DETAILED ACTION
This FINAL office action is in response to Applicant’s request for continued examination and amendment filed February 27, 2026. Applicant’s February 27th amendment amended claims 1, 4, 8, 15, 20. Claims 1-20 are pending. Claims 1, 8 and 15 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 .
Response to Amendment
The 35 U.S.C. 101 rejection of claims 1-20 in the previous office action is maintained.
The 35 U.S.C. 112a rejection of claim 4 in the previous office action is withdrawn in response to Applicant's amendments to the claim.
Response to Arguments
Applicant's arguments filed February 27, 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues that the claims are patent eligible under 35 U.S.C. 101 as the claims are not directed to/do not constitute an abstract idea (Remarks: Paragraphs 3-4, Page 13) and the claims integrate the abstract idea into a practical application (e.g. improving surplus distribution model; claims/invention directed to improved/optimized surplus distribution model; optimizes surplus distribution for the greatest overall benefit of the recipients; optimization of machine learning to more effectively directed surplus products; Specification: Paragraphs 12, 16; Remarks: Last Paragraph Page 13; Page 14; Paragraph 1, Page 15)
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are not directed to/do not constitute an abstract idea, the examiner respectfully disagrees.
The claims are directed to the fundamental economic practice of product distribution planning. Product distribution planning, including product distribution planning for surplus products, is directed to methods of organizing human activity as it is a fundamental economic practice in the subcategories of sales activities and/or commercial interactions.
More specifically the claims output a recommend for the distribution of a second quantity of surplus products based on the demand for a first quantity of surplus products and subsequently provide control instructions to an automated handling system (Representative Claim 1: “…outputting by the processor ant at least one retrained distribution model, second data indicating a second recommendation for distribution of a second quantity of surplus products to the organization based on the demand for the first quantity of surplus products at the organization…providing by the processor control instructions to an automated handling system to prepare the second quantity of surplus products for shipment”).
With respect to the method step directed to outputting a recommending a distribution of second quantity of surplus product is directed to insignificant extra/post solution activity, mere data output, wherein the claim fails to recite who or what the recommendation is outputted to. Further, the claims do not recite whether or not the recommendation is received, viewed or implemented.
With respect to the method step directed to providing control instructions to an automated handling system is similarly directed to insignificant post/extra solution activity, mere data output, wherein the claim fails to recite an automated handling system actually receiving and/or executed the provided instructions. That the intended use of the provided instructions is for preparing the second quantity of surplus products for shipment recites non-functional descriptive material, an intended use of the data provided, and has not been given patentable weight. More specifically providing control instructions to an automated handling system to prepare the second quantity of surplus products for shipment is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea (i.e. distribution planning) using a generic computer, generic ‘automated handling system’ (only disclosed in specification Paragraph 61). The recited ‘automated handling system’ is used for their well-known, conventional, routine and widely prevalent purpose. See MPEP 2106.05(d). The ‘‘automated handling system’ is both disclosed and claimed at a high level such that it amounts to using a generic computer/processor to apply the abstract idea using generic ‘‘automated handling system’. The limitation only recites outcomes/results without any details as to how the outcomes are accomplished. See MPEP 2106.05(f)(1).
Additionally, the claims are directed to a mental process practically capable of being performed in the human mind via observation, evaluation, judgement and opinion. Representative claim 1: The step of training at least one surplus distribution model may be performed in the human mind using judgement and evaluation. The step of processing one or more demand signals to optimize the at least one surplus distrbution model may be performed in the human mind by evaluation and judgement. The step of processing satellite imagery in real-time for prioritizing recipient organizations based on the satellite imagery may be performed in the human mind using judgement and opinion. The step of processing the at least one surplus distribution model may be performed in the human mind using evaluation. The step of outputting the first data indicating the first recommendation is directed to insignificant extra-solution activity (i.e. data output), further a human via pen and paper is practically capable of outputting a recommendation for distribution of a first quantity of surplus product. The step of receiving data indicating the first quantity of products were distributed is directed to insignificant extra solution activity (i.e. data gathering) and may be performed in the human mind using observation of data. The step of outputting the second data indicating the second recommendation is directed to insignificant extra-solution activity (i.e. data output), further a human via pen and paper is practically capable of outputting a recommendation for distribution of a second quantity of surplus product. The step of providing control instructions to an automated handling system is directed to insignificant extra-solution activity, further a human is more than capable of providing control instructions to an automated handling system.
