DETAILED ACTION
This action is in reply to the submission filed on 12/23/2025.
Status of Claims
Applicant’s amendments to claims 1, 8, 10-12, 14 and 18-20 are acknowledged. Claims 1-20 are currently pending and have been examined.
Response to Remarks
Examiner thanks Applicant for clarification of paragraph 41 of the specification’s reference to the figures, as well as clarification of claims 10 and 19’s claim objections, which have been addressed.
Applicant's remarks filed 12/23/2025 have been fully considered and have been found not persuasive in full. The use of DNA code equivalent to maintain the previously claimed control data structure is seen as using computing technology in its ordinary capacity to embody the abstract idea. Database techniques claimed at this level, without support in the specification for a tailored implementation, are not seen as significantly more or a practical application of the abstract idea. While DNA code equivalence, or converting from a binary code base to a 4 base, is not seen as a mental process, it is seen as a computerized embodiment of the mental process. Substituting “DNA code equivalent” in the claims for “computerized database” renders the claims functionally equivalent as currently filed. The data is being used to perform the claimed functions (store all segmentation information, manage an entire supply chain by deriving actions based on elements within the data, and as a carrier for the entity workflows). This data is seen to perform said functions regardless of the DNA code equivalent embodiment. Then, this is seen as using said technology in its ordinary capacity to perform said ideas.
Regarding page 9 of remarks, Examiner is unsure of any machine learning in the independent claims, or how using DNA code equivalents relates to any specific improvements in machine learning. Rather, Applicant’s disclosure was reviewed and not found to teach any such improvements.
Regarding page 10, the Office is careful not to equate the specific goal of machine learning optimization regarding multiple tasks as in Desjardins, with any improvements to supply chain management systems. Further, the language in the present applications disclosure recited on page 10 contains no teachings indicating improvements, rather than merely a DNA code equivalent embodiment. It is noted claim 8 used machine learning to provide updated segmentation criteria as output.
Regarding page 11, Examiner sees the present claims as directed towards segmenting products into segments, generating control data structures, applying supply chain rules and integrating decisions into workflows with respect to a supply chain. Meanwhile, the claims in Enfish as directed to a means for configuring computer memory according to a logical table, as well as detailing said logic table. Enfish is necessarily rooted in computing technology, while present claims are managing workflows in supply chains using structured data encoded in a DNA code equivalent using computing technology. Then it is seen that the DNA code equivalent encoding, machine learning in claim 8, and computing embodiments as a whole and individually are using said computing technology in its ordinary capacity to perform the abstract idea of mental evaluation of data generation, structuring, value determinations, and rules analysis for decision-based outputs.
Regarding pages 12 and 13, McRo is directed to a method for animating lip syncing of 3-d characters using phoneme sequences of morph weight streams, rather than segmenting products, generating structured data to combine segments and product characteristics, and encoding said data into DNA code equivalents. Further, Examiner does not see in the specification training a machine learning model on multiple tasks with an acceptable level of performance. Regarding page 14, the machine learning in claim 8 does not, nor the application’s disclosure, teaches training a machine learning model on multiple tasks with an acceptable level of performance.
Regarding the prior art rejections, and page 19 of remarks, Examiner does not see any segment level optimization in the claims beyond that of updated segmentation criteria in claim 8, rather the claim language includes scope involving using segment-based rules to trigger optimal decisions based on segmentation elements, characteristics and values. As in, the segments are not optimized. If they were, Evans teaches in paragraph 57 an auto-segmenter that suggests segmentation schemes based on data inputs. Both Evans and present invention use product segments and characteristics to drive supply chain decisions. Further, the claims include individual product optimization. Claim 14 and page 20 of remarks recite “recommendation for a decision with respect to a given product.” This is seen as individual product optimization.
Additional art is cited to teach the added limitations concerning DNA code equivalents.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: the claims fall under statutory categories of processes and/or machines.
