Detailed Action
Status of Claims
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 in reply to the Amendment filed on 5/12/2026.
Claims 1-20 are currently pending and have been examined. Claims 1-2, 7, 10-11, 16, and 19 have been amended. The claim objections have been overcome by amendment.
Claim Rejection - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-9 are directed to a process, claims 10-18 are directed to a machine, and claims 19-20 are directed to an article of manufacture. Therefore, claims 1-20 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 1, 10, and 19 at least the following limitations that are believed to recite an abstract idea:
identifying a first product number of a first industrial product manufactured by a first manufacturer;
determining to utilize a model to identify an equivalent industrial product manufactured by a second manufacturer, wherein the model is trained on industrial product information and configured to process natural language queries;
generating a prompt for the model, by populating a natural-language prompt template with the first product number,
wherein the prompt comprises: a natural-language request to provide a second product number of a second industrial product manufactured by the second manufacturer and capable of substitution for the first industrial product;
submitting the prompt to the model;
receiving, from the model in response to the prompt, a response comprising the second product number of the second industrial product manufactured by the second manufacturer;
storing the first product number in association with the second product number and indicating a model-generated match; and
providing to a user, a notification comprising the second product number as the model-generated match for the first product number.
The above limitations recite the concept of product substitution recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-20 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
The method being computer implemented
Generative artificial intelligence
A large language model (LLM)
A database
A user interface
A system comprising: one or more processors; and one or more memories operably coupled to the one or more processors and having stored thereon software instructions that, upon execution by the one or more processors, cause the one or more processors to perform steps
A computer-readable storage media device having program instructions stored thereon to perform competitive product matching, wherein the program instructions, upon execution by one or more processors, cause the one or more processors to perform steps
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 2-9, 1-18, and 20 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
The method being computer implemented
Generative artificial intelligence
A large language model (LLM)
A database
A user interface
A system comprising: one or more processors; and one or more memories operably coupled to the one or more processors and having stored thereon software instructions that, upon execution by the one or more processors, cause the one or more processors to perform steps
A computer-readable storage media device having program instructions stored thereon to perform competitive product matching, wherein the program instructions, upon execution by one or more processors, cause the one or more processors to perform steps
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Rejection – 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claims 1, 3-8, 10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Clarke et al (US 20240331011 A1), hereinafter Clarke, in view of Bromley et al (US 10043222 B1), hereinafter Bromley, and further in view of Grenader et al (US 20240362497 A1), hereinafter Grenader.
Regarding Claim 1, Clarke discloses a computer-implemented method for competitive product matching, the method comprising:
identifying a first product number of a first product manufactured by a first manufacturer (Clarke: “procurement system 202 can request potential substitute products from a separate system … As part of the request or requests, procurement system 202 can include some type of identifier associated with the one or more selected products (e.g., SKU, etc.) in order to provide a valid request for potential substitute products.” [0094] – “a distributor can act as a supplier, by manufacturing (e.g., either directly or by contracting with a manufacturer) items” [0052]);
determining to utilize an Artificial Intelligence (AI) model to identify an equivalent product manufactured by a second manufacturer (Clarke: “depending on which triggers are implemented for substitution evaluations, … then process 900 can proceed to 914. …At 914, recommended substitute items can be selected corresponding to the selected item. … based on an output of unsupervised product equivalency learning model 812, an output of supervised product equivalency learning model 814” [0113-0114] - “product equivalency learning model 814 can be implemented … using different types and combinations of algorithms such as …neural networks, and/or other similar types of supervised machine learning algorithms. … supervised product equivalency learning model 814 … maps inputs (e.g., products selected by a purchasing user via procurement system 202) to outputs (e.g., potential substitute products that can replace the selected products).” [0105] – “ substitute product identification 1932 identifies a different type of … product (e.g., associated with different attributes such as … a different distributor, etc.)” [0128]),
