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 .
Status of Claims
The office action is being examined in response to the application filed by the applicant on May 13th, 2024.
Claims 1 - 22 are pending and have been examined.
This action is made NON-FINAL.
The examiner would like to note that this application is now being handled by examiner Bill Chen.
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 – 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and therefore does not recite patent-eligible subject matter. Claim 1 is the most representative of the independent claim set 1, 15 and 20.
Step 2A Prong 1: The abstract idea is defined by the elements of:
via a user interface (UI) (140), receiving entry of a SWO report (136);
applying at least one automated analysis to the SWO report to detect information missing from the SWO report and/or to generate a completeness score (138) for the SWO report;
via the UI, providing an indication (142) of the information missing from the SWO report and/or the completeness score for the SWO report; and
storing the SWO report in a SWO database (111)
These limitations collectively describe evaluating the completeness or quality of information and notifying a user of deficiencies. Determining whether information is missing from a report and assigning a complete score are activities that can be performed mentally by a human reviewer. A supervisor could read service work order reports, determine whether required information is missing from it, assign a completeness score, and then inform the service engineer of the missing information. Accordingly, the claim recites a mental process, which is one of the enumerated groupings of abstract ideas, refer to MPEP 2106.04(a).
Additionally, the claimed method concerns managing and improving service work order documentation within a workplace environment. Evaluating reports for completeness and directing a user to correct missing information constitutes managing human activity in the context of business or workplace operations. Therefore, the claim also falls within the grouping of certain methods of organizing human activity. Thus, claim 1 recites a judicial exception.
Step 2A Prong 2: For independent claim 1, the claims do not integrate an abstract idea into a practical application. The additional elements recited in claim 1 beyond the abstract idea include:
a non-transitory computer readable medium
at least one electronic processor
a user interface (UI)
a service work order (SWO) database
These elements are described at a high level of generality and perform generic computer functions such as receiving data, analyzing data, displaying results, and storing data. The claim does not (1) improve the functioning of the of the electronic processor, (2) improve the functioning of the SWO database, (3) improve user interface technology, (4) recite any specific algorithm that improves computer performance, or (5) modify the operation of a medical imaging device or any other machine. The claim therefore merely uses generic computer components as tools to automate the abstract idea of evaluating report completeness.
Accordingly, the abstract idea is not integrated into a practical application.
Step 2B: For independent claim 1, because claim 1 is directed to a judicial exception and does not integrate the exception into a practical application, the analysis proceeds to Step 2B to determine whether the claim includes additional elements that amount to significantly more than the abstract idea. Claim 1 does not include any such additional elements. As discussed above, the claim merely performs the abstract idea of evaluating completeness of a report using generic computing components. Therefore, claim 1 does not include an inventive concept sufficient to transform the abstract idea into a patent-eligible subject matter.
For dependent claims 2 – 14, 16 – 19, and 21 – 22, these claims cover or fall under the same abstract ideas of a mental process or certain methods of organizing human activity, such as:
artificial neural network (ANN) training, threshold filtering, retraining, database generation (claims 2 – 6 and 18 – 19)
natural language processing (NLP) analysis, detecting missing information, suggesting text (claims 7, 10 – 12, and 21 – 22)
diagnostic model, root cause determination, predicting maintenance operation (claims 13 – 14)
providing completeness scores and prompting update if below a threshold (claims 8 – 9 and 16 – 17)
Each group of dependent claims merely adds generic machine learning techniques and NLP analysis, threshold-based evaluation, feedback prompting, or predictive analytics—which are all forms of data analysis and mental processes implemented on generic computing components. None of the dependent claims integrate the abstract idea into a practical application or recite significantly more than the abstract idea. Accordingly, claims 1 – 22 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 – 11 and 13 – 22 are rejected under 35 U.S.C. 102(a) as unpatentable over Rathore (US20180373579 A1).
Regarding claims 1, 15 and 20:
Rathore teaches:
via a user interface (UI), receiving entry of a SWO report; [¶0040; Fig. 3]: A user interface is connected to the device. [¶0016; Fig. 1A]: The disclosure teaches a data quality system receiving metadata from a plurality of server devices associated with a system. Further, [¶0021]: Reports may be received and outputted by the system via a client device.
applying at least one automated analysis to the SWO report to detect information missing from the SWO report and/or to generate a completeness score for the SWO report; [¶0055 - 0058; Fig. 4]: The data quality system is able to perform analyses on the quality of data, as a result reducing and eliminating errors. The system aggregates the data related to specific topics and utilizes averages in order to determine a threshold granularity. [¶0069]: Techniques are set in place to identify whether data includes missing data.
via the UI, providing an indication of the information missing from the SWO report and/or the completeness score for the SWO report; and [¶0055 - 0058; Fig. 4]: The data quality system is able to perform analyses on the quality of data, as a result reducing and eliminating errors. Similarly, [¶0069]: teaches a set of techniques that may be used to process data and determine values for a plethora of data sets, including missing data.
storing the SWO report in a SWO database. [¶0016 – 0017; Fig. 1A]: Data is stored in a server device and may by stored in other sources as well.
