DETAILED ACTION
Claims 1-11 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to tracing article and supplier relationships without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claim 11), Apparatus (claims 1-10)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 10, 11] a model that has defined a relationship between an article and an article forming the article and a supplier thereof, and
receive an input of traceability information indicating a relationship between an article and at least one article forming the article and a supplier thereof;
estimate, by using the model, one or more articles and suppliers thereof that are lacking in the input traceability information;
output the estimated one or more articles and suppliers thereof for confirmation by a user;
store one or more articles and suppliers thereof confirmed by the user as determined traceability information; and
update the model with the determined traceability information.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations presented above, including the gathering of information, updating a model, performing traceability, presenting information, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to tracing article and supplier relationships, which (under its broadest reasonable interpretation) is an example of managing relationships and interactions between people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 recites a traceability information creation support system, comprising:
a processor; and
a storage device, wherein the storage device holds an article and a model that has learned a relationship between an article and an article forming the article and a supplier thereof.
Claim 1 also stores one or more articles and suppliers thereof confirmed by the user in the storage device.
Claim 1 updates the model by learning the determined traceability information.
Claim 10 incorporates additional elements similar to those recited in claim 1.
Furthermore, claim 10 discloses that the estimating is performed using a large language learning system.
Claim 11 incorporates additional elements similar to those recited in claim 1.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 40-47).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶ 147 – “Accordingly, by using a general-purpose model, such as so-called generative Al, creation of traceability information including even information on suppliers that a company itself cannot grasp directly can be supported.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 2] wherein the estimating the one or more articles and the suppliers thereof that are lacking in the input traceability information includes estimating, by using the model, one or more articles that form an article included in the input traceability information and suppliers thereof, and
repeat estimating the one or more articles forming the article included in the input traceability information and the suppliers thereof until reaching an article that is further impartible;
output all the estimated one or more articles and suppliers thereof for the confirmation by the user; and
store an article and a supplier thereof confirmed by the user among all the output one or more articles and the suppliers thereof as the determined traceability information.
[Claim 3] wherein the estimating the one or more articles and the suppliers thereof that are lacking in the input traceability information includes estimating, by using the model, one or more articles forming an article included in the input traceability information and suppliers thereof, and
estimate an article forming the article included in the input traceability information and a supplier thereof;
output the estimated article and supplier thereof for the confirmation by the user; and
repeat storing an article and a supplier thereof confirmed by the user as the determined traceability information until reaching an article that is further impartible.
[Claim 4] wherein the estimating the one or more articles and the suppliers thereof that are lacking in the input traceability information includes estimating, by using the model, one or more articles formed by an article included in the input traceability information and suppliers thereof, and
repeat estimating the one or more articles formed by the article included in the input traceability information and the suppliers thereof until reaching an article corresponding to a final product;
output all the one or more estimated articles and the suppliers thereof for the confirmation by the user; and
store an article and a supplier thereof confirmed by the user among all the output one or more articles and the suppliers thereof as the determined traceability information.
[Claim 5] wherein the estimating the one or more articles and the suppliers thereof that are lacking in the input traceability information includes estimating, by using the model, one or more articles formed by an article included in the input traceability information and suppliers thereof, and
estimate an article formed by the article included in the input traceability information and a supplier thereof;
output the estimated article and supplier thereof for the confirmation by the user; and
repeat storing an article and a supplier thereof confirmed by the user as the determined traceability information until reaching an article that corresponds to a final product.
[Claim 6] holds traceability information for the model that includes one or more relationships between an article and an article forming the article and a supplier thereof, and
update the traceability information for the model by adding the determined traceability information; and
update the model by providing the updated traceability information with a weight greater than the weight of other traceability information being added to the added determined traceability information in the updated traceability information.
[Claim 7] output, when the estimated has been performed, by using the model, a plurality of combinations of articles and suppliers thereof as a plurality of candidates for one article and a supplier thereof that are lacking in the input traceability information, an accuracy of the estimation of each of the plurality of combinations for the confirmation by the user, and
wherein the confirmation by the user includes selecting one of the plurality of candidates.
[Claim 8] acquire a risk value of each of the suppliers included in the determined traceability information; and
determine whether to notify the user of a risk of the each of the suppliers based on the risk value of the each of the suppliers and an accuracy of the estimation of the each of the suppliers.
[Claim 9] notify the user of the risk value of the each of the suppliers when the risk value of the each of the suppliers is higher than a predetermined criterion and the accuracy of the estimation of the each of the suppliers is higher than a predetermined criterion.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations presented above, including the gathering of information, updating a model, performing traceability, presenting information, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to tracing article and supplier relationships, which (under its broadest reasonable interpretation) is an example of managing relationships and interactions between people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Risk is evaluated in claims 8 and 9 and this is another example of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of their independent claims.
Claim 1 recites a traceability information creation support system, comprising:
a processor; and
a storage device, wherein the storage device holds an article and a model that has learned a relationship between an article and an article forming the article and a supplier thereof.
Claim 1 also stores one or more articles and suppliers thereof confirmed by the user in the storage device.
Claim 1 updates the model by learning the determined traceability information.
Claims 2-9 recite that the processor is configured to perform various operations.
Claim 2 stores an article and a supplier thereof confirmed in the storage device.
Claim 3 repeats storing an article and a supplier thereof confirmed by the user in the storage device as the determined traceability information until the processor reaches an article that is further impartible.
Claim 4 stores an article and a supplier thereof confirmed in the storage device.
Claim 5 repeats storing an article and a supplier thereof confirmed by the user in the storage device as the determined traceability information until the processor reaches an article that corresponds to a final product.
Claim 6 recites wherein the storage device holds traceability information for learning that includes one or more relationships between an article and an article forming the article and a supplier thereof, and
wherein the processor is configured to:
update the traceability information for learning by adding the determined traceability information; and
update the model by learning the updated traceability information for learning with a weight greater than the weight of other traceability information being added to the added determined traceability information in the updated traceability information for learning.
Claim 10 incorporates additional elements similar to those recited in claim 1.
Furthermore, claim 10 discloses that the estimating is performed using a large language learning system.
Claim 11 incorporates additional elements similar to those recited in claim 1.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 40-47).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶ 147 – “Accordingly, by using a general-purpose model, such as so-called generative Al, creation of traceability information including even information on suppliers that a company itself cannot grasp directly can be supported.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 –
(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.
(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.
Claims 1-7 and 11 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Panda et al. (WO 2022/132040 A1).
