DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
For the purpose of prior art consideration, the effective filing date of the instant application is based on the application filed in Europe on July 24th, 2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Whether a Claim is to a Statutory Category
In the instant case, claims 1-7 recite a method/process, claim 8 recites a non-transitory computer readable medium/ machine and claims 9-15 recite a system/ machine that is performing a series of functions. Therefore, these claims fall within the four statutory categories of invention of a machine and a process. Step 1 is satisfied.
Step2A – Prong 1: Does the Claim Recite a Judicial Exception
Exemplary claim 1 (and similarly claims 8 and 9) recites the following abstract concepts that are found to include an enumerated “abstract idea”:
A computer-implemented method executed by at least one processor of a computing system, said computer-implemented method comprising:
receiving product data including a list of components of a product and, for each component of said list of components, a list of types of materials,
for each type of material of said types of materials, retrieving from a life cycle inventory database a plurality of candidate materials and associated environmental impact data,
generating, by the computing system, vector embeddings representing the candidate materials and storing the vector embeddings in a vector store,
generating a component context embedding representing the corresponding component,
computing semantic similarity scores between the component context embedding and the vector embeddings of the candidate materials,
selecting, based on the semantic similarity scores, a subset of candidate materials,
generating a structured prompt comprising the selected subset of candidate materials,
inputting the structured prompt into a large language model configured to select one material from the selected subset,
retrieving environmental impact values associated with the selected material from the life cycle inventory database, and
aggregating, by the processor, the environmental impact values that are retrieved indexed by component and material,
to generate a total environmental impact value for the product.
[Emphasis added to show the abstract idea as bolded being executed by unbolded additional elements that do not meaningfully limit the abstract idea]
This method claim is grouped within the "mathematical concepts” grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test because the claims involve a series of steps for mathematical calculations to generate a total environmental impact value for the product, which is a process that is encompassed by the abstract idea of mathematical concepts. See e.g., MPEP 2106.04(a)(2). Accordingly, claim 1 (and similarly claims 8 and 9) is found to recite abstract idea(s).
Step2A – Prong 2: Does the Claim Recite Additional Elements that Integrate the Judicial Exception into a Practical Application
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test, the additional elements of the claims such as computer, processor, computing system, life cycle inventory database, vector store and a large language model merely use a computer as a tool to perform an abstract idea and/or generally link the use of a judicial exception to a particular technological environment. Specifically, the computer, processor, computing system, life cycle inventory database, vector store and a large language model performs the steps or functions of mathematical calculations to generate a total environmental impact value for the product. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer (or technical elements disclosed at a high level of generality such as computer, processor, computing system, life cycle inventory database, vector store and a large language model) performing functions of receiving, retrieving, generating, storing, computing, selecting, inputting and aggregating that correspond to acts required to carry out the abstract idea (MPEP 2106.05(f) and (h)). Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
Step2B: Does the Claim Amount to Significantly More
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element analysis of Step 2A Prong 2 is equally applied to Step 2B. “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis.” MPEP 2106.05(d). The courts have recognized the following computer functions as well‐understood, routine, and conventional (“WURC”) functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. Exemplary claim 1 recites the following limitations that the courts have found to be WURC:
Claim 1 includes limitations relating to receiving or transmitting data over a network receiving product data …; retrieving from a life cycle inventory database a plurality of candidate materials and associated environmental impact data; retrieving environmental impact values associated with the selected material from the life cycle inventory database; as claimed) data. See MPEP 2106.05(d)(II) where courts found to be WURC - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
Claim 1 includes several limitations relating to performing repetitive calculations (generating, by the computing system, vector embeddings representing the candidate materials…; generating a component context embedding …; computing semantic similarity scores between the component context embedding and the vector embeddings of the candidate materials; selecting, based on the semantic similarity scores, a subset of candidate materials; generating a structured prompt comprising the selected subset of candidate materials; inputting the structured prompt into a large language model configured to select one material from the selected subset; aggregating, by the processor, the environmental impact values…; to generate a total environmental impact value for the product; as claimed). See MPEP 2106.05(d)(II) where courts found to be WURC - ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
Claim 1 includes several limitations relating to storing and retrieving information in memory (storing the vector embeddings in a vector store; as claimed). See MPEP 2106.05(d)(II) where courts found to be WURC - iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Accordingly, when viewed alone and in ordered combination, these additional elements are not found to recite significantly more than the underlying abstract idea.
