No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…) ), claims 1 and 11 do not recite any additional elements that amount to significantly more than the abstract idea. As discussed above, Claims 1, 13, and 17 additionally recite: obtaining data pertaining to a request from a user for at least one device (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting); the predicting is using one or more artificial intelligence techniques (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). generating, based at least in part on the predicted labeling information, at least one image of at least one label to be applied to the at least one device (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting) performing one or more automated actions based at least in part on the at least one generated image of the at least one label (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). the method of claim 1 is computer-implemented…wherein the method is performed by at least one processing device comprising a processor coupled to a memory (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the processing device to perform the limitations of claim 13 (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). the apparatus comprising at least one processing device comprising a processor coupled to a memory; the at least processing device being configured to perform the limitations of claim 17 (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea. Regarding dependent claims 2, 14, and 18: Step 2a Prong One: incorporates the rejection of claims 1, 13, and 17. Step 2a Prong Two: the claims additionally recite wherein processing at least a portion of the obtained data using one or more artificial intelligence techniques comprises processing the at least a portion of the obtained data using at least one ensemble boosting-based multi-output classifier (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Step 2b: the claims additionally recite wherein processing at least a portion of the obtained data using one or more artificial intelligence techniques comprises processing the at least a portion of the obtained data using at least one ensemble boosting-based multi-output classifier (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Regarding dependent claim 3: Step 2a Prong One: incorporates the rejection of claim 2. Step 2a Prong Two: the claims additionally recite wherein processing the at least a portion of the obtained data using at least one ensemble boosting-based multi-output classifier comprises using one or more of at least one gradient boosting classifier and at least one extreme gradient boosting classifier (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Step 2b: the claims additionally recite wherein processing the at least a portion of the obtained data using at least one ensemble boosting-based multi-output classifier comprises using one or more of at least one gradient boosting classifier and at least one extreme gradient boosting classifier (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Regarding dependent claim 4: Step 2a Prong One: incorporates the rejection of claim 1. Step 2a Prong Two: the claims additionally recite wherein processing at least a portion of the obtained data using one or more artificial intelligence techniques comprises processing the at least a portion of the obtained data using one or more shallow learning algorithms comprising at least one of one or more ensemble decision tree bagging and boosting techniques, at least one k-nearest neighbors (K-NN) algorithm, and one or more support vector machines (SVMs) (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Step 2b: the claims additionally recite wherein processing at least a portion of the obtained data using one or more artificial intelligence techniques comprises processing the at least a portion of the obtained data using one or more shallow learning algorithms comprising at least one of one or more ensemble decision tree bagging and boosting techniques, at least one k-nearest neighbors (K-NN) algorithm, and one or more support vector machines (SVMs) (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Regarding dependent claims 5, 15, and 19: Step 2a Prong One: incorporates the rejection of claims 1, 13, and 17. Step 2a Prong Two: the claims additionally recite wherein generating the at least one image of the at least one label comprises inserting one or more items of content, corresponding to at least a portion of the predicted labeling information, into one or more portions of at least one device label template selected in accordance with at least one of the predicted labeling information and the obtained data (insignificant extra-solution activity as discussed in MPEP 2106.05(g)). Step 2b: the claims additionally recite wherein generating the at least one image of the at least one label comprises inserting one or more items of content, corresponding to at least a portion of the predicted labeling information, into one or more portions of at least one device label template selected in accordance with at least one of the predicted labeling information and the obtained data (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting). Regarding dependent claim 6: Step 2a Prong One: incorporates the rejection of claim 1. Step 2a Prong Two: the claims additionally recite wherein generating the at least one image of at least one label to be applied to the at least one device comprises generating the at least one image of at least one label to be applied to at least one of a device component of the at least one device and a packaging component associated with the at least one device (insignificant extra-solution activity as discussed in MPEP 2106.05(g) , and a field of use and technological environment as discussed in MPEP 2106.05(h) (i.e. the label to be applied to a device or packaging)). Step 2b: the claims additionally recite wherein generating the at least one image of at least one label to be applied to the at least one device comprises generating the at least one image of at least one label to be applied to at least one of a device component of the at least one