[Claim 1] Panda discloses a traceability information creation support system (p. 10: 27 – p. 11: 6 – “The communication network 124 helps to register and/or track the process of BOM curation. It communicates with the user(s) and suppliers and maintains or creates a record of the communication. Once the quotes are ready, the communication network 124 sends email (or other - e.g. fax, postal mail) notifications to the corresponding suppliers and requests them to fill in one or more fields associated with the quotes or items identified in the quotes. It also monitors any deadlines associated with the quotes and notifies the suppliers accordingly. The communication network 124 also consolidates the responses from suppliers. The Auxiliary block 120 coordinates the entire or a part of the method of curation of BOM starting from requesting suppliers to fill in item information to gathering and consolidating quotes for further processing.”), comprising:
a processor (p. 19: 10-13 – “The methods may also be provided on a computer-readable medium comprising computer program code that, when executed by a processor of a computer system (such as the computer system 300), causes the computer system to perform the method 400, the method 100 and others described herein.”); and
a storage device, wherein the storage device holds an article and a model that has learned a relationship between an article and an article forming the article and a supplier thereof (p 7: 25 – p. 8: 3 – “Figure 1 is a block diagram of a part of a system 100 for producing a curated BOM. System 100 comprises a core block or machine learning module 110. The machine learning module 110 comprises Al for intelligent part search 112 (part search machine learning model), Al for part-supplier matching/searching 114 (part of a supplier matching machine learning model), and Al for best supplier selection 116 (part of a supplier matching machine learning model). System 100 also comprises an auxiliary block or module 120 that supports the machine learning module 110. The Auxiliary block 120 contains a Document Automation module 122 and Communication Network 124.”; p. 16: 21 – p. 17: 2 – “At step 420, the part search machine learning model (network 112) may identify one or more specifications that are not fulfilled by the known part(s) (unfulfilled specification(s)). At step 420, the supplier matching machine learning model (networks 114, 214) may be executed to consult supplier parts lists to identify one or more part identifier(s) of new parts (new part(s)) corresponding to the unfulfilled specification(s). System 300 may add the part identifier(s) of the new part(s) to a database of all known parts stored in the memory 304. Over repeated iterations of method 400, system 300 progressively expands its database of all known parts. System 300 may re-apply the part search machine learning model (networks 112 or 212) to the list to determine a part number for one or more updated known parts corresponding to the list. System 300 may update the known part(s) based on the one or more updated known parts.”), and
wherein the processor (p. 19: 10-13) is configured to:
receive an input of traceability information indicating a relationship between an article and at least one article forming the article and a supplier thereof (p. 12: 15 – p. 13: 14 – “Networks of the core block 200 enable quick and efficient BOM curation and BOM price estimation. A designer may iterate multiple times during the product development stage. During the iterative process, the neural networks of the system can efficiently revise and curate BOMs taking into account the design iterations and user revisions. The system 100 or 200 may be coupled as a plug-in with an existing CAD/designing software to integrate the BOM curation process with the design process. During the design stage, as the user keeps on adding components one by one to his/her product, systems according to the embodiments may identify information about the newly added item and recommend a part number for that newly added item. The network for part number recommendation 212 generates recommendations of part numbers. The network for part number recommendation 212 may use an unsupervised machine learning approach, i.e., Uniform Manifold Approximation and Projection (UMAP). Network 212 may be trained on a huge dataset of electronic items with corresponding specifications. UMAP reduces these multi-dimensional specification data into a low dimensional space by finding correlations between them. It may also cluster the specification data as per their field of application. When a user provides input about the field of application, the UMAP network of network 212 matches that field with the most relevant field and recommends a part number. Network 212 also uses a neural collaborative filtering technique and suggests alternative parts to the user. Network 212 uses the specifications provided by the user for a part and also utilizes the historical purchase records of the user to recommend alternative parts. Systems 100 or 200 may also comprise program code to decompose a design model file such as a CAD model and provide the user with a detailed analysis of the items used in a product in the design model file. The systems may also enable the generation of a quick price estimate of the product by finding the best/preferred supplier for individual components and using price information associated with the best/preferred supplier for the individual components. In some embodiments, systems 100 or 200 may analyze a schematic/CAD model of a circuit board, decompose the model to identify all parts or a subset of parts of the circuit board, and then recommend part numbers and a price or prices for the circuit board.”);
estimate, by using the model, one or more articles and suppliers thereof that are lacking in the input traceability information (p. 9: 6 – p. 10: 30 – “Characterising the items enables quotes to be generated for the parts thus characterised. It is expected, however, that the network 112 will not know all parts it encounters in all instances. This is particularly the case new technologies, the parts for which are either entirely new or can change very rapidly. To accommodate this the network 112 learns about the new items that it encounters for the first time. In response to identifying a new item, the system optimizes its weights to account for the new item - i.e. the new item becomes known to, or identifiable by, the system and thus ultimately becomes a known item. By identifying parts, network 112 facilitates the split of the raw BOM 134 into multiple subgroups while keeping similar products in similar product category groups. Each group is therefore likely to include related products that can be supplied by an individual supplier or group of suppliers. Accordingly, the network 112 identifies the parts. The BOM splitting block 140 then splits the raw BOM into multiple, quotable groups. The BOM splitting block 140 may receive information from the quote creation block 150 to determine how to split the BOM, may split the BOM based on common keywords in specifications or based on previous quotes. The quote creation block 150 then uses the groups supplied by block 140 to generate a plurality of quotes or requests for quotes, each request for a quote being associated with a subgroup or category of BOM parts identified by the network 112. At this stage the quotes do not specify a supplier. Once the quotes are available for a BOM, the Al for Part-Supplier matching network 114 processes the quotes and suggests the best supplier(s) for each item in the quotes. Network 114 may be a part of a supplier selection machine learning model. Network 114 implements a neural collaborative filtering technique for the matching process. Network 114 comprises two sparse vectors, one represents the parts, and the other represents suppliers. It then uses embedding layers within network 114 to obtain latent vectors. The latent vectors allow loss function minimization and supplier selection. The raw BOM 134 may comprise an incomplete or a partial list of specifications/attributes of items. However, networks 112 and 114 still match partially specified items based on records in a master database accessible to system 100. The master database may comprise a historical purchase database associated with past purchases of a user.”);
output the estimated one or more articles and suppliers thereof for confirmation by a user (p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”);
store one or more articles and suppliers thereof confirmed by the user in the storage device as determined traceability information (p. 11: 13-22 -- “The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”); and
update the model by learning the determined traceability information (p. 11: 14-22 – “The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”).