Other than the recitation of an organization (a business, a human entity) and the generic a processor, system, computer readable storage medium having program code, data processing system 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. The limitations directed to a generic computer including a processor, system, computer readable storage medium having program code, data processing system are each recited at a high level of generality and amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Further the mere nominal recitation of a generic computer (i.e., processor, system, computer readable storage medium having program code, data processing system (each used for their well-understood, conventional and routine purpose) does not take the claim limitation out of the mental processes grouping. The claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of distribution planning. In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 612 (Fed. Cir. 2016). The recited technology (processor, storage medium, etc.), are used as a “conduit for the abstract idea,” not to provide a technological solution to a specific technological problem. Id.; see also id. at 611–13 (holding claims reciting the use of a cellular telephone and a network server to classify an image and store the image based on its classification to be abstract because the patent did “not describe a new telephone, a new server, or a new physical combination of the two” and did not address “how to combine a camera with a cellular telephone, how to transmit images via a cellular network, or even how to append classification information to that data”).
As for the steps directed to training a surplus distribution model using machine learning, modifying satellite imagery using the machine learning and retraining a surplus distribution model using machine learning to output a recommended quantity of surplus products to distribute are recited as being performed are each recited at a high level of generality and are performed by a generic computer/data processing system. The computer is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic computer. The recited training a surplus distribution model using machine learning, modifying satellite imagery using the machine learning and retraining a surplus distribution model using machine learning steps provide nothing more than mere instructions to implement an abstract idea on a generic computer. The machine learning is used to generally apply the abstract idea without limiting how the machine learning, trained or retrained machine learning functions. The machine learning, trained or retrained machine learning are recited described at a high level such that it amounts to using a computer with a generic machine learning to apply the abstract idea. The limitations only recite outcomes without any details as to how the outcomes are accomplished (i.e. black box). The recitation of the machine learning, trained or retrained machine learning in this claim does not negate the mental nature of these limitations because the machine learning, trained or retrained machine learning are merely used at a tool to perform an otherwise mental process.
As for the recited data cleansing step utilizing a extract, transform and load (ETL) data integrator (software per se), the ETL data integrator is recited at a high level of generality and at best, as disclosed, is directed/utilizes well-known, well-understood, conventional and routine techniques to extract, transform and load data utilizing a generic computer/processor. As such that the data is ‘cleansed’ using the ETL data integrator amounts to no more than mere instructions to apply the abstract idea using a generic ETL data integrator on a generic computer. The recitation of the ETL data integrator in this claim does not negate the mental nature of these limitations because the ETL data integrator is merely used at a tool to perform an otherwise mental process (e.g. a person is readily capable of ‘cleansing’ data by preventing extraneous data from medical journals from being utilized). The recited ETL data integrator is at best the equivalent of merely adding the words apply it to the judicial exception.
Nothing in Applicant’s disclosures suggests that the Applicant intended to accomplish any of the steps recited in the claims through anything other than well understood technology used in a routine and conventional manner. Therefore, the claims lack an inventive concept. See also, e.g., Elec. Power Grp., 830 F.3d at 1355 (holding claims lacked inventive concept where “[n]othing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information”); Content Extraction, 776 F.3d at 1348 (holding claims lacked an inventive concept where the claims recited the use of “existing scanning and processing technology”).
Reevaluating the steps of outputting the first data indicating the first recommendation, receiving data indicating the first quantity of surplus product were distribution, and outputting second data indicating a second recommendation which are considered insignificant extra solution activity, these limitations are mere data gathering and data output recited at a high level of generality and amount to nothing more than receiving and/or outputting data which are both well-understood, routine and conventional activities. The limitations remain insignificant extra solution activity even upon reconsideration. Even when considered in combination the additional elements represent mere instructions to apply an exception and insignificant extra solution activity which cannot provide an inventive concept.