wherein the AI model comprises a model trained on product information (Clarke: “ Master item groupings 802 can include groupings of different products for one or more facilities and/or organizations … Master item groupings 802 can be compiled manually, semi-manually, or automatically, and can be used to train supervised product equivalency learning model 814” [0104] – “data associated with the order and any substitutions that were made as part of the order can be used as feedback to train one or more models (e.g., models 812 and 814” [0101]);
generating a prompt for the AI model, wherein the prompt comprises: a request to provide a second product number of a second product manufactured by the second manufacturer and capable of substitution for the first product (Clarke: “generating inputs for providing to unsupervised product equivalency learning model 812, and evaluating product equivalents generated by unsupervised product equivalency learning model” [0107] – “performing the substitution evaluation comprises providing data associated with the selected product as input to a machine learning model and identifying the substitute product based on an output of the machine learning model.” Claim 10 – “ As part of the request or requests, procurement system 202 can include some type of identifier associated with the one or more selected products (e.g., SKU, etc.) in order to provide a valid request for potential substitute products.” [0094]);
submitting the prompt to the AI model (Clarke: “generating inputs for providing to unsupervised product equivalency learning model 812” [0107] – “providing data associated with the selected product as input to a machine learning model” Claim 10);
receiving, from the AI model in response to the prompt, a response comprising the second product number of the second product manufactured by the second manufacturer (Clarke: “At 914, recommended substitute items can be selected corresponding to the selected item. For example, process 900 can generate the recommended substitute items based on an output of unsupervised product equivalency learning model 812, an output of supervised product equivalency learning model 814” [0114] – See also Figure 15, where a product number is included with the substitute item.);
storing the first product number in association with the second product number in a database and indicating a model-generated match (Clarke: “if the user does accept a recommendation to replace the selected item in the shopping cart with a recommended substitute item, an associated order guide and/or the shopping cart can be updated accordingly. At 825, also if the user does accept a recommendation to replace the selected item in the shopping cart with a recommended substitute item, a positive feedback signal can be generated and provided as feedback to unsupervised product equivalency learning model 812 and/or feedback graph 816.” [0111] – “Feedback graph 816 can then be used for a variety of purposes, including for training supervised product equivalency learning model 814” [0107] – “Procurement system 202 can also query one or more databases, including databases associated with procurement system 202, in order to request potential substitute products.” [0094]); and
providing to a user, via a user interface, a notification comprising the second product number as the model-generated match for the first product number (Clarke: “At 918, process 900 can present recommended substitute items to the user via the user interface. For example, the recommended substitute items can be presented to the user on shopping cart page 1000 as suggested replacements 1030” [0115] – “The substitute products provided to the user via suggested replacements 1030 can be suggested as substitutes for a manner of reasons, as detailed herein, including … recommendations generated using artificial intelligence (e.g., using unsupervised product equivalency learning model 812 and/or supervised product equivalency learning model 814).” [0118]).
While Clarke teaches that the products may be items such as supplies and “maintenance, repair, and operating items (MRO),” [0002], and that the first product number is an input to the AI model, it does not specifically teach that the products are industrial products; that the AI model is a generative artificial intelligence (GAI) model comprising a large language model (LLM) configured to process natural language queries; and that the prompt is generated by populating a natural-language prompt template with the input, the prompt comprising a natural-language request.
However, Bromley teaches systems for suggesting parts substitutions in a product-fabrication setting (Bromley: Abstract), including that the products are industrial products (Bromley: “the part number of the 10 KΩ resistor part may be used as the substitute part number for the other two resistor parts” Col. 7, lines 15-25 – “ the part number 3124C0000 is itself suggested as a substitute for part number 3144A0009.” Col. 11, lines 45-55– See also Col’s 4-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Clarke would continue to teach storing the first product number in association with the second product number, except that now it would also teach that the products are industrial products, according to the teachings of Bromley. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved efficiency in industrial fabrication (Bromley: Col. 5, lines 50-55).
While Clarke/Bromley teach generating a prompt for an AI model comprising a first product number as an input, they do not teach that the AI model is a generative artificial intelligence (GAI) model comprising a large language model (LLM) configured to process natural language queries; and that the prompt is generated by populating a natural-language prompt template with the input, the prompt comprising a natural-language request.