[Claim 2] Panda discloses wherein the estimating the one or more articles and the suppliers thereof that are lacking in the input traceability information includes estimating, by the processor, by using the model, one or more articles that form an article included in the input traceability information and suppliers thereof (p. 9: 6 – p. 10: 30 – “Characterising the items enables quotes to be generated for the parts thus characterised. It is expected, however, that the network 112 will not know all parts it encounters in all instances. This is particularly the case new technologies, the parts for which are either entirely new or can change very rapidly. To accommodate this the network 112 learns about the new items that it encounters for the first time. In response to identifying a new item, the system optimizes its weights to account for the new item - i.e. the new item becomes known to, or identifiable by, the system and thus ultimately becomes a known item. By identifying parts, network 112 facilitates the split of the raw BOM 134 into multiple subgroups while keeping similar products in similar product category groups. Each group is therefore likely to include related products that can be supplied by an individual supplier or group of suppliers. Accordingly, the network 112 identifies the parts. The BOM splitting block 140 then splits the raw BOM into multiple, quotable groups. The BOM splitting block 140 may receive information from the quote creation block 150 to determine how to split the BOM, may split the BOM based on common keywords in specifications or based on previous quotes. The quote creation block 150 then uses the groups supplied by block 140 to generate a plurality of quotes or requests for quotes, each request for a quote being associated with a subgroup or category of BOM parts identified by the network 112. At this stage the quotes do not specify a supplier. Once the quotes are available for a BOM, the Al for Part-Supplier matching network 114 processes the quotes and suggests the best supplier(s) for each item in the quotes. Network 114 may be a part of a supplier selection machine learning model. Network 114 implements a neural collaborative filtering technique for the matching process. Network 114 comprises two sparse vectors, one represents the parts, and the other represents suppliers. It then uses embedding layers within network 114 to obtain latent vectors. The latent vectors allow loss function minimization and supplier selection. The raw BOM 134 may comprise an incomplete or a partial list of specifications/attributes of items. However, networks 112 and 114 still match partially specified items based on records in a master database accessible to system 100. The master database may comprise a historical purchase database associated with past purchases of a user.”), and
wherein the processor (p. 19: 10-13) is configured to:
repeat estimating the one or more articles forming the article included in the input traceability information and the suppliers thereof until the processor reaches an article that is further impartible (p. 12: 15 – p. 13: 14 -- “Networks of the core block 200 enable quick and efficient BOM curation and BOM price estimation. A designer may iterate multiple times during the product development stage. During the iterative process, the neural networks of the system can efficiently revise and curate BOMs taking into account the design iterations and user revisions. The system 100 or 200 may be coupled as a plug-in with an existing CAD/designing software to integrate the BOM curation process with the design process. During the design stage, as the user keeps on adding components one by one to his/her product, systems according to the embodiments may identify information about the newly added item and recommend a part number for that newly added item. The network for part number recommendation 212 generates recommendations of part numbers…Systems 100 or 200 may also comprise program code to decompose a design model file such as a CAD model and provide the user with a detailed analysis of the items used in a product in the design model file. The systems may also enable the generation of a quick price estimate of the product by finding the best/preferred supplier for individual components and using price information associated with the best/preferred supplier for the individual components. In some embodiments, systems 100 or 200 may analyze a schematic/CAD model of a circuit board, decompose the model to identify all parts or a subset of parts of the circuit board, and then recommend part numbers and a price or prices for the circuit board.” The basic BOM components of a product and the components that are ordered from suppliers are examples of impartible components.);
output all the estimated one or more articles and suppliers thereof for the confirmation by the user (p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”); and
store an article and a supplier thereof confirmed by the user among all the output one or more articles and the suppliers thereof in the storage device as the determined traceability information (p. 11: 13-22 -- “The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”).
[Claim 3] Panda discloses wherein the estimating the one or more articles and the suppliers thereof that are lacking in the input traceability information includes estimating, by the processor, by using the model, one or more articles forming an article included in the input traceability information and suppliers thereof (p. 9: 6 – p. 10: 30 – “Characterising the items enables quotes to be generated for the parts thus characterised. It is expected, however, that the network 112 will not know all parts it encounters in all instances. This is particularly the case new technologies, the parts for which are either entirely new or can change very rapidly. To accommodate this the network 112 learns about the new items that it encounters for the first time. In response to identifying a new item, the system optimizes its weights to account for the new item - i.e. the new item becomes known to, or identifiable by, the system and thus ultimately becomes a known item. By identifying parts, network 112 facilitates the split of the raw BOM 134 into multiple subgroups while keeping similar products in similar product category groups. Each group is therefore likely to include related products that can be supplied by an individual supplier or group of suppliers. Accordingly, the network 112 identifies the parts. The BOM splitting block 140 then splits the raw BOM into multiple, quotable groups. The BOM splitting block 140 may receive information from the quote creation block 150 to determine how to split the BOM, may split the BOM based on common keywords in specifications or based on previous quotes. The quote creation block 150 then uses the groups supplied by block 140 to generate a plurality of quotes or requests for quotes, each request for a quote being associated with a subgroup or category of BOM parts identified by the network 112. At this stage the quotes do not specify a supplier. Once the quotes are available for a BOM, the Al for Part-Supplier matching network 114 processes the quotes and suggests the best supplier(s) for each item in the quotes. Network 114 may be a part of a supplier selection machine learning model. Network 114 implements a neural collaborative filtering technique for the matching process. Network 114 comprises two sparse vectors, one represents the parts, and the other represents suppliers. It then uses embedding layers within network 114 to obtain latent vectors. The latent vectors allow loss function minimization and supplier selection. The raw BOM 134 may comprise an incomplete or a partial list of specifications/attributes of items. However, networks 112 and 114 still match partially specified items based on records in a master database accessible to system 100. The master database may comprise a historical purchase database associated with past purchases of a user.”), and
wherein the processor (p. 19: 10-13) is configured to:
estimate an article forming the article included in the input traceability information and a supplier thereof (p. 9: 6 – p. 10: 30 – “Characterising the items enables quotes to be generated for the parts thus characterised. It is expected, however, that the network 112 will not know all parts it encounters in all instances. This is particularly the case new technologies, the parts for which are either entirely new or can change very rapidly. To accommodate this the network 112 learns about the new items that it encounters for the first time. In response to identifying a new item, the system optimizes its weights to account for the new item - i.e. the new item becomes known to, or identifiable by, the system and thus ultimately becomes a known item. By identifying parts, network 112 facilitates the split of the raw BOM 134 into multiple subgroups while keeping similar products in similar product category groups. Each group is therefore likely to include related products that can be supplied by an individual supplier or group of suppliers. Accordingly, the network 112 identifies the parts. The BOM splitting block 140 then splits the raw BOM into multiple, quotable groups. The BOM splitting block 140 may receive information from the quote creation block 150 to determine how to split the BOM, may split the BOM based on common keywords in specifications or based on previous quotes. The quote creation block 150 then uses the groups supplied by block 140 to generate a plurality of quotes or requests for quotes, each request for a quote being associated with a subgroup or category of BOM parts identified by the network 112. At this stage the quotes do not specify a supplier. Once the quotes are available for a BOM, the Al for Part-Supplier matching network 114 processes the quotes and suggests the best supplier(s) for each item in the quotes. Network 114 may be a part of a supplier selection machine learning model. Network 114 implements a neural collaborative filtering technique for the matching process. Network 114 comprises two sparse vectors, one represents the parts, and the other represents suppliers. It then uses embedding layers within network 114 to obtain latent vectors. The latent vectors allow loss function minimization and supplier selection. The raw BOM 134 may comprise an incomplete or a partial list of specifications/attributes of items. However, networks 112 and 114 still match partially specified items based on records in a master database accessible to system 100. The master database may comprise a historical purchase database associated with past purchases of a user.”);
output the estimated article and supplier thereof for the confirmation by the user (p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”); and
