DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 2/9/2026.
Claims 1, 13, and 19 have been amended.
Claims 1-20 are currently pending and have been examined.
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 Amendments
The previously pending 35 USC 103 rejections have been withdrawn in response to Applicant’s claim amendments. Please see below for reasoning.
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are not directed toward an abstract idea. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that the claims are not recited as being performed in the human mind, but rather Organizing Human Activity. The Examiner asserts that throwing in the words “machine learning” does not make the claims eligible because the machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant cites other pieces of case law regarding 101, but does not tie them in with the claims or show how they are related. Applicant’s arguments are not persuasive.
Applicant argues the claims are related to Example 40. The Examiner respectfully disagrees. The claims have nothing to do with networking protocols or flow of data. The claims are analyzing sourcing percentages to make allocation decisions, which is abstract. Applicant does not properly identify the additional elements. Applicant’s arguments are not persuasive.
Regarding the August 4th memo and the Desjardins the Examiner asserts the claimed limitations have been analyzed independently and as a whole (See rejection below). The Examiner again asserts that throwing in the words “machine learning” does not make the claims eligible because the machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s claims recite nothing more than a general purpose computer for implementing the abstract idea. Applicant’s arguments are not persuasive.
Regarding 2B the Examiner asserts Applicant’s claims recite nothing more than a general purpose computer for implementing the abstract idea (See MPEP 2106.05). Applicant does not properly identify the additional elements. Applicant’s arguments are not persuasive.
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 non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 19-20 are directed toward a process and claims 1-18 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a system for generating a business planning recommendation for a project, the system comprising: a processor; a memory; a location analysis module; a sourcing analysis module; a business recommendation module, wherein the memory, the location analysis module, the sourcing analysis module, and the business recommendation module are communicatively coupled to the processor; and one or more machine-readable instructions stored in the memory that cause the system to perform at least the following when executed by the processor: generating, by a location analysis machine learning model of the location analysis module, weighted location metrics based on location metrics received at the location analysis module, wherein the weighted location metrics are dynamically adjustable by the location analysis machine learning model in real-time; generating, by the location analysis machine learning model of the location analysis module, a location allocation decision based on the weighted location metrics, the location allocation decision based on an allocation of a percentage of the project to house at each location of at least two locations of an enterprise, such that a first location housing percentage of the allocation of the percentage of the project is housed at a first location of the at least two locations of the enterprise and a second location housing percentage of the allocation of the percentage of the project is housed at a second location of the at least two locations of the enterprise; generating, by a sourcing analysis machine learning model of the sourcing analysis module, weighted sourcing metrics based on sourcing input parameters received at the sourcing analysis module, wherein the weighted sourcing metrics are dynamically adjustable by the sourcing analysis machine learning model in real-time; generating, by the sourcing analysis machine learning model of the sourcing analysis module, a sourcing allocation decision based on the weighted sourcing metrics, the sourcing allocation decision based on an allocation of a percentage of the project to source internally within the enterprise and externally outside the enterprise, such that an internal sourcing percentage of the allocation of the percentage of the project to source internally is associated with internally sourcing within the enterprise and an external sourcing percentage of the allocation of the percentage of the project to source externally is associated with externally sourcing outside the enterprise; and generating, by a business recommendation machine learning model of the business recommendation module, a business plan recommendation comprising a respective combined location and sourcing allocation decision for each location of the first location and the second location of the at least two locations based on a combination of the location allocation decision between the first location and the second location and the sourcing allocation decision overall across the at least two locations such that (i) the combined location and sourcing allocation decision for the first location includes the first location housing percentage of the location allocation decision, at least a first portion of the internal sourcing percentage of the sourcing allocation decision, and at least a first portion of the external sourcing percentage of the sourcing allocation decision, and (ii) the combined location and sourcing allocation decision for the second location includes the second location housing percentage of the location allocation decision, at least a second portion of the internal sourcing percentage of the sourcing allocation decision, and at least a second portion of the external sourcing percentage of the sourcing allocation decision (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing weighted location based metrics to allocate percentages at each location for sourcing purposes and recommending a business plan to execute the allocation and sourcing, which is a commercial interaction.
