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 .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 1/26/2026, Applicant responded on 5/26/2026. Amended claim 1-2, 5-6, 8-9, 12-13, 15-16, and 19-20.
Claims 1-21 are pending in this application and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/26/2026 has been entered.
Response to Amendment
Applicant's amendments to claims 1-2, 5-6, 8-9, 12-13, 15-16, and 19-20 are not sufficient to overcome the 35 USC 101 set forth in the previous action.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “...Claims 1-2, 5-6, 8-9, 12-13, 15-16, and 19-20 have been amended as noted above. Claims 2-7 depend from Claim 1. Claims 9-14 depend from Claim 8. Claims 16-21 depend from Claim 15. Withdrawal of the rejection under 35 U.S. C. § 101 is therefore respectfully requested.…” The Examiner respectfully disagrees.
While Applicant’s amendments advance prosecution, the claims, are recite and direct to, …consolidating shipping package objects…, which is a problem directed to organizing human activity and a mental process, as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. The alleged solutions are solutions directed to solving abstract ideas, which are still abstract ideas. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer, machine learning. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, machine learning, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 or amount to significantly more under Step 2B. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
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 1-21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 8, 15) recite, “A … method comprising:
receiving an object request, wherein the object request is received through an …, and wherein the object request corresponds to a set of objects associated with an object distribution system and a destination for distribution of the set of objects;
processing the object request through a distribution consolidation … algorithm to generate a set of object distribution times corresponding to a set of nodes associated with the object distribution system, wherein the distribution consolidation … algorithm is trained using a dataset that includes sample object requests and sample object distribution times corresponding to sample nodes associated with different object distribution systems;
updating the … to provide the set of object distribution times, wherein when the … is updated, an object distribution time is selected from the set of object distribution times through the …;
obtaining transit data associated with the …, wherein the transit data indicates transit times from the set of nodes to an endpoint corresponding to the … and the destination;
processing the object distribution time, the transit data, and data corresponding to the set of nodes through the distribution consolidation … algorithm to identify a set of times for transiting the set of objects from the set of nodes to the endpoint for consolidated distribution of the set of objects at the destination according to the object distribution time;
generating a set of object distribution requests, wherein the set of object distribution requests includes the object request and the set of times, and wherein when the set of object distribution requests is received by the set of nodes, the set of nodes transits the set of objects to the endpoint according to the set of times to allow for consolidation of the set of objects at the endpoint for consolidated distribution at the destination according to the object distribution time;
processing new data corresponding to actual object consolidations associated with different object requests corresponding to different locations as the different object requests are received, wherein the new data is processed to generate feedback corresponding to different object distribution times generated by the distribution consolidation … algorithm in response to the different object requests; and
dynamically re-training the distribution consolidation … algorithm according to the feedback and as the feedback is generated, wherein when the distribution consolidation … algorithm is re-trained, the distribution consolidation ... algorithm generates new object distribution times according to new object requests.“
Analyzing under Step 2A, Prong 1:
The limitations regarding, …receiving an object request, wherein the object request is received through an …, and wherein the object request corresponds to a set of objects associated with an object distribution system and a destination for distribution of the set of objects; processing the object request through a distribution consolidation … algorithm to generate a set of object distribution times corresponding to a set of nodes associated with the object distribution system, wherein the distribution consolidation … algorithm is trained using a dataset that includes sample object requests and sample object distribution times corresponding to sample nodes associated with different object distribution systems; updating the … to provide the set of object distribution times, wherein when the … is updated, an object distribution time is selected from the set of object distribution times through the …; obtaining transit data associated with the …, wherein the transit data indicates transit times from the set of nodes to an endpoint corresponding to the … and the destination; processing the object distribution time, the transit data, and data corresponding to the set of nodes through the distribution consolidation … algorithm to identify a set of times for transiting the set of objects from the set of nodes to the endpoint for consolidated distribution of the set of objects at the destination according to the object distribution time; generating a set of object distribution requests, wherein the set of object distribution requests includes the object request and the set of times, and wherein when the set of object distribution requests is received by the set of nodes, the set of nodes transits the set of objects to the endpoint according to the set of times to allow for consolidation of the set of objects at the endpoint for consolidated distribution at the destination according to the object distribution time; processing new data corresponding to actual object consolidations associated with different object requests corresponding to different locations as the different object requests are received, wherein the new data is processed to generate feedback corresponding to different object distribution times generated by the distribution consolidation … algorithm in response to the different object requests; and dynamically re-training the distribution consolidation … algorithm according to the feedback and as the feedback is generated, wherein when the distribution consolidation … algorithm is re-trained, the distribution consolidation ... algorithm generates new object distribution times according to new object requests…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the identified limitations; therefore, the claims recite a mental process.