Independent claim 8 is directed to non-transitory computer readable medium executing a method for performing the functions of receiving, retrieving, generating, storing, computing, selecting, inputting and aggregating relating to mathematical concepts without additional elements beyond technical elements disclosed at a high level of generality such as a non-transitory computer readable medium, computer, computing system, processor, life cycle inventory database, vector store and a large language model that provide significantly more than the abstract idea of mathematical concepts of mathematical calculations to generate a total environmental impact value for the product as noted above regarding claim 1. For these reasons as well, this independent claim is also not patent eligible.
Independent claim 9 describes a system performing the functions of receiving, retrieving, generating, storing, computing, selecting, inputting and aggregating relating to mathematical concepts without additional elements beyond technical elements disclosed at a high level of generality such as a processor, memory, life cycle inventory database, vector store, computer and a large language model that provide significantly more than the abstract idea of mathematical concepts of mathematical calculations to generate a total environmental impact value for the product as noted above regarding claim 1. Therefore, this independent claim is also not patent eligible.
Dependent claims 2-7 and 10-15 further describe the abstract idea of mathematical concepts. Dependent claims 2-7 and 10-15 add non-functional descriptive material and respective functions of retrieving, extracting, receiving, inputting, obtaining, receiving, determining, generating, applying and accessing steps that are executed by a life cycle inventory database, language model module, database communication link, large language model, external product data source, semantic similarity scoring, embedding model, processor, network interfaces and as disclosed in independent claims 1 and 9, however these additional steps remain disclosed at a high level of generality and do not amount to more than mere computer implementation of the abstract idea, which does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. Therefore, dependent claims 2-7 and 10-15 are also not patent eligible. Further, the dependency of these claims on ineligible independent claims 1 and 9 also renders dependent claims 2-7 and 10-15 as not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sousa et al. (US 2008/0319812 A1) in view of Steingrimsson et al. (US 2023/0028912 A1).
Regarding Claim 1, 8 and 9, modified Sousa teaches:
A computer-implemented method executed by at least one processor of a computing system, said computer-implemented method comprising: / A non-transitory computer readable medium storing a computer program comprising instructions which, when the computer program is executed by a computer, cause the computer to carry a computer-implemented method executed by at least one processor of the computer, said computer-implemented method comprising:/ A system comprising: at least one processor; memory storing instructions; a life cycle inventory database; and …; wherein the at least one processor is configured to execute the instructions to cause the system to implement a computer-implemented method comprising (See Sousa ¶ [0020-0021] – computer program executed by a processor, [0036] – A web services framework integrates Life Cycle Assessment (LCA) software technology with existing product design, manufacturing planning, product data management, supply chain management, financial planning, and distribution management tools [0040] – memory and [0045] - The AI/recommendations and optimization engine is configured to send and receive information to and from the LCA calculator), comprising:
receiving product data including a list of components of a product and, for each component of said list of components, a list of types of materials (See Sousa ¶ [0043] – the LCA calculator can receive product information from a bill-of-materials (BOM) [list by example] that can include material types (i.e., name of the material), volume or amount of materials used, and the units, manufacturing processes and additional product system information, such as transportation mode and distances and energy use estimates),
for each type of material of said types of materials, retrieving from a life cycle inventory database a plurality of candidate materials and associated environmental impact data (See Sousa ¶ [0045] – the recommendation engine includes rules and information about materials and their impact on the environment. For example, the recommendation engine can receive the LCA score from the LCA calculator and output a list of alternative materials and amounts and design strategies that can be used in the design),
…,
…,
…,
selecting, …, a subset of candidate materials (See Sousa ¶ [0045] - the recommendation engine includes rules and information about materials and their impact on the environment … the recommendation engine can receive the LCA score from the LCA calculator and output a list of alternative materials [selecting a subset of candidate materials by example] and amounts and design strategies that can be used in the design),