Dependent claims 2-12, 14-18, and 20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a system, the system comprising: a processor; a memory; a location analysis module; a sourcing analysis module; a business recommendation module, wherein the memory, the location analysis module, the sourcing analysis module, and the business recommendation module are communicatively coupled to the processor; and one or more machine-readable instructions stored in the memory that cause the system to perform at least the following when executed by the processor: by a location analysis machine learning model of the location analysis module, by the location analysis machine learning model of the location analysis module, by a sourcing analysis machine learning model of the sourcing analysis module, by the sourcing analysis machine learning model of the sourcing analysis module, by a business recommendation machine learning model of the business recommendation module (claims 1 and 13)”; “by a location analysis machine learning model of a location analysis module, by the location analysis machine learning model of the location analysis module, by a sourcing analysis machine learning model of a sourcing analysis module, by the sourcing analysis machine learning model of the sourcing analysis module, by a business recommendation machine learning model of a business recommendation module (claim 19)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-12, 14-18, and 20 further narrow the abstract idea and dependent claims 7 and 16 additionally recite “a graphical user interface (GUI) communicatively coupled to the processor (claims 7 and 16)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, Method; and System Independent claims 1, 13, and 19 recite “a system, the system comprising: a processor; a memory; a location analysis module; a sourcing analysis module; a business recommendation module, wherein the memory, the location analysis module, the sourcing analysis module, and the business recommendation module are communicatively coupled to the processor; and one or more machine-readable instructions stored in the memory that cause the system to perform at least the following when executed by the processor: by a location analysis machine learning model of the location analysis module, by the location analysis machine learning model of the location analysis module, by a sourcing analysis machine learning model of the sourcing analysis module, by the sourcing analysis machine learning model of the sourcing analysis module, by a business recommendation machine learning model of the business recommendation module (claims 1 and 13)”; “by a location analysis machine learning model of a location analysis module, by the location analysis machine learning model of the location analysis module, by a sourcing analysis machine learning model of a sourcing analysis module, by the sourcing analysis machine learning model of the sourcing analysis module, by a business recommendation machine learning model of a business recommendation module (claim 19)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0019-0023 and Figures 1. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-12, 14-18, and 20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 7 and 16 additionally recite “a graphical user interface (GUI) communicatively coupled to the processor (claims 7 and 16)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable over 35 USC 103
Claims 1-20 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 1, 13, and 19 disclose a system and method for analyzing weighted location based metrics to allocate percentages at each location for sourcing purposes and recommending a business plan to execute the allocation and sourcing, with specific recitations including such that a first location housing percentage of the allocation of the percentage of the project is housed at a first location of the at least two locations of the enterprise and a second location housing percentage of the allocation of the percentage of the project is housed at a second location of the at least two locations of the enterprise, such that an internal sourcing percentage of the allocation of the percentage of the project to source internally is associated with internally sourcing within the enterprise and an external sourcing percentage of the allocation of the percentage of the project to source externally is associated with externally sourcing outside the enterprise, and the sourcing allocation decision overall across the at least two locations such that (i) the combined location and sourcing allocation decision for the first location includes the first location housing percentage of the location allocation decision, at least a first portion of the internal sourcing percentage of the sourcing allocation decision, and at least a first portion of the external sourcing percentage of the sourcing allocation decision, and (ii) the combined location and sourcing allocation decision for the second location includes the second location housing percentage of the location allocation decision, at least a second portion of the internal sourcing percentage of the sourcing allocation decision, and at least a second portion of the external sourcing percentage of the sourcing allocation decision.
Regarding a possible 103 rejection: The closest prior art of record is:
Singh et al. (US 2022/0156693 A1) – which discloses developing optimized cargo solutions.
Murthy et al. (US 2014/0257901 A1) – which discloses integrate services, projects, and assets for resource management using modular analytic tools.
Johnson et al. (US 2008/0114628 A1) – which discloses enterprise proposal management.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1, 13, and 19, such as the recitations above. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a system for generating a business planning recommendation for a project, the system comprising: a processor; a memory; a location analysis module; a sourcing analysis module; a business recommendation module, wherein the memory, the location analysis module, the sourcing analysis module, and the business recommendation module are communicatively coupled to the processor; and one or more machine-readable instructions stored in the memory that cause the system to perform at least the following when executed by the processor: generating, by a location analysis machine learning model of the location analysis module, weighted location metrics based on location metrics received at the location analysis module, wherein the weighted location metrics are dynamically adjustable by the location analysis machine learning model in real-time; generating, by the location analysis machine learning model of the location analysis module, a location allocation decision based on the weighted location metrics, the location allocation decision based on an allocation of a percentage of the project to house at each location of at least two locations of an enterprise, such that a first location housing percentage of the allocation of the percentage of the project is housed at a first location of the at least two locations of the enterprise and a second location housing percentage of the allocation of the percentage of the project is housed at a second location of the at least two locations of the enterprise; generating, by a sourcing analysis machine learning model of the sourcing analysis module, weighted sourcing metrics based on sourcing input parameters received at the sourcing analysis module, wherein the weighted sourcing metrics are dynamically adjustable by the sourcing analysis machine learning model in real-time; generating, by the sourcing analysis machine learning model of the sourcing analysis module, a sourcing allocation decision based on the weighted sourcing metrics, the sourcing allocation decision based on an allocation of a percentage of the project to source internally within the enterprise and externally outside the enterprise, such that an internal sourcing percentage of the allocation of the percentage of the project to source internally is associated with internally sourcing within the enterprise and an external sourcing percentage of the allocation of the percentage of the project to source externally is associated with externally sourcing outside the enterprise; and generating, by a business recommendation machine learning model of the business recommendation module, a business plan recommendation comprising a respective combined location and sourcing allocation decision for each location of the first location and the second location of the at least two locations based on a combination of the location allocation decision between the first location and the second location and the sourcing allocation decision overall across the at least two locations such that (i) the combined location and sourcing allocation decision for the first location includes the first location housing percentage of the location allocation decision, at least a first portion of the internal sourcing percentage of the sourcing allocation decision, and at least a first portion of the external sourcing percentage of the sourcing allocation decision, and (ii) the combined location and sourcing allocation decision for the second location includes the second location housing percentage of the location allocation decision, at least a second portion of the internal sourcing percentage of the sourcing allocation decision, and at least a second portion of the external sourcing percentage of the sourcing allocation decision (as required by independent claims 1, 13, and 19)”, thus rendering claims 1, 13, 19 and their dependent claims as allowable over the prior art.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW D HENRY/Primary Examiner, Art Unit 3625