DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are pending.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 4/19/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
4. The drawings have been reviewed and are accepted as being in compliance with the provisions of 37 CFR 1.121.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Step 1: Claim 1 recites “A system…” therefore the claim is a machine; Claim 19 recites “a non-transitory computer readable…” therefore the claim is a manufacture.
Claim 20 recites “computer implemented method …”, the claim recites a series of steps and therefore is a process, receive a resource file from a user…
Step 2A Prong One: Claims 1, 8, and 15 recite the limitations: sort, normalize, determining, updating, apply, generate, specifically “sort at least one resource line item in the resource file based on the at least one supplier entity name; normalize the at least one supplier entity name to generate at least one normalized supplier entity name;
determine the at least one normalized supplier entity name matches an authenticated supplier entity name of an entity name master record; update, based on the normalized supplier entity name, a source entity management database, wherein the source entity management database comprises a plurality of authenticated supplier entity names and a plurality of previous variable data for each of the authenticated supplier entity names and at least one updated variable data for the normalized supplier entity name; apply the at least one pre-determined variable standard and the selected category to a source entity suggestion model to output at least one suggested source entity name that matches the pre-determined variable standard and the selected category
generate, based on the at least one suggested source entity name, a suggested source entity interface component to configure a graphical user interface of a user device associated with the user.”
“receive a resource file from a user, the resource file comprising at least one supplier entity name and at least one resource line item; receive a user indication, wherein the user indication comprises at least one pre- determined variable standard and a selected category” and “generate, based on the at least one suggested source entity name, a suggested source entity interface component to configure a graphical user interface of a user device associated with the user.”
These limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components.
That is, other than reciting a "processing device" “executing the computer readable code”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “determining” in the context of this claim encompasses a user mentally, and with the aid of pen and paper, grouping and evaluating data, providing supplier data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites the additional elements “normalized”, “authenticated”, and “suggested” having the entity name on a master record.” The limitations are a mere generic transmission and presentation of collected and analyzed data (MPEP 2106.05(g).
A claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they are considered insignificant extra-solution activity. Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities listed above, including “sorting” “authenticate” these limitations are a mere generic transmission and presentation of collected and analyzed data. The limitations performed by generate a combination of data or “output” based on data and update them based on comparison of data ( it is recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner. (see MPEP 2106.04(a)(2). There are no additional elements that amount to significantly more than the above-identified judicial exception (abstract idea).
Koninklijke KPN N.V. v.Gemalto M2M GmbH, 942 F.3d 1143, 1149 (Fed. Cir.2019) (quoting Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1257 (Fed. Cir. 2016)). In the context of software patents (which includes machine learning patents), the step-one inquiry determines “whether the claims focus on ‘the specific asserted improvement in computer capabilities . . . or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool.’” Id. (alteration in original) (quoting Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303 (Fed. Cir. 2018)).
As per Claim 2 , The claims recite the additional limitations: wherein, in an instance where the at least one normalized supplier entity name matches an authenticated supplier entity name, categorize the at least one supplier entity name in an identified category associated with the authenticated supplier entity name. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources, which recites a mere mental step or an abstract idea.
As per Claim 3, The claims recite the additional limitations: wherein the processing device is further configured to: receive at least one resource agreement between a supplier entity and the user; and apply an agreement engine to the at least one resource agreement, wherein the agreement engine outputs at least one key performance indicator of the resource agreement and automatically updates the source entity management database with the key performance indicators, wherein at least one key performance indicator is at least one updated variable data for the supplier entity. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources, which recites a mere mental step or an abstract idea.
As per Claim 4, The claims recite the additional limitations: wherein, in an instance where at least one supplier entity name does not match an authenticated supplier entity name, apply a supplier entity name machine learning model to the at least one supplier entity name. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources, which recites a mere mental step or an abstract idea.
As per Claim 5, The claims recite the additional limitations: wherein, in the instance where the at least one supplier entity name does not match an authenticated supplier entity name, the at least one processing device is further configured to: search the at least one supplier entity name in a user database, the user database comprising a plurality of authenticated supplier entity names and at least one associated category for each authenticated supplier entity name of the user database; generate a determined result based on the at least one supplier entity name matching at least one authenticated supplier entity name of the user database; categorize the at least one supplier entity name based on the associated category for the matched authenticated supplier entity name of the user database; and determine, based on the associated category, a normalized supplier entity name. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources, which recites a mere mental step or an abstract idea.