device and a packaging component associated with the at least one device (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting, and a field of use and technological environment as discussed in MPEP 2106.05(h) (i.e. the label to be applied to a device or packaging)). Regarding dependent claims 7, 16, and 20: Step 2a Prong One: incorporates the rejection of claims 1, 13, and 17. Step 2a Prong Two: the claims additionally recite wherein performing one or more automated actions comprises automatically generating at least one device label, in accordance with the at least one generated image, to be applied to the at least one device in connection with the request from the user (insignificant extra-solution activity as discussed in MPEP 2106.05(g), and a field of use and technological environment as discussed in MPEP 2106.05(h) (i.e. the label to be applied to a device)). Step 2b: the claims additionally recite wherein performing one or more automated actions comprises automatically generating at least one device label, in accordance with the at least one generated image, to be applied to the at least one device in connection with the request from the user (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and output, and a field of use and technological environment as discussed in MPEP 2106.05(h) (i.e. the label to be applied to a device)). Regarding dependent claim 8: Step 2a Prong One: incorporates the rejection of claim 7. Step 2a Prong Two: the claims additionally recite wherein generating at least one device label comprises generating the at least one device label upon obtaining approval by the user of the at least one generated image (insignificant extra-solution activity as discussed in MPEP 2106.05(g)). Step 2b: the claims additionally recite wherein generating at least one device label comprises generating the at least one device label upon obtaining approval by the user of the at least one generated image (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting). Regarding dependent claim 9: Step 2a Prong One: incorporates the rejection of claim 1. Step 2a Prong Two: the claims additionally recite wherein performing one or more automated actions comprises automatically outputting, to the user for approval, one or more of the predicted labeling information and the at least one generated image (insignificant extra-solution activity as discussed in MPEP 2106.05(g)). Step 2b: the claims additionally recite wherein performing one or more automated actions comprises automatically outputting, to the user for approval, one or more of the predicted labeling information and the at least one generated image (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting). Regarding dependent claim 10: Step 2a Prong One: incorporates the rejection of claim 1. Step 2a Prong Two: the claims additionally recite wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques using at least the at least one generated image of the at least one label (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Step 2b: the claims additionally recite wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques using at least the at least one generated image of the at least one label (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Regarding dependent claim 11: Step 2a Prong One: incorporates the rejection of claim 1. Step 2a Prong Two: the claims additionally recite wherein obtaining data pertaining to the request from the user for the at least one device comprises obtaining information pertaining to at least one of an order by the user for the at least one device and shipping instructions for the at least one device to the user (insignificant extra-solution activity as discussed in MPEP 2106.05(g)). Step 2b: the claims additionally recite wherein obtaining data pertaining to the request from the user for the at least one device comprises obtaining information pertaining to at least one of an order by the user for the at least one device and shipping instructions for the at least one device to the user (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere information gathering and outputting). Regarding dependent claim 12: Step 2a Prong One: incorporates the rejection of claim 1. Step 2a Prong Two: the claims additionally recite training the one or more artificial intelligence techniques using multi-dimensional features derived from historical device labeling information across multiple users and multiple devices (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Step 2b: the claims additionally recite training the one or more artificial intelligence techniques using multi-dimensional features derived from historical device labeling information across multiple users and multiple devices (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as recited in the dependent claims discussed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components, and limitations describing a field of use or technological environment. The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea, and limitations describing a field of use or technological environment. Claim Rejections – 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim 1-7, 11, and 13-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Segev et al. (US 20210073449 A1) . With respect to claims 1, 13, and 17, Segev teaches a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform a method; an apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured to perform the method ( e.g. paragraph 0005, disclosed systems and methods using combination of hardware and software, including non-transitory computer-readable storage media storing program instructions which are executable by at least one processing device; paragraphs 0119-0120, describing processors and describing systems, methods, and computer readable media, including a processor using a machine learning model to suggest equipment and equipment placement in rooms ); and the computer-implemented method comprising: obtaining data pertaining to a request from a user for at least one device ( e.g. paragraph 0138, accessing floor plan, such as in response to user input; paragraph 