generating a structured prompt comprising the selected subset of candidate materials (See Sousa ¶ [0058] - the user describes a concept and inputs the bill-of-materials [candidate materials by example] and additional product system information, such as transportation mode and distances and energy use estimates. The information may be entered directly through the UI, or could be mined from other 3rd party tools… the LCA calculator or recommendation engine may request further goals and parameters from the user [structured prompt by example] based on a subset of the information entered),
inputting the structured prompt into a … model configured to select one material from the selected subset (See Sousa ¶ [0047] – a neural network and mathematical models can perform modeling functions that receive a collection of inputs, perform a computational algorithm, and produce an output with LCA results and other sustainability information, [0058] - the user describes a concept and inputs the bill-of-materials [candidate materials by example] and additional product system information, such as transportation mode and distances and energy use estimates. The information may be entered directly through the UI, or could be mined from other 3rd party tools… the LCA calculator or recommendation engine may request further goals and parameters from the user [structured prompt by example] based on a subset of the information entered and [0060] - the recommendation engine can utilize the LCA calculations, was well as information in the social network, and the KM system to output suggested design strategies for improving a product, such as indicating that a certain material or product generally shows a high score in global warming. The sustainable design strategy received from the recommendation engine can be to reduce the amount of a certain material [one material from the selected subset by example], to use alternative recycled or renewable materials, or to reuse materials contained in the product),
retrieving environmental impact values associated with the selected material from the life cycle inventory database (See Sousa ¶ [0051] – the component impacts can include scores, impacts over lifetime, and effects on impact categories. The lifecycle impacts can include CO2 scores, impacts per phase, and impact categories … the comparison can include images of the product and the reference with an impact reduction percent (%), the respective impacts per functional unit (e.g., Okala millipoints/hour of use), the respective total impacts over the product lifetime, an estimated lifetime, the component with the highest impact factor (e.g., Rotomold HDPE), the most affected impact category (e.g., human toxicity), and the lifecycle phase most impacted by the System Bill Of Materials (SBOM)), and
aggregating, by the processor, the environmental impact values that are retrieved indexed by component and material (See Sousa ¶ [0048] – the recommendation engine may provide the user with a suggested substitute material to use in a design. In general, the substitute material can be more environmentally friendly than the original material … aggregate appropriate news and information regarding sustainable product design and manufacturing including new products, methods, evaluation systems and regulations. The knowledge management and collaboration system … can allow users within a company to share product information across the company such that users in a company can perform sustainability analysis at the component level. The information for these components can be included in a larger system design. The KM system can facilitate organization of sustainability data based on product components),
to generate a total environmental impact value for the product (See Sousa ¶ [0043] – the LCA calculator can receive product information from a bill-of-materials (BOM) that can include material types (i.e., name of the material), volume or amount of materials used, and the units, manufacturing processes and additional product system information and [0051] – the component impacts can include scores, impacts over lifetime, and effects on impact categories. The lifecycle impacts can include CO2 scores, impacts per phase, and impact categories … the comparison can include … the respective total impacts over the product lifetime, an estimated lifetime, the component with the highest impact factor (e.g., Rotomold HDPE), the most affected impact category (e.g., human toxicity), and the lifecycle phase most impacted by the System Bill Of Materials (SBOM)).
While Sousa teaches an artificial intelligence recommendation engine using neural networks and mathematical learning models to perform product life cycle analysis to determine an environmental impact of said product based on the materials used in said product (Sousa ¶ [0038], [0043], [0045-0047] and [0051]), Sousa does not explicitly teach generating, by the computing system, vector embeddings representing the candidate materials and storing the vector embeddings in a vector store. This is taught by Steingrimsson (See Steingrimsson ¶ [0356] - aggregation of the observed cost from the Bill of Materials for individual subsystems from the spacer cart example, [0747] – Word2Vec represents a group of related models that have been used to produce word embeddings. These models consist of shallow, two-layer neural networks that can be trained to reconstruct linguistic contexts of words. Word2Vec accepts as input a large corpus of text and generates a vector space [vector store], with typical dimension of the order of several hundreds, with each unique word in the corpus being assigned to a corresponding vector in the space. Word vectors are located in the vector space such that words that share common contexts in the corpus are positioned close to one another in the space).