As per Claim 6, The claims recite the additional limitations: wherein, in the instance where the at least one supplier entity name does not match an authenticated supplier entity name, the at least one processing device is further configured to: submit the at least one supplier entity name to a search engine via an application programing interface to generate a search result; download the search result from the application programming interface; apply a natural language processor to the search result to generate at least one keyword from the search result; determine, based on the at least one keyword from the search result, a category associated with the at least one keyword, wherein the category is associated with a plurality of keywords including the at least one keyword; and determine, based on the category associated with the supplier entity name, the normalized supplier entity name. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources, which recites a mere mental step or an abstract idea.
As per Claim 7, The claims recite the additional limitations:, wherein the source entity suggestion model comprises a bayes theorem. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources. (See COFFELT V. NVIDIA CORPORATION, The
calculations claimed can be done by a human mentally or with a pen and paper.” It further added that “analyzing information by steps people [can] go through in their minds, or by mathematical algorithms, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 8, The claims recite the additional limitations: wherein the source entity suggestion model is a machine learning model, the processing device is further configured to: collect a plurality of previously tagged variables, wherein the plurality of previously tagged variables comprises at least one of a positive feedback or a negative feedback; generate, based on the collected plurality of previously tagged variables, a previously tagged variable training dataset; and apply the previously tagged variable training dataset to the source entity suggestion model to train the source entity suggestion model. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources. Merely observing tokens, again these limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 9, The claims recite the additional limitations: wherein the at least one processing device is further configured to categorize the at least one normalized supplier entity name as a medical device manufacturer or a medical equipment manufacturer. The user can identify based on different sources, which again is comparing data, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 10, The claims recite the additional limitations: wherein the resource line item comprises data of at least one of a supplier’s name, a service type of a supplier of the supplier entity name, an amount owed to the supplier, a due date of payment to the supplier, at least one payment term, or a balance due to the supplier. The user can identify based on different sources, which again is comparing data, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 11, The claims recite the additional limitations: wherein the entity name master record comprises a plurality of authenticated supplier entity names and at least one category for each authenticated supplier entity name, and wherein the normalized supplier entity name is matched to at least one authenticated supplier entity name, and wherein the plurality of authenticated supplier entity names comprises a plurality of entity types for each authenticated supplier entity name. The user can identify based on different sources, using a tool, “suppliers” or entity name, which again is comparing data, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 12, The claims recite the additional limitations: wherein the processing device is further configured to: receive a user identifier and at least one electronic record associated with the user; receive at least one source entity response from at least one source entity; update a predictive plan database with the at least one electronic record, wherein the predictive plan database comprises a plurality of electronic records associated with a plurality of recipients and a plurality of source entities; determine a previous transaction amount for each of the plurality of electronic records, wherein the previous transaction amount is based on at least one previous pre- determined variable standard for each source entity; and determine, based on the previous pre-determined variable standard and the previous transaction amount associated with each source entity of the plurality of source entities, at least one suggested source entity name which complies with the pre- determined variable standard and the transaction amount. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources, which recites a mere mental step or an abstract idea.
As per Claim 13, The claims recite the additional limitations: wherein each authenticated supplier entity name comprises an integer-based supplier name identifier. The user can identify based on different sources, which again is comparing data, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 14, The claims recite the additional limitations: wherein the processing device is further configured to: store, by an electronic record management module, a plurality of electronic records associated with each source entity of the plurality of authenticated source entity names, wherein the plurality of electronic records comprise data associated with each resource transaction. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources. (See COFFELT V. NVIDIA CORPORATION, The
calculations claimed can be done by a human mentally or with a pen and paper.” It further added that “analyzing information by steps people [can] go through in their minds, or by mathematical algorithms, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 15, The claims recite the additional limitations: wherein the variable data comprises data associated with at least one of a location, a service level, a service type, a transaction amount, a service term, a special amount discount, or a potential risk. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources. (See COFFELT V. NVIDIA CORPORATION, The calculations claimed can be done by a human mentally or with a pen and paper.” It further added that “analyzing information by steps people [can] go through in their minds, or by mathematical algorithms, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 16, The claims recite the additional limitations:, wherein the generation of the at least one normalized supplier entity name 1s generated by the processing device being further configured to: apply the at least one supplier entity name to a categorization machine learning model and at least one resource line item; and output, by the categorization machine learning model, the normalized supplier entity name and an associated category of the normalized supplier entity name. The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources. (See COFFELT V. NVIDIA CORPORATION, The calculations claimed can be done by a human mentally or with a pen and paper.” It further added that “analyzing information by steps people [can] go through in their minds, or by mathematical algorithms, without more . . . [are] mental processes within the abstract-idea category.