For the reasons outlined above, the claims recite a method of organizing human activity and/or a mental process, i.e., an abstract idea, and that the additional element recited in the claim beyond the abstract idea (e.g. processor, computer program product, computer readable storage medium having program code stored, machine learning, ETL data integrator) is no more than generic technological components used as a tool to perform the recited abstract idea. As such, it does not integrate the abstract idea into a practical application. See Alice Corp., 573 U.S. at 223-24 (“[Wholly generic computer implementation is not generally the sort of ‘additional featur[e]’ that provides any ‘practical assurance that the process is more than a drafting effort designed to monopolize the [abstract idea] itself.’” (quoting Mayo, 566 U.S. at 77)).
With regards to Applicant’s argument that the method steps directed to modifying satellite imagery in real-time…modifying a weigh metric…causing prioritization of recipient organizations based on the satellite imagery is not directed to an abstract idea (fundamental economic practice or mental process), the examiner respectfully disagrees. As the claims make clear the purpose of the modification of satellite imagery is to prioritize which organizations based on weighting (prioritizing) organizations to account for a natural disaster coinciding with relocation of people and demand for medicine. This is clearly a business solution to a business problem – which organizations, during a disaster, should receive surplus products (medicine) first. Prioritizing limited resources, like medicines in disaster areas, is a well-known, common, and routine economic practice. Prioritizing the distribution of emergency supplies, medicines or the like during a natural disaster occurred well-prior to the advent of computers or computer technologies. Further, a human is more than capable of reviewing satellite imagery in real time and weighting/prioritizing recipient organizations during a natural disaster. Prioritizing recipient organizations does not improve the functioning of the underlying computer/processor, does not represent an improvement to another technical field or technology (i.e. distribution planning, supply distribution during a natural disaster or the like are not technical fields). The prioritization of recipients does not represent an improvement in machine learning itself. At best, prioritizing recipient organizations using machine learning, represents an improvement in the abstract itself.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims integrate the abstract idea into a practical application, the examiner respectfully disagrees.
As discussed above claims are directed to the fundamental economic practice of product distribution planning – specifically recommending how to distribute surplus products as well as being directed to a mental process practically capable of being performed in the human mind via observation, evaluation, judgement and opinion.
Having determined under step one of the Mayo/Alice framework that the claims are directed to an abstract idea, we next consider under Step 2B of the Guidance, the second step of the Mayo/Alice framework, whether the claims include additional elements or a combination of elements that provides an “inventive concept,” i.e., whether an additional element or combination of elements adds specific limitations beyond the judicial exception that are not “well-understood, routine, conventional activity” in the field (which is indicative that an inventive concept is present) or simply appends well-understood, routine, conventional activities previously known to the industry to the judicial exception.
Under step two of the Mayo/Alice framework, the elements of each claim are considered both individually and “as an ordered combination” to determine whether the additional elements, i.e., the elements other than the abstract idea itself, “transform the nature of the claim” into a patent-eligible application. Alice Corp., 573 U.S. at 217 (citation omitted); see Mayo, 566 U.S. at 72-73 (requiring that “a process that focuses upon the use of a natural law also contain other elements or a combination of elements, sometimes referred to as an ‘inventive concept,’ sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the natural law itself’ (emphasis added) (citation omitted)).
Here the only additional element recited in the claims beyond the abstract idea are the organization (which is a person, business, human entity) and the generic processor, computer program product, computer readable storage medium having program code stored, machine learning, automated handling system i.e., generic technological components. See Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Applicant has not identified any additional elements recited in the claim that, individually or in combination, provides significantly more than the abstract idea.
The claims do not integrate the abstract idea into a practical application.