Steingrimsson further teaches generating a component context embedding representing the corresponding component (See Steingrimsson ¶ [0461] - A component, in this context, can consist of a part or another assembly. One can obtain assembly components by utilizing the GetComponents (IAssemblyDoc) method. Through proper calls to GetComponents( ) one can iterate over each component, extract the associated data, and then use. In particular, one can iterate over each component and extract the features, through proper calls to GetFeatures( ), [0747] – Word2Vec represents a group of related models that have been used to produce word embeddings).
Steingrimsson further teaches computing semantic similarity scores between the component context embedding and the vector embeddings of the candidate materials (See Steingrimsson ¶ [0356], [0461] and [0747] as noted above teaching – the component context embedding and the vector embeddings of the candidate materials by example and [0586] - Word2Vec represents each distinct word with a vector containing a specific list of numbers. The vectors are carefully selected such that the cosine similarity between the vectors characterizes the level of semantic similarity between the words represented by those vectors).
Steingrimsson also teaches the use of a large language model (See Steingrimsson ¶ [0586] – Word2Vec is a technique for natural language processing. The Word2Vec algorithm utilizes a neural network model to infer word association from a large corpus of text, thereby teaching use of large language model by example). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the learning model based life cycle analysis system of Sousa the use of vectorize material and component information and large language model input to select materials for product designs as taught by Steingrimsson for the purpose of identifying design oversights early, aiding with human decision making, and providing productivity improvements (cost savings) to its users (Steingrimsson ¶ [0012]), thereby increasing the accuracy and efficiency of the model based product life cycle analysis system of Sousa.
Regarding Claim 2 and 10, modified Sousa teaches:
The computer-implemented method/ system according to claim 1 and 9, wherein the structured prompt further comprises, for each candidate material in the subset of candidate materials that is selected, one or more of a material description retrieved from the life cycle inventory database and an activity domain identifier associated therewith (See Sousa ¶ [0050] – the user interface can include a series of screens for creating a new product project… the UI screens can include tab objects configured to present data objects relating to product definition, assessment scope, assessment goals, and access … the product definition tab can include data fields for a product name, a client or division, a product category, and a text box for description … The user may also select lifecycle phases and transportation elements to be included in the assessment [activity domain by example] and [0058] - the user describes a concept and inputs the bill-of-materials [candidate materials by example] and additional product system information, such as transportation mode and distances and energy use estimates. The information may be entered directly through the UI, or could be mined from other 3rd party tools… the LCA calculator or recommendation engine may request further goals and parameters from the user [structured prompt by example] based on a subset of the information entered).
Regarding Claim 3, modified Sousa teaches:
The computer-implemented method according to claim 2, wherein the material comprises a summary (See Sousa ¶ [0051] – the UI screen can include a graphical summary of a comparison, by product component, of the life cycle greenhouse gases impact) …
While Sousa teaches an artificial intelligence recommendation engine using neural networks and mathematical models to perform product life cycle analysis to determine an environmental impact of said product based on the materials used in said product that are described by at least a summary of features relating to said materials (Sousa ¶ [0051] and [0056]), Sousa does not explicitly teach that said summary was obtained by features extraction from a material description record stored in the life cycle inventory database. This is taught by Steingrimsson (See Steingrimsson ¶ [0627] - the algorithm for recursively extracting the assembly/component dependence, along with the mass properties and the bounding box… By forcing a rebuild of the assembly in Step 5, we ensure the mass properties [feature by example] can be properly extracted). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in model based life cycle analysis system of Sousa the use of feature extraction as taught by Steingrimsson for the purpose of identifying design oversights early, aiding with human decision making, and providing productivity improvements (cost savings) to its users (Steingrimsson ¶ [0012]), thereby increasing the accuracy and efficiency of the model based product life cycle analysis system of Sousa.
Regarding Claim 4 and 11, modified Sousa teaches:
The computer-implemented method/ system according to claim 1 and 9, further comprising,
receiving a product name (See Sousa ¶ [0050] – the user interface can include a series of screens for creating a new product project… the UI screens can include tab objects configured to present data objects relating to product definition … the product definition tab can include data fields for a product name),
inputting the product name into the … model to obtain the list of components and corresponding types of materials (See Sousa ¶ [0043] – the LCA calculator can receive product information from a bill-of-materials (BOM) that can include material types (i.e., name of the material), volume or amount of materials used, and the units, manufacturing processes and additional product system information, [0047] – a neural network and mathematical models can perform modeling functions that receive a collection of inputs, perform a computational algorithm, and produce an output with LCA results and other sustainability information and [0050] – product name).