repeat storing an article and a supplier thereof confirmed by the user in the storage device as the determined traceability information until the processor reaches an article that is further impartible (p. 7: 16-17 -- “The systems may also store a record of various design iterations associated with the product and the relevant curated BOM for each design iteration.”; p. 10: 26-28 – “The communication network 124 helps to register and/or track the process of BOM curation. It communicates with the user(s) and suppliers and maintains or creates a record of the communication.”; p. 11: 13-22 -- “The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; p. 12: 3-24 – “A user can start the process of curation of a BOM with a single item search 224 or the user can upload a list of items with specifications 222 as part of input 200. Network 212 processes the input 200 and generates part numbers from the specifications 222 of items 224. Network 212 may generate multiple part numbers for a single part as per the standards of different manufacturers. The user may also specify a field of application for the product for which the BOM is being curated. Network 212 may take into account the field of application for the product when generating customized part numbers. Network 212 may then search for the items in a parts database accessible to the system 200 and provide the user with a detailed description of each item and cost as part of output 230. Network 212 may also recommend alternative items and part numbers to the user. If the user agrees with the suggestions of network 212, then the system consolidates all information and forms a curated BOM 232 with part numbers and part prices as part of BOM cost data 234. Networks of the core block 200 enable quick and efficient BOM curation and BOM price estimation. A designer may iterate multiple times during the product development stage. During the iterative process, the neural networks of the system can efficiently revise and curate BOMs taking into account the design iterations and user revisions. The system 100 or 200 may be coupled as a plug-in with an existing CAD/designing software to integrate the BOM curation process with the design process. During the design stage, as the user keeps on adding components one by one to his/her product, systems according to the embodiments may identify information about the newly added item and recommend a part number for that newly added item. The network for part number recommendation 212 generates recommendations of part numbers.“; p. 13: 7-14 – “Systems 100 or 200 may also comprise program code to decompose a design model file such as a CAD model and provide the user with a detailed analysis of the items used in a product in the design model file. The systems may also enable the generation of a quick price estimate of the product by finding the best/preferred supplier for individual components and using price information associated with the best/preferred supplier for the individual components. In some embodiments, systems 100 or 200 may analyze a schematic/CAD model of a circuit board, decompose the model to identify all parts or a subset of parts of the circuit board, and then recommend part numbers and a price or prices for the circuit board.”; All aspects of the BOM curation, including part and supplier approval, are tracked and stored. The design process itself as well as the analysis of each of multiple components in a BOM may be performed in an iterative manner, thereby implying that storing is also iteratively performed as more information is gleaned during the process. Additionally, the basic BOM components of a product and the components that are ordered from suppliers are examples of impartible components.).
[Claim 4] Panda discloses wherein the estimating the one or more articles and the suppliers thereof that are lacking in the input traceability information includes estimating, by the processor, by using the model, one or more articles formed by an article included in the input traceability information and suppliers thereof (p. 9: 6 – p. 10: 30 – “Characterising the items enables quotes to be generated for the parts thus characterised. It is expected, however, that the network 112 will not know all parts it encounters in all instances. This is particularly the case new technologies, the parts for which are either entirely new or can change very rapidly. To accommodate this the network 112 learns about the new items that it encounters for the first time. In response to identifying a new item, the system optimizes its weights to account for the new item - i.e. the new item becomes known to, or identifiable by, the system and thus ultimately becomes a known item. By identifying parts, network 112 facilitates the split of the raw BOM 134 into multiple subgroups while keeping similar products in similar product category groups. Each group is therefore likely to include related products that can be supplied by an individual supplier or group of suppliers. Accordingly, the network 112 identifies the parts. The BOM splitting block 140 then splits the raw BOM into multiple, quotable groups. The BOM splitting block 140 may receive information from the quote creation block 150 to determine how to split the BOM, may split the BOM based on common keywords in specifications or based on previous quotes. The quote creation block 150 then uses the groups supplied by block 140 to generate a plurality of quotes or requests for quotes, each request for a quote being associated with a subgroup or category of BOM parts identified by the network 112. At this stage the quotes do not specify a supplier. Once the quotes are available for a BOM, the Al for Part-Supplier matching network 114 processes the quotes and suggests the best supplier(s) for each item in the quotes. Network 114 may be a part of a supplier selection machine learning model. Network 114 implements a neural collaborative filtering technique for the matching process. Network 114 comprises two sparse vectors, one represents the parts, and the other represents suppliers. It then uses embedding layers within network 114 to obtain latent vectors. The latent vectors allow loss function minimization and supplier selection. The raw BOM 134 may comprise an incomplete or a partial list of specifications/attributes of items. However, networks 112 and 114 still match partially specified items based on records in a master database accessible to system 100. The master database may comprise a historical purchase database associated with past purchases of a user.”), and
wherein the processor (p. 19: 10-13) is configured to:
repeat estimating the one or more articles formed by the article included in the input traceability information and the suppliers thereof until the processor reaches an article corresponding to a final product (p. 12: 15 – p. 13: 14 -- “Networks of the core block 200 enable quick and efficient BOM curation and BOM price estimation. A designer may iterate multiple times during the product development stage. During the iterative process, the neural networks of the system can efficiently revise and curate BOMs taking into account the design iterations and user revisions. The system 100 or 200 may be coupled as a plug-in with an existing CAD/designing software to integrate the BOM curation process with the design process. During the design stage, as the user keeps on adding components one by one to his/her product, systems according to the embodiments may identify information about the newly added item and recommend a part number for that newly added item. The network for part number recommendation 212 generates recommendations of part numbers…Systems 100 or 200 may also comprise program code to decompose a design model file such as a CAD model and provide the user with a detailed analysis of the items used in a product in the design model file. The systems may also enable the generation of a quick price estimate of the product by finding the best/preferred supplier for individual components and using price information associated with the best/preferred supplier for the individual components. In some embodiments, systems 100 or 200 may analyze a schematic/CAD model of a circuit board, decompose the model to identify all parts or a subset of parts of the circuit board, and then recommend part numbers and a price or prices for the circuit board.” The basic BOM components of a product and the components that are ordered from suppliers are examples of impartible components.);
output all the one or more estimated articles and the suppliers thereof for the confirmation by the user (p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”); and
store an article and a supplier thereof confirmed by the user among all the output one or more articles and the suppliers thereof in the storage device as the determined traceability information (p. 7: 16-17 -- “The systems may also store a record of various design iterations associated with the product and the relevant curated BOM for each design iteration.”; p. 10: 26-28 – “The communication network 124 helps to register and/or track the process of BOM curation. It communicates with the user(s) and suppliers and maintains or creates a record of the communication.”; p. 11: 13-22 -- “The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; p. 12: 3-24 – “A user can start the process of curation of a BOM with a single item search 224 or the user can upload a list of items with specifications 222 as part of input 200. Network 212 processes the input 200 and generates part numbers from the specifications 222 of items 224. Network 212 may generate multiple part numbers for a single part as per the standards of different manufacturers. The user may also specify a field of application for the product for which the BOM is being curated. Network 212 may take into account the field of application for the product when generating customized part numbers. Network 212 may then search for the items in a parts database accessible to the system 200 and provide the user with a detailed description of each item and cost as part of output 230. Network 212 may also recommend alternative items and part numbers to the user. If the user agrees with the suggestions of network 212, then the system consolidates all information and forms a curated BOM 232 with part numbers and part prices as part of BOM cost data 234. Networks of the core block 200 enable quick and efficient BOM curation and BOM price estimation. A designer may iterate multiple times during the product development stage. During the iterative process, the neural networks of the system can efficiently revise and curate BOMs taking into account the design iterations and user revisions. The system 100 or 200 may be coupled as a plug-in with an existing CAD/designing software to integrate the BOM curation process with the design process. During the design stage, as the user keeps on adding components one by one to his/her product, systems according to the embodiments may identify information about the newly added item and recommend a part number for that newly added item. The network for part number recommendation 212 generates recommendations of part numbers.“; p. 13: 7-14 – “Systems 100 or 200 may also comprise program code to decompose a design model file such as a CAD model and provide the user with a detailed analysis of the items used in a product in the design model file. The systems may also enable the generation of a quick price estimate of the product by finding the best/preferred supplier for individual components and using price information associated with the best/preferred supplier for the individual components. In some embodiments, systems 100 or 200 may analyze a schematic/CAD model of a circuit board, decompose the model to identify all parts or a subset of parts of the circuit board, and then recommend part numbers and a price or prices for the circuit board.”; The basic BOM components of a product and the components that are ordered from suppliers are examples of impartible components.).