0144, functional requirements specified by user; paragraph 0151, functional requirement input by user; paragraph 0153, equipment including various devices, etc., having a function in the room; paragraph 0154, technical specification for equipment; paragraph 0156, technical specification data for equipment previously input/uploaded by user; paragraph 0158, receiving technical specification along with the functional requirement; paragraph 0189, Fig. 3A, accessing floor plan and receiving functional requirements applied to rooms within floor plan, such as functional requirement for different cameras with different specifications; paragraph 0191, user able to manually modify specifications/locations; paragraphs 0421-0426, Fig. 12A, accessing functional requirements and technical specifications, such as input by a user, where the technical specification may identify various characteristics for equipment to be installed in a floor plan; i.e. receiving user input, including data identifying/specifying equipment, such as a device, for placement in a room ); predicting labeling information for the at least one device by processing at least a portion of the obtained data using one or more artificial intelligence techniques ( e.g. paragraph 0120, machine learning model suggesting equipment and equipment placement models in rooms; paragraph 0136, BIM analysis using machine learning method resulting in equipment associated with the room; paragraph 0159-0161, generative analysis using machine learning/artificial intelligence to identify placement locations for equipment, including identified type, brand, model, etc.; paragraph 0189, generative analysis to identify technical specifications and placement locations for cameras within floor plan; outputting the technical specifications as well as the equipment placements locations, as well as a materials list; paragraph 0427, performing floor plan analysis, such as by using machine learning methods; paragraph 0429-0431, floor plan analysis allowing system to ascertain room features associated with functional requirements and technical specifications, including information relevant to equipment selection and configuration within the room; generatively analyzing room features with reference to functional requirements and technical specifications to determine customized equipment configuration, the generative analysis including use of machine learning/artificial intelligence; paragraph 0433-0437, describing resulting customized equipment configurations as including various properties, parameters, settings, etc. of equipment; paragraph 0443, generating manufacturer dataset including the customized equipment configuration, the manufacturer dataset including a set of instructions for an equipment manufacturer specifying details relating to the manufacture, calibration, programming, packaging, labeling , shipment, and installation of equipment; paragraph 0449, manufacturer data set generated to enable manufacturer to customize the equipment and package it in a manner displaying the room identifier, and further including a representation of the customization performed on the equipment, the room identifier, and any other information included in the manufacturer data set; room identifier and other information printed on packaging or label of the packaging; paragraph 0455, manufacturer data set including label layout; paragraph 0456-0457, Figs. 12A-B, describing label layout for customized label 1270; i.e. ML/AI techniques are used to generate a manufacturer dataset for equipment, and this information is further used to generate a label for the equipment ); generating, based at least in part on the predicted labeling information, at least one image of at least one label to be applied to the at least one device ( e.g. paragraph 0202, classification tag/label to be placed on the equipment; generating image file; printing tag or label, etc.; paragraph 0443, generating manufacturer dataset including details relating to labeling of the equipment; paragraph 0449, including various information from manufacturer dataset printed on packaging or label of packaging for the equipment; paragraphs 0456-457, Figs. 12A-B, describing customized label 1270 generated for equipment based on manufacturer dataset information; requiring label on every piece of equipment; label layout including image, graphical representation, etc.; i.e. the label is generated including/as an image capable of being printed so that it can be physically affixed to equipment packaging ); and performing one or more automated actions based at least in part on the at least one generated image of the at least one label ( e.g. paragraph 0456, manufacturer fulfilling custom order and labeling each equipment in a custom, automated way; paragraph 0460, affixing customized label 1270 to package; i.e. the equipment order can be fulfilled, including packaging and labeling the equipment, in an automated manner (using the label, and therefore based at least in part on the generated label image) ); wherein the method is performed by at least one processing device comprising a processor coupled to a memory ( e.g. paragraph 0005, disclosed systems and methods using combination of hardware and software, including non-transitory computer-readable storage media storing program instructions which are executable by at least one processing device; paragraphs 0119-0120, describing processors and describing systems, methods, and computer readable media, including a processor executing instructions loaded into memory ). With respect to claims 2, 14, and 18, Segev teaches all of the limitations of claims 1, 13, and 17 as previously discussed, and further teaches wherein processing at least a portion of the obtained data using one or more artificial intelligence techniques comprises processing the at least a portion of the obtained data using at least one ensemble boosting-based multi-output classifier ( e.g. paragraph 0129, 0140, 0268, 0380, 0402, 0404, 0485, 0512, 0669, etc., AI methods described include using boosting algorithms such as XGBoost; compare with specification of the instant application, page 12, lines 