Further, …receiving an object request, wherein the object request is received through an …, and wherein the object request corresponds to a set of objects associated with an object distribution system and a destination for distribution of the set of objects; processing the object request through a distribution consolidation … algorithm to generate a set of object distribution times corresponding to a set of nodes associated with the object distribution system, wherein the distribution consolidation … algorithm is trained using a dataset that includes sample object requests and sample object distribution times corresponding to sample nodes associated with different object distribution systems; updating the … to provide the set of object distribution times, wherein when the … is updated, an object distribution time is selected from the set of object distribution times through the …; obtaining transit data associated with the …, wherein the transit data indicates transit times from the set of nodes to an endpoint corresponding to the … and the destination; processing the object distribution time, the transit data, and data corresponding to the set of nodes through the distribution consolidation … algorithm to identify a set of times for transiting the set of objects from the set of nodes to the endpoint for consolidated distribution of the set of objects at the destination according to the object distribution time; generating a set of object distribution requests, wherein the set of object distribution requests includes the object request and the set of times, and wherein when the set of object distribution requests is received by the set of nodes, the set of nodes transits the set of objects to the endpoint according to the set of times to allow for consolidation of the set of objects at the endpoint for consolidated distribution at the destination according to the object distribution time; processing new data corresponding to actual object consolidations associated with different object requests corresponding to different locations as the different object requests are received, wherein the new data is processed to generate feedback corresponding to different object distribution times generated by the distribution consolidation … algorithm in response to the different object requests; and dynamically re-training the distribution consolidation … algorithm according to the feedback and as the feedback is generated, wherein when the distribution consolidation … algorithm is re-trained, the distribution consolidation ... algorithm generates new object distribution times according to new object requests…, under the broadest reasonable interpretation, are human managing and consolidating shipping packages from different shipping distribution organizations and shipping distribution facilities, therefore it is, commercial interactions, managing personal behavior or relationships or interactions between people. Thus, the claims recite certain methods of organizing human activity.
Accordingly, the claims recite a mental process, certain methods of organizing human activity, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 8, 15: computer-implemented, interface associated with an object distribution processor, machine learning, A system, comprising: one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system, A non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system
Claim 4, 11, 18: an instance of a SaaS-based system accessed through one or more application programming interface (API) calls
Claim 6, 13, 20: machine learning
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “receiving …”, “obtaining…”, “…updating…”, “generating…”, “…processing…, “, “…generate feedback…”, “…dynamically re-training…”, “…generates new…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “receiving …”, “obtaining…”, “…processing…”, “…dynamically re-training…”, data output – “…updating…”, “generating…”, “…generate feedback…” ,“…generates new…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0028] In an embodiment, to access the object distribution optimization system 102, the object distribution system 104 calls a representational state transfer (REST) application programming interface (API) of the object distribution optimization system 102. In this REST API call, the object distribution system 104 may provide a set of credentials that may be used to authenticate the object distribution system 104. For example, in the REST API call, the object distribution system 104 may provide its username and corresponding password to the object distribution optimization system 102. The object distribution optimization system 102 may evaluate the provided credentials to determine whether the credentials are valid. If so, the object distribution optimization system 102 may provide the object distribution system 104 with an access token that may be used by the object distribution system 104 to access the object distribution optimization system 102 without providing its credentials for a period of time (e.g., until the access token expires).
[0037] The machine learning algorithm or artificial intelligence implemented by the object distribution optimization system 102 may be dynamically trained in real-time using supervised training techniques. For instance, a dataset of input object requests (e.g., prior object requests associated with different recipients, hypothetical object requests generated for training of the machine learning algorithm or artificial intelligence, etc.) and known object distribution times for different distribution system nodes associated with the input object requests (e.g., prior object distribution times for actual distribution system nodes associated with prior object requests, hypothetical object distribution times for different distribution system nodes associated with hypothetical object requests, etc.) can be selected for training of the machine learning algorithm or artificial intelligence. Additionally, the dataset may include any applicable parameters and/or performance characteristics associated with the different distribution system nodes that may be used for object distributions. These parameters and/or performance characteristics, along with the known object distribution times, may be used to define a ground truth for dynamic evaluation of the machine learning algorithm or artificial intelligence to ensure that the machine learning algorithm or artificial intelligence is generating accurate object distribution times for any identified distribution system nodes associated with different object distribution requests.
[0054] The object distribution system 208-2 may provide the object request and object distribution requests for a particular recipient 210 to an object distribution orchestration system 204 of the object distribution optimization system 202. The object distribution orchestration system 204, in an embodiment, is a SaaS-based system that can be accessed by an object distribution system via authorized API calls. The object distribution orchestration system 204 may be configured to identify one or more object distribution times for each distribution system node associated with an object distribution system 208-2 based on transit data associated with an object distribution processor 206, and a defined set of parameters that each object distribution location needs; as well as any times corresponding to pending object distributions scheduled for the recipient 210.
[0152] The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the methods of some examples. The required structure for a variety of these systems will appear from the description below. In addition, the techniques are not described with reference to any particular programming language, and various examples may thus be implemented using a variety of programming languages.
[0153] In various implementations, the system operates as a standalone device or may be connected (e.g., networked) to other systems. In a networked deployment, the system may operate in the capacity of a server or a client system in a client-server network environment, or as a peer system in a peer-to-peer (or distributed) network environment.
[0154] The system may be a server computer, a client computer, a personal computer (PC), a tablet PC, a laptop computer, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, an iPhone, a Blackberry, a processor, a telephone, a web appliance, a network router, switch or bridge, or any system capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that system.
[0172] Some portions of this description describe examples in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.
[0173] Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some examples, a software module is implemented with a computer program object comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
[0174] Examples may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-21 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