As per Claim 17, The claims recite the additional limitations: wherein the processing device is further configured to: collect a plurality of categorization variables associated with a plurality of supplier entity names, wherein the categorization variables comprise at least one of a general ledger code, a transaction date, a source organization, a source process center, or a source facilities type; generate a categorization training dataset, wherein the categorization training dataset comprises the plurality of categorization variables; and apply the categorization training dataset to the categorization machine learning model to train the categorization machine learning model. Analyzing information by steps people [can] go through in their minds, or by mathematical algorithms, without more . . . [are] mental processes within the abstract-idea category.
Koninklijke KPN N.V. v.Gemalto M2M GmbH, 942 F.3d 1143, 1149 (Fed. Cir.2019) (quoting Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1257 (Fed. Cir. 2016)). In the context of software patents (which includes machine learning patents), the step-one inquiry determines “whether the claims focus on ‘the specific asserted improvement in computer capabilities . . . or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool.’” Id. (alteration in original) (quoting Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303 (Fed. Cir. 2018)).
As per Claim 18, The claims recite the additional limitations: wherein the processing device is further configured to: providing, by an entity terminal module, user data associated with at least one authenticated supplier entity name, wherein the user data comprises at least a username and at least one user record, and wherein the at least one user record comprises at least one completed resource transfer or service transfer between the username and an authenticated service entity name. The limitations performed by a module being a tool.; generates a combination of data or “output” based on data and update them based on comparison of data ( it is recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner. (see MPEP 2106.04(a)(2). There are no additional elements that amount to significantly more than the above-identified judicial exception (abstract idea).
Koninklijke KPN N.V. v.Gemalto M2M GmbH, 942 F.3d 1143, 1149 (Fed. Cir.2019) (quoting Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1257 (Fed. Cir. 2016)). In the context of software patents (which includes machine learning patents), the step-one inquiry determines “whether the claims focus on ‘the specific asserted improvement in computer capabilities . . . or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool.’” Id. (alteration in original) (quoting Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303 (Fed. Cir. 2018)).
As per Claims 19-20, being the product and method claims corresponding to the system claim 1 respectively and rejected under the same reason set forth in connection of the rejections of Claim 1.
Claim Rejections - 35 USC § 103
7. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
8. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Makhija et al (US 2022/0027826), in view of Fireman et al (US 2009/0099862), hereinafter “Makhija” and “Fireman” respectively.
As per Claim 1, Makhija discloses:
A system for using machine learning to categorize and select suggested source entities, the system comprising: a memory device with computer-readable program code stored thereon; at least one processing device operatively coupled to the at least one memory device and the at least one communication device, wherein executing the computer-readable code is configured to cause the at least one processing device to: receive a resource file from a user, the resource file comprising at least one supplier entity name and at least one resource line item; (Par [0069], “benchmarks for resource qualifications” and par [0123], “several line item descriptions” and see Figures 1-3) sort at least one resource line item in the resource file based on the at least one supplier entity name; (Par [0010], “identifying one or more suppliers for executing the recommended strategy based on the object characteristic data set; and encapsulating one or more recommended awarding scenario on the category workbench user interface for selection. The method includes receiving a response to a questionnaire based on the object characteristic data set from one or more recommended suppliers for identifying the one or more suppliers.” And Figures 1-3C) normalize the at least one supplier entity name to generate at least one normalized supplier entity name; (Par [0057], “The parameters may include entity name, existing contract details, pricing information, object procured, date of procurement, place, etc. It shall be understood to a person skilled in the art that the parameters may vary depending on the request and source of request like from an entity or auto generated request from an application after completion of an operation of the application or generation of an auto-set demand trigger through the application.” And Par [0134], “The present invention trains a data classifier on each level 1 label with the normalized taxonomy used as the output in a supervised learning setting.”)