However, Grenader teaches AI systems for answering user questions(Grenader: [Abstract]), including that:
the AI model is a generative artificial intelligence (GAI) model comprising a large language model (LLM) configured to process natural language queries (Grenader: “a large language model (LLM) …is implemented within or as a generative large language model AI entity” [0045] – “the class into which the user question belongs is determined by inspection (e.g., presence of keywords) or analysis (e.g., natural language analysis). … user question 125 are handled by LLM agent type processing ” [0071]); and
the prompt is generated by populating a natural-language prompt template with the input, the prompt comprising a natural-language request (Grenader: “a natural language processor 206 can be used to identify and/or extract specific portions from the selected chunks, which extracted specific portions can in turn be used to populate the fields of prompt template 367. As shown, performance of the operations within prompt generation module 374 result in one or more LLM prompts 327, which in turn are provided to a selected LLM (not shown) with the expectation that the LLM will return a large language model system answer (e.g., LLM answer 226, as shown) corresponding to the user question.” [0161] – “ variable assignment technique affords a wide range of prompts to an LLM, where the prompt can be constructed in real time (e.g., at the time that the user poses a question) and in accordance with information available at that moment in time.” [0169]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Clarke/Bromley would continue to teach determining to utilize a Generative Artificial Intelligence (GAI) model to identify an equivalent industrial product manufactured by a second manufacture & generating a prompt for the GAI model, except that now it would also teach that the AI model is a generative artificial intelligence (GAI) model comprising a large language model (LLM) configured to process natural language queries; and that the prompt is generated by populating a natural-language prompt template with the input, the prompt comprising a natural-language request, according to the teachings of Grenader. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to generate AI prompts that are more likely to generate answers useful to the inquirer (Grenader: [0005]).
Regarding Claim 3, Clarke/Bromley/Grenader teach the computer-implemented method of claim 1, wherein the identifying the first product number comprises: receiving, via the user interface, a query, comprising: the first product number, and a request to identify a corresponding product manufactured by the second manufacturer (Clarke: “Suggested replacements 1030 can be presented responsive to receiving a user selection of replace element 1026” [0118] – see figure 15.),
Wherein Bromley further teaches that the products are industrial products (Bromley: “the part number of the 10 KΩ resistor part may be used as the substitute part number for the other two resistor parts” Col. 7, lines 15-25 – “ the part number 3124C0000 is itself suggested as a substitute for part number 3144A0009.” Col. 11, lines 45-55– See also Col’s 4-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bromley with Clarke/Grenader for the reasons identified above with respect to claim 1.
Regarding Claim 4, Clarke/Bromley/Grenader teach the computer-implemented method of claim 3,
wherein the notification further comprises: an indication that the second product was generated by the GAI model, and a user prompt requesting approval to use the second product as an acceptable substitute for the first product (Clarke: “The shopping cart page shown in FIG. 16 shows suggested replacements for the selected … product that can be presented to the user … suggested replacements in this specific example are corporate recommended replacements, ... However, as noted, substitution evaluations for different selected products can be performed to dynamically identify and present suggested substitute products for replacing selected products in a variety of manners, including using one or more of unsupervised product equivalency learning model 812, supervised product equivalency learning model 814.” [0124] – “include a swap element 1034 associated with substitute product identification 1032 and a swap element 1038 associated with substitute product identification 1036. Both swap element 1034 and swap element 1038 are selectable user interface elements that can be selected by the user based on an interaction between the user and the user interface (via the user device) in order to replace the selected product with the corresponding suggested substitute product. ” [0119]); and
wherein the method further comprises: receiving, from the user, an indication that the second product is an acceptable substitute for the first product; and in response to receiving the indication from the user, storing the first product number in association with the second product number as a community-approved match (Clarke: “if the user does accept a recommendation to replace the selected item in the shopping cart with a recommended substitute item, an associated order guide … can be updated accordingly. At 825, also if the user does accept a recommendation to replace the selected item in the shopping cart with a recommended substitute item, a positive feedback signal can be generated and provided as feedback to unsupervised product equivalency learning model 812 and/or feedback graph 816 ” [0107]).