While Sousa teaches an artificial intelligence recommendation engine using neural networks and mathematical models to perform product life cycle analysis to determine an environmental impact of said product based on the materials used in said product (Sousa ¶ [0043], [0045-0047] and [0051]), Sousa does not explicitly teach that said models include a large language model. This is taught by Steingrimsson (See Steingrimsson ¶ [0586] – Word2Vec is a technique for natural language processing. The Word2Vec algorithm utilizes a neural network model to infer word association from a large corpus of text, thereby teaching use of large language model by example). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in model based life cycle analysis system of Sousa the use of large language models as taught by Steingrimsson for the purpose of identifying design oversights early, aiding with human decision making, and providing productivity improvements (cost savings) to its users (Steingrimsson ¶ [0012]), thereby increasing the accuracy and efficiency of the model based product life cycle analysis system of Sousa.
Regarding Claim 5 and 12, modified Sousa teaches:
The computer-implemented method/ system according to claim 1 and 9, wherein the list of components and, said types of materials are received from an external product data source (See Sousa ¶ [0042] - the knowledge management system 116 may include both proprietary and open source programs (e.g., Drupal, Fast Search, third-party Wiki tools, PBWiki, Basecamp and bulletin board systems) [external product data source by example], [0043] – the LCA calculator can receive product information from a bill-of-materials (BOM) [list by example] that can include material types (i.e., name of the material), volume or amount of materials used, and the units, manufacturing processes and additional product system information and [0051] – The UI can include component impact navigation buttons … the component impacts can include scores, impacts over lifetime, and effects on impact categories … the component with the highest impact factor … and the lifecycle phase most impacted by the System Bill Of Materials (SBOM) [component list by example]).
Regarding Claim 6 and 13, modified Sousa teaches:
The computer-implemented method/ system according to claim 1 and 9, further comprising, when one or several requested types of material are not listed in the life cycle inventory database, determining a substitute material type … and retrieving candidate materials corresponding to the substitute material type (See Sousa ¶ [0048] – The Sustainable Design Decision Support System provides news, information, best practices, case studies, and heuristics on life-cycle thinking. The social networking system can provide information and education services to educate the users about sustainability, building sustainable products and how the system operates ... the recommendation engine may provide the user with a suggested substitute material to use in a design … the substitute material can be more environmentally friendly than the original material … The KM system can facilitate organization of sustainability data based on product components. The KM system can also include a collection of implementation notes to provide users with information on how to implement a new material or sustainable design strategy).
While Sousa teaches an artificial intelligence recommendation engine using neural networks and mathematical models to perform product life cycle analysis to determine an environmental impact of said product based on the materials used in said product (Sousa ¶ [0043], [0045-0047] and [0051]), Sousa does not explicitly teach using semantic similarity scoring. This is taught by Steingrimsson (See Steingrimsson ¶ [0586] - Word2Vec represents each distinct word with a vector containing a specific list of numbers. The vectors are carefully selected such that the cosine similarity between the vectors characterizes the level of semantic similarity between the words represented by those vectors). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in model based life cycle analysis system of Sousa the use semantic similarity scoring as taught by Steingrimsson for the purpose of identifying design oversights early, aiding with human decision making, and providing productivity improvements (cost savings) to its users (Steingrimsson ¶ [0012]), thereby increasing the accuracy and efficiency of the model based product life cycle analysis system of Sousa.
Regarding Claim 7 and 14, modified Sousa teaches:
The computer-implemented method/ system according to claim 1 and 9, … (See claims 1 and 9 above).
While Sousa teaches an artificial intelligence recommendation engine using neural networks and mathematical learning models to perform product life cycle analysis to determine an environmental impact of said product based on the materials used in said product (Sousa ¶ [0038], [0043], [0045-0047] and [0051]), Sousa does not explicitly teach that generating the vector embeddings comprises applying an embedding model to material description text. This is taught by Steingrimsson (See Steingrimsson ¶ [0356] - aggregation of the observed cost from the Bill of Materials for individual subsystems from the spacer cart example, [0747] – Word2Vec represents a group of related models that have been used to produce word embeddings. These models consist of shallow, two-layer neural networks that can be trained to reconstruct linguistic contexts of words).