determine the at least one normalized supplier entity name matches an authenticated supplier entity name of an entity name master record; (Par [0071], “also includes an authentication mechanism to ensure each recommended supplier is validated automatically” and par [0136], “This meta-data for all extracted clusters could have several use cases such as Master Data Management, Supplier Recommendation etc.”) update, based on the normalized supplier entity name, a source entity management database, wherein the source entity management database comprises a plurality of authenticated supplier entity names and a plurality of previous variable data for each of the authenticated supplier entity names and at least one updated variable data for the normalized supplier entity name; (Par [0006], “an updated analysis of organizational spend in comparison to market data as well as identifying Key performance indicators for determining areas of improvement, are extremely essential and quantifying data associated with such factors for assessment by category managers is not possible.” See Figures 1-2 and 3C-3G)
receive a user indication, wherein the user indication comprises at least one pre- determined variable standard and a selected category; (Par [0012], “a category workbench application user interface configured for triggering a sourcing module to initiate at least one task based on a received demand from at least one data source” and par [0114], “This variable is used to control the number of suppliers being selected for allocation for all items. It identifies the supplier who is supplying at least one unit of any item across all items…” and se Figures 3C-3K). apply the at least one pre-determined variable standard and the selected category to a source entity suggestion model to output at least one suggested source entity name that matches the pre-determined variable standard and the selected category; and (claim 31, “by receiving the recommended strategy processed by the server and applying an AI based dynamic processing logic to the strategy to automate at least one task.” And par [0137], “For an entity that would like to procure a certain item, the item can be queried across all the detected clusters and the list of suppliers for the best match cluster could be retrieved.” And see Figures 3G-4) generate, based on the at least one suggested source entity name, a suggested source entity interface component to configure a graphical user interface of a user device associated with the user. (Par [0145], “In one embodiment the category management system includes an organizer configured to generate a set of quantitative and qualitative data on the dashboard to analyze trends in supply chain. The quantitative data includes market indices, commodity prices, stock price of supplier, delivery turn-around time (TAT), changes in market shares, demand and supply forecasts, expected lead times” and see Figures 4-5F).
Makhija disclose an authentication mechanism, but not specifically “authenticated”
Fireman discloses the above claimed features as follows: (Par [0169], “Tags may also be promiscuous, i.e., attending all requests alike, or secure, which may require authentication and control of typical password management and secure key distribution issues. A tag may as well be prepared to be activated or deactivated in response to specific reader commands.”)
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of Fireman specifically a login authentication control into the method of Makhija to take advantage on applying a security measure according to user’s needs. The modification would have been obvious because one of the ordinary skills in the art would implement security to provide the entities with the best correlation possible.
As per Claim 2, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein, in an instance where the at least one normalized supplier entity name matches an authenticated supplier entity name, categorize the at least one supplier entity name in an identified category associated with the authenticated supplier entity name. (Par [0056], “supplier data on web etc., a data solver and optimizer 112 for processing variables, bid optimization and recommend suppliers. The data solver and optimizer 112 is configured for identifying constraint associated with suppliers before processing, a processor 113 configured for performing various functions including but not limited to selecting appropriate data attributes, identifying positioning of the data attributes,…” and see Figure 1-2).
As per Claim 3, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the processing device is further configured to: receive at least one resource agreement between a supplier entity and the user; (Par [0008], “having a good sense of inventory with contractual agreements can help set the pace” and see Figures 1-2) and apply an agreement engine to the at least one resource agreement, wherein the agreement engine outputs at least one key performance indicator of the resource agreement and automatically updates the source entity management database with the key performance indicators, (Par [0006], “an updated analysis of organizational spend in comparison to market data as well as identifying Key performance indicators for determining areas of improvement, are extremely essential and quantifying data associated with such factors for assessment by category managers is not possible.” See Figures 1-2 and 3C-3G)
wherein at least one key performance indicator is at least one updated variable data for the supplier entity. (Par [0006], “…in comparison to market data as well as identifying Key performance indicators for determining areas of improvement, are extremely essential and quantifying data associated with such factors for assessment by category managers is not possible.” See Figures 1-2 and 3C-3G).
