DETAILED ACTION
This FINAL office action is in response to Applicant’s amendment filed January 6, 2026. Applicant’s January 6th amendment amended claims 1 and 11. Currently Claims 1-20 are pending. Claims 1 and 11 are the independent claims.
The instant application is a continuation of Application No, 18218967 now U.S. Patent No. 12002084. Application No, 18218967 is a continuation of Application No. 17230816 now U.S. Patent No. 11734749.
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.
Response to Arguments
Applicant's arguments filed January 6, 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 similar to the Ex parte Dejardins et al. decision (e.g. addresses technical problem of dynamic feedback/incomplete/stale training data sets in neural networks, one skilled in the art would understand that the claimed process is a technical improvement in neural network based predictive modeling – real-time replacement of stale/incomplete availability data; feedback look integrating real-time data thereby improving the functioning of the computer-implemented prediction system itself; Specification: Paragraphs 5, 46; Remarks: 13-15); and the claims integrate abstract idea into a practical application (Remarks: Paragraph 1, Page 16).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., real-time replacement of stale/incomplete data, incorporating real-time fulfillment data, dynamic feedback, predictive modeling, fresh data, feedback loop integrating real-time data; Remarks 13-15) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Examiner notes that none of the phrases real-time, realtime, feedback, dynamic feedback, incomplete data, loop, or the like appear anywhere in Applicant’s disclosure.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are similar to the recent Appeals Review Panel review of Ex parte Desjardins et al., related to U.S. Patent Application No. 16/319,040, assigned to DeepMind Technologies Limited, the examiner respectfully disagrees.
As discussed below the claims remain directed to assisting a human (shopper) to fulfill items from a warehouse location predicted to have available inventory items and is proximate to the shopper – i.e. human task assignment. Alternatively, the claims are directed to order fulfillment a well-known economic (business) practice. Additionally, the claims are directed to a mental process capable of being performed in the human mind via observation, evaluation, judgement and opinion. The non-abstract computing elements (processor, memory, computer system, network, device, shopper device, mobile application (software per se) and shopper user interface) are directed to a generic computer, recited at a high level of generality and amount to nothing more than mere instructions to implement the exception on a generic computer. The steps of training, updating, retraining, storing, and accessing neural network item availability model is similarly recited at high level of generality and amounts to no more than mere instructions to implement the abstract idea by generally applying the neural network model without limiting how the neural network model functions.
While the Desjardins decision cautions against overbroad application of 35 U.S.C. 101 to artificial intelligence inventions, such inventions not categorically excluded from patentability, the thrust of the decision made clear that improvements to an AI model itself can be sufficient for the purpose of patent eligibility, even when the claims recite, on their face, an ostensibly “abstract idea.” Specifically, the Appeals Review Panel found that the claims under review provided a technical improvement in the functioning of machine learning models by enabling continual learning, reducing storage requirements, and preserving performance across tasks. In particular, the decision emphasized that the claimed invention addresses a technical problem ("catastrophic forgetting") and improves the operation of AI systems, not just through generic computer implementation but by a specific training strategy. To support this determination, 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”.
None of Applicant’s arguments, disclosure or claims recite of disclose at any level of detail that the generically applied neural network item availability model represents or provides an improvement in machine learning itself.
Independent claims 1 and 11 recite accessing a neural network item availability model that is trained to predict whether a target item is available, updating the neural network item availability model by applying training examples, retraining the neural network item availability model with additional examples, retraining the neural network item availability model by adjusting one or more parameters, storing the retrained neural network item availability model and accessing the stored retrained neural network item availability model. While the claims recite the training/retraining and application of a neural network item availability model, the neural network item availability model is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic neural network on a generic computer, also recited at a high level of generality. The neural network is used to generally apply the abstract idea without limiting how the neural network functions. The neural network is described at a high level such that it amounts to using a generic computer with generic machine learning to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. Further nowhere in Applicant’s disclosure is there any discussion at any level that the trained/retrained/applied neural network item availability model improves the general field of machine learning or addresses a technical problem in the field of machine learning or provides an improvement to a specific machine learning model, algorithm, technique or the like.
As for the recited training, updating and retraining of the neural network item availability model - machine learning algorithms, commonly and routinely – if not inherently – work by ‘learning’ from previous iterations/applications/instances and apply/utilizes those stored learns for future applications (i.e. iteratively learning is old, well-known, common and routine; does not represent a technical improvement to the field of machine learning, does not improve the functioning of the underlying computer, processor or memory).
With regards to argued Specification Paragraph 5, discloses that the online concierge system selects a warehouse having a minimum distance to the location of an identified order thereby allowing the system to reduce the distance traveled by a shopper fulfilling the order and that the system may identify items available at a location of the warehouse that is not best able to fulfill the user order. This paragraph fails to disclose machine learning, artificial intelligence or the claimed neural network much alone disclose an improvement to the field of machine learning as argued. This paragraph, like the remainder of Applicant’s discloses, fails to disclose or discuss at any level a technical solution to a technical problem, an improvement to an underlying technology, an improvement to another technical field or the like.