Wherein Bromley further teaches that the products are industrial products (Bromley: “the part number of the 10 KΩ resistor part may be used as the substitute part number for the other two resistor parts” Col. 7, lines 15-25 – “ the part number 3124C0000 is itself suggested as a substitute for part number 3144A0009.” Col. 11, lines 45-55– See also Col’s 4-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bromley with Clarke/Grenader for the reasons identified above with respect to claim 1.
Regarding Claim 5, Clarke/Bromley/Grenader teach the computer-implemented method of claim 1, further comprising: providing, to an engineer, a request for approval to use the second industrial product as an acceptable substitute for the first industrial product; receiving, from the engineer, an indication that the second industrial product is an acceptable substitute for the first industrial product; and in response to receiving the indication from the engineer, storing the first product number in association with the second product number as an engineer-approved match (Bromley: “The results of optimization, … are preferably returned to the design engineer for consideration of the proposed parts substitutions for adoption and incorporation into the given design.” Col. 7, lines 45-55 – “the design engineer(s) may …conduct certain signal integrity analyses in order to validate or reject the consolidating substitutions suggested by the optimization results. The changes validated and accepted by this exploration process 110 are then incorporated into the design ” Col. 8, lines 35-45).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bromley with Clarke/Grenader for the reasons identified above with respect to claim 1.
Regarding Claim 6, Clarke/Bromley/Grenader teach the computer-implemented method of claim 1, further comprising: training the GAI model with the industrial product information comprising one or more of: product specifications, product certifications, product regulations, product catalogs, or a combination thereof (Clarke: “master item groupings 802 can define a … grouping including various similar types …that can be ordered by a facility …Master item groupings 802 can be compiled manually, semi-manually, or automatically, and can be used to train supervised product equivalency learning model” [0104] – “data associated with the order and any substitutions that were made as part of the order can be used as feedback to train one or more models (e.g., models 812 and 814 as detailed below, feedback graph 816 as detailed below, etc.). Moreover, data associated with the order can be used for purchase order approvals. Based on data associated with the order, a reason for replacing a selected product with a suggested substitute product can be inferred (e.g., due to stockout, limited stock, order guide compliance, etc.)” [0101]),
Wherein Bromley further teaches that the products are industrial products (Bromley: “the part number of the 10 KΩ resistor part may be used as the substitute part number for the other two resistor parts” Col. 7, lines 15-25 – “ the part number 3124C0000 is itself suggested as a substitute for part number 3144A0009.” Col. 11, lines 45-55– See also Col’s 4-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bromley with Clarke/Grenader for the reasons identified above with respect to claim 1.
Regarding Claim 7, Clarke/Bromley/Grenader teach the computer-implemented method of claim 1, wherein:
the first product number is one of a plurality of product numbers for industrial products manufactured by the first manufacturer (Clarke: “procurement system 202 can request potential substitute products from a separate system … As part of the request or requests, procurement system 202 can include some type of identifier associated with the one or more selected products (e.g., SKU, etc.) in order to provide a valid request for potential substitute products.” [0094] – “a distributor can act as a supplier, by manufacturing (e.g., either directly or by contracting with a manufacturer) items” [0052]);
the determining to utilize the GAI model comprises determining to utilize GAI assistance for a subset of the plurality of product numbers, the subset including the first product number; the prompt further includes each product number of the subset of product numbers (Clarke: “generating inputs for providing to unsupervised product equivalency learning model 812, and evaluating product equivalents generated by unsupervised product equivalency learning model” [0107] – “performing the substitution evaluation comprises providing data associated with the selected product as input to a machine learning model and identifying the substitute product based on an output of the machine learning model.” Claim 10 – “ As part of the request or requests, procurement system 202 can include some type of identifier associated with the one or more selected products (e.g., SKU, etc.) in order to provide a valid request for potential substitute products.” [0094]); and
the response from the GAI model further comprises a second plurality of product numbers including the second product number, each of the second plurality of product numbers being provided as substitution recommendations for a corresponding product number from the subset of product numbers (Clarke: “At 914, recommended substitute items can be selected corresponding to the selected item. For example, process 900 can generate the recommended substitute items based on an output of unsupervised product equivalency learning model 812, an output of supervised product equivalency learning model 814” [0114] – “the recommended substitute items can be presented to the user on shopping cart page 1000 as suggested replacements 1030, as shown in FIG. 10 and discussed below. If the user accepts at least one of the recommended substitute items, then … update the shopping cart by replacing the selected item with the accepted substitute item recommended to the user.” [0115] - See Figures 10 & 15.).