As per Claim 4, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein, in an instance where at least one supplier entity name does not match an authenticated supplier entity name, apply a supplier entity name machine learning model to the at least one supplier entity name. (Par [0063], “In an exemplary embodiment, the Artificial intelligence engine 109 employs machine learning techniques that learn patterns and generate insights from the data. Further, the AI engine with ML employs deep learning that utilizes artificial neural networks to mimic biological neural network in human brains. The artificial neural networks analyze data to determine associations and provide meaning to unidentified data.” And par [0057], “after expiry of a contract or a demand directly by the entity etc., The parameters may include entity name, existing contract details, pricing information, object procured” and see Figure 1)
As per Claim 5, the rejection of Claim 4 is incorporated and Makhija further discloses: wherein, in the instance where the at least one supplier entity name does not match an authenticated supplier entity name, the at least one processing device is further configured to: search the at least one supplier entity name in a user database, the user database comprising a plurality of authenticated supplier entity names and at least one associated category for each authenticated supplier entity name of the user database; (Par [0063], “In an exemplary embodiment, the Artificial intelligence engine 109 employs machine learning techniques that learn patterns and generate insights from the data. Further, the AI engine with ML employs deep learning that utilizes artificial neural networks to mimic biological neural network in human brains. The artificial neural networks analyze data to determine associations and provide meaning to unidentified data.” And par [0057], “…The parameters may include entity name, existing contract details, pricing information, object procured” and See Figures 1-3A) generate a determined result based on the at least one supplier entity name matching at least one authenticated supplier entity name of the user database; (Par [0058], “The ALU enables processing of binary integers to assist in formation of at least one table of data attributes where the OSDM and entity specific data model (ESDM) or either similar data models are applied to the data table for obtaining supplier score of recommending suppliers.” And see Figures 1-3A)
categorize the at least one supplier entity name based on the associated category for the matched authenticated supplier entity name of the user database; (Par [0056], “The mechanism 104 also includes an object specific data model mechanism (OSDM) as part of the data model database within entity specific data in the data lake 105. The object includes item or service for sourcing as a supply chain operation.”)
and determine, based on the associated category, a normalized supplier entity name. (Par [0075], “The autonomous sourcing and category management system enables more secured process considering the issues inherent with cloud environments.” And See Figures 1-3A)
As per Claim 6, the rejection of Claim 4 is incorporated and Makhija further discloses: wherein, in the instance where the at least one supplier entity name does not match an authenticated supplier entity name, the at least one processing device is further configured to: submit the at least one supplier entity name to a search engine via an application programing interface to generate a search result; (Par [0138], “…where a search by the AI engine is performed for very quick and accurate results…”) download the search result from the application programming interface; (Par [0157], “downloaded to…”) apply a natural language processor to the search result to generate at least one keyword from the search result; (Par [0092], “The negotiation script is generated based on one or more negotiation data models trained through natural language processing (NLP) of a historical dataset with logistic regression and median calculations to predict recommendations.”) determine, based on the at least one keyword from the search result, a category associated with the at least one keyword, wherein the category is associated with a plurality of keywords including the at least one keyword; (Par [0130], “a convolution neural network is used for classification that focuses on presence of keywords rather than sequence for feature extraction as spend description is a short text containing a series of keywords” and See Figures 1-3A) and determine, based on the category associated with the supplier entity name, the normalized supplier entity name. (Par [0126] and See Claim 6 and Figures 1-3A)
As per Claim 7, the rejection of Claim 1 is incorporated and Fireman further discloses: wherein the source entity suggestion model comprises a bayes theorem. (Par [0026] According to one exemplary embodiment, the method may include where the (c) may include: performing at least one of: stochastic analysis, and/or Bayesian analysis; or deterministic analysis.).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of Fireman specifically a calculation related to bayes theorem into the method of Makhija to take advantage on applying a statistical technique to measure a special space according to user’s needs. The modification would have been obvious because one of the ordinary skills in the art would implement calculating an estimated approach to obtain the best data result as possible.