[Claim 5] Panda discloses wherein the estimating the one or more articles and the suppliers thereof that are lacking in the input traceability information includes estimating, by the processor, by using the model, one or more articles formed by an article included in the input traceability information and suppliers thereof (p. 9: 6 – p. 10: 30 – “Characterising the items enables quotes to be generated for the parts thus characterised. It is expected, however, that the network 112 will not know all parts it encounters in all instances. This is particularly the case new technologies, the parts for which are either entirely new or can change very rapidly. To accommodate this the network 112 learns about the new items that it encounters for the first time. In response to identifying a new item, the system optimizes its weights to account for the new item - i.e. the new item becomes known to, or identifiable by, the system and thus ultimately becomes a known item. By identifying parts, network 112 facilitates the split of the raw BOM 134 into multiple subgroups while keeping similar products in similar product category groups. Each group is therefore likely to include related products that can be supplied by an individual supplier or group of suppliers. Accordingly, the network 112 identifies the parts. The BOM splitting block 140 then splits the raw BOM into multiple, quotable groups. The BOM splitting block 140 may receive information from the quote creation block 150 to determine how to split the BOM, may split the BOM based on common keywords in specifications or based on previous quotes. The quote creation block 150 then uses the groups supplied by block 140 to generate a plurality of quotes or requests for quotes, each request for a quote being associated with a subgroup or category of BOM parts identified by the network 112. At this stage the quotes do not specify a supplier. Once the quotes are available for a BOM, the Al for Part-Supplier matching network 114 processes the quotes and suggests the best supplier(s) for each item in the quotes. Network 114 may be a part of a supplier selection machine learning model. Network 114 implements a neural collaborative filtering technique for the matching process. Network 114 comprises two sparse vectors, one represents the parts, and the other represents suppliers. It then uses embedding layers within network 114 to obtain latent vectors. The latent vectors allow loss function minimization and supplier selection. The raw BOM 134 may comprise an incomplete or a partial list of specifications/attributes of items. However, networks 112 and 114 still match partially specified items based on records in a master database accessible to system 100. The master database may comprise a historical purchase database associated with past purchases of a user.”), and
wherein the processor (p. 19: 10-13) is configured to:
estimate an article formed by the article included in the input traceability information and a supplier thereof (p. 9: 6 – p. 10: 30 – “Characterising the items enables quotes to be generated for the parts thus characterised. It is expected, however, that the network 112 will not know all parts it encounters in all instances. This is particularly the case new technologies, the parts for which are either entirely new or can change very rapidly. To accommodate this the network 112 learns about the new items that it encounters for the first time. In response to identifying a new item, the system optimizes its weights to account for the new item - i.e. the new item becomes known to, or identifiable by, the system and thus ultimately becomes a known item. By identifying parts, network 112 facilitates the split of the raw BOM 134 into multiple subgroups while keeping similar products in similar product category groups. Each group is therefore likely to include related products that can be supplied by an individual supplier or group of suppliers. Accordingly, the network 112 identifies the parts. The BOM splitting block 140 then splits the raw BOM into multiple, quotable groups. The BOM splitting block 140 may receive information from the quote creation block 150 to determine how to split the BOM, may split the BOM based on common keywords in specifications or based on previous quotes. The quote creation block 150 then uses the groups supplied by block 140 to generate a plurality of quotes or requests for quotes, each request for a quote being associated with a subgroup or category of BOM parts identified by the network 112. At this stage the quotes do not specify a supplier. Once the quotes are available for a BOM, the Al for Part-Supplier matching network 114 processes the quotes and suggests the best supplier(s) for each item in the quotes. Network 114 may be a part of a supplier selection machine learning model. Network 114 implements a neural collaborative filtering technique for the matching process. Network 114 comprises two sparse vectors, one represents the parts, and the other represents suppliers. It then uses embedding layers within network 114 to obtain latent vectors. The latent vectors allow loss function minimization and supplier selection. The raw BOM 134 may comprise an incomplete or a partial list of specifications/attributes of items. However, networks 112 and 114 still match partially specified items based on records in a master database accessible to system 100. The master database may comprise a historical purchase database associated with past purchases of a user.”);
output the estimated article and supplier thereof for the confirmation by the user (p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”); and
repeat storing an article and a supplier thereof confirmed by the user in the storage device as the determined traceability information until the processor reaches an article that corresponds to a final product (p. 7: 16-17 -- “The systems may also store a record of various design iterations associated with the product and the relevant curated BOM for each design iteration.”; p. 10: 26-28 – “The communication network 124 helps to register and/or track the process of BOM curation. It communicates with the user(s) and suppliers and maintains or creates a record of the communication.”; p. 11: 13-22 -- “The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; p. 12: 3-24 – “A user can start the process of curation of a BOM with a single item search 224 or the user can upload a list of items with specifications 222 as part of input 200. Network 212 processes the input 200 and generates part numbers from the specifications 222 of items 224. Network 212 may generate multiple part numbers for a single part as per the standards of different manufacturers. The user may also specify a field of application for the product for which the BOM is being curated. Network 212 may take into account the field of application for the product when generating customized part numbers. Network 212 may then search for the items in a parts database accessible to the system 200 and provide the user with a detailed description of each item and cost as part of output 230. Network 212 may also recommend alternative items and part numbers to the user. If the user agrees with the suggestions of network 212, then the system consolidates all information and forms a curated BOM 232 with part numbers and part prices as part of BOM cost data 234. Networks of the core block 200 enable quick and efficient BOM curation and BOM price estimation. A designer may iterate multiple times during the product development stage. During the iterative process, the neural networks of the system can efficiently revise and curate BOMs taking into account the design iterations and user revisions. The system 100 or 200 may be coupled as a plug-in with an existing CAD/designing software to integrate the BOM curation process with the design process. During the design stage, as the user keeps on adding components one by one to his/her product, systems according to the embodiments may identify information about the newly added item and recommend a part number for that newly added item. The network for part number recommendation 212 generates recommendations of part numbers.“; p. 13: 7-14 – “Systems 100 or 200 may also comprise program code to decompose a design model file such as a CAD model and provide the user with a detailed analysis of the items used in a product in the design model file. The systems may also enable the generation of a quick price estimate of the product by finding the best/preferred supplier for individual components and using price information associated with the best/preferred supplier for the individual components. In some embodiments, systems 100 or 200 may analyze a schematic/CAD model of a circuit board, decompose the model to identify all parts or a subset of parts of the circuit board, and then recommend part numbers and a price or prices for the circuit board.”; All aspects of the BOM curation, including part and supplier approval, are tracked and stored. The design process itself as well as the analysis of each of multiple components in a BOM may be performed in an iterative manner, thereby implying that storing is also iteratively performed as more information is gleaned during the process. Additionally, the basic BOM components of a product and the components that are ordered from suppliers are examples of impartible components.).