Regarding Claim 15, modified Sousa teaches:
The system according to claim 9, further comprising network interfaces and wherein said life cycle inventory database and said … model are accessible via said network interfaces (See Sousa ¶ [0042-0044] – the logic layer can include a social networking system, a knowledge management and collaboration system, an LCA calculator, an AI/recommendation and optimization engine, and a content management system (CMS). In operation, the users are using the system to answer direct LCA questions and perform LCA-centric “what if” scenarios in an effort to design their products more sustainably. The users can access the system through either a rich GUI (e.g., a Rich Internet Application), or via a third-party tool (e.g., a CAD tool such as SolidWorks®, a PDM tool, or other PLM and ERP tools) with a tool specific plug-in. Both the UI and third-party tools are connected to a web services (i.e., a web services API). The LCA calculator can be configured to process this information and return a sustainability analysis … the LCA calculator can receive product information from a bill-of-materials (BOM) [list/ database by example] that can include material types (i.e., name of the material), volume or amount of materials used, and the units, manufacturing processes and additional product system information and [0061] - the user can view information from the logic layer and data layer … the recommendation engine can output a series of links to direct the user to content based on the assessment results).
While Sousa teaches an artificial intelligence recommendation engine using neural networks and mathematical models to perform product life cycle analysis to determine an environmental impact of said product based on the materials used in said product (Sousa ¶ [0043], [0045-0047] and [0051]), Sousa does not explicitly teach that said models include a language model. This is taught by Steingrimsson (See Steingrimsson ¶ [0586] – Word2Vec is a technique for natural language processing. The Word2Vec algorithm utilizes a neural network model to infer word association from a large corpus of text, thereby teaching use of large language model by example). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the learning model based life cycle analysis system of Sousa the use of vectorize material and component information and large language model input to select materials for product designs as taught by Steingrimsson for the purpose of identifying design oversights early, aiding with human decision making, and providing productivity improvements (cost savings) to its users (Steingrimsson ¶ [0012]), thereby increasing the accuracy and efficiency of the model based product life cycle analysis system of Sousa.
Response to Remarks
Applicant's arguments filed 05/12/2026 have been fully considered but they are not persuasive.
Rejection under 35 U.S.C. § 112:
The amendments to claims 1-15 resolve the previous issues concerning 35 U.S.C. § 112 and the previous rejection is withdrawn.
Rejection under 35 U.S.C. § 101:
The amendments to claims 1-15 do not improve patent eligibility for the claimed invention of the instant application and the previous rejection under 35 U.S.C. § 101 is maintained.
Contrary to the applicant’s assertion that claimed invention improves the operation of the LLM-driven selection system by (i) reducing the candidate space before invoking the LLM, (ii) structuring and limiting the prompt to a defined set, and (iii) constraining output to selection from that set - reducing error, improving determinism, and reducing unnecessary computation that amounts to far more than generic computer implementation, as amended, the claims do not reflect a technical improvement in a manner that is obvious to one of ordinary skill in the art. As a whole, the claims merely show data collection, data analysis and an output based on said analysis as executed by technical elements disclosed at a high level of generality that does not amount to more than computer implementation of the abstract idea. Independent claims 1, 8 and 9 merely show use of a large language model to select a material from a subset of candidate materials, not how said model makes said selection. This leaves the claims as a whole without clear reflection of an improvement to the underlying technology, but rather only to the abstract idea itself.
Rejection under 35 U.S.C. § 103:
The examiner agrees with the applicant in that the amendments to claims 1-15 leaves said claims as no longer taught by the prior art combination of the Sousa and Cella. This ground for rejection is withdrawn and Cella is no longer relied on as prior art. However, as described above in the current rejection under 35 U.S.C. § 103, the invention of the instant application as currently disclosed by the amended claim limitations remains unpatentable over Sousa in view of Steingrimsson.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW S WERONSKI whose telephone number is (571)272-5802. The examiner can normally be reached M-F 8 am - 5 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd A. Obeid can be reached at 5712703324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW S WERONSKI/Examiner, Art Unit 3627
/MICHAEL JARED WALKER/Primary Examiner, Art Unit 3627
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