DETAILED ACTION
This non-final office action is in response to Applicant’s submission filed April 18, 2025. Claims 1-20 are pending. Claims 1-, 9 and 16 are the independent claims.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding independent Claims 1, 9 and 16, the claims are directed to the abstract idea of supply chain planning. This is a process (i.e. a series of steps) which (Statutory Category – Yes –process).
The claims recite a judicial exception, a method for organizing human activity, supply chain planning (Judicial Exception – Yes – organizing human activity). Specifically, the claims are directed to generate/transmit supply chain ‘optimization’ data to a client device for presentation on a graphical user interface (i.e. display data to a human user), wherein supply chain planning is a fundamental economic practice that falls into the abstract idea subcategories of sales activities and/or commercial interactions. That the data is associated with a ‘commodity’ merely recites non-functional descriptive material (i.e. intended use). See 2106.04(a). Further all of the steps of “generate”, “generate”, “save”, “generate”, and “transmit” recite functions of the supply chain planning are also directed to an abstract idea that falls into the abstract idea subcategories of sales activities and/or commercial interactions. The intended purpose of independent claims 1, 9 and 16 appears to be to display to a human user supply chain ‘optimization’ data.
Accordingly, the claims recite an abstract idea – fundamental economic practice, specifically in the abstract idea subcategories of sales activities and/or commercial interactions. The exceptions are the additional limitations of generic computer elements: computing system, one or more processors, one or more memories, hardware modules, one or more computing systems, data processing sub-module, client device, graphical user interface, computer readable medium storing instructions including instruction. See 2106.04(a).
Accordingly, the claims recite an abstract idea under Step 2A, Prong One, we proceed to Step 2A, Prong Two. Considering whether the additional elements set forth in the claim integrate the abstract idea into a practical application (See 2106.04(a)), the previously identified non-abstract elements directed to generic computing components include: computing system, one or more processors, one or more memories, hardware modules, one or more computing systems, data processing sub-module, client device, graphical user interface, computer readable medium storing instructions including instruction. These generic computing components are merely used to receive/access, process or display data as described extensively in Applicant’s specification (Specification: Figure 1). Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. Moreover, when viewed as a whole with such additional elements considered as an ordered combination, the claim modified by adding a generic computer would be nothing more than a purely conventional computerized implementation of applicant's supply chain planning in the general field of business management/planning and would not provide significantly more than the judicial exception itself. Note McRo, Inc. v. Bandai Namco Games America Inc. (837 F.3d 1299 (Fed. Cir. 2016)), guides: "[t]he abstract idea exception prevents patenting a result where 'it matters not by what process or machinery the result is accomplished."' 837 F.3d at 1312 (quoting O'Reilly v. Morse, 56 U.S. 62, 113 (1854)) (emphasis added). The claims are not directed to a particular machine nor do they recite a particular transformation (MPEP § 2106.05(b)).
Additionally, the claims do not recite any specific claim limitations that would provide a meaningful limitation beyond generally linking the use of the judicial exception to a particular technological environment. Nor do the claims present any other issues as set forth in the MPEP 2106.04(a) regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. Rather, the claims merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea. Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), claims 1-20 do not integrate the judicial exception into a practical application.
Regarding the use of the generic (known, conventional) recited computing system, one or more processors, one or more memories, hardware modules, one or more computing systems, data processing sub-module, client device, graphical user interface, computer readable medium storing instructions including instruction," the Supreme Court has held "the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. 208, 223. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. The claims as a whole do not recite more than what was well-known, routine and conventional in the field (see MPEP § 2106.05(d)). In light of the foregoing and under the MPEP 2106.04(a), that each of the claims, considered as a whole, is directed to a patent-ineligible abstract idea that is not integrated into a practical application and does not include an inventive concept.
Regarding the one or more trained inference models to generate missing data items, the examiner notes that the one or more trained inference models are trained external/outside of the scope of the invention as claimed. Further the one or more trained inference models are recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic one or more trained inference models on a generic computer, also recited at a high level of generality. The one or more trained inference models are used to generally apply the abstract idea without limiting how the trained neural network functions. The one or more trained inference models are described at a high level such that it amounts to using a generic computer with a generic one or more trained inference models to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Additionally, the claims recite a judicial exception, a mental processes, which can be performed in the human mind or via pen and paper (Judicial Exception – Yes – mental process).
The claimed steps of generate normalized commodity data, generate missing data items and generate supply chain optimization data all describe the abstract idea. These limitations as drafted are directed to a process that under its reasonable interpretation covers performance of the steps in the mind but for the recitation of the generic computer components. Other than the recitation of a computing system, one or more processors, one or more memories, hardware modules, one or more computing systems, data processing sub-module, client device, graphical user interface, computer readable medium storing instructions nothing in the claimed steps precludes the step from practically being performed in the mind. The claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea because the extraction module is directed to insignificant pre-solution activity (i.e. data gathering). The step of transmit the supply chain ‘optimization’ data (just data) for presentation is directed to insignificant post-solution activity (i.e. data output). The mere nominal recitation of a generic processor/computer does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process. (Judicial Exception recited – Yes – mental process).
The claims do not integrate the abstract idea into a practical application. The generic computing system, one or more processors, one or more memories, hardware modules, one or more computing systems, data processing sub-module, client device, graphical user interface, computer readable medium storing instructions are each recited at a high level of generality merely performs generic computer functions of retrieving, processing or displaying data. The generic processor/computer merely applies the abstract idea using generic computer components. The elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component.
Regarding the recited one or more trained inference models to generate missing data items in a structured data set, the examiner notes that the one or more trained inference models are trained external/outside of the scope of the invention as claimed. Further the one or more trained inference models are recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using generic one or more trained inference models on a generic computer, also recited at a high level of generality. The one or more trained inference models are used to generally apply the abstract idea without limiting how the one or more trained inference models’ function. The one or more trained inference models are described at a high level such that it amounts to using a generic computer with generic one or more trained inference models to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. The recitation of one or more trained inference models in this claim does not negate the mental nature of these limitations because the one or more trained inference models are merely used at a tool to perform an otherwise mental process.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Integrated into a Practical Application – No).
As discussed above the additional elements in the claims amount to no more than a mere instruction to apply the abstract idea using generic computing components, wherein mere instructions to apply an judicial exception using generic computer components cannot integrate a judicial exception into a practical application or provide an inventive concept. For the transmit step(s) that were considered extra-solution activity, this has been re-evaluated and determined to be well-understood, routine, conventional activity in the field. Applicant’s specification does not provide any indication that the computer/processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is ineligible (Provide Inventive Concept – No).
The claims are ineligible under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding dependent claims 2-8, 10-15 and 17-20 the claims are directed to the abstract idea of supply chain planning and merely further limit the abstract idea claimed in independent claims 1, 9 and 16.
Claims 2, 10 and 17 further limits the abstract idea by interfacing with one or more computer systems, identifying a format, extracting the commodity data and normalizing the extracted commodity data (a more detailed abstract idea remains an abstract idea). Claims 3, 11 and 18 are further limit the abstract idea by identifying whether the computer readable text is in a known format, parsing the computer readable text according to rules, extracting the commodity data using a trained artificial intelligence model when the text data format is not known (a more detailed abstract idea remains an abstract idea). Claim 4, 12 and 19 further limit the abstract idea by identifying missing data items, identifying one or more trained inference models to output missing data items, identifying input types for the models, retrieving current data inputs having respective types, and inputting the current data (a more detailed abstract idea remains an abstract idea). Claims 5 and 13 further limit the abstract idea by recursively inputting historical data inputs into an initialized inference model, recursively comparing outputs of the models, recursively updating the initialized reference models and saving a most recent update to the initialized data models (a more detailed abstract idea remains an abstract idea). Claims 6 and 14 further limit the abstract idea by limiting the historical data input to include ONE or more of infrastructure or weather or agronomic or economic data (a more detailed abstract idea remains an abstract idea). Claims 7, 15 and 20 further limit the abstract idea by selecting a data processing sub-module, retrieving a relevant portion of the structured data, and inputting the relevant portion of structured data (a more detailed abstract idea remains an abstract idea). Claim 8 further limits the abstract idea by limiting the sub-module to ONE or more of best market optimizer OR constrained optimization OR storage allocation planning OR price elasticity optimization OR infrastructure planning OR what-if analysis OR competitor analysis OR data chat module (a more detailed abstract idea remains an abstract idea).
None of the limitations considered as an ordered combination provide eligibility because taken as a whole the claims simply instruct the practitioner to apply the abstract idea to a generic computer.
Further regarding claims 1-20, Applicant’s specification discloses that the claimed elements directed to a computing system, one or more processors, one or more memories, hardware modules, one or more computing systems, data processing sub-module, client device, graphical user interface, computer readable medium storing instructions at best merely comprise generic computer hardware which is commercially available (Specification: Figure 1). More specifically Applicant’s claimed features directed to a system do not represent custom or specific computer hardware circuits, instead the terms merely refers to commercially available software and/or hardware. Thus, as to the system recited, "the system claims are no different from the method claims in substance. The method claims recite the abstract idea implemented on a generic computer; the system claims recite a handful of generic computer components configured to implement the same idea." See Alice Corp. Pry. Ltd., 134 S.Ct. at 2360.
Accordingly, the claims merely recite manipulating data utilizing generic computer hardware (e.g. memory, processor, etc.). Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. Further the lack of detail of the claimed embodiment in Applicant’s disclosure is an indication that the claims are directed to an abstract idea and not a specific improvement to a machine.
Accordingly given the broadest reasonable interpretation and in light of the specification the claims are interpreted to include the process steps being performed by a human mind or via pen and paper. The claim limitations which recite a computer implemented method is at best recite generic, well-known hardware. However, the recited generic hardware simply performs generic computer function of displaying or processing data. Generic computers performing generic, well known computer functions, alone, do not amount to significantly more than the abstract idea. Further the recited memories are part of every conventional general-purpose computer.
Applicant has not demonstrated that a special purpose machine/computer is required to carry out the claimed invention. A special purpose machine is now evaluated as part of the significantly more analysis established by the Alice decision and current 35 U.S.C. 101 guidelines. It involves/requires more than a machine only broadly applying the abstract idea and/or performing conventional functions.
Applicant’s specification discloses that the claimed elements directed to a computing system, one or more processors, one or more memories, hardware modules, one or more computing systems, data processing sub-module, client device, graphical user interface, computer readable medium storing instructions including instructions merely comprise generic computer hardware which is commercially available (Specification: Figure 1). More specifically Applicant’s claimed features directed to a system and components do not represent custom or specific computer hardware circuits, instead the term system merely refers to commercially available software and/or hardware. Thus, as to the system recited, "the system claims are no different from the method claims in substance. The method claims recite the abstract idea implemented on a generic computer; the system claims recite a handful of generic computer components configured to implement the same idea." See Alice Corp. Pry. Ltd., 134 S.Ct. at 2360.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1, 7, 9 , 15, 16 and 20, the term “relevant” (Claim 1: inputting a ‘relevant’ portion of the structure data) in claims 1, 9 and 16 is a relative term which renders the claim indefinite. The term “relevant” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. A relevant portion has been interpreted to include any subset, single data item/value or all data items for the purposes of examination. Appropriate correction required.
Claim Rejections - 35 USC § 102
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 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)(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 1-4, 7-12, 15, 16, 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Evans et al., U.S. Patent No. 12566772.
Regarding claims 1, 9 and 16, Evans et al. discloses a system and method comprising:
One or more processors, one or more memories storing computer/machine readable instructions, a plurality of hardware modules (Figures 13A, 13B; Column 28, Lines 50-58; Column 29);
Generate normalized (standardized, harmonize, cleansed, etc.) ‘commodity’ data from electronic versions of ‘commodity’ data hosted on one or more computing systems (data ingestion, enterprise data systems; Figure 1, Element 170; Figure 3, Element 360), via a subsystem (structured data extract module; Figure 2, Element 220; Figures 3, 7; Figure 9, Element 930; Column 5, Lines 40-68; Column 24, Lines 66-68; Column 25, Lines 1-25; Claims 2, 3);
Generate (impute) missing data items in a structured data set using ONE or more trained inference (AI, ML, statistical, etc.) models, via a subsystem (data inference module)(Column 6, Lines 54-63; Column 8, Lines 38-65; Figure 9, Element 940);
Save the normalized and generated missing data items in a data store as part of the structured data set, via a subsystem (Figure 9, Element 950; Column 4, Lines 31-59; Column 25, Lines 60-68; Column 26, Lines 1-15);
Generate, in response to received user input, supply chain ‘optimization’ data by inputting a ‘relevant’ portion of the structure data into a selected data processing sub-module (routine, model, software, code, program, application, etc.), via a subsystem (Column 9; Column 27, 28; Figure 2, Element 230; Figure 4; Figure 5, Element 520, 540); and
Transmit the supply chain ‘optimization’ data to a client device, via a subsystem (Column 2, Line 60-68; Column 28; Lines 33-50; Figure 4; Figure 5, Element 460; Figure 7, Element 730; Claims 1, 9) (for presentation on a graphical user interface is directed to a wished for/intended use of the transmitted data).
Regarding Claims 2, 10 and 17, Evans et al. discloses a system and method further comprising:
Interfacing with one or more computing systems that host the electronic versions of the ‘commodity’ data (data ingestion, enterprise data systems; Figure 1, Element 170; ; Figure 2, Element 220; Figure 3, Element 360);
Identifying a format in which the electronic version of the commodity data are hosted by the one or more computer systems (e.g. file name, data type, etc.; Figure 8 – profiling data, harmonization, disambiguation; Column 2, Lines 20-38; Column 5, Lines 30-68; Column 6, Lines 1-19; Column 23, Lines 10-68; Column 24, Lines 1-20);
Extracting (ingest) the commodity data from the one or more computing systems based on the identified format (Column 2, Lines 20-38; Column 5, Lines 30-68; Column 6, Lines 1-19; Column 23, Lines 10-68; Column 24, Lines 1-20); and
Normalizing the extract commodity data to conform to preconfigured data formats (disambiguation, consolidation, transformation, etc; Column 8, Lines 25-60; Columns 23, 24; Figures 8, 9).