Regarding Claim 8, Clarke/Bromley/Grenader teach the computer-implemented method of claim 1, wherein:
the prompt further comprises an instruction to search for specifications for the first product from the first manufacturer (Clarke: “procurement system 202 can request potential substitute products from a separate system … As part of the request or requests, procurement system 202 can include some type of identifier associated with the one or more selected products (e.g., SKU, etc.) in order to provide a valid request for potential substitute products.” [0094] – “a distributor can act as a supplier, by manufacturing (e.g., either directly or by contracting with a manufacturer) items” [0052]); and
the request to provide the second product number in the prompt comprises a request to determine the second product number based on the specifications for the first product (Clarke: “supervised product equivalency learning model 814 can use master item groupings 802 to determine potential substitute products that are associated with a given selected product. Master item groupings 802 can include groupings of different products … including various similar types ” [0104]).
Wherein Bromley further teaches that the products are industrial products (Bromley: “the part number of the 10 KΩ resistor part may be used as the substitute part number for the other two resistor parts” Col. 7, lines 15-25 – “ the part number 3124C0000 is itself suggested as a substitute for part number 3144A0009.” Col. 11, lines 45-55– See also Col’s 4-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bromley with Clarke/Grenader for the reasons identified above with respect to claim 1.
Regarding claims 10, 12-17, the limitations of claims 10, 12-17 are closely parallel to the limitations of claims 1, 3-8, with the additional limitations of a system comprising: one or more processors; and one or more memories operably coupled to the one or more processors and having stored thereon software instructions that, upon execution by the one or more processors, cause the one or more processors to perform steps (Clarke: [0065-0067]), and are rejected on the same basis.
Regarding claims 19-20, the limitations of claims 19-20 are closely parallel to the limitations of claims 1 and 3, with the additional limitations of a computer-readable storage media device having program instructions stored thereon, wherein the program instructions, upon execution by one or more processors, cause the one or more processors to perform steps (Clarke: [0065-0067], Claim 1), and are rejected on the same basis.
Claims 9 & 18 are rejected under 35 U.S.C. 103 as being unpatentable over Clarke/Bromley/Grenader, and further in view of Xu et al (US 20200380578 A1) hereinafter Xu
Regarding Claim 9, Clarke/Bromley/Grenader teach the computer-implemented method of claim 1, but do not specifically teach that the prompt further comprises a request to generate a confidence score indicating a likelihood that the second industrial product is an acceptable substitute for the first industrial product; and the response further comprises the confidence score.
However, Xu teaches systems for automatically identifying substitute items (Xu: Abstract), including that the prompt further comprises a request to generate a confidence score indicating a likelihood that the second industrial product is an acceptable substitute for the first industrial product; and the response further comprises the confidence score (Xu: “The machine learning algorithms may determine acceptance or confidence scores (e.g., probabilities) between an anchor item and a substitute item for the anchor item. … upon receiving a request for an item substitution for a particular item, item substitution computing device 102 may execute one or more of the machine learning algorithms to determine item substitutes for the particular item.” [0078] – “generate an adjacent matrix that identifies confidence or acceptance scores between an anchor item (e.g., an out of stock item) and a substitute item for the anchor item … generates a graph identifying confidence scores between attribute vectors … item substitution computing device 102 employs a generative graph convolutional network algorithm, such as one based on a Bayesian latent factor model) to determine the substitute item. ” [0030-0032]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Clarke/Bromley/Grenader would continue to teach submitting the prompt to the GAI model, except that now it would also teach that the prompt further comprises a request to generate a confidence score indicating a likelihood that the second industrial product is an acceptable substitute for the first industrial product; and the response further comprises the confidence score, according to the teachings of Xu. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to more reliably identify a substitute item (Xu: [0004]).