Step 2A Prong 1: Claim 1 recites: segmenting products into product segments, generating a control data structures for each segment that includes segmentation elements and a value or characteristic for each element, rendering each structure as a code equivalent encoding all segmentation information comprising the segmentation elements and corresponding values or characteristics for a corresponding product; publishing the code to a product catalogue, making the code accessible to workflows of systems and personell, providing the structures to a system workflow causing system to perform actions based on structure, wherein an entire supply chain for the product is managed via the code by driving actions, decisions, or recommendations based on the segmentation elements, characteristics, and characteristic values defined in the code, and wherein the code is carried along the entity workflows to trigger optimal decisions using segment-based supply chain rules. Claim 14 recites segmenting products including elements associated with a supply chain, determining a value or characteristic for each element based on supply chain data and segmentation criteria defined by the entity of the products, generating a data structure for each segment identifying the elements and an assigned corresponding characteristic or value, rendering the structure as code for a product, encoding the elements and values/characteristics, assigning supply chain decisions to each segment based on said data structure and supply chain rules, and integrating said decisions into a workflow of a system, causing the system to perform an action, to provide at least one decision, or recommendation, wherein the rules are linked to the code to a given element, as a whole, or combinations of elements, and the rules are evaluated against real-time product specific supply chain data using the segmentation elements and values/characteristics encoded to generate an action, decision, or recommendation. Claim 19 recites generating a data structure for a supply chain of a product by defining a supply chain segment to a product, the segment includes segmentation elements corresponding to the product, assigning a value or characteristic to each element based on criteria and relevant supply chain data, producing the structure using the elements and values/characteristics, rendering the structure as code, encoding the elements and values/characteristics, publishing the code to a product catalogue maintained by the entity by updating fields in the catalogue with the code, integrating the structure into a workflow causing the workflow to perform an action, provide a decision, or make a recommendation for a certain decision, based on supply chain rules associated with the elements or structure as a whole. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes, including an observation, evaluation, judgment or opinion. Analyzing data and outputting recommendations, as claimed, related to product segments in a supply chain can be done by the human mind. Additionally, the limitations also cover fundamental economic activity, including mitigating risk of supply chain inefficiencies.
Step 2A Prong 2: Said judicial exception is not integrated into a practical application because the claims as a whole, looking at the additional elements: encoding data to DNA code equivalent, a server with processor and non-transitory computer-readable storage medium with executable instructions, individually and in combination, merely use a computer (see MPEP 2106.05f.) The claims use these machines in their ordinary capacity for the purpose of applying the abstract idea(s). Therefore, these limitations are invoking computers or other machinery merely as a tool to perform an existing process, such that it amounts to no more than mere instructions to apply the exception. Then, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, and the claim is directed to an abstract idea.
Step 2B: Said claims recite additional elements as listed above, which are not sufficient to amount to significantly more than the judicial exception because, as mentioned in Step 2A Prong 2, they use computers or other machinery to perform an abstract idea in such a way that amounts to no more than mere instructions to apply the exception using computers or other machinery. Mere instructions to apply an exception using computers or other machinery cannot provide an inventive concept. Therefore, the claim is not patent eligible.
Claim 2 recites obtaining data. Claim 3 recites cleaning data. Cleaning in the disclosure is defined in paragraph 37 of the specification, as can be including averages, deviations, conversions. It is also defined in para. 66 of the specification, as being corrected or modified to correct obvious errors defined by cleansing rules. Claim 4 recites cleansing includes calculating statistical values for each element. Claim 5 recites obtaining segmentation criteria defined by the entity for each element. Claim 6 recites determining the value/characteristic for each element of each segment based on a corresponding statistical value and criteria. Claim 7 recites rendering the structures as string representing a product segment for a product and identifying each segmentation element and value/characteristic. A string is interpreted by para. 43 of the specification and Figure 1B as including a format like “YES_HIGH_HIGH_POORLOW.” These claims recite data analysis as in the independent claims and are considered as including mental processes.
