DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1–9 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or joint inventor regards as the invention.
The subject matter of the claims cannot be determined with reasonable certainty because several claim limitations recite vague, relative, or purely functional language without sufficient structural or algorithmic definition.
With respect to claim 1, the limitation “an optimization subsystem engine comprising a plurality of programming instructions” is indefinite because it is unclear what structural or algorithmic components constitute the “engine.” The term appears to represent a generic functional component, and the claim does not specify the architecture, modules, or algorithms that define the boundaries of this subsystem.
The limitation “generate a multi-dimensional data model representing relationships and dependencies between received and integrated data” is indefinite because the claim does not specify: the structure of the model, the type of dimensions, the representation used for the relationships. The term “multi-dimensional data model” may encompass a wide range of data structures (e.g., relational databases, graphs, matrices, tensors), and therefore the scope of the limitation is unclear.
The limitation “analyze the data model using one or more optimization algorithms” is indefinite because the claim does not identify or define the algorithms being used.
Without specifying the class or structure of the algorithms, the scope of the limitation is unclear because virtually any analytical algorithm could fall within the scope of this limitation.
The limitation “create a dynamic schedule for order preparation, pickup, and delivery” is indefinite because the claim does not define the meaning of “dynamic” in this context. It is unclear whether the schedule: updates continuously, updates periodically, updates upon receiving new data. Thus the boundaries of the claimed scheduling operation are not reasonably certain.
The limitation “identify potential inefficiencies” is indefinite because the claim does not specify: what constitutes an inefficiency, how inefficiencies are measured, what criteria are used for identifying inefficiencies. Therefore the scope of this limitation is unclear.
The limitation “transmit adaptive preparation instructions to business computing devices” is indefinite because the claim does not define: what makes the instructions “adaptive”, how the instructions adapt to the system state, what parameters are included in the instructions.
Thus, the scope of this limitation cannot be determined with reasonable certainty.
Claim 2 recites “a data integration module that processes and normalizes data.”
The terms “processes” and “normalizes” are indefinite because the claim does not define: the specific processing steps, the normalization technique, the data formats being normalized.
The scope of the module therefore cannot be reasonably determined.
Claim 3 recites “determines optimal routes considering multiple factors.”
The phrase “optimal routes” is indefinite because the claim does not define: the optimization objective, the weighting of factors, the criteria used to determine optimality. Additionally, the phrase “multiple factors” is vague because the claim does not specify which factors are required.
Claim 4 recites “a machine learning subsystem that improves system performance.”
The phrase “improves system performance” is indefinite because it does not specify: what performance metric is being improved, how improvement is measured.
Thus, the scope of the limitation is unclear.
Claim 6 recites “customizable insights and predictive analytics.” The term “insights” is indefinite because it does not have a clear technical meaning and the claim does not specify what constitutes an “insight.”
Claim 7 recites “role-specific experiences.” The term “experiences” is vague because it is unclear whether this refers to: graphical user interfaces, permissions, data views. Thus the claim does not clearly define the scope of the limitation.
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 1–9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, i.e., a judicial exception (an abstract idea), without reciting additional elements that amount to significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Step 1: Statutory Category (MPEP §2106.03)
Claims 1–9 are directed to a system, which falls within one of the four statutory categories of invention (i.e., machine, process, manufacture, or composition of matter).
Therefore, the claims are evaluated under the two-step framework described in Alice Corp. v. CLS Bank International and the USPTO 2019 Revised Patent Subject Matter Eligibility Guidance.
Step 2A – Prong One
Claim 1 recites limitations including: receiving real-time data from mobile devices associated with delivery agents, receiving routing information and order information, integrating external data sources, generating a multi-dimensional data model, analyzing the data model using optimization algorithms, generating delivery schedules, predicting resource requirements, identifying inefficiencies, transmitting preparation instructions.
These limitations describe collecting, analyzing, and using logistics data to coordinate order preparation and delivery operations.
This corresponds to managing delivery logistics and scheduling resources, which is a commercial interaction and business management activity.
Such activities fall within the category of “certain methods of organizing human activity”, which is a recognized category of abstract ideas. Accordingly, claim 1 recites an abstract idea.
Claims 2–9 depend from claim 1 and therefore also recite the same abstract idea.
Step 2A – Prong Two
The claim additionally recites the following elements: a computing system comprising processors, mobile devices associated with delivery agents, business computing devices, an optimization subsystem engine, routing module, machine learning subsystem, analytics module, user interface system, integration and extensibility layer.
These elements are described at a high level of generality and perform generic computing functions, including: receiving data, storing data, processing data, transmitting instructions.
These claims do not recite: a specific improvement to computer functionality, a specialized data processing architecture, a new networking protocol, or a technological solution to a technological problem.
Instead, these claims merely apply the abstract idea using generic computer components as tools.
Using generic computer components to implement an abstract idea does not integrate the abstract idea into a practical application.
Accordingly, the claims do not integrate the abstract idea into a practical application.
Step 2B – Inventive Concept
Because these claims are directed to an abstract idea and do not integrate the exception into a practical application, the claims must be evaluated to determine whether they include additional elements that amount to significantly more than the abstract idea.