27-28, describing XGBoost as an example of an “ensemble boosting, multi-output classification algorithm” ). With respect to claim 3, Segev teaches all of the limitations of claim 2 as previously discussed, and further teaches wherein processing the at least a portion of the obtained data using at least one ensemble boosting-based multi-output classifier comprises using one or more of at least one gradient boosting classifier and at least one extreme gradient boosting classifier ( e.g. paragraph 0129, 0140, 0268, 0380, 0402, 0404, 0485, 0512, 0669, etc., AI methods described include using boosting algorithms such as XGBoost; compare with specification of the instant application, page 12, lines 27-28, describing XGBoost as an example of an “ensemble boosting, multi-output classification algorithm” ). With respect to claim 4, Segev teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein processing at least a portion of the obtained data using one or more artificial intelligence techniques comprises processing the at least a portion of the obtained data using one or more shallow learning algorithms comprising at least one of one or more ensemble decision tree bagging and boosting techniques, at least one k-nearest neighbors (K-NN) algorithm, and one or more support vector machines (SVMs) ( e.g. paragraph 0129, 0140, 0268, 0380, 0402, 0404, 0485, 0512, 0669, etc., AI methods described include using boosting algorithms such as XGBoost; paragraph 0212, AI/ML algorithms including support vector machines, random forests, nearest neighbors algorithms, etc. ). With respect to claims 5, 15, and 19, Segev teaches all of the limitations of claims 1, 13, and 17 as previously discussed, and further teaches wherein generating the at least one image of the at least one label comprises inserting one or more items of content, corresponding to at least a portion of the predicted labeling information, into one or more portions of at least one device label template selected in accordance with at least one of the predicted labeling information and the obtained data ( e.g. paragraphs 0455-0457, Fig. 12B, manufacturer dataset including label layout specifying how information is to presented, on a product label, including what information is included, the position of the included information, the manner in which it is to be included, spacing/formatting requirements, etc.; different portions 1272-1278 of customized label including specified information, such as product type, model number, image, and bar code, identification of building/project, floor, and room, graphical indication of room placement and orientation, and installation instructions; i.e. as cited above, the ML/AI methods are used to generate the manufacturer dataset, including a label layout/template, and information about the equipment which is to be included in the label layout, and the label is created by including designated information in designated parts of the label layout ). With respect to claim 6, Segev teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein generating the at least one image of at least one label to be applied to the at least one device comprises generating the at least one image of at least one label to be applied to at least one of a device component of the at least one device and a packaging component associated with the at least one device ( e.g. paragraph 0443, generating manufacturer dataset including details relating to labeling of the equipment; paragraph 0449, including various information from manufacturer dataset printed on packaging or label of packaging for the equipment; packaging of equipment includes enclosing the equipment in a box or other container; customized packaging includes within the packaging a representation of the customization performed on the equipment, the room identifier, and any other information included in the manufacturer data set; paragraph 0454, packaging graphical representation of the room with the customized equipment by including it in the packaging such as printed on a paper insert, instructions, installation guide, etc., on the surface of the packaging, on the label, including in a machine readable code or electronically scannable medium, etc.; paragraphs 0456-457, Figs. 12A-B, describing customized label 1270 generated for equipment based on manufacturer dataset information; requiring label on every piece of equipment; label layout including image, graphical representation, etc.; i.e. the label is generated including/as an image capable of being printed so that it can be physically affixed to equipment packaging ). With respect to claims 7, 16, and 20, Segev teaches all of the limitations of claims 1, 13, and 17 as previously discussed, and further teaches wherein performing one or more automated actions comprises automatically generating at least one device label, in accordance with the at least one generated image, to be applied to the at least one device in connection with the request from the user ( e.g. paragraph 0202, classification tag/label to be placed on the equipment; generating image file; printing tag or label, etc.; paragraph 0449, printing label/printing on packaging; paragraph 0454, printing on packaging, printing on label; paragraph 0456, manufacturer fulfilling custom order and labeling each equipment in a custom, automated way; paragraph 0460, affixing customized label 1270 to package; i.e. the equipment order can be fulfilled, including packaging and labeling the equipment, in an automated manner (using the label, and therefore based at least in part on the generated label image) ). With respect to claim 11, Segev teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein obtaining data pertaining to the request from the user for the at least one device comprises obtaining information pertaining to at least one of an order by the user for the at least one device and shipping instructions for the at least one device to the user ( e.g. paragraph 0197, customized settings associated with piece of equipment including details regarding optional or separately-ordered accessory; paragraph 0198, customize setting may be user input for the equipment, and