Claim 8 recites providing data, elements and criteria to a machine learning model, receiving cleansed data and updated segmentation criteria as output from the model, and generating the structure using said data and criteria. This is seen as using a high-level claimed ML model to apply said mental processes of cleansing/correcting data and updating criteria.
Claim 9 recites publishing the structures for system access. Claim 10 recites sending the structures to the system via an application programming interface (API). Claim 11 recites updating structures at a preconfigured time interval based on relevant supply chain data. Claim 12 recites using updated relevant supply chain to generate reports and providing the reports through an interface. Claim 13 recites providing compliance data within a report using predefined rules associated with a structure of a segment. This is seen as using computing technology such as APIs and interfaces in its ordinary capacity to perform said abstract idea, including sending and receiving data.
Claim 15 recites storing the structures as records within a datastore. Claim 16 recites linking the rules to records as a whole, combinations of fields of a given, or a single field of the given record. Claim 17 recites providing the structures and rules to the system. Claim 18 recites integrated the structures into a workflow. Claim 20 recites providing a report at a segmentation element level for the product supply chain through an interface at a configured level of time based on real-time data and supply chain rules. This is interpreted as the report is based on real-time data and supply chain rules, not the time interval. These claims are seen as using an interface as a tool to perform said abstract idea of mental processes of data analysis and output.
For these reasons the claims are not subject matter eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 9, 12, 14-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Evans (US 2021/0158259) in view of Anido (US 2023/0073349).
Claim 1. Evans teaches a method, comprising:
segmenting available products of an entity into product segments, each product segment associated with a given product; (paragraph 44 showing segmentation of products)
generating control data structures, each control data structure for each product segment, (para. 37 showing data structure generation) each control data structure includes segmentation elements and a value or characteristic for each segmentation element; and (para. 57 showing characteristics of segments) (para. 57 showing supply chain parameters for different segments as it pertains to products within segment, and is relevant to said chain) (para. 58 showing mapping of said data herein referred to as the control data structure)
wherein the data encodes all segmentation information comprising the segmentation elements and corresponding values or characteristics for a corresponding product; (para. 58 showing mapping of said data herein referred to as the control data structure)
publishing the data to a product catalogue maintained by the entity, making the data accessible to workflows of systems and personnel throughout the entity; (para. 49 showing module for enabling access to data within a system)
providing the control data structures to a workflow of a system causing the system to perform one or more actions within the workflow based on the control data structures; (para. 44 showing optimization module as workflow performing analysis for recommendation of supply chain network improvements)
wherein an entire supply chain for the corresponding product is managed via the data structure by driving actions, decisions, or recommendations based on the segmentation elements, characteristics, and characteristic values defined within the data structure, and wherein the data structure is carried along the entity workflows to trigger optimal decisions using segment-based supply chain rules. (para. 44 showing optimization module as workflow performing analysis for recommendation of supply chain network improvements, including use of segmentation data)
Evans does not, but Anido, in the art of inventory monitoring, teaches:
rendering each control data structure as a Deoxyribonucleic Acid (DNA) code equivalent. (Paragraphs 227-234 showing Anido’s system and data stored in DNA digital storage)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of supply chain optimization using data strings in Evans, with the known technique of digital DNA storage in Anido, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for not losing data when power is removed. See para. 227 of Anido.
Claim 2. Evans as modified by Anido teaches the method of claim 1, wherein segmenting further includes obtaining supply chain data maintained by the entity. (para. 37 showing obtaining data from system of entity)
Claim 3. Evans as modified by Anido teaches the method of claim 2, wherein obtaining further includes cleaning the obtained supply chain data. (para. 38 showing data transformation for compatibility. Examiner interprets this cleaning to include data transformation to ensure compatibility. See also para. 50 for data cleaning, and para. 54 for data transformation through mathematical operations.)