[Claim 6] Panda discloses wherein the storage device holds traceability information for learning that includes one or more relationships between an article and an article forming the article and a supplier thereof (p. 7: 16-17 -- “The systems may also store a record of various design iterations associated with the product and the relevant curated BOM for each design iteration.”; p. 10: 26-28 – “The communication network 124 helps to register and/or track the process of BOM curation. It communicates with the user(s) and suppliers and maintains or creates a record of the communication.”; p. 11: 13-22 -- “The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; p. 12: 3-24 – “A user can start the process of curation of a BOM with a single item search 224 or the user can upload a list of items with specifications 222 as part of input 200. Network 212 processes the input 200 and generates part numbers from the specifications 222 of items 224. Network 212 may generate multiple part numbers for a single part as per the standards of different manufacturers. The user may also specify a field of application for the product for which the BOM is being curated. Network 212 may take into account the field of application for the product when generating customized part numbers. Network 212 may then search for the items in a parts database accessible to the system 200 and provide the user with a detailed description of each item and cost as part of output 230. Network 212 may also recommend alternative items and part numbers to the user. If the user agrees with the suggestions of network 212, then the system consolidates all information and forms a curated BOM 232 with part numbers and part prices as part of BOM cost data 234. Networks of the core block 200 enable quick and efficient BOM curation and BOM price estimation. A designer may iterate multiple times during the product development stage. During the iterative process, the neural networks of the system can efficiently revise and curate BOMs taking into account the design iterations and user revisions. The system 100 or 200 may be coupled as a plug-in with an existing CAD/designing software to integrate the BOM curation process with the design process. During the design stage, as the user keeps on adding components one by one to his/her product, systems according to the embodiments may identify information about the newly added item and recommend a part number for that newly added item. The network for part number recommendation 212 generates recommendations of part numbers.“; p. 13: 7-14 – “Systems 100 or 200 may also comprise program code to decompose a design model file such as a CAD model and provide the user with a detailed analysis of the items used in a product in the design model file. The systems may also enable the generation of a quick price estimate of the product by finding the best/preferred supplier for individual components and using price information associated with the best/preferred supplier for the individual components. In some embodiments, systems 100 or 200 may analyze a schematic/CAD model of a circuit board, decompose the model to identify all parts or a subset of parts of the circuit board, and then recommend part numbers and a price or prices for the circuit board.”; The basic BOM components of a product and the components that are ordered from suppliers are examples of impartible components.), and
wherein the processor (p. 19: 10-13) is configured to:
update the traceability information for learning by adding the determined traceability information (p. 11: 14-22 – “The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; p. 16: 14 – p. 17: 2 – “At step 420, the part search machine learning model (network 112) may identify one or more specifications that are not fulfilled by the known part(s) (unfulfilled specification(s)). At step 420, the supplier matching machine learning model (networks 114, 214) may be executed to consult supplier parts lists to identify one or more part identifier(s) of new parts (new part(s)) corresponding to the unfulfilled specification(s). System 300 may add the part identifier(s) of the new part(s) to a database of all known parts stored in the memory 304. Over repeated iterations of method 400, system 300 progressively expands its database of all known parts. System 300 may re-apply the part search machine learning model (networks 112 or 212) to the list to determine a part number for one or more updated known parts corresponding to the list. System 300 may update the known part(s) based on the one or more updated known parts. The part search machine learning model (network 112 or 212) may be configured to identify one or more specifications that are missing from the list of specifications (missing specification(s)). The part search machine learning model (network 112 or 212) may identify at least one of one or more parts and one or more specifications (addition(s)) based on the missing specification(s) and update the list of specifications based on the addition(s). The part search machine learning model (network 112 or 212) may determine a part identifier for one or more known parts or updated known parts (i.e. a list including previously new parts) corresponding to the updated list of specifications.“); and
update the model by learning the updated traceability information for learning with a weight greater than the weight of other traceability information being added to the added determined traceability information in the updated traceability information for learning (p. 9: 10-12 – “In response to identifying a new item, the system optimizes its weights to account for the new item - i.e. the new item becomes known to, or identifiable by, the system and thus ultimately becomes a known item.”; p. 11: 9-22 – “Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”).
[Claim 7] Panda discloses wherein the processor (p. 19: 10-13) is configured to output, when the processor has estimated, by using the model, a plurality of combinations of articles and suppliers thereof as a plurality of candidates for one article and a supplier thereof that are lacking in the input traceability information, an accuracy of the estimation of each of the plurality of combinations for the confirmation by the user (p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; Approval of components, designs, and/or suppliers by a user is an example of confirming accuracy of the estimations.; p. 12: 3-24 – “A user can start the process of curation of a BOM with a single item search 224 or the user can upload a list of items with specifications 222 as part of input 200. Network 212 processes the input 200 and generates part numbers from the specifications 222 of items 224. Network 212 may generate multiple part numbers for a single part as per the standards of different manufacturers. The user may also specify a field of application for the product for which the BOM is being curated. Network 212 may take into account the field of application for the product when generating customized part numbers. Network 212 may then search for the items in a parts database accessible to the system 200 and provide the user with a detailed description of each item and cost as part of output 230. Network 212 may also recommend alternative items and part numbers to the user. If the user agrees with the suggestions of network 212, then the system consolidates all information and forms a curated BOM 232 with part numbers and part prices as part of BOM cost data 234. Networks of the core block 200 enable quick and efficient BOM curation and BOM price estimation. A designer may iterate multiple times during the product development stage. During the iterative process, the neural networks of the system can efficiently revise and curate BOMs taking into account the design iterations and user revisions. The system 100 or 200 may be coupled as a plug-in with an existing CAD/designing software to integrate the BOM curation process with the design process. During the design stage, as the user keeps on adding components one by one to his/her product, systems according to the embodiments may identify information about the newly added item and recommend a part number for that newly added item. The network for part number recommendation 212 generates recommendations of part numbers.“), and
wherein the confirmation by the user includes selecting one of the plurality of candidates (p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; p. 19: 4-9 – “Method 400 may further comprise optional steps 470, 480 and 490. At step 470, system 300 receives confirmation of final supplier(s) based on the curated BOM generated at 460. At step 480, system 300 generates at least one of a purchase order for each supplier of the final supplier(s); an agreement for execution by a user and each supplier of the final supplier(s). At step 490, the system sends at least one of the purchase orders and the agreement to each respective supplier.”).