Regarding claim 2:
Rathore teaches:
scoring the historical SWO reports according to a predetermined completeness score threshold; and [¶0067]: The data quality system is able to determine scores for predicted uses of data.
training at least one artificial neural network (ANN) (132) to detect information missing from the SWO report and/or to generate a completeness score for an SWO report, the training using historical SWO reports having a completeness score exceeding a predetermined completeness score threshold; [¶0065]: The data quality system may be coupled to machine learning in order to be trained on information identifying different data sets and sources.
wherein the at least one automated analysis includes the trained at least one ANN. [¶0065]: Machine learning is paired to the disclosure.
Regarding claim 3:
Rathore teaches:
generating the SWO database (111) storing the historical SWO reports having a completeness score exceeding predetermined completeness score threshold [¶0058]: The data quality system is responsible for storing and aggregating data to satisfy a certain threshold.
Regarding claim 4:
Rathore teaches:
receiving additional historical SWO reports; [¶0016; Fig. 1A]: The disclosure teaches a data quality system receiving metadata from a plurality of server devices associated with a system. Further, [¶0021]: Reports may be received and outputted by the system via a client device.
scoring the additional historical SWO reports according to a predetermined completeness score threshold; and [¶0067]: The data quality system is able to determine scores for predicted uses of data.
updating the SWO database (111) with the additional historical SWO reports having a completeness score exceeding predetermined completeness score threshold. [¶0014]: The data quality system may update data at a source of the data instead of each time data is utilized. “This conserves processing resources of hardware resources of an organization by reducing or eliminating a need to fix the data each time the data is used.”
Regarding claim 5:
Rathore teaches:
retraining the ANN (132) with the additional historical SWO reports stored in the SWO database (111). [¶0065]: The data quality system may be coupled to machine learning in order to be trained on information identifying different data sets and sources.
Regarding claim 6:
Rathore teaches:
receiving a user input from a service engineer (SE) indicative of the historical SWO report should be stored in the SWO database (111). [¶0043]: An input component is coupled to the disclosure, allowing the device to receive information via user input.
Regarding claim 7:
Rathore teaches:
wherein the at least one automated analysis includes: at least one natural language processing (NLP) analysis to detect information missing from the SWO report [¶0064]: The data quality system may use natural language processing to further identify terms/phrases.
Regarding claims 8 and 16:
Rathore teaches:
applying the at least one automated analysis to the SWO report to generate a completeness score for the SWO report, and the providing includes providing the completeness score for the SWO report. [¶0019]: The data quality system may use machine learning in order to effectively identify errors associated with pieces of data. [¶0022; Fig. 1B]: As a result, the system is able to automatically replace the detected error-containing data stored within the server(s).
Regarding claims 9 and 17:
Rathore teaches:
wherein, if the completeness score for the SWO report does not exceed the predetermined completeness score threshold, then the providing of the completeness score for the SWO report includes: outputting the indication to via the UI to update the SWO report. [¶0023; Fig. 1B]: The data quality system provides logistics and information to be displayed regarding the processed data.
Regarding claims 10 and 21:
Rathore teaches:
applying the at least one automated analysis to the SWO report to detect information missing from the SWO report, and the providing includes providing the detected missing information for the SWO report. [¶0055 - 0058; Fig. 4]: The data quality system is able to perform analyses on the quality of data, as a result reducing and eliminating errors. The system aggregates the data related to specific topics and utilizes averages in order to determine a threshold granularity. [¶0069]: Techniques are set in place to identify whether data includes missing data.
Regarding claims 11 and 22:
Rathore teaches:
outputting the indication to via the UI to suggest text to add to the SWO report allow the SWO report to exceed the predetermined completeness score threshold. [¶0069]: “For example, the set of metrics may include… whether the data includes missing data, whether the data includes minimum and/or maximum value violations, “
Regarding claim 13:
Rathore teaches:
training a diagnostic model to determine a root cause of a fault of the medical imaging device; and [¶0065 – 0067]: Machine learning is paired to the data quality system in order to be trained on data to predict an intended use of the data—“data quality system 230 may process the data using machine learning to predict an intended use of the data and may then identify errors in the data based on the predicted intended use. “
using the trained diagnostic model to predict a maintenance operation to be performed on the medical imaging device. [¶0065 – 0067]: Machine learning is paired to the data quality system in order to be trained on data to predict an intended use of the data—“data quality system 230 may process the data using machine learning to predict an intended use of the data and may then identify errors in the data based on the predicted intended use. “
Regarding claim 14:
Rathore teaches:
data-mining completed SWO reports to determine a root cause of a fault of the medical imaging device. [¶019 – 0021]: The disclosure utilizes machine learning in order to identify errors associated with data within reports. Additionally, the data quality system “may fix errors in a particular manner for an intended use of the data, to satisfy a set of rules, to match other data with the same intended use, and/or the like. In some cases, the data quality system may output, for display via the client device, a report that indicates proposed modifications to the data and may request approval from a user of the client device prior to fixing errors related to the data.”
Regarding claim 18:
Rathore teaches:
retrieving historical SWO reports; [¶0040; Fig. 3]: A user interface is connected to the device. [¶0016; Fig. 1A]: The disclosure teaches a data quality system receiving metadata from a plurality of server devices associated with a system. Further, [¶0021]: Reports may be received and outputted by the system via a client device.
scoring the historical SWO reports according to a predetermined completeness score threshold; [¶0067]: The data quality system is able to determine scores for predicted uses of data.
training at least one artificial neural network (ANN) (132) to generate a completeness score for an SWO report, the training using historical SWO reports having a completeness score exceeding a predetermined completeness score threshold, wherein the at least one automated analysis includes the trained at least one ANN; and [¶0065]: The data quality system may be coupled to machine learning in order to be trained on information identifying different data sets and sources.
generating the SWO database (111) storing the historical SWO reports having a completeness score exceeding predetermined completeness score threshold. [¶0075 – 0077]: After generating the reports, the device processes the data towards the set of server devices to store the generated reports.
Regarding claim 19:
Rathore teaches:
receiving additional historical SWO reports; [¶0040; Fig. 3]: A user interface is connected to the device. [¶0016; Fig. 1A]: The disclosure teaches a data quality system receiving metadata from a plurality of server devices associated with a system. Further, [¶0021]: Reports may be received and outputted by the system via a client device.
scoring the additional historical SWO reports according to a predetermined completeness score threshold; [¶0067]: The data quality system is able to determine scores for predicted uses of data.
updating the SWO database with the additional historical SWO reports having a completeness score exceeding predetermined completeness score threshold; and [¶0014]: The data quality system may update data at a source of the data instead of each time data is utilized. “This conserves processing resources of hardware resources of an organization by reducing or eliminating a need to fix the data each time the data is used.”
retraining the ANN with the additional historical SWO reports stored in the SWO database. [¶0065]: The data quality system may be coupled to machine learning in order to be trained on information identifying different data sets and sources.
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.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Rathore (US20180373579 A1) in view of Mavrieudus (US20200185085 A1)
Regarding claim 12:
Rathore does not teach the limitation below. Thus, Mavrieudus teaches:
extracting textual features from the historical SWO reports to provide labels for the historical SWO reports. [¶0040]: Feature extraction is taught in order to identify component names/identifiers and then associate the extracted features to be classified by category.
It would have been obvious to one or ordinary skill in the art before the earliest effective filing date of the invention to have combined Rathore’s method and systems for processing data to improve the quality of datasets with extracting textual features in order to provide labels for historical SWO reports, as taught by Mavrieudus, in order to effectively and efficiently organize and label metadata corresponding to their respective groupings.
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Mukherjee (US7949444 B2) is pertinent because it is directed to “A natural language data extraction module extracts problem data and related solution data from the narrative data, and a database module populates an aircraft service information database with the extracted problem data and the related extracted solution data.”
Muthusamy (US20080215387 A1) is pertinent because it is directed to “a software process that provides: an increase in the productivity in the validation process; tracking of the validation inventory, paperless execution of validation protocols; automation of revalidation schedule with alert features; increased validation efficiency; and ensures compliance to FDA regulations.”
Wilczek (US20160092423 A1) is pertinent because it is directed to “computer-implemented template recognition using optical character recognition (OCR) technology, and more specifically to feedback validation of electronically generated forms for more accurate data extraction and categorization.”
Lott (US20050060317 A1) is pertinent because it is directed to “a method and system for improving the requirements engineering process. More specifically, the present invention relates to a method and system for the creation of rules documents and/or interface specifications and the automated generation of computer-implemented message transformation and/or validation software based on those business rules and interface definitions.”
Lin (US20210279566 A1) is pertinent because it is directed to “training a contrastive neural network in an active learning environment. More specifically, the embodiments relate identifying novel patterns in a new dataset for a prediction accuracy assessment to further train the contrastive neural network.”
Horowitz (US11568153 B2) is pertinent because it is directed to “evaluating natural language text. More particularly, in certain embodiments, the present disclosure is related to a narrative evaluator.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bill Chen whose telephone number is (571)270-0660. The examiner can normally be reached Monday - Friday 8:30am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached on (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BILL CHEN/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626