As per Claim 8, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the source entity suggestion model 1s a machine learning model, the processing device is further configured to: collect a plurality of previously tagged variables, wherein the plurality of previously tagged variables comprises at least one of a positive feedback or a negative feedback; generate, based on the collected plurality of previously tagged variables, a previously tagged variable training dataset; and apply the previously tagged variable training dataset to the source entity suggestion model to train the source entity suggestion model. (Par [0126-0127], “The Data lake 305 provides feedback on the category workbench 301 for recommending supplier.” And see par [0056], “Since supply chain operations include multiple functions within the sourcing operation like supplier recommendation, item categorization, demand sensing etc., the support mechanism 114 includes sub-processors 116 for carrying out multiple tasks…” See Figures 2-3B, having risk and ratings coming from positive or negative feedback).
Makhija disclose a categorization, but not specifically “tagged”
Fireman discloses the above claimed features as follows: (Par [0169], “Tags may also be promiscuous, i.e., attending all requests alike, or secure, which may require authentication and control of typical password management and secure key distribution issues. A tag may as well be prepared to be activated or deactivated in response to specific reader commands.”)
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of Fireman specifically a login authentication control into the method of Makhija to take advantage on applying a security measure according to user’s needs and also a tagged approach according to entities. The modification would have been obvious because one of the ordinary skills in the art would implement security to provide the entities with the best correlation possible.
As per Claim 9, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the at least one processing device 1s further configured to categorize the at least one normalized supplier entity name as a medical device manufacturer or a medical equipment manufacturer. (Par [0005], “…where the organization segments its spending on goods and services in different category as part of category management. The segmentation arranges goods and services in discrete groups depending on the functions of these goods and services. Some of the categories include office management, HR, Professional Services, Security, IT, Transport, travel, medical, industrial products and services etc.”).
As per Claim 10, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the resource line item comprises data of at least one of a supplier’s name, a service type of a supplier of the supplier entity name, an amount owed to the supplier, a due date of payment to the supplier, at least one payment term, or a balance due to the supplier. (Par [0147], “FIG. 5B shows an interface 500B, with details of the spend profile including spend by category, spend by region, spend by business unit, spend by Payment terms, contracted Vs non-contracted spend by category and spend trends. The supplier profile includes information about top suppliers with supplier category spend, supplier region, business unit spend, payment terms spend.”).
As per Claim 11, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the entity name master record comprises a plurality of authenticated supplier entity names and at least one category for each authenticated supplier entity name, and wherein the normalized supplier entity name is matched to at least one authenticated supplier entity name, and wherein the plurality of authenticated supplier entity names comprises a plurality of entity types for each authenticated supplier entity name.
As per Claim 12, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the processing device is further configured to: receive a user identifier and at least one electronic record associated with the user; receive at least one source entity response from at least one source entity; (Par [0077-0078]. “The AI engine enables creation of data scripts for processing multiple tasks including generation of relevant questionnaire, supplier recommendation, optimization, and object characteristic data set. In step 215 floating the RFP to selected suppliers and receiving supplier response in 216.” And par [0127], “The data lake 305 receives information related to supplier attributes from multiple data sources, crawled data from web related to supplier profile or newsfeed, questionnaire and outcomes and historical spend data.”)
update a predictive plan database with the at least one electronic record, wherein the predictive plan database comprises a plurality of electronic records associated with a plurality of recipients and a plurality of source entities; (Par [0054], “The category workbench application interface 101A triggers a plurality of predictive data models to identify one or more category of objects eligible for sourcing”)
determine a previous transaction amount for each of the plurality of electronic records, wherein the previous transaction amount is based on at least one previous pre- determined variable standard for each source entity; (claim 31, “by receiving the recommended strategy processed by the server and applying an AI based dynamic processing logic to the strategy to automate at least one task.” And par [0137], “For an entity that would like to procure a certain item, the item can be queried across all the detected clusters and the list of suppliers for the best match cluster could be retrieved.” And see Figures 3G-4) and determine, based on the previous pre-determined variable standard and the previous transaction amount associated with each source entity of the plurality of source entities, at least one suggested source entity name which complies with the pre-determined variable standard and the transaction amount. (Par [0084], “[0084] In an embodiment, the data sources include expiring contracts, blanket pay orders, should cost models built on market indices and prices, transactional spend data, demand planning, ERPs, budgets, supply planning, newsfeeds, Merger and Acquisition information, bankruptcy, innovation and spin-off.” And see Figures 3C-3K).