With regards to argued Specification Paragraph 46, this paragraph discloses the updating of training data sets for a machine learning model (Figure 5), noting that it may be more difficult to model/predict availability of items that are not frequently ordered. Further this paragraph discloses that machine learning (at a high level) is one way to improvement the accuracy of item availability predictions and that the modeling engine can build statistically meaningful connections between machine learning factors and predicted item availability. At best this paragraph discloses a wished-for improvement in the abstract idea itself – i.e. more accurate/reliable item availability predictions – which is a well-known, routine and conventional business problem not a technical problem inherent in computers, computer networks or artificial intelligence/machine learning. A more accurate or more reliable item availability prediction does not represent a technical problem, does not improve the underlying computer/computer technology and does not improve the field of machine learning, neural networks or artificial intelligence itself. This paragraph fails to disclose a technical problem related to machine learning and fails to disclose an improvement to the field of machine learning itself as argued. This paragraph, like the remainder of Applicant’s discloses, fails to disclose or discuss at any level a technical solution to a technical problem, an improvement to an underlying technology, an improvement to another technical field or the like.
Accordingly, the claims are nothing like those in the Desjardins decision and are therefore not patent eligible under 35 U.S.C. 101.
Examiner suggests Applicant review the recently update MPEP § 2106.04(d)(1) (below) as well as the recently posted 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (2024 AI SME Update) in the Federal Register on July 17, 2024 (https://www.federalregister.gov/public-inspection/2024-15377/guidance-2024-update-on-patent-subject-matter-eligibility-including-on-artificial-intelligence ) and specifically review the three new examples 47-49 announced by the 2024 AI SME Update which provide exemplary SME analyses under 35 U.S.C. 101 of hypothetical claims related to AI inventions (https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf).
MPEP § 2106.04(d)(1)
In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field. Second, if the specification sets forth an improvement in technology or a technical field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
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.
While the claims may represent an improvement to the fundamental economic process of order fulfillment and/or human task assignment, the claims in no way either claimed or disclosed represent a practical application (e.g. claims do not provide a technical solution to a technical problem; claims do not improve any of the underlying technology (processor, memory, computer system, network, device, shopper device, mobile application (software per se) and shopper user interface). At best the claims recite an improvement in the abstract idea itself.
Under the 2019 Revised Guidance, the claims are evaluated to determine if additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure ("MPEP") §§ 2106.05(a)-(c), (e)- (h)). See 2019 Revised Guidance, 84 Fed. Reg. at 51-52, 55. A claim that integrates a judicial exception into a practical application applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. See 2019 Revised Guidance, 84 Fed. Reg. at 54.
For example, limitations that are indicative of "integration into a practical application" include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP § 2106.05(a);
Applying the judicial exception with, or by use of, a particular machine - see MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP § 2106.05(e).
In contrast, limitations that are not indicative of "integration into a practical application" include:
Adding the words "apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP § 2106.05(±);
Adding insignificant extra-solution activity to the judicial exception- see MPEP § 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
See 2019 Revised Guidance, 84 Fed. Reg. at 54-55 ("Prong Two").
In view of the 2019 Revised Guidance, 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 the generic processor, memory, computer system, network, device, shopper device, mobile application (software per se) and shopper user interface. These generic computer hardware merely performs generic computer functions of receiving, processing and providing data and represent a purely conventional implementation of applicant’s order fulfillment in the general field of business management 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 computer components. The claims do present any other issues as set forth in the 2019 Revised Guidance regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. See Revised Guidance, 84 Fed. Reg. at 55. Rather, the claims on appeal 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.
The method steps directed to the neural network item availability model (a mathematical model for prediction item availability at a warehouse; e.g. the accessing, updating, training, and retraining) are recited at a high level of generality and is merely a black box into which data is inputted and automagically predicted item availabilities are outputted. The neural network model is merely performing a mathematical operation using a generic computer. The neural network model is recited at a high level of generality and amounts to mere instructions to apply the abstract idea using a generic computer applying a generic neural network model. The neural network model is generally applied without limiting how the neural network model functions. The neural network limitations only recite outcomes without any details about how the outcomes are accomplished.
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.
Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), the claims do not integrate the judicial exception into a practical application.
Accordingly, the claims 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 and 11, the claims are directed to the abstract idea of human task assignment. 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, human task assignment (shopper) (Judicial Exception – Yes – organizing human activity). Specifically, the claims are directed to causing a shopper (human) to fulfill items from a selected/identified warehouse location that is predicted to have available inventory items (e.g. products, goods, groceries, SKUs, etc.) and is in close proximity to the shopper, wherein human task assignment (shopper) is a fundamental economic practice that falls into the abstract idea managing personal behavior or relationships or interactions between people. Further all of the steps of “receiving”, “identifying”, “selecting”, “determining”, “accessing”, “obtaining”, “updating”, “obtaining”, “retraining”, “determining”, “generating”, “selecting” “determining”, “transmitting”, “causing”, “receiving”, “generating”, “retraining”, “storing”, “accessing”, and “applying” recite functions of the human task assignment are also directed to an abstract idea that falls into the abstract idea managing personal behavior or relationships or interactions between people. The step of determining a predicted availability and generating an availability value are also directed to an abstract idea because it is a mathematical concept.