Claim 4. Evans as modified by Anido teaches the method of claim 3, wherein cleaning further includes calculating statistical values for each segmentation element from cleansed data. (para. 54 mathematical operations for cleaning; para. 72 showing statistical computations of attribute data)
Claim 5. Evans as modified by Anido teaches the method of claim 4, wherein calculating further includes obtaining segmentation criteria defined by the entity for each segmentation element. (para. 103 showing supply chain parameters and configuration for said calculations)
Claim 6. Evans as modified by Anido teaches the method of claim 5, wherein generating further includes determining the value or the characteristic for each segmentation element of each product segment based on a corresponding calculated statistical value and corresponding segmentation criteria. (para. 103 showing value/characteristic as outcome probabilities in determination using statistical methods.)
Claim 9. Evans as modified by Anido teaches the method of claim 1, wherein providing further includes publishing the control data structures for access by the system associated with the entity. (para. 49 showing data access configured for use)
Claim 12. Evans as modified by Anido teaches the method of claim 1 further comprising:
using updated supply chain relevant to corresponding products and the entity and the control data structures to generate reports; and (para. 89 showing graphical output of data)
providing the reports to the entity through an interface. (para. 41 showing said output in an interface)
Claim 14. Evans teaches a method, comprising:
segmenting products of an entity into segments, (para. 44 showing segmentation of products) each segment including segmentation elements associated with a supply chain of a corresponding product; (para. 57 showing characteristics of segments)
determining a value or characteristic for each segmentation element of each segment based on supply chain data maintained by the entity and based on segmentation criteria defined by the entity for each segmentation element; (para. 57 showing supply chain parameters for different segments as it pertains to products within segment, and is relevant to said chain)
generating control data structures, each control data structure for each segment of each product, (para. 37 showing data structure generation) wherein each control data structure identifies the segmentation elements and a corresponding value or characteristic assigned to each segmentation element; (para. 58 showing mapping of said data herein referred to as the control data structure)
rendering each control data structure for a corresponding product, wherein the data encodes the segmentation elements and corresponding values or characteristics; (para. 58 showing mapping of said data herein referred to as the control data structure)
assigning one or more supply chain decisions to each segment of each product based on a corresponding control data structure and based on supply chain rules associated with the corresponding control data structure; and (para. 65 showing rules and parameters set for workflow)
integrating at least one supply chain decision into a workflow of a system causing the system to perform at least one action, to provide the at least one supply chain decision, or to provide at least one recommendation with respect to at least one supply chain of at least one product managed by the entity; (para. 44 showing optimization module as workflow performing analysis for recommendation of supply chain network improvements)
wherein supply chain rules are linked to the data structure as a whole, to a given segmentation element of the data, or to combinations of segmentation elements of the data, and the supply chain rules are evaluated against real-time product specific supply chain data using the segmentation elements and corresponding values or characteristics encoded in the data to generate an automated action, an automated decision, or an automated recommendation for a decision with respect to a given product. (para. 44 showing optimization module as workflow performing analysis for recommendation of supply chain network improvements)
Evans does not, but Anido, in the art of inventory monitoring, teaches:
rendering each control data structure as a Deoxyribonucleic Acid (DNA) code equivalent. (Paragraphs 227-234 showing Anido’s system and data stored in DNA digital storage)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of supply chain optimization using data strings in Evans, with the known technique of digital DNA storage in Anido, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for not losing data when power is removed. See para. 227 of Anido.
Claim 15. Evans as modified by Anido teaches the method of claim 14, wherein generating further includes storing the control data structures as records within a data store. (para. 45 showing recordation of results in archive)
Claim 16. Evans as modified by Anido teaches the method of claim 15, wherein assigning further includes linking the supply chain rules to corresponding records as a whole, combinations of fields of a given, or a single field of the given record. (para. 57 showing assigning algorithms to different groupings of segments)
Claim 17. Evans as modified by Anido teaches the method of claim 14 further comprising, providing the control data structures and the supply chain rules to the system or to a different system that is associated with the entity or is associated with a different entity. (paras. 48 and 49 showing data access configured for use)
Claim 19. Evans teaches a system, comprising:
at least one server comprising at least one processor and a non- transitory computer-readable storage medium with executable instructions; and the executable instructions when executed by the at least one processor cause the at least one processor to perform operations comprising: (Evans para. 117 showing server, para. 118 showing processor and RAM)
generating a control data structure for a supply chain of a product associated with at least one entity (para. 37 showing data structure generation) by defining a supply chain segment to the product, (para. 44 showing segmentation of products)
the supply chain segment includes segmentation elements corresponding to the product, (para. 57 showing characteristics of segments)
assigning a value or a characteristic to each segmentation element based on segmentation criteria for a corresponding segmentation element and based on data that is relevant to the supply chain, and (para. 57 showing supply chain parameters for different segments as it pertains to products within segment, and is relevant to said chain)
producing the control data structure using the segmentation elements and corresponding assigned values or characteristics; and (para. 58 showing mapping of said data herein referred to as the control data structure)
rendering the control data structure that encodes the segmentation elements and corresponding values or characteristics for the product; (para. 58 showing mapping of said data herein referred to as the control data structure)
publishing the data to a product catalogue maintained by the at least one entity by updating fields in the product catalogue with the data; (para. 49 showing module for enabling access to data within a system)
integrating the control data structure into at least one workflow associated with at least one system of the at least one entity causing the at least one workflow to perform an action, to provide a decision, or to make a recommendation for a certain decision with respect to the product (para. 44 showing optimization module as workflow performing analysis for recommendation of supply chain network improvements)
based on real-time data relevant to the supply chain received by the at least one system and (para. 41 showing updating workflow with new data; paragraphs 13 and 20 showing real time data analysis)
based on supply chain rules associated with the segmentation elements of the control data structure or associated with the control data structure as a whole; (para. 65 showing rules and parameters set for workflow)
wherein the data condenses, summarizes, captures, and quantifies at least one entity's knowledge of the product's supply chain, and wherein all segmentation information encoded in the data is carried along the entity workflows to trigger optimal decisions using segment-based supply chain rules. (para. 44 showing optimization module as workflow performing analysis for recommendation of supply chain network improvements)
Evans does not, but Anido, in the art of inventory monitoring, teaches:
rendering each control data structure as a Deoxyribonucleic Acid (DNA) code equivalent. (Paragraphs 227-234 showing Anido’s system and data stored in DNA digital storage)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of supply chain optimization using data strings in Evans, with the known technique of digital DNA storage in Anido, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for not losing data when power is removed. See para. 227 of Anido.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Evans in view of Anido, and in further view of Stoettinger (US 2020/0364495 A1).
Claim 7. Evans as modified by Anido teaches the method of claim 6.
Evans teaches transforming data portions into strings. (para. 54 string format for dates) It does not teach wherein determining further includes rendering the control data structures as strings, each string represents a corresponding product segment for a corresponding product of the entity, and each string identifies each segmentation element and a corresponding value or characteristic. However, Stoettinger does: (para. 52 of Stoettinger showing attributes of a product combined into one long data string.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of supply chain optimization using data strings in Evans, with the known technique of combining elements into one string in Stoettinger because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for concatenation of data into one string. (Stoettinger para. 52 showing said combination of data.)
Claims 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Evans in view of Adino, and in further view of McEntire (US 11,704,581).