Regarding claim 18, the limitations of claim 18 are closely parallel to the limitations of claim 9, and are rejected on the same basis.
Allowable Over Prior Art of Record
Claims 2 & 11 are allowable over prior art. The combination of elements and the claim as a whole are not found in the prior art.
Claims 2 & 11 are allowable over prior art though rejected on other grounds (e.g. 101) as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention. In the present application, claims 2 and 11 are allowable over prior art. The most related prior art patent of record include Clarke which teaches determining if supplier recommendations are available for suggested replacements [0108], including corporate recommendation separate from those generated by AI [0118], and generating recommendations based on user feedback of substitution recommendations [0107]; Bromley which teaches design engineers giving consideration to substitutions prior to approval; and Chawda et al (US-20230326212-A1) which teaches engineer approval for deviations, such as recommended substitutions, as well as user feedback on model outputs for recommendations.
However, none of these references disclose the ability for: determining that there are no engineer-approved product matches for the first industrial product; and determining that there are no community-approved matches for the first industrial product, wherein generating the prompt for the GAI model is performed in response to determining there are no engineer-approved or community-approved matches, as claimed.
Each of these references fail to disclose or render obvious the combination of limitations in the dependent claims 2 and 11, alone or in obvious combination. Therefore, at least for the combination of elements recited in claim, claims 2 and 11 are allowable over prior art though rejected on other grounds (e.g. 101) as discussed above.
Response to Arguments
Applicant's arguments filed 5/12/2026 have been fully considered but they are not persuasive.
Claim Rejection – 35 USC §101
Applicant argues that “claim 1 is not a method of organizing human activity,” as it “is noted directed to any of the enumerated subgroupings.” Applicant states that “claim 1 is directed to the identification of product matches leveraging a GAI model, and is at most only tangentially related to sales.”
Examiner disagrees. With reference to the rejection above, the claims recite a steps that amount to a concept for product substitution recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. With reference to MPEP 2106.04, commercial interactions are not confined merely to a “transaction itself” as argued, but include a broader range of marketing or sales activities or behaviors, and business relations; recommending substitute products falls within this categorization. The recited abstract idea further falls within managing personal behavior or relationships or interactions between people in that it recites, similar to the exemplary concepts such as filtering content, abstract steps for following rules or instructions.
Applicant further argues that “claim 1 is integrated into a practical application,” providing “a specific technological solution for addressing technical challenges associated with identifying equivalent industrial products across disparate manufacturer product catalogs.” Applicant argues that “identifying technically compatible substitute industrial products is difficult based on the vast number of competitor products across different manufacturers,” and that “the claimed invention improves the technical process of industrial product matching itself” by “leverage[ing] a trained GAI model in order to automatically identify technically compatible substitute industrial products,” resulting in “greatly speeding up the process.”
Examiner disagrees. The alleged problem of “identifying equivalent industrial products across disparate manufacturer product catalogs” is abstract and distinct from computer technology, such than an alleged solution to this problem is a business solution to a business problem rather than a technical solution to a problem rooted in computer technology [MPEP 2106.05(a)]. Furthermore, the alleged ability for “speeding up the process” of evaluating a “vast number of competitor products” relies only on the improved speed or efficiency inherent with applying the abstract idea on a computer, which does not integrate a judicial exception into a practical application [MPEP 2106.05(f)]. Rather, the additional elements are invoked as mere instructions to apply the identified abstract idea to a technological environment, providing only a general linking to computer technology [MPEP 2106.05(f)].
Claim Rejection – 35 USC §103
Applicant’s arguments with respect to the independent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, newly relied-upon reference Grenader is relied upon to teach the newly amended limitations regarding the LLM and natural-language prompt template.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US-20230326212-A1 teaches industrial tool substitution recommendations, including recommending substitute tools from a different vendor that requires engineer approval.
US-20200175564-A1 teaches substitute recommendation systems using a trained machine learning model.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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