The step(s) of accessing a neural network item availability model that is trained to predict whether a target item is available at a target warehouse and retraining the neural network item availability model are also directed to an abstract idea because it is a mathematical concept (predict item availability). The recited neural network model is merely a black box into which data is inputted and automagically predicted item availabilities are outputted. The neural network item availability model is merely performing a mathematical operation using a generic computer. The neural network item availability model is recited at a high level of generality and amounts to mere instructions to apply the abstract idea using a generic computer applying a generic neural network item availability model. The neural network item availability model is generally applied without how the neural network model functions. The neural network limitations only recite outcomes without any details about how the outcomes are accomplished.
The intended purpose of independent claims 1 and 11 appears to be to transmit to a human user a set of items from a warehouse location to fulfill a request from a selected warehouse.
Accordingly, the claims recite an abstract idea – fundamental economic practice, specifically in the abstract idea managing personal behavior or relationships or interactions between people. The exceptions are user (a human) and the additional limitations of generic computer elements: processor, memory, computer system, network, device, shopper device, mobile application (software per se) and shopper user interface.
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, memory, computer system, network, and device. These generic computing components are merely used to receive and process data as described extensively in Applicant’s specification. 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 human task assignment (shopper) in the general field of order/inventory 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 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, memory, computer system, network, and device," 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 identifying a plurality of candidate warehouse locations, selecting a set of items, determining an availability score, updating the item availability model, retraining the item availability model, determining a predict availability, generating the availability score for the candidate warehouse location, selecting a warehouse location, generating a second set of training examples, applying the retrained neural network item availability 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, memory, computer system, network, device, shopper device, mobile application (software per se) and shopper user interface 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 receiving a request to specify an order, accessing a neural network availability model, obtaining a plurality of training examples, obtaining a set of additional training examples, receiving data indicating whether each item is found at the warehouse, storing the retrained neural network and accessing the retrained neural network are directed to insignificant pre-solution activity (i.e. data gathering). The steps of transmitting the predicted availability and causing the shopper to fulfill the request are 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 processor, memory, computer system, network, device, shopper device, mobile application (software per se) and shopper user interface are recited at a high level of generality merely performs generic computer functions of receiving and processing 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. 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 accessing, obtaining, and presenting steps that were considered extra-solution activity, this has been re-evaluated and determined to be well-understood, routine, conventional activity in the field. Applications 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).
The recited neural network model item availability model is generally applied to the abstract idea and is performed by a generic computer recited at a high level of generality. The recite computer/processor/system amounts to no more than mere instructions to apply the abstract idea using the generic computer. Similarly, the recite neural network item availability model (e.g. accessing a neural network item availability model, updating…., training…, retraining…., etc.) recites nothing more than mere instructions to implement the abstract idea on a generic computer using a generic neural network item availability model. The neural network item availability model is used to generally apply the abstract idea without limiting how the neural network item availability model functions. The recited steps amount to using a generic computer with a generic neural network model to apply the abstract idea. These limitations only recite outcomes without any details about how the outcomes are accomplished. The recitation of the neural network item availability model in the claims does not negate the mental nature of these limitations because the neural network model is merely used at a tool to perform an otherwise mental process.
For these reasons, these limitations remain extra-solution activity even upon reconsideration. Even when considered in combination the additional elements represent mere instructions to apply the abstract idea and insignificant extra-solution activation, which cannot provide an 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 2-10 and 12-20, the claims are directed to the abstract idea of human task assignment (shopper) and merely further limit the abstract idea claimed in independent claims 1 and 11.
Claims 2 and 12 further limits the abstract idea by determining a probability of the user purchasing different items and selecting items based on probabilities (a more detailed abstract idea remains an abstract idea). Claims 3 and 13 further limit the abstract idea by ranking items previously ordered and selecting items (a more detailed abstract idea remains an abstract idea). Claims 4 and 14 further limit the abstract idea by selecting items based on a threshold probability (a more detailed abstract idea remains an abstract idea). Claims 5 and 15 further limit the abstract idea by selecting a warehouse having a maximum availability score (a more detailed abstract idea remains an abstract idea). Claims 6 and 16 further limit the abstract idea by periodically updating the training examples (a more detailed abstract idea remains an abstract idea). Claims 7 and 17 further limit the abstract idea by determining a confidence score associated with the determined availability score (a more detailed abstract idea remains an abstract idea). Claims 8 and 18 further limit the abstract idea by determining a geographic region maintained by the system and identifying warehouse locations within the geographic region (a more detailed abstract idea remains an abstract idea). Claims 9 and 19 further limit the abstract idea by determining orders received by other users, determining a purchase rate, determining the selection location having maximum rate (a more detailed abstract idea remains an abstract idea). Claims 10 and 20 further limit the abstract idea by receiving an order query and comparing the query to the selected warehouse location (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, memory, computer system, network, and device at best merely comprise generic computer hardware which is commercially available. 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 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.
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