At best the claims represent an improvement to a well-known business problem of product distribution planning (e.g. increase the effectiveness in surplus product distribution as argued by Applicant) by outputting first/second recommendation for the distribution of first/second quantity of surplus products to an organization. The invention neither as claimed nor disclosed integrates the abstract idea into a practical application as the claimed/disclosed invention does not provide a technical solution to a technical problem, does not improve any of the underlying technology (e.g. processor, computer program product, computer readable storage medium having program code stored, machine learning, ETL) or another technological field. There is a fundamental difference between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other — a distinction that the Federal Circuit applied in Enfish, in rejecting a § 101 challenge at the first stage of the Mayo/Alice framework because the claims at issue focused on a specific type of data structure, i.e., a self-referential table, designed to improve the way a computer stores and retrieves data in memory, and not merely on asserted advances in uses to which existing computer capabilities could be put. See Enfish, 822 F.3d at 1335-36. Here the claims simply use a computer as a tool and nothing more.
With regards to the machine learning steps (training a surplus distribution model using machine learning, modifying satellite imagery in real time using machine learning and retraining a surplus distribution model using machine learning to output a recommended quantity of surplus products to distribute the training), these steps are being performed by a generic computer/data processing system wherein the computer is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic computer. The recited machine learning is used to generally apply the abstract idea without limiting how the trained/retrained machine learning functions. The machine learning is described at a high level such that it amounts to using a computer with a generic machine learning to apply the abstract idea. The limitations only recite outcomes without any details as to how the outcomes are accomplished. The recitation of the machine learning in this claim does not negate the mental nature of these limitations because the machine learning is merely used at a tool to perform an otherwise mental process. The recited processor/computer program product are at best the equivalent of merely adding the words apply it to the judicial exception.
With regards to the extract, transform and load (ETL) data integrator (software per se), the ETL data integrator is recited at a high level of generality and at best, as disclosed, is directed/utilizes well-known, well-understood, conventional and routine techniques to extract, transform and load data utilizing a generic computer/processor. As such that the data is ‘cleansed’ using the ETL data integrator amounts to no more than mere instructions to apply the abstract idea using a generic ETL data integrator on a generic computer. The recitation of the ETL data integrator in this claim does not negate the mental nature of these limitations because the ETL data integrator is merely used at a tool to perform an otherwise mental process (e.g. a person is readily capable of ‘cleansing’ data by preventing extraneous data from medical journals from being utilized). The recited ETL data integrator is at best the equivalent of merely adding the words apply it to the judicial exception.
In view of MPEP 2106 one must consider whether there are additional elements set forth in the claims that integrate the judicial exception into a practical application. The identified additional non-abstract elements recited in the independent claims are organization (which is a person, business) and the generic computing elements including the processor, computer program product, computer readable storage medium having program code stored, machine learning, automated handling system. This generic computer hardware merely performs generic computer functions of processing and outputting data and represent a purely conventional implementation of applicant’s surplus distribution planning in the general field of distribution planning and do not represent significantly more than the abstract idea. See at least MPEP § 2106.05(a) ("Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field").
These recited additional elements are merely generic technological components. The claims do present any other issues as set forth in MPEP 2106 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.
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. 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.
Applicant has not identified any additional elements recited in the claim that, individually or in combination, provides significantly more than the abstract idea.
Further it is noted that there is a fundamental difference between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other — a distinction that the Federal Circuit applied in Enfish, in rejecting a § 101 challenge at the first stage of the Mayo/Alice framework because the claims at issue focused on a specific type of data structure, i.e., a self-referential table, designed to improve the way a computer stores and retrieves data in memory, and not merely on asserted advances in uses to which existing computer capabilities could be put. See Enfish, 822 F.3d at 1335-36. Here the claims simply use a computer as a tool and nothing more.
For the reasons outlined above, the claims recite both a mental process and a method of organizing human activity, i.e., an abstract idea, and that the additional element recited in the claim beyond the abstract idea (e.g. process, computer program product, etc.) is no more than a generic computer component used as a tool to perform the recited abstract idea. As such, it does not integrate the abstract idea into a practical application. See Alice Corp., 573 U.S. at 223-24 (“[Wholly generic computer implementation is not generally the sort of ‘additional featur[e]’ that provides any ‘practical assurance that the process is more than a drafting effort designed to monopolize the [abstract idea] itself.’” (quoting Mayo, 566 U.S. at 77)).
