DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 13, 2026 has been entered. Claims 1 and 10 have been amended. Claims 1-18 are presented for examination.
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 Arguments
Applicant's arguments filed April 13, 2026 have been fully considered but they are not persuasive.
Applicant argues that “[t]he claims therefore are not directed to merely organizing human activity. Instead, they recite a specific technological process implemented through networked computer systems and machine-learning-based analysis of uploaded job media” (page 9 of Applicant’s response) and “[t]hese limitations [listed on pages 9-10 of Applicant’s response] require computer-implemented processing of digital media and coordinated operation across multiple networked systems and data repositories. Such operations cannot practically be performed in the human mind. Accordingly, the claims do not recite a mental process.” (Page 10 of Applicant’s response) The Examiner respectfully disagrees. The use of uploaded video capture and uploaded job files simply automates the gathering of information that could otherwise be performed by a human. A human user can review uploaded video and photograph capture to perform evaluations on the gleaned information and to generate a closeout package. A human user could also reconcile invoices and purchase order data. The implied retrieval of the data from across ERP and customer systems is simply a high level recitation of the gathering of data that could otherwise be performed by a human. Additionally, the machine learning is applied at a high level and presents a general link to technology. Furthermore, as explained in the rejection, the evaluated process is related to “managing completed jobs associated with a plurality of customers” (Spec: ¶ 1), which (under its broadest reasonable interpretation) is an example of managing business relations and interactions between people (i.e., organizing human activity). Also, parsing data related to one or more jobs associated with one or more customers is an example of filtering content, which Courts have identified as both an example of a mental process and of organizing human activity.
On page 9 of Applicant’s response, Applicant states, “These features tie the claimed process to a specific technical environment involving infrastructure service operations, ERP systems, uploaded job media, and machine-learning-based verification of physical site work. The claims therefore apply any alleged abstract idea in a particular technological setting to achieve a concrete operational result.” The Examiner views the claims as merely presenting a general link to a technical environment. The additional elements are only generally applied to implement the abstract ideas. There is no special arrangement of additional elements, for example. There are also no specific technical details presented in the claims beyond, in effect, performing the various operations via processing systems and via machine learning at a high level. As explained in the rejection, the processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)). Applicant has not presented persuasive evidence to the contrary.
Additionally explained in the rejection is that the machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶ 32 – “The typical machine learning training process collects data samples, extracts a set of features from these samples, and feeds the features into a machine learning model to determine and recognize patterns related to customer requests. The output of this training process is one or more machine learning models that can remedy customer job request discrepancies/insufficiencies and streamline the process from receiving customer job requests to job completion.”; ¶ 29 – “Such training can include providing the system historical job requests, i.e., from a training data store 208, and input characteristics associated with various customers. The training data can be collected from customer systems 204 and/or historical job data collected by the present management system 200. The training can include any of supervised and unsupervised learning for the one or more machine learning models.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human. Applicant has not presented persuasive evidence to the contrary.
Applicant asserts the following:
The claimed invention improves the technological process used by infrastructure service providers to verify completion of field work and manage billing. Traditionally, verification of infrastructure maintenance or installation work requires manual review of photos, videos, and documentation, followed by manual generation of closeout packages and billing. The claimed invention improves this process. These improvements reduce manual processing of job documentation and improve the reliability and speed of infrastructure job-closeout verification. Accordingly, the claims reflect an improvement in a technological workflow for managing infrastructure service operations, and therefore are not directed to an abstract idea. (Page 10 of Applicant’s response)
Any improved reliability and speed come from the capabilities of a general-purpose computer. As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)). Applicant has not presented persuasive evidence to the contrary.
On page 11 of the response, Applicant submits:
Applicant respectfully submits that the pending claims, as amended, now explicitly address the concerns raised in the Advisory Action regarding lack of technical specificity in the detecting step. In particular, the independent claims have been amended to recite that completion of a job is detected by processing uploaded video capture associated with the job and applying a machine learning model to that video data to identify predefined physical site components, including antennas, coaxial cabling, and connectors, and determining, based on the identified components and uploaded files, that the job satisfies completion criteria. These limitations define a specific technological mechanism for analyzing digital video data and identifying infrastructure components, rather than merely reciting a desired result. The claims therefore no longer operate at a high level of generality, but instead recite particular processing applied to specific types of digital inputs using a trained machine learning model.