Regarding Claims 3, 11 and 18 Evans et al. discloses a system and method further comprising:
When the format in which the electronic versions includes computer readable text data (Column 2, Lines 20-38; Column 5, Lines 30-68; Column 6, Lines 1-19);
Identifying whether the computer readable text is contained in a known format (profiling; Column 26, Lines 18-68; Figure 8, Elements 810, 820, 830);
Parsing the computer readable text data according to pre-configured rules (profiling, mapping, Figure 3, Element 310, 350; Figure 8, Element 840) to extract (ingest) the commodity data when the text data is contained in the known format (Column 26, Lines 50-68; Column 27, Lines 1-15);
Extracting the commodity data from the computer readable text data using a trained artificial intelligence model when the computer readable text data is not contained in a known format (mapping AI mapping models; Figure 3, Element 320, 370; Column 5, Lines 30-68; Column 6, Lines 1-19; Column 26, Lines 50-68; Column 27, Lines 1-15).
Regarding Claims 4, 12 and 19, Evans et al. discloses a system and method further comprising:
Identifying missing data items (null, zero) within the structured data set saved in the data store (Column 6, Lines 54-63; Column 8, Lines 38-65; Column 25, Lines 25-60)
Identifying one or more trained inference models that are configured to output (impute) the missed data items based on a respect type of each of the missing data items (Column 6, Lines 54-63; Column 7, Lines 38-63; Column 8, Lines 38-65; Figure 9, Element 940);
Retrieving current data inputs having respective types matched to the input data types (Column 7, Lines 38-63; Column 8, Lines 38-65); and
Inputting the current data inputs into the identified one or more trained inference models to generate the missing data items (Column 6, Lines 54-63; Column 7, Lines 38-63; Column 8, Lines 38-65; Figure 9, Element 940).
Regarding Claims 7, 15 and 20 Evans et al. discloses a system and method further comprising:
Selecting a data processing sub-module based on received user input (Figure 10; Column 14, Lines 58-68; Column 15);
Retrieving a ‘relevant’ portion of the structured data set from the data store, the ‘relevant’ portion being based on the received user input and the selected data processing sub-module’ (Figure 10; Figure 12, Elements 120, 1240; Column 11, Lines 10-40); and
Inputting the relevant ‘portion’ of the structured data into the selected processing sub-module to generate supply chain optimization data (Figure 2, Elements 150, 230; Figure 4; Figure 7, Elements 720, 730; Figure 12, Elements 120, 1240)
Regarding Claim 8 Evans et al. discloses a system and method wherein the data processing sub-module includes at least ONE or more of best market optimizer OR constrained optimization OR storage allocation planning OR price elasticity optimization OR infrastructure planning OR what-if analysis OR competitor analysis OR data chat module (Figure 2, Element 230; Figure 7; Column 10, Lines 13-26; Column 28, Lines 33-50).
Allowable Subject Matter
Claims 5, 6, 13 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable, over the prior art, if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 5, 6, 13 and 14 remain rejected over 35 U.S.C. 101 and therefore and not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wedl, U.S. Patent No. 12112385, discloses a system and method for data extraction/parsing comprising a parser logic/matcher and extraction module for parsing/extracting data from known and new/unknown data types using machine learning (DETX 11, Claim 3).
Freier et al., U.S. Patent Publication No. 20230289911 discloses a system and method for supply chain planning/management including data standardization/normalization (Paragraph 102) and utilizing inference models to infer missing data (Paragraphs 58, 102).
Pathak et al, U.S. Patent Publication No. 20230289911, discloses a supply chain management system and method for determining missing ‘commodity’ data using machine learning (NN, Paragraphs 17, 106).
Mamou et al., U.S. Patent Publication No. 20050240592, discloses a supply chain management system and method comprising well-known Extract, Transform, Load (ETL) subsystems to normalize/standardize and extract data SCM data from multiple systems (Paragraphs 89, 200-202, 241; Figure 37).
Breeding-Allison et al., U.S. Patent Publication No. 20240420026 discloses a supply chain management system and method comprising machine learning, normalizing data, impute missing values, and extracting data from multiple sources.
not commodity
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SCOTT L. JARRETT
Primary Examiner
Art Unit 3625
/SCOTT L JARRETT/Primary Examiner, Art Unit 3625
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