Examiner agrees that the invention as disclosed may result in improvements to a surplus distribution model, improve/optimize surplus distribution modeling, and/or optimize surplus distribution for the greatest overall benefit of the recipients, as argued, however these improvements are at best improvements in the abstract idea itself (i.e. improvements to the well-known business process of distribution planning). These argued improvements do not represent a technical solution to a technical problem. These argued improvements do not represent an improvement to any of the underlying technological elements nor do they represent an improvement in another technical field (e.g. distribution planning is not a technical field).
With regards to Applicant’s argument that the claims optimize of machine learning to more effectively directed surplus products, the examiner respectfully disagrees. Initially it is noted that the claims only recite training at least one surplus distribution model by using machine learning implemented using a processor, optimizing the at least one surplus distribution model by processing one or more demand signals and modifying a weighting metric by processing satellite imagery in real time using machine learning – the claims do not recite optimization of machine learning as argued. At best the claims, as discussed above, optimize the distribution of surplus products (a business solution to a business problem) and utilize machine learning recited at a high level of generality performed via a generic computer as a tool/conduit for the abstract idea. Applicant’s disclosure does not disclose nor do the claims recite an improvement in machine learning/artificial intelligence itself.
Regarding argued Specification Paragraph 12, this paragraph discloses the optimization of the surplus distribution model and the application of machine learning artificial intelligence models (“…the present arrangements improve machine learning applicable to optimizing delivery of surplus products to those in need of surplus products”). This paragraph does not disclose at any level of detail an improvement in machine learning artificial intelligence models itself nor provide an improvement to any of the underlying technology or another technical field (i.e. surplus distribution planning is not a technology or technical field – it is a fundamental economic practice).
Similarly, argued Specification Paragraph 16, discloses that the system uses AI and machine learning to optimize distribution of the surplus products to have greatest beneficial impact, i.e. to optimize the business/economic process of distribution planning for surplus products. This paragraph does not disclose at any level of detail an improvement in machine learning artificial intelligence models itself nor provide an improvement to any of the underlying technology or another technical field.
As made clear in both MPEP 2106.04 and the recent Desjardins decision in order for an invention involving machine learning/artificial intelligence the claims and disclosure must recite/claim improvements to an AI model itself. Specifically, the guidance emphasized that eligible invention addresses a technical problem and improves the operation of AI systems, not just through generic computer implementation. Further the Appeals Review Panel looked to the specification which, on its own, disclosed how the invention would improve functioning of an AI model--in particular, the specification explained how the proposed invention would use less “storage capacity” and lead to “reduced system complexity." These improvements, which the Appeals Review Panel found were incorporated into the claims as a whole, constituted an “improvement to how the machine learning model itself operates”. Applicant’s disclosure fails to provide any discussion at any level that the claimed.
Accordingly, the claims do not integrate the abstract idea into a practical application and are not patent eligible under 35 U.S.C. 101.
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, 8 and 15, the claims are directed to the abstract idea of product distribution planning (Title: “Distribution of Surplus Products….”). 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, product distribution planning (Judicial Exception – Yes – organizing human activity). Specifically, the claims are directed to output first/second recommendation for the distribution of first/second quantity of surplus products to an organization, wherein product distribution planning is a fundamental economic practice that falls into the abstract idea subcategories of sales activities and/or commercial interactions. Further all of the steps of “training”, “processing”, “modifying”, “processing”, “receiving”, “retraining”, “outputting” and “providing” recite functions of the product distribution 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, 8 and 15 appears to be output ‘recommendations’ for distributing surplus products to an organization, wherein the outputted data may or may not be viewed or acted upon.
As for the steps directed to outputting a recommending a distribution of second quantity of surplus product is directed to insignificant extra/post solution activity, mere data output, wherein the claim fails to recite who or what the recommendation is outputted to. Further, the claims do not recite whether or not the recommendation is received, viewed or implemented.
As for the steps directed to providing control instructions to an automated handling system is similarly directed to insignificant post/extra solution activity, mere data output, wherein the claim fails to recite an automated handling system actually receiving and/or executed the provided instructions. That the intended use of the provided instructions is for preparing the second quantity of surplus products for shipment recites non-functional descriptive material, an intended use of the data provided, and has not been given patentable weight. The recited providing control instructions to an automated handling system to prepare the second quantity of surplus products for shipment is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea (i.e. distribution planning) using a generic computer, generic ‘automated handling system’ (only disclosed in specification Paragraph 61). The recited ‘automated handling system’ is used for their well-known, conventional, routine and widely prevalent purpose. See MPEP 2106.05(d). The ‘‘automated handling system’ is both disclosed and claimed at a high level such that it amounts to using a generic computer/processor to apply the abstract idea using generic ‘‘automated handling system’. The limitation only recites outcomes/results without any details as to how the outcomes are accomplished. See MPEP 2106.05(f)(1).
As for the steps directed to training a surplus distribution model using machine learning, modifying by processing satellite imagery in real time using machine learning and retraining a surplus distribution model using machine learning to output a recommended first/second quantity of surplus products to distribute to first/second organization the training, modifying and retraining are performed by a generic computer/data processing system. Further the steps directed to training and retraining using machine learning at least one surplus distribution model are directed to a mathematical concept/operation wherein the recited training/retraining covers the performance of the mathematical calculations/operations (i.e. first/second quantity of surplus products to distribute), this limitation falls within the mathematical groupings of abstract ideas. The computer is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic computer. The recited training, modifying and retraining by machine learning implemented using a processor (representative claim 1) provide nothing more than mere instructions to implement an abstract idea on a generic computer. The machine learning is used to generally apply the abstract idea without limiting how the trained machine learning functions. The machine learning is recited described at a high level such that it amounts to using a computer with a generic machine learning to apply the abstract idea. The limitations only recite outcomes without any details as to how the outcomes are accomplished.
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 additional limitations of generic computer elements: processor, system, computer program product, computer readable storage mediums having program code stored, ETL data integrator (software per se), automated handling system as well as the ‘machine learning’ (algorithm/model).
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, the previously identified non-abstract elements directed to generic computing components include: processor, system, computer program product, computer readable storage mediums having program code stored, ETL data integrator (software per se), automated handling system as well as the ‘machine learning’ (algorithm/model). These generic computing components are merely used to obtain/receive and process information as described extensively in Applicant’s 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 product distribution planning in the general field of business management 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 MPEP 2106 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 processor, system, computer program product, computer readable storage mediums having program code stored, ETL data integrator (software per se), automated handling system as well as the ‘machine learning’ (algorithm/model).," 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, 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.
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 training by machine learning…at least one surplus distribution model, processing one or more demand signals, processing one or more demand signals, modifying by processing satellite imagery in real time using the machine learning, processing using the at least one surplus distribution model the current data and retraining the at least one surplus distribution model 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 processor, system, computer program product, computer readable storage mediums having program code stored, ETL data integrator (software per se), automated handling system as well as the ‘machine learning’ (algorithm/model) 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 steps of outputting second data indicating a second recommendation for distribution of a second quantity of surplus products and providing control instructions to an automated handling system are directed to insignificant post-solution activity (i.e. data output – see discussion above). The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping.
As for the steps directed to training a surplus distribution model using machine learning. Modifying by processing satellite imagery and retraining a surplus distribution model using machine learning to output a recommended first/second quantity of surplus products to distribute to first/second organization the training and retraining are performed by a generic computer/data processing system. Further the steps directed to training/retraining using machine learning at least one surplus distribution model are directed to a mathematical concept/operation wherein the recited training/retraining covers the performance of the mathematical calculations/operations (i.e. first/second quantity of surplus products to distribute), this limitation falls within the mathematical groupings of abstract ideas. The computer is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic computer. The recited training/retraining by machine learning and modifying satellite imagery in real time using machine learning are implemented using a processor (representative claim 1) provide nothing more than mere instructions to implement an abstract idea on a generic computer. The machine learning is used to generally apply the abstract idea without limiting how the machine learning, trained machine learning or retrained machine learning function. The machine learning is recited described at a high level such that it amounts to using a computer with a generic machine learning to apply the abstract idea. The limitations only recite outcomes without any details as to how the outcomes are accomplished. The recitation of the machine learning in this claim does not negate the mental nature of these limitations because the machine learning is merely used at a tool to perform an otherwise mental process.