The claims do not present the “specific technological mechanism” for identifying predefined physical site components, including antennas, coaxial cabling, and connectors, and determining, based on the identified components and uploaded files, that the job satisfies completion criteria. Simply stating that video and files are uploaded and machine learning is used does not qualify as a “specific technological mechanism,” but instead as a general application of the additional elements and as a general link to technology. A human user can identify predefined physical site components, including antennas, coaxial cabling, and connectors, and determine, based on the identified components and uploaded files, that the job satisfies completion criteria.
Applicant argues that “the claims are directed to an improvement in a technical workflow involving digital media analysis and enterprise system integration, rather than to a mere business practice.” (Page 12 of Applicant’s response) The Examiner respectfully disagrees. The claims do not present any details regarding how digital media analysis itself and enterprise system integration itself are improved from a technical standpoint. The claims lack specific technical details beyond the use of the various additional elements at a high level of generality.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “managing completed jobs associated with a plurality of customers” (Spec: ¶ 1) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 10-18), Article of Manufacture (claims 1-9)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims perform the following:
[Claims 1, 10] receiving files associated with a job performed for a customer of an infrastructure service provider, one or more files associated with a job performed for a customer of an infrastructure service provider, the files including any of video capture images and documents associated with maintenance or installation work at a site [NOTE: “Any of video capture images and documents,” in effect, presents these options in the alternative.];
creating a model with customer data associated with one or more customers of the infrastructure service provider, wherein the customer data comprises historical customer payment behaviors and purchase order histories;
parsing data related to one or more jobs;
detecting completion of a job by reviewing uploaded video and photograph capture including analyzing image data derived from the video capture to analyze visual content associated with the job, evaluating the uploaded video and photograph capture to identify one or more predefined site components including antennas, coaxial cabling, and connectors, and determining, based on the identified site components and the uploaded files, that the job satisfies completion criteria;
generating, based on the uploaded files and identified site components, a closeout package verifying maintenance or installation work against customer or operator guidelines;
transmitting the generated closeout package to an external customer for review and approval; and
performing an action based on approval of the closeout package, wherein the action comprises (i) automatically generating and transmitting an invoice associated with the closeout package, (II) reconciling the invoice with purchase order data, and (iii) scheduling a follow-up notification al a customer-specific time, including grouping multiple invoices for a given customer into a single notification to optimize collections.
It is noted that the word “automatically” may simply mean “in direct response to” (which a human user can perform).
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can parse data (including data found in paper files) related to jobs to identify patterns of completed jobs and perform an action in response to completion of a job(s). A human user can also determine when to send invoices for completed jobs. A human user can also perform actions including generating and sending out invoices, reconciling invoices, and scheduling notifications based on historical payment behaviors. A human user can review uploaded video and photograph capture to perform evaluations on the gleaned information and to generate a closeout package. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “managing completed jobs associated with a plurality of customers” (Spec: ¶ 1), which (under its broadest reasonable interpretation) is an example of managing business relations and interactions between people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Parsing data related to one or more jobs associated with one or more customers is an example of filtering content. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.”
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 includes a non-transitory computer-readable medium comprising instructions that, when executed, cause one or more processors to perform the recited steps.
Claims 1 and 10 receive one or more uploaded files via an upload interface linked to an Enterprise Resource Planning (ERP) system.
Claims 1 and 10 train a machine learning model with customer data associated with one or more customers of the infrastructure service provider and determine, via the machine learning model, that one or more of the jobs are completed.
Claims 1 and 10 recite that the parsed data is stored in the ERP system.
Claims 1 and 10 recite detecting completion of a job by, processing uploaded video and photograph capture including analyzing image data derived from the video capture to analyze visual content associated with the job, applying the machine learning model to the uploaded video and photograph capture to identify one or more predefined site components including antennas, coaxial cabling, and connectors, and determining via the machine learning model, based on the identified site components and the uploaded files, that the job satisfies completion criteria.