may include combining one or more pieces of equipment each with their own separate order codes, fabricated or delivered within common chassis, housing, rack, or enclosure, delivered to a project, etc.; paragraph 0438, customized equipment setting including order codes for aggregated equipment components; paragraph 0456, Fig. 12B, customized label including identification of project, building, floor, and room; equipment is ordered, such as for construction project, etc.; data file transmitted to manufacturer, enabling manufacturer to fulfill the custom order and label each piece of equipment in a custom way; paragraph 0497, customizing equipment setting involving generating a custom order code for ordering the customized equipment from vendor/manufacturer ) . Claim Rejections – 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-fti This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102€, (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a). 07-21-aia AIA Claim s 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Segev in view of Lameris et al. (US 20180104898 A1) . With respect to claim 8, Segev teaches all of the limitations of claim 7 as previously discussed. Segev does not explicitly disclose wherein generating at least one device label comprises generating the at least one device label upon obtaining approval by the user of the at least one generated image. However, Lameris teaches wherein generating at least one device label comprises generating the at least one device label upon obtaining approval by the user of the at least one generated image ( e.g. paragraph 0026, object identification system including label generation device to create a label for the object once identified; printing label for the object; paragraph 0044, Figs. 3 and 4, user interface allowing user to select candidate match in table to identify object; displaying user interface to enable operator to print label for identified object, including area 402 to display the label to be generated and button 404 to confirm that the label is correct; generating the label in response to button 404 being activated; paragraph 0050, presenting label to operator to have operator confirm that the label is correct ). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Segev and Lameris in front of him to have modified the teachings of Segev (directed to mass customization of equipment, including using ML/AI techniques, and including generating custom labels for the equipment based on determined equipment information and a layout), to incorporate the teachings of Lameris (directed to identifying objects and generating labels for the objects) to include the capability (in the system of Segev) for the user to confirm/approve the generated label as correct and to generate the label upon obtaining this confirmation/approval (as taught by Lameris). One of ordinary skill would have been motivated to perform such a modification in order to enable identification of objects quickly and with minimal intervention, including generation of labels to identify the object in subsequent processing steps such as packaging and shipping as described in Lameris (paragraph 0020). With respect to claim 9, Segev teaches all of the limitations of claim 1 as previously discussed. Segev does not explicitly disclose wherein performing one or more automated actions comprises automatically outputting, to the user for approval, one or more of the predicted labeling information and the at least one generated image. However, Lameris teaches wherein performing one or more automated actions comprises automatically outputting, to the user for approval, one or more of the predicted labeling information and the at least one generated image ( e.g. paragraph 0026, object identification system including label generation device to create a label for the object once identified; printing label for the object; paragraph 0044, Figs. 3 and 4, user interface allowing user to select candidate match in table to identify object; displaying user interface to enable operator to print label for identified object, including area 402 to display the label to be generated and button 404 to confirm that the label is correct; generating the label in response to button 404 being activated; paragraph 0050, presenting label to operator to have operator confirm that the label is correct ). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Segev and Lameris in front of him to have modified the teachings of Segev (directed to mass customization of equipment, including using ML/AI techniques, and including generating custom labels for the equipment based on determined equipment information and a layout), to incorporate the teachings of Lameris (directed to identifying objects and generating labels for the objects) to include the capability (in the system of Segev) to automatically output the generated label for the user to confirm/approve (as taught by Lameris). One of ordinary skill would have been motivated to perform such a modification in order to enable identification of objects quickly and with minimal intervention, including generation of labels to identify the object in subsequent processing steps such as packaging and shipping as described in Lameris (paragraph 0020) . 07-21-aia AIA Claim s 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Segev in view of Sakurai (US 20250061704 A1) . With respect to claim 10, Segev teaches all of the limitations of claim 1 as previously discussed. Segev does not explicitly disclose wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques using at least the at least one generated image of the at least one label. However, Sakurai teaches wherein performing one or more automated actions comprises automatically training at least a portion of the one or more artificial intelligence techniques using at least the at least one generated image of the at least one label ( e.g. paragraphs 0042-0044, captured images of product 300, such as a printer, with a label L affixed to front surface; different types of labels L1 and L2 as examples of labels, the labels L1 and L2 being affixed to different products, etc.; paragraph 0078, storing training data including label region information and class information indicating type of label (such as label L1 or L2); paragraph 0091-0097, Fig. 11B, training object detection model such that output data indicates appropriate label region of input image and appropriate label type; acquiring composite image data, inputting to object detection model, generating output data; calculating loss using output data and training data, adjusting parameters of object detection model using loss value, and when finish condition is satisfied, storing trained object detection model; paragraphs 0104-0109, Fig. 12B, similarity describing training of image generation model using image data, including normal image data for training models relative to corresponding labels L1 and L2 ). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Segev and Sakurai in front of him to have modified the teachings of Segev (directed to mass customization of equipment, including using ML/AI techniques, and including generating custom labels for the equipment based on determined equipment information and a layout), to incorporate the teachings of Sakruai (directed to machine learning techniques using device labels) to include the capability (in the system of Segev) to automatically train the machine learning model using device label information (as taught by Lameris, i.e. including generated equipment labels as taught by Segev). One of ordinary skill would have been motivated to perform such a modification in order to allow for accurate detection by the model while suppressing the burden of training the model and an excessive increase of data volume, and reducing the burden of preparing input image data for training as described in Sakurai (paragraph 0009). With respect to claim 12, Segev teaches all of the limitations of claim 1 as previously discussed. Segev does not explicitly disclose the method further comprising: training the one or more artificial intelligence techniques using multi-dimensional features derived from historical device labeling information across multiple users and multiple devices. However, Sakurai teaches the method further comprising: training the one or more artificial intelligence techniques using multi-dimensional features derived from historical device labeling information across multiple users and multiple devices ( e.g. paragraphs 0042-0044, captured images of product 300, such as a printer, with a label L affixed to front surface; different types of labels L1 and L2 as examples of labels, the labels L1 and L2 being affixed to different products, etc.; labels include background B1, characters X1 indicating various kinds of information such as brand logo, product number, lot number, and marks; paragraph 0078, storing training data including label region information and class information indicating type of label (such as label L1 or L2); paragraph 0091-0097, Fig. 11B, training object detection model such that output data indicates appropriate label region of input image and appropriate label type; acquiring composite image data, inputting to object detection model, generating output data; calculating loss using output data and training data, adjusting parameters of object detection model using loss value, and when finish condition is satisfied, storing trained object detection model; paragraphs 0104-0109, Fig. 12B, similarity describing training of image generation model using image data, including normal image data for training models relative to corresponding labels L1 and L2; paragraph 0156, user of inspection process/operator of the inspection for the product to which label L to be inspected is affixed; paragraph 0174, user of training process of image generation model; paragraph 0183, extracting features of labels and defects in labels; extracting features of labels themselves for each type of label; i.e. where first and second different labels applied to different products/devices provide historical devices labeling information across multiple devices; moreover, the labels themselves including a plurality of different features such as background, text/characters, marks, numbers, etc. and being used in a variety of scenarios corresponding to multiple users such as a user of an inspection process involving the label and a user of a training process for a model to recognize/generate the label ). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Segev and Sakurai in front of him to have modified the teachings of Segev (directed to mass customization of equipment, including using ML/AI techniques, and including generating custom labels for the equipment based on determined equipment information and a layout), to incorporate the teachings of Sakruai (directed to machine learning techniques using device labels) to include the capability (in the system of Segev) to automatically train the machine learning model using device label information, such as labels actually used (i.e. historical labels) for multiple different devices in multiple scenarios and users (such as in an inspection process and in a training process), and including multiple different dimensions of features (such as background information, text/numbers, marks, etc.) (as taught by Lameris, i.e. including generated equipment labels as taught by Segev). One of ordinary skill would have been motivated to perform such a modification in order to allow for accurate detection by the model while suppressing the burden of training the model and an excessive increase of data volume, and reducing the burden of preparing input image data for training as described in Sakurai (paragraph 0009). It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L STANLEY whose telephone number is (469)295-9105. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM CST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar, can be reached at telephone number (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR for authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /JEREMY L STANLEY/ Primary Examiner, Art Unit 2127 Application/Control Number: 18/393,987 Page 2 Art Unit: 2127
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