The additional elements recited in the claims include: generic processors, mobile devices, business computing devices, data integration modules, routing modules, machine learning subsystems, analytics modules, user interfaces, integration layers.
These elements represent well-understood, routine, and conventional activities previously known in the field of computer-implemented logistics systems.
The specification describes these elements functionally and generically, indicating that they are conventional computing components performing their expected functions.
Merely applying the abstract idea using generic computer technology does not amount to significantly more than the abstract idea.
Therefore, the claims do not include an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
Claims 1–9 are directed to the abstract idea of coordinating logistics and delivery operations using data analysis and scheduling algorithms.
These claims do not integrate the abstract idea into a practical application and do not include additional elements that amount to significantly more than the abstract idea. Accordingly, claims 1–9 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–9 are rejected under 35 U.S.C. 103 as being unpatentable over Sahasrabudhe et al. (US 11,501,247, hereinafter Sahasrabudhe) in view of Fornell et al. (US 2022/0358468, hereinafter Fornell) and further in view of Xu et al. (US 2024/0428321, hereinafter Xu).
With respect to claim 1, Sahasrabudhe discloses the feature of a computing system comprising a plurality of processors configured to perform delivery routing optimization operations (see for example paragraphs [0024]–[0027] and Fig. 1).
an optimization subsystem executing instructions to process delivery data and compute optimized delivery assignments (see for example paragraphs [0034], [0038], and Fig. 3).
receiving real-time data from mobile devices associated with delivery agents, including location and operational metrics (see for example paragraphs [0042], [0045], and Fig. 4).
generating a multi-dimensional data model representing relationships between delivery agents, orders, and routing parameters (see for example paragraphs [0050], [0053] and Fig. 7).
analyzing the data model using optimization algorithms to generate dynamic schedules for pickup and delivery operations (see for example paragraphs [0056] and [0061] and Fig. 8).
predicting delivery resource requirements using machine-learning models trained on historical delivery data (see for example paragraphs [0063] and [0066]).
Sahasrabudhe does not disclose the feature of receiving dynamic routing information including delivery routes, travel conditions, and driver status information, receiving order information from business computing devices including estimated preparation times and order details, integrating external data sources such as traffic conditions and environmental data when determining optimized delivery routes, identifying inefficiencies in routing operations and dynamically adjusting delivery routes accordingly, transmitting preparation timing instructions and coordination signals to merchant computing systems to optimize order preparation and pickup timing.
However, Fornell teaches the feature of:
receiving dynamic routing information including delivery routes, travel conditions, and driver status information (see for example paragraphs [0030], [0034], and Fig. 5).
integrating external data sources such as traffic conditions and environmental data when determining optimized delivery routes (see for example paragraphs [0037], [0041], and Fig. 6).
identifying inefficiencies in routing operations and dynamically adjusting delivery routes accordingly (see for example paragraphs [0046] and [0049]), and
Xu teaches the feature of receiving order information from business computing devices including estimated preparation times and order details (see for example paragraphs [0033] and [0057]), and
transmitting preparation timing instructions and coordination signals to merchant computing systems to optimize order preparation and pickup timing (see for example paragraphs [0047] and [0057]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine the teachings of Sahasrabudhe , Fornell , and Xu . Since , one of ordinary skill in the art would have been motivated to combine these teachings in order to improve delivery efficiency by synchronizing order preparation with optimized courier routing while incorporating real-time traffic and operational data, thereby reducing delivery delays and improving logistics resource utilization. Such a combination merely involves the predictable use of known delivery optimization and routing technologies performing their established functions,
With respect to claim 2, Fornell further discloses the feature of aggregating and normalizing routing and location data received from multiple external systems and mobile devices before performing route optimization (see for example paragraphs [0035], [0037], and Fig. 4).
With respect to claim 3, Fornell further discloses the feature of a routing engine configured to determine optimized routes for multiple delivery vehicles including multi-stop route planning (see for example paragraphs [0040], [0044], and Fig. 6).
With respect to claim 4, Sahasrabudhe further discloses the feature of machine-learning models trained on historical delivery data to improve prediction of delivery times and optimize resource allocation (see for example paragraphs [0063], [0067], and Fig. 9).
With respect to claim 5, Sahasrabudhe further discloses the feature of configurable optimization parameters that allow balancing of different objectives including delivery speed, cost efficiency, and customer satisfaction (see for example paragraphs [0058] and [0060]).
With respect to claim 6, Sahasrabudhe further discloses the feature of analytics models generating predictive insights about delivery demand and system performance (see for example paragraphs [0065] and [0068]).
With respect to claim 7, Fornell further discloses the feature of user interface dashboards providing operational information to dispatch operators and delivery agents through role-specific views (see for example paragraphs [0051] and Fig. 7).
With respect to claim 8, Sahasrabudhe further discloses the feature of a high-dimensional data representation used to process delivery system parameters in real time (see for example paragraphs [0050]–[0054]).
With respect to claim 9, Fornell further discloses the feature of application programming interfaces (APIs) enabling integration with third-party logistics systems and merchant platforms (see for example paragraphs [0048], [0053], and Fig. 8).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROKIB MASUD whose telephone number is (571)270-5390. The examiner can normally be reached Mon-Fri 8:00-5:00.
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/ROKIB MASUD/Primary Examiner, Art Unit 3627
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