Claims 1 and 10 recite transmitting the generated closeout package to at least one of the ERP system and an external customer system for review and approval.
Claims 1 and 10 recite performing an action based on approval of the closeout package, wherein the action comprises (i) automatically generating and transmitting, via the ERP system, an invoice associated with the closeout package, (ii) reconciling the invoice with purchase order data obtained from the customer system, and (iii) scheduling a follow-up notification al a customer-specific time determined by the machine learning model based on historical payment behaviors. including grouping multiple invoices for a given customer into a single notification to optimize collections.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 10-15, 52-53; ¶ 10 – “It should be appreciated by those of ordinary skill in the art that FIG. 6 depicts the digital device 100 in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components (182, 184, 186, 188, and 190) are communicatively coupled via a local interface 192. The local interface 192 can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art.”).
The use of video capture simply automates the gathering of information that could otherwise be performed by a human. A human user could also reconcile invoices and purchase order data. The implied retrieval of the data from across ERP and customer systems is simply a high level recitation of the gathering of data that could otherwise be performed by a human. Additionally, the machine learning is applied at a high level and presents a general link to technology.
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶ 32 – “The typical machine learning training process collects data samples, extracts a set of features from these samples, and feeds the features into a machine learning model to determine and recognize patterns related to customer requests. The output of this training process is one or more machine learning models that can remedy customer job request discrepancies/insufficiencies and streamline the process from receiving customer job requests to job completion.”; ¶ 29 – “Such training can include providing the system historical job requests, i.e., from a training data store 208, and input characteristics associated with various customers. The training data can be collected from customer systems 204 and/or historical job data collected by the present management system 200. The training can include any of supervised and unsupervised learning for the one or more machine learning models.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims perform the following:
[Claims 2, 11] automatically transmitting the closeout package to a customer for review and approval prior to releasing an invoice.
[Claims 3, 12] wherein the model is adapted to identify files related to the completion of a job, and wherein generating the closeout package includes identifying specific site components associated with a completed job, including antennas, coaxial cabling and connectors, and verifying that the components conform to customer or operator guidelines.
[Claims 4, 13] wherein the action includes sending an invoice for jobs determined to be completed, and wherein the steps further comprise: sending a follow up notification.
[Claims 5, 14] wherein sending the follow up notification is configured to occur at a particular time, and wherein the model is adapted to determine a particular time based on historical payment behaviors of the one or more customers.
[Claims 6, 15] wherein the steps comprise grouping invoices associated with a specific customer and sending a follow up notification for the group of invoices at a particular time.
[Claims 8, 17] wherein the customer data includes historical customer data.
[Claims 9, 18] wherein the steps further comprise: generating a closeout package for jobs determined to be completed.
The dependent claims incorporate the abstract ideas identified above in regard to the independent claims.
It is noted that the word “automatically” may simply mean “in direct response to” (which a human user can perform).
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can parse data (including data found in paper files) related to jobs to identify patterns of completed jobs and perform an action in response to completion of a job(s). A human user can also determine when to send invoices for completed jobs. A human user can also perform actions including generating and sending out invoices, reconciling invoices, and scheduling notifications based on historical payment behaviors. A human user can review uploaded video and photograph capture to perform evaluations on the gleaned information and to generate a closeout package. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “managing completed jobs associated with a plurality of customers” (Spec: ¶ 1), which (under its broadest reasonable interpretation) is an example of managing business relations and interactions between people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Parsing data related to one or more jobs associated with one or more customers is an example of filtering content. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.”
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of the independent claim from which each depends.
Claim 1 includes a non-transitory computer-readable medium comprising instructions that, when executed, cause one or more processors to perform the recited steps.
Claims 1 and 10 receive one or more uploaded files via an upload interface linked to an Enterprise Resource Planning (ERP) system.
Claims 1 and 10 train a machine learning model with customer data associated with one or more customers of the infrastructure service provider and determine, via the machine learning model, that one or more of the jobs are completed.
Claims 1 and 10 recite that the parsed data is stored in the ERP system.
Claims 1 and 10 recite detecting completion of a job by, processing uploaded video and photograph capture including analyzing image data derived from the video capture to analyze visual content associated with the job, applying the machine learning model to the uploaded video and photograph capture to identify one or more predefined site components including antennas, coaxial cabling, and
connectors, and determining via the machine learning model, based on the identified site components and the uploaded files, that the job satisfies completion criteria.
Claims 1 and 10 recite transmitting the generated closeout package to at least one of the ERP system and an external customer system for review and approval.
Claims 1 and 10 recite performing an action based on approval of the closeout package, wherein the action comprises (i) automatically generating and transmitting, via the ERP system, an invoice associated with the closeout package, (ii) reconciling the invoice with purchase order data obtained from the customer system, and (iii) scheduling a follow-up notification al a customer-specific time determined by the machine learning model based on historical payment behaviors. including grouping multiple invoices for a given customer into a single notification to optimize collections.
Claims 2 and 11 recite uploading the generated closeout package to the ERP system and automatically transmitting the closeout package to a customer’s external system for review and approval prior to releasing an invoice.
Claims 3 and 12 recite wherein the data includes one or more files, and wherein the machine learning model is adapted to identify files related to the completion of a job and wherein the data includes one or more files, and wherein the model is adapted to identify files related to the completion of a job, and wherein generating the closeout package includes processing video capture to identify specific site components associated with a completed job, including antennas, coaxial cabling and connectors, and verifying that the components conform to customer or operator guidelines.
Claims 5 and 14 recite wherein the machine learning model is adapted to determine a particular time based on historical payment behaviors of the one or more customers.
Claims 7 and 16 recite wherein the training includes any of supervised and unsupervised learning.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 10-15, 52-53; ¶ 10 – “It should be appreciated by those of ordinary skill in the art that FIG. 6 depicts the digital device 100 in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components (182, 184, 186, 188, and 190) are communicatively coupled via a local interface 192. The local interface 192 can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art.”).
The use of video capture simply automates the gathering of information that could otherwise be performed by a human. A human user could also reconcile invoices and purchase order data. The implied retrieval of the data from across ERP and customer systems is simply a high level recitation of the gathering of data that could otherwise be performed by a human. Additionally, the machine learning is applied at a high level and presents a general link to technology.
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶ 32 – “The typical machine learning training process collects data samples, extracts a set of features from these samples, and feeds the features into a machine learning model to determine and recognize patterns related to customer requests. The output of this training process is one or more machine learning models that can remedy customer job request discrepancies/insufficiencies and streamline the process from receiving customer job requests to job completion.”; ¶ 29 – “Such training can include providing the system historical job requests, i.e., from a training data store 208, and input characteristics associated with various customers. The training data can be collected from customer systems 204 and/or historical job data collected by the present management system 200. The training can include any of supervised and unsupervised learning for the one or more machine learning models.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Allowable Subject Matter
Claims 1-18 are allowed over the prior art. The claims remain rejected under 35 U.S.C. § 101.
The following is a statement of reasons for the indication of allowable subject matter:
As set forth in the non-final Office action dated July 2, 2025, Christensen (US 2023/0186243), Dasgupta et al. (US 2022/0180413), and Bennett et al. (US 2014/0337188) address the various aspects of the claims conceptually (noting that independent claims 1 and 10 have been amended to recite more detailed aspects of the concepts recited in dependent claims 4-6, 8-9, 13-15, and 17-18). Additionally, Raj Susairaju et al. (US 2023/0032429) addresses more specific details regarding the use of machine learning to schedule a follow-up notification at a customer-specific time based on historical payment behaviors (Raj Susairaju: ¶¶ 63-65). However, the Examiner finds that one of ordinary skill in the art at the time of Applicant’s invention would not have found it obvious, in light of the aforementioned references, to create the specific invention (including the particular interoperations of the various recited limitations) as presented in independent claims 1 and 10. Therefore, claims 1-18 are deemed to be allowable over the prior art of record.
Conclusion
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
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