[Claim 11] Claim 11 recites limitations already addressed by the rejection of claim 1 above; therefore, the same rejection applies.
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 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Panda et al. (WO 2022/132040 A1), as applied to claim 1 (for claims 8 and 9), in view of Al-Sinan et al. (US 2021/0304297).
[Claim 8] Panda does not explicitly disclose wherein the processor is configured to:
acquire a risk value of each of the suppliers included in the determined traceability information; and
determine whether to notify the user of a risk of the each of the suppliers based on the risk value of the each of the suppliers and an accuracy of the estimation of the each of the suppliers.
However, Al-Sinan discloses that supplier performance is evaluated to determine if suppliers should be considered for future dealings (Al-Sinan: ¶ 58 – “Supplier Performance Evaluation 230: Maintaining a record of the supplier's performance is important to provide a basis for future dealings and whether the supplier should be considered or excluded from future bidding.”). Suppliers may be invited to participate in a bidding process if they are prequalified, well-known, and are deemed to be low-risk tendering (Al-Sinan: ¶ 40 – “In some cases, mainly low-risk tendering and public tendering, bidding can be open, meaning that any supplier can participate in the bidding. In other cases, it may be preferred to invite only prequalified and well-known suppliers, or limit the bidding to a single source.”).
An AI Text Generator GPT-2 (i.e., an example of a large language learning system) may score suppliers and approve only those who meet a minimum passing score (indicative of each supplier’s ability to deliver work on a procurement request) to remain under consideration, as explained in ¶¶ 127-129 of Al-Sinan:
[0127] In traditional procurement processes, some organizations may elect to perform the technical evaluation prior to the commercial evaluation to avoid being influenced by suppliers who may propose lower prices based on quality of the service. However, this is not the case in the APS system, where there is no human intervention. Therefore, these two activities can be executed simultaneously without sacrificing quality. In fact, each supplier can be assigned a technical score in addition to a commercial score. The bid ranking can be a weighted average of the two scores.
[0128] The technical evaluation questionnaire can assess the supplier's ability to deliver the work and can be linked to the scope of work and other elements from the procurement request variable matrix. Standard sections of the questionnaire can cover supplier mobilization, allocated resources, manpower qualifications, and equipment specifications. These elements can be evaluated and authenticated in a similar way as the prequalification evaluation using similar underlying principles. Other sections of the questionnaire can be scope-dependent and can rely heavily on machine learning algorithms to determine the answers. The system can be loaded with previous technical questionnaire which can be fed into an AI Text Generator GPT-2 to process the training data and create relevant questionnaires. Scoring supplier responses can be executed using machine learning, where the greatest-adhering supplier responses can be given the highest scores and other responses can be ranked accordingly. However, some elements of the questionnaire can be evaluated as pass or fail in which suppliers can be excluded from the evaluation when not meeting a given mandatory requirement.
[0129] A technical evaluation of the technical proposal questionnaire can produce a technical score. Using a predefined minimum passing score (for example, 70%), suppliers who achieve the minimum passing score can remain under consideration while those failing can be excluded.
In other words, Al-Sinan explains how risk values related to a supplier’s ability to deliver a request as part of a procurement process (which is understood to contribute to an understanding of traceability) may be used to determine which suppliers qualified for further consideration.
Panda explains that users can approve a machine learning model-generated recommendation, which helps improve the learning process by (in effect) confirming the users’ approvals as indications of accuracy of the recommendations estimated for each respective user (Panda: p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; Approval of components, designs, and/or suppliers by a user is an example of confirming accuracy of the estimations.).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Panda wherein the processor is configured to:
acquire a risk value of each of the suppliers included in the determined traceability information; and
determine whether to notify the user of a risk of the each of the suppliers based on the risk value of the each of the suppliers and an accuracy of the estimation of the each of the suppliers
in order to facilitate a more granular understanding of which factors affect supplier reliability and predicted performance in regard to delivering a desired component, product, etc., thereby providing the machine learning system in Panda with more capabilities to more accurately make recommendations that are more likely to be of greater interest and relevance to the users seeking reliable suppliers.
[Claim 9] Panda does not explicitly disclose wherein the processor is configured to notify the user of the risk value of the each of the suppliers when the risk value of the each of the suppliers is higher than a predetermined criterion and the accuracy of the estimation of the each of the suppliers is higher than a predetermined criterion.
However, Al-Sinan discloses that supplier performance is evaluated to determine if suppliers should be considered for future dealings (Al-Sinan: ¶ 58 – “Supplier Performance Evaluation 230: Maintaining a record of the supplier's performance is important to provide a basis for future dealings and whether the supplier should be considered or excluded from future bidding.”). Suppliers may be invited to participate in a bidding process if they are prequalified, well-known, and are deemed to be low-risk tendering (Al-Sinan: ¶ 40 – “In some cases, mainly low-risk tendering and public tendering, bidding can be open, meaning that any supplier can participate in the bidding. In other cases, it may be preferred to invite only prequalified and well-known suppliers, or limit the bidding to a single source.”).
An AI Text Generator GPT-2 (i.e., an example of a large language learning system) may score suppliers and approve only those who meet a minimum passing score (indicative of each supplier’s ability to deliver work on a procurement request) to remain under consideration, as explained in ¶¶ 127-129 of Al-Sinan:
[0127] In traditional procurement processes, some organizations may elect to perform the technical evaluation prior to the commercial evaluation to avoid being influenced by suppliers who may propose lower prices based on quality of the service. However, this is not the case in the APS system, where there is no human intervention. Therefore, these two activities can be executed simultaneously without sacrificing quality. In fact, each supplier can be assigned a technical score in addition to a commercial score. The bid ranking can be a weighted average of the two scores.
[0128] The technical evaluation questionnaire can assess the supplier's ability to deliver the work and can be linked to the scope of work and other elements from the procurement request variable matrix. Standard sections of the questionnaire can cover supplier mobilization, allocated resources, manpower qualifications, and equipment specifications. These elements can be evaluated and authenticated in a similar way as the prequalification evaluation using similar underlying principles. Other sections of the questionnaire can be scope-dependent and can rely heavily on machine learning algorithms to determine the answers. The system can be loaded with previous technical questionnaire which can be fed into an AI Text Generator GPT-2 to process the training data and create relevant questionnaires. Scoring supplier responses can be executed using machine learning, where the greatest-adhering supplier responses can be given the highest scores and other responses can be ranked accordingly. However, some elements of the questionnaire can be evaluated as pass or fail in which suppliers can be excluded from the evaluation when not meeting a given mandatory requirement.
[0129] A technical evaluation of the technical proposal questionnaire can produce a technical score. Using a predefined minimum passing score (for example, 70%), suppliers who achieve the minimum passing score can remain under consideration while those failing can be excluded.
In other words, Al-Sinan explains how risk values related to a supplier’s ability to deliver a request as part of a procurement process (which is understood to contribute to an understanding of traceability) may be used to determine which suppliers qualified for further consideration.