Claim 8. Evans as modified by Anido teaches the method of claim 1, wherein generating further includes
providing supply chain data maintained by the entity, the segmentation elements, and segmentation criteria defined by the entity as input to a machine learning model (model), (para. 10 showing said optimization module including machine learning (ML), see para. 44)
receiving updated segmentation criteria for each segmentation element as output from the model, and (para. 44 showing ML for updating segmentation and parameters)
generating each control data structure for each product segment using the cleansed data and the updated segmentation criteria. (para. 56 showing results from optimization module, said module including ML, criteria and cleansed data)
Evans teaches receiving updated segmentation criteria as model output. It does not teach receiving cleansed data as ML output. It uses a data transformer model which does not explicitly use ML. (para. 54 showing cleaning in data transformer module) However, McEntire teaches using ML for data cleaning. (column 52, line 65 through column 53, line 28)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of machine learning and data cleaning in Evans, with the known technique of data cleaning using machine learning because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for improved cleansing through the use of machine learning. (Said citation of McEntire showing how ML can clean data.)
Claim 10. Evans as modified by Anido teaches the method of claim 1, wherein providing further comprises sending the control data structures to the system. (para. 49 showing data transmission to the system)
Evans teaches data access through an interface. (para. 49 showing access interface) It does not teach APIs. However, McEntire teaches sending data through an API. (Column 14, lines 42-54 showing data transmission through API).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of supply chain optimization in Evans, with the known technique of using APIS for data packaging, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for interoperability in data interfacing.
Claims 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Evans in view of Anido, and in further view of Najmi (US 2016/0217406).
Claim 11. Evans as modified by Anido teaches the method of claim 1 further comprising:
updating the control data structures for corresponding products and the entity based on updated supply chain data relevant to the corresponding products and the entity. (para. 44 showing updating said data output based on new input data)
Evans does not, but Najmi teaches updating data at a preconfigured interval of time. (para. 34 showing system updates to supply chain data after a time period.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of iterative supply chain optimization, with the known technique of determining time periods for updating data in Najmi, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for consistent data updates. (Najmi para. 34 showing said system of updating after a time period being used by planners.)
Claim 20. Evans as modified by Anido teaches the system of claim 19, wherein the executable instructions when executed by the at least one processor further cause the at least one processor to perform additional operations comprising:
providing a report at a segmentation element level of detail for the supply chain of the product (para. 89 showing graph output at a segment level) through at least one interface to the at least one entity (para. 41 showing said output in an interface) based on the real-time data and the supply chain rules.
Evans does not teach providing a report at a configured interval of time. However, Najmi does at para. 34 showing continual update or reporting/publishing after time periods).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of iterative supply chain optimization, with the known technique of determining time periods for updating data in Najmi, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for consistent data updates. (Najmi para. 34 showing said system of updating after a time period being used by planners.)
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Evans in view of Anido, and in further view of Evans (US 2023/0306347), hereinafter termed Evans ‘347.
Claim 13. Evans as modified by Anido teaches the method of claim 1. Evans teaches monitoring compliance (paras. 48 and 64 monitoring compliance of rules for optimization) and providing reports for each product using predefined rules associated with a corresponding control data structure of a corresponding product segment for the corresponding product, (para. 89 showing graphical output of data) but not providing compliance data within a report.
However, Evans ‘347 teaches further comprising
providing compliance data within a report. (para. 67 showing reporting of compliance data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of compliance and reporting regarding supply chain optimization in Evans, with the known technique of reporting compliance data in Evans ‘347, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for said reporting. (Evans ‘347 para. 67 showing reports including said compliance data).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Evans in view of Anido, and in further view of Adelmann (US 2023/0011806).
Claim 18. Evans as modified by Anido teaches the method of claim 14. It does not teach further comprising: integrating the control data structures into at least one additional workflow of at least one additional system associated with a different entity. However, Adelmann does at para. 89, showing adding another source into the workflow.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of supply chain optimization using product segments in Evans, with the known technique of using secondary sourcing into workflows in Adelmann, because applying the known technique would have yielded predictable results and resulted in an improved system by allowing for adjusting of workflows for implementation of desired inputs. (Adelmann para. 89 showing selection of data sourcing for workflows.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, this action is made final. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AARON TUTOR/Primary Examiner, Art Unit 3627