With regards to the recited data cleansing step utilizing an extract, transform and load (ETL) data integrator (software per se), the ETL data integrator is recited at a high level of generality and at best, as disclosed, is directed/utilizes well-known, well-understood, conventional and routine techniques to extract, transform and load data utilizing a generic computer/processor. As such that the data is ‘cleansed’ using the ETL data integrator amounts to no more than mere instructions to apply the abstract idea using a generic ETL data integrator on a generic computer. The recitation of the ETL data integrator in this claim does not negate the mental nature of these limitations because the ETL data integrator is merely used at a tool to perform an otherwise mental process (e.g. a person is readily capable of ‘cleansing’ data by preventing extraneous data from medical journals from being utilized). The recited ETL data integrator is at best the equivalent of merely adding the words apply it to the judicial exception.
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 processor, system, computer program product, computer readable storage mediums having program code stored, ETL data integrator (software per se), automated handling system as well as the ‘machine learning’ (algorithm/model) are recited at a high level of generality merely performs generic computer functions of processing and outputting data. The generic processor 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. 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. As explained above the computer and the machine learning are at best equivalent to merely adding the words apply it to the abstract idea. For the receiving and outputting steps that were considered extra-solution activity, this has been re-evaluated and determined to be well-understood, routine, conventional activity in the field. The limitations remain insignificant extra-solution activity even upon reconsideration and even when considered in combination. The additional elements (machine learning, processor, etc.) represent mere instructions to apply the abstract idea and insignificant extract solution activity (outputting), which cannot provide an inventive concept.
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 over a network 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 2-7, 9-14 and 16-20, the claims are directed to the abstract idea of product distribution planning and merely further limit the abstract idea claimed in independent claims 1, 8 and 15.
Claims 2, 9 and 16 further limits the abstract idea by limiting the current data to a medical deficiency and the recommendation to distribute surplus products that alleviate the medical deficiency (a more detailed abstract idea remains an abstract idea). Claims 3, 10 and 17 further limit the abstract idea by limiting the first recommendation to a recommendation to distribute foods or vitamins to alleviate the deficiency (a more detailed abstract idea remains an abstract idea). Claim 4 further limits the abstract idea by limiting the retrained surplus distribution model to autoregressive distributed lag OR autoregressive integrated moving average model (a more detailed abstract idea remains an abstract idea, mathematical concept/operation). Claims 5 and 12 further limits the abstract idea by outputting recipe recommendations (a more detailed abstract idea remains an abstract idea). Claim 6 and 13 further limit the abstract idea by limiting the recipes to include substitutes not available as a surplus product (a more detailed abstract idea remains an abstract idea). Claims 7 and 14 further limit the abstract idea by generating an immutable record using blockchain (a more detailed abstract idea remains an abstract idea). Claims 11 and 19 further limit the abstract idea by outputting third data comprising a recipe for a meal to be made from the delivered surplus products (insignificant post solution activity, a more detailed abstract idea remains an abstract idea). Claim 20 further limits the abstract idea by ‘directing’ the medicine to a recipient (as disclosed in at least specification Paragraphs 61, 71 – directing is placing an order with a retail or distribution system and/or notifying individuals surplus products are available, i.e. insignificant post solution activity, 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 processor, system, computer program product, computer readable storage mediums having program code stored, ETL data integrator (software per se), automated handling system at best merely comprise generic computer hardware which is commercially available (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 refer 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. storage medium, 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 storing, accessing, 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 system, processor, interface, component and memory 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 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 memory, processor, interface or similar generic computer structures which at best recite generic, well-known hardware. However, the recited generic hardware simply performs generic computer function of storing, accessing, 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT L JARRETT whose telephone number is (571)272-7033. The examiner can normally be reached M-TH 6am-4:30PM.
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SCOTT L. JARRETT
Primary Examiner
Art Unit 3625
/SCOTT L JARRETT/Primary Examiner, Art Unit 3625