As per Claim 13, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein each authenticated supplier entity name comprises an integer-based supplier name identifier. (Par [0057], “The parameters may include entity name, existing contract details, pricing information, object procured, date of procurement, place, etc. It shall be understood to a person skilled in the art that the parameters may vary depending on the request and source of request like from an entity or auto generated request from an application after completion of an operation of the application or generation of an auto-set demand trigger through the application.” And par [0098], “The auto optimization by the system through a data solver and optimizer includes operating with mixed Integer and Non-Integer scripts to accomplish minimizing cost or maximizing savings and constraints.”).
As per Claim 14, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the processing device is further configured to: store, by an electronic record management module, a plurality of electronic records associated with each source entity of the plurality of authenticated source entity names, wherein the plurality of electronic records comprise data associated with each resource transaction. (Par [0069], “product and service categories, volume tier discounts, new technologies, substitute products, low cost alternatives, standardization or reuse opportunities, benchmarks for resource qualifications and experience, intervals for price negotiations, futures, forwards, and options to fix or cap prices of commodity purchases in liquid markets, currency hedging for materials which are predominantly imported, Value chain for opportunities for Vertical integration, Should cost by leveraging data model to negotiate on billing rates, material and equipment price, supplier mark-up/profit, and current inventory management practices.”).
As per Claim 15, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the variable data comprises data associated with at least one of a location, a service level, a service type, a transaction amount, a service term, a special amount discount, or a potential risk. (Par [0144], “The actionable insights include category spend monitoring data, category classification and positioning data, supply market analysis data, supplier spend monitoring data, cost driver data, strategy data, opportunity identification data, risk assessment data. The system includes the AI engine coupled to the processor and configured for tracking and monitoring a plurality of parameters driving one or more supply chain operations. The plurality of parameters includes category strategies, key projects, supplier risk factors,..”).
As per Claim 16, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the generation of the at least one normalized supplier entity name is generated by the processing device being further configured to: apply the at least one supplier entity name to a categorization machine learning model and at least one resource line item; (Par [0134], “This data classifier is also used as a feature encoder as the feature space learned by this classifier at the prefinal layer captures a separable space across items. To perform item discovery, all the data belonging to a level 1 label is encoded through its corresponding level 1 classifier. The resultant feature vectors capture the semantic meaning of the line item description.”)
and output, by the categorization machine learning model, the normalized supplier entity name and an associated category of the normalized supplier entity name. (Par [0056], “item categorization, demand sensing etc., the support mechanism 114 includes sub-processors 116 for carrying out multiple tasks simultaneously. Further, the sourcing operation includes tasks like supplier recommendation, bid optimization and negotiation with suppliers. The mechanism 104 includes an auto-negotiator 117 coupled to the AI engine 109 configured for negotiating with the supplier based on a negotiation script generated by the AI engine 109.” And par [0134-135]).
As per Claim 17, the rejection of Claim 1 is incorporated and Makhija further discloses: wherein the processing device is further configured to: collect a plurality of categorization variables associated with a plurality of supplier entity names, wherein the categorization variables comprise at least one of a general ledger code, a transaction date, a source organization, a source process center, or a source facilities type; (Par [0066], “the historical database 119 stores transaction data including spend data, object data, contract data etc., from one or more entities, a supplier database 120 configured for storing supplier related data, an operational database 121 configured for storing a set of parameters identified from a received”)
generate a categorization training dataset, wherein the categorization training dataset comprises the plurality of categorization variables; (Par [0135], “The level 4 label is most granular representation of categorization available through the data taxonomy. Further granularity can be achieved through the clustering approach. For example, clustering on a level 4 label of “Meals” could result in “Sandwiches”, “Buffet Meals”, “Doughnuts” etc.”) and apply the categorization training dataset to the categorization machine learning model to train the categorization machine learning model. (Par [0056], “item categorization, demand sensing etc., the support mechanism 114 includes sub-processors 116 for carrying out multiple tasks simultan