Panda explains that users can approve a machine learning model-generated recommendation, which helps improve the learning process by (in effect) confirming the users’ approvals as indications of accuracy of the recommendations estimated for each respective user (Panda: p. 10: 5-12 – “Networks 112 and 114 may access to the master database and populate or predict missing parts or attributes of a partially specified specification. System 100 may also prompt the user to either verify the missing or partially specified specifications or to accept the suggested specifications. Network 114 accesses the master database and retrieves historical supplier data relating to the user requesting curation of a BOM. Network 114 may subsequently widen its search and include other curated supplier databases. After performing an analysis of relevant suppliers, network 114 populates the quotes in the quote creation block 150 with the identified matching suppliers and makes the quotes available for the next stage.”; p. 11: 7-22 – “Quotes can be sent out by the supplier selection block 180 to suppliers 160 who return quotes for various items. Quotes once received (and, in some cases, stored) at quote receiving block 195 are then processed by the "Al for Supplier Selection" network 116. Network 116 may also be referred to or be part of a supplier selection machine learning model. Network 116 comprises a set of one or more selection criteria and corresponding weights, for selecting suppliers based on their quotes. The selection criteria are used for supplier score calculation. The user can specify his/her selection criteria and can customize the selection process as executed by the network 116. The user has also the option to agree or disagree with the network suggested suppliers. Results regarding the selection of a preferred or best supplier for a quote or a part may be stored in a best supplier block 190, and can use these results in calculating supplier scores. Network 116 calculates weighted average scores for each supplier and picks the supplier with the highest score as the preferred one. Network 116 then deploys a few MLP layers to optimize its weights. In response to a user disagreeing or discarding a prediction or recommendation generated by network 116, network 116 initiates a retraining and optimization process to adapt the supplier selection process to the user preferences.”; Approval of components, designs, and/or suppliers by a user is an example of confirming accuracy of the estimations.). Being approved by a user (as opposed to not being approved by a user) may be the threshold of predetermined criterion for establishing acceptable accuracy.
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Panda wherein the processor is configured to notify the user of the risk value of the each of the suppliers when the risk value of the each of the suppliers is higher than a predetermined criterion and the accuracy of the estimation of the each of the suppliers is higher than a predetermined criterion in order to facilitate a more granular understanding of which factors affect supplier reliability and predicted performance in regard to delivering a desired component, product, etc., thereby providing the machine learning system in Panda with more capabilities to more accurately make recommendations that are more likely to be of greater interest and relevance to the users seeking reliable suppliers. Additionally, by informing users of potentially higher than preferred risk associated with certain suppliers, users of Panda would have been able to make more informed decisions, especially if they still choose to consider a higher-risk supplier(s).
[Claim 10] Claim 10 recites limitations already addressed by the rejections of claims 1 and 8 above; therefore, the same rejections apply.
Furthermore, Panda does not explicitly discloses that the estimating is performed using a large language learning system. However, Al-Sinan discloses that supplier performance is evaluated to determine if suppliers should be considered for future dealings (Al-Sinan: ¶ 58 – “Supplier Performance Evaluation 230: Maintaining a record of the supplier's performance is important to provide a basis for future dealings and whether the supplier should be considered or excluded from future bidding.”). Suppliers may be invited to participate in a bidding process if they are prequalified, well-known, and are deemed to be low-risk tendering (Al-Sinan: ¶ 40 – “In some cases, mainly low-risk tendering and public tendering, bidding can be open, meaning that any supplier can participate in the bidding. In other cases, it may be preferred to invite only prequalified and well-known suppliers, or limit the bidding to a single source.”).
An AI Text Generator GPT-2 (i.e., an example of a large language learning system) may score suppliers and approve only those who meet a minimum passing score (indicative of each supplier’s ability to deliver work on a procurement request) to remain under consideration, as explained in ¶¶ 127-129 of Al-Sinan:
[0127] In traditional procurement processes, some organizations may elect to perform the technical evaluation prior to the commercial evaluation to avoid being influenced by suppliers who may propose lower prices based on quality of the service. However, this is not the case in the APS system, where there is no human intervention. Therefore, these two activities can be executed simultaneously without sacrificing quality. In fact, each supplier can be assigned a technical score in addition to a commercial score. The bid ranking can be a weighted average of the two scores.
[0128] The technical evaluation questionnaire can assess the supplier's ability to deliver the work and can be linked to the scope of work and other elements from the procurement request variable matrix. Standard sections of the questionnaire can cover supplier mobilization, allocated resources, manpower qualifications, and equipment specifications. These elements can be evaluated and authenticated in a similar way as the prequalification evaluation using similar underlying principles. Other sections of the questionnaire can be scope-dependent and can rely heavily on machine learning algorithms to determine the answers. The system can be loaded with previous technical questionnaire which can be fed into an AI Text Generator GPT-2 to process the training data and create relevant questionnaires. Scoring supplier responses can be executed using machine learning, where the greatest-adhering supplier responses can be given the highest scores and other responses can be ranked accordingly. However, some elements of the questionnaire can be evaluated as pass or fail in which suppliers can be excluded from the evaluation when not meeting a given mandatory requirement.
[0129] A technical evaluation of the technical proposal questionnaire can produce a technical score. Using a predefined minimum passing score (for example, 70%), suppliers who achieve the minimum passing score can remain under consideration while those failing can be excluded.
In other words, Al-Sinan explains how risk values related to a supplier’s ability to deliver a request as part of a procurement process (which is understood to contribute to an understanding of traceability) may be used to determine which suppliers qualified for further consideration.
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Panda such that the estimating is performed using a large language learning system in order to reduce the need for human-based processing, thereby taking advantage of the following resulting benefits, as described in Al-Sinan:
¶ 5 – “First, an autonomous procurement system can run an entire procurement process without human intervention, from pro forma contract creation through the selection of a winning bidder. Second, negotiations can be conducted in a structured way instead of in an intuitive way. Third, the system can comprehend specific organizations' policies and procedures, and their applicability in different procurement scenarios.”
¶ 32 – “In addition to resulting in payroll savings in an organization, eliminating human intervention can enhance the integrity of the procurement process. For example, developing a highly-skilled professional procurement staff can be time-consuming and costly, unlike a self-reliant solution provided using an autonomous system. Moreover, machine learning techniques can provide solutions during a lessons learned segment of a procurement process, such as an organizations' failure to capture all issues and training challenges involved with procurement.”
¶ 63 – “Pro forma development is one of the most challenging activities in the procurement process since it requires cognitive capabilities that depend heavily on the competence and experience of procurement professionals. Although traditional programming can have limitations when performing this task, natural language processing (NLP) and machine learning (ML) can create a pro forma of similar, if not superior, quality to that processed by a human being. NLP and ML can be included in the first activity executed by the system when a user initiates a procurement request, as NLP and ML can provide key inputs to many other modules of the system.”
¶ 135 – “Using the APS to handle negotiations can eliminate psychological influences that humans may encounter or inject in typical negotiation processes.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Romo et al. (US 2025/0285198) – Determines risk priority ratings associated with dynamic sourcing.
Meadow et al. (US 2018/0357365) – Performs product authentication and tracking.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights