DETAILED ACTION
The following FINAL Office Action is in response to Applicant’s Response filed on 04/17/2026.
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 . 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 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.
Status of Claims
Claims 1-20 were previously pending and subject to a non-final Office Action mailed 02/05/2026. Claims 1, 9-11, and 19-20 were amended. Claims 1-20 are currently pending and are subject to the final Office Action below.
Response to Arguments
35 USC § 112
Applicant has amended Claims 9-10 and 19-20 to clarify the claims. Accordingly, the 35 U.S.C. 112(b) rejections of Claims 9-10 and 19-20 have been rendered moot and thus, have been withdrawn.
35 USC § 101
Applicant’s arguments, see pages 9-13, filed 04/17/2026, with respect to the 35 U.S.C. 101 rejections of Claims 1-20 have been fully considered and are not persuasive.
Applicant argues that Claims 1-20 are not directed to an abstract idea and further that the claimed features could not be practically performed in the human mind and/or correspond to mental activities. Examiner respectfully disagrees and notes that the identified abstract idea is not mental processes, but “certain methods of organizing human activity”. Specifically, commercial interactions or business relations in light of paragraph 4 of Applicant’s specification.
Examiner further noting that the amended claims are still directed to commercial interactions or business relations. The “processor of the electronic device”, “binary classification deep learning model”, “text similarity algorithm”, and “display device of an on-site manager” are considered additional elements. See rejection below for further analysis of each additional element.
Applicant further argues that the claims improve data processing specifically computing efficiency, accuracy, or real-time processing in a way that solves a technical problem. Examiner respectfully disagrees. First, “handling subjective/variable report data” is not a technical problem, but a “commercial” or “business” problem as noted in paragraph 4 of Applicant’s specification “A report may depend on the subjective perspective and judgment of a reporter which may differ from information about an actual incident… significant manpower or property loss may occur to handle the reported incident. An on-site manager may not be able to handle an incident in a timely manner”.
Additionally, the improvements Applicant provided are improvements in the abstract idea itself as on-site managers may be able to accurately/efficiently process reports in a timely manner. See MPEP 2106.05(a)(II) “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Accordingly, the 35 U.S.C. 101 rejection of Claims 1-20 is maintained.
35 USC § 103
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Examiner relies upon new references Subramanian and Liu along with previously cited art to teach the claimed limitations.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent Claims 1 and 11 recite the limitation of “by applying a binary classification deep learning model trained on preprocessed past incident information to extract features from text of the new incident information and compute a probability score exceeding a threshold derived from judgement weights”.
The closest description of the limitation is in paragraph 81 of Applicant’s specification “In addition, the binary classification deep learning model 420 may primarily determine that the new incident information is obtained by a false or mistaken report when the probability exceeds a preset numerical value. For example, the binary classification deep learning model 420 may output a SoftMax function as a result using the received text information and calculate the probability that the new incident information is obtained by a false or mistaken report, based on the SoftMax score for the SoftMax function. The binary classification deep learning model 420 may transmit, to the false or mistaken report determination module 430, the SoftMax score and whether the output new incident information is obtained by a false or mistaken report.”
Paragraph 83 states “The false or mistaken report determination module 430 may calculate the false or mistaken report score using the received pieces of information and determine that the reported new incident is obtained by a false or mistaken report when the false or mistaken report score exceeds a predetermined reference. The predetermined reference may be determined by an on-site manager, a policy maker, or a system operator (e.g., a National Police Agency, etc.).”
Thus, Applicant’s specification supports “compute a probability score exceeding a threshold” where the threshold is preset numerical value or predetermined referenced set by an on-site manager, policy maker, or system operator. Applicant’s specification does not support “a threshold derived from judgement weights”.
Accordingly, the limitations emphasized above are not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a join inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Dependent Claims 2-10 and 12-20 inherit the rejection as they do not cure deficiencies of the independent claims.
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 an abstract idea without significantly more.
Step 1
Claims 1-10 are directed to a method (i.e., a process) and Claims 11-20 are directed to an electronic device (i.e., a machine). Therefore, the claims all fall within one of the four statutory categories of invention.
Step 2A Prong 1
Independent Claim 1 and Claim 11 recite the limitations of:
receiving new incident information and reporter information about a reported new incident…;
determining, in real time …, whether the new incident information is obtained by a false or mistaken report, based on the new incident information and the reporter information by … to extract features from text of the new incident information and compute a probability score exceeding a threshold derived from judgement weights;
determining, when the new incident information is determined to be true, a similarity with one or more pieces of related incident information associated with the reported new incident… by… that computes vector embeddings of the text and measures cosine similarity to select related incident information within a predefined similarity threshold; and
outputting to …, among the one or more pieces of related incident information, similar incident information to respond to the reported new incident, based on the similarity
Certain Methods of Organizing Human Activity
The limitations stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (commercial interactions or managing personal behavior or relationships or interactions between people). Specifically, business relations or social activities in light of Applicant’s specification paragraph 4 “A report of an incident (e.g., a 112 report, etc.) may depend on the subjective perspective and judgment of a reporter, which may differ from information about an actual incident. In addition, when a reported incident is obtained by a false or mistaken report, significant manpower or property loss may occur to handle the reported incident. An on-site manager may not be able to handle an incident in a timely manner due to a time delay in finding an on-site response measure, based on the report content of a new incident, and may inappropriately handle the incident due to failure to find an appropriate on-site response measure”. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. Claim 1 recites the additional elements of an electronic device, a network interface from a reporting device, a processor of the electronic device, “applying a binary classification deep learning model trained on preprocessed past incident information”, a database that stores past incident information, a text similarity algorithm, and a display device of an on-site manager. Claim 11 recites the additional elements of an electronic device comprising a processor, a network interface from a reporting device, “applying a binary classification deep learning model trained on preprocessed past incident information”, a database that stores past incident information, a text similarity algorithm, and a display device of an on-site manager.
The additional elements of an electronic device, a processor of the electronic device, and a database that stores past incident information are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
The additional elements of “applying a binary classification deep learning model trained on preprocessed past incident information” and “a text similarity algorithm” are merely indicating a field of use or technological environment in which to apply a judicial exception. Specifying that the abstract idea of “determining” was to be implemented by “applying a binary classification deep learning model trained on preprocessed past incident information” and further, specifying that the abstract idea of “determining” was to be implemented by a text similarity algorithm. These narrowing limitations are merely an attempt to limit the use of the abstract idea to a particular technological environment – the field of machine learning and natural language processing (respectively). See MPEP 2106.05(h).
The additional elements of a network interface from a reporting device and a display device of an on-site manager are also merely indicating a field of use or technological environment in which to apply a judicial exception. Requiring that the abstract idea of commercial interaction or business relations be performed using a network interface from a reporting device (the reporting user or “reporter”) and a display device of an on-site manager (on-site manager). Thus, the limitations simply attempted to limit the use of the abstract idea to computer environments.
Accordingly, the additional elements do not integrate the abstract idea into a practical application, whether individually or viewed in an ordered combination, because mere instructions to apply the exception using a generic computer component and field of use does not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an electronic device, a processor of the electronic device, and a database that stores past incident information amount to no more than mere instructions to apply the exception using a generic computer component.
Again, the additional elements of “applying a binary classification deep learning model trained on preprocessed past incident information” and “a text similarity algorithm” are specifying that the abstract idea of “determining” was to be implemented by “applying a binary classification deep learning model trained on preprocessed past incident information” and further, specifying that the abstract idea of “determining” was to be implemented by a text similarity algorithm. These narrowing limitations are merely an attempt to limit the use of the abstract idea to a particular technological environment – the field of machine learning and natural language processing (respectively).
The additional elements of a network interface from a reporting device and a display device of an on-site manager are also merely indicating a field of use or technological environment in which to apply a judicial exception. Requiring that the abstract idea of commercial interaction or business relations be performed using a network interface from a reporting device (the reporting user or “reporter”) and a display device of an on-site manager (on-site manager). Thus, the limitations simply attempted to limit the use of the abstract idea to computer environments.
None of the steps/functions of Claim 1 and Claim 11 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to organizing human activity, mere instructions to apply the judicial exception, and field of use, thus, the analysis does not change when considered as an ordered combination.
The additional elements of Claim 1 and Claim 11 amount to no more than mere instructions to implement the abstract idea on a computer. Even when considered in combination, these additional elements represent mere instructions to apply an exception using a generic computer component and field of use which cannot provide an inventive concept. Thus, the additional elements do not meaningfully limit the claim. Accordingly, Claim 1 and Claim 11 are ineligible.
Dependent Claims 2 and 12, Claims 6 and 16, Claims 7 and 17, and Claims 10 and 20 merely add additional limitations that narrow down the abstract idea identified above.
Dependent Claims 3 and 13 merely add additional limitations (“select” and “determine the similarity”) that narrow down the abstract idea identified above. The claims also recite an additional element of a text similarity algorithm. Such a feature merely limits the claims to the natural language processing field i.e., to execution by a text similarity algorithm which is simply an attempt to limit the use of the abstract idea to a particular technological environment. See MPEP 2106.05(h).
Dependent Claims 4 and 14 and Claims 5 and 15 merely add additional limitations (determining, determining, and determining) that narrow down the abstract idea identified above. Claims 5 and 15 also recite an additional element of a binary classification deep learning model that trains the past incident information. Such a feature merely limits the claims to the machine learning field i.e., to execution by a binary classification deep learning model that trains the past incident information which is simply an attempt to limit the use of the abstract idea to a particular technological environment.
Dependent Claims 8 and 18 and Claims 9 and 19 merely add additional limitations (storing, when… storing, and when… storing) that narrow down the abstract idea identified above. Examiner noting that the limitations of “storing” may also be interpreted as an additional element. Specifically, insignificant extra-solution activity of data storage which is a computer function that is well-understood, routine, and conventional – “storing and retrieving information in memory” in MPEP 2106.05(d)(II)(iv).
Thus, taken alone and when viewed as an ordered combination, nothing in dependent claims 2-10 and 12-20 amount to significantly more than the judicial exception. Claims 1-20 are ineligible.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 8, 11-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gratton et al. (US2021/0081559) in view of Subramanian et al. (US2020/0074359) in view of Embree et al. (US2005/0160330) in view of Liu et al. (US2021/0398137).
As per independent Claim 1 and Claim 11,
Gratton teaches an operating method of an electronic device, the operating method comprising:/ An electronic device comprising: a processor, wherein the processor is configured to: (para. 109-117)
receiving new incident information and reporter information about a reported new incident via a network interface from a reporting device (figure 1A and para. 141 (and para. 129) raw signals such as social posts, 911 calls, crowd sourced information where the content of raw signals can include images, video, audio, text, etc.; para. 142-151 where in para. 151 the raw signal is processed and the normalized signal (time, location, context, content (“new incident information”), type, and source (“reporter information”)) is sent to event detection infrastructure; para. 401-402 where data can come from any number of different sources such as user input, social media systems, cameras, etc.; para. 885 person near the scene is live streaming or posts a picture to a social media site and para. 887 location data for a mobile telephone that placed the 911 call)
determining, in real time using a processor of the electronic device, whether the new incident information is obtained by a false or mistaken report, based on the new incident information and the reporter information (para. 196-197 event detection infrastructure can determine event truthfulness (how likely an event is actually an event versus a hoax, fake, misinterpreted, etc.); para. 206 determine truthfulness based on source (“reporter information”), type, age, and content (“new incident information”) of normalized signals; see also para. 215-230 for entire truthfulness module description; para. 111-116 processor; para. 190-192 “live” extraction of signals and para. 129 where signals are ingested in real-time; para. 327 notification is sent at moment zero or in live-time)
determining, when the new incident information is determined to be true, a similarity with one or more pieces of related incident information associated with the reported new incident in a database that stores past incident information (para. 224 based on the truth score exceeding a threshold, event detection infrastructure can trigger an event detection for the event (“determined to be true” – see para. 217-218); Figure 17 and para. 384-391 where in para. 385 receiving an event feed of events detected from one or more normalized signals and specifically para. 386-388 where characteristics of the event are compared to characteristics of prior events; see also Figure 16 and para. 380-383; para. 375-379 “The impact prediction module can maintain an event history database of prior events and corresponding impacts. As new events are detected, the impact prediction module can refer to the event history database and compare the new events to prior events”)
outputting to a display device of an on-site manager, among the one or more pieces of related incident information, similar incident information to respond to the reported new incident, based on the similarity (Para. 398 timely notification of predicted impacts allow entities to better prepare or take measures to address the predicted impacts; Para. 377-379 impact prediction module can formulate predicted impacts of new events based on impacts of prior similar events and send the predicted impacts (types and areas) to a notification module which notifies entities such as hospitals, blood banks, delivery services; see also para. 389-397; para. 400 entities include social workers, first responders, hospitals, etc. who desire to be made aware of relevant events; see para. 637-640 explosion occurs at a factory and fire, police, and power company are notified; para. 327 notification sent to electronic device associated with entity)
Examiner noting that Gratton teaches para. 215-230 where in para. 218 truthfulness is represented by a percentage indicating a probability that an associated event is actually true, para. 220 truthfulness exceeds a specified threshold registered by an entity. Examiner further noting that Gratton teaches the “context” of the incident information.
Gratton does not teach, but Subramanian teaches:
By applying a binary classification deep learning model trained on preprocessed past incident information (para. 12 determine whether the reports include an issue (are fraudulent, are inaccurate, etc.); para. 16-17 receive data related to reports in the form of text “text of a historical expense report input to an expense reporting system, text of a historical audit report, and/or the like”; para. 23 facilitate training of a model to identify issues in a report based on attributes included in the data; para. 28 prepare or pre-process the data; para. 30-35 different types of machine learning models specifically para. 32 binary classification of historical data to train the machine learning model; para. 43-54 and figure 1G specifically para. 48 input the report into the super model and para. 49-50 the model outputs a score)
To extract features from text of the new incident information (para. 18-20 process the data and para. 21 text processing technique, para. 22 process the data using a machine learning model; para. 36-37 analysis and generating reports; para. 43-54 and figure 1G where in para. 45-46 receive a report to be processed)
And compute a probability score exceeding a threshold (para. 51 determining whether the score satisfies a threshold where in para. 47 “the score may indicate a likelihood of the expense report including an issue. For example, the score may indicate a likelihood of the expense report including a fraudulent expense, a likelihood of the expense report failing an audit, a likelihood of the expense report including data that does not match the features of the super model, and/or the like”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Subramanian with the motivation of increasing efficiency and accuracy.
See para. 13-14 “Some implementations described herein provide a report analysis platform that is capable of processing reports (e.g., thousands, millions, or more reports) associated with an organization utilizing a machine learning model and detecting issues in the reports. In this way, the report analysis platform can process a significant majority (e.g., 90 percent or more), or all, of the reports generated by the organization in a quick and efficient manner. This improves an accuracy of processing reports to identify an issue relative to other techniques. In addition, this increases a throughput of an organization's capability to process reports associated with the organization, thereby reducing or eliminating a risk of missed reports that include an issue. Further, this conserves resources of the organization (e.g., monetary resources, time resources, computing resources, and/or the like) that would otherwise be consumed as a result of using other techniques for processing reports.”
Subramanian suggests “derived from judgment weights” (para. 53 (and also 98) “For example, the report analysis platform may flag the expense report as possibly including an error and/or for further review based on the score satisfying a threshold. Additionally, or alternatively, the report analysis platform may flag attributes associated with the expense report when the score satisfies a threshold. For example, the report analysis platform may flag an individual, a location, a vendor, and/or the like associated with the expense report when the score satisfies a threshold. In some implementations, and continuing with the previous example, the report analysis platform may process old expense reports associated with the flagged attributes, may process any new expense reports associated with the flagged attributes, and/or the like.”; para. 36 determine a pattern of compliant/non-compliant historical reports by attribute included in the data associated with the historical reports)
Gratton/Subramanian does not teach, but Embree teaches:
derived from judgment weights (para. 54 user issue reporting performance table with records of past performance of a user in reporting issues – false positive rate, historical accuracy or correctness in reporting uses; para. 59 issue table with record for each issue including identity of the reporting entity and reported entity; para. 67-68, 74 update performance data of reporting entities; figure 9 and para. 72 if reporting entity has been highly reliable and accurate in reporting issues, the module will assign a higher performance priority to the issue; para. 73 “the user performance module 138 may factor in both the past performance of a reporting entity, and a reported entity when calculating the performance priority 172. The module 138 can also attribute different weights to information concerning the reporting entity and the reported entity. For example, a higher weighting may be attributed to the past performance of the reporting entity”; see also para. 75-78)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Embree with the motivation of increasing the accuracy of the determination by accounting for the reliability of the source.
See para. 2-4 “The above issues pertaining to the processing of issue reports are amplified by a number of factors, such as an increase in the complexity or rules pertaining to the operation of a system (e.g., an online resource of forum), and an increase in the number of sources from which issue reports may originate” and para. 97 “This has the effect of allowing the historical accuracy (or other performance metrics) associated with a reporting entity (e.g., a human reporting user) to be factored into the prioritization of response activities to an issue.”
Gratton/Subramanian/Embree does not teach, but Liu teaches:
using text similarity algorithm that computes vector embeddings of the text and measures cosine similarity to select related incident information within a predefined similarity threshold (para. 45 training AI model by converting textural representation of historical incidents into vector representations; para. 49-50 receives an incident and parses the incident to identify the textural data; para. 51 the received incident is converted into a textural representation and then the vector representation of the received incident is compared with representations of historical incidents based on similarity measurements such as cosine similarity; para. 52 for example; “similarity threshold” in para. 51 K nearest neighbors of the newly occurred incident based on the similarity measurements and para. 52 relevant incidents are the top K incidents with the highest cosine similarity scores)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Liu with the motivation of increasing accuracy.
See Para. 3-4 “Just by retrieving for related incidents based on textual descriptions may easily miss many of these contextually dependent incidents. Accordingly, there is a requirement to accurately identifying related incident using textual data and contextual data” and para. 51 “By using this technique, the disclosed technology is not only able to identify related incidents that is not only textually similar, but also contextually relevant to each another as illustrated in FIG. 7.”
As per dependent Claim 2 and Claim 12,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 1 and the electronic device of claim 11.
Gratton teaches:
selecting the one or more pieces of related incident information, based on the new incident information; and determining the similarity (Para. 386 comparing characteristics of the event to characteristics of a plurality of prior events specifically comparing event category, event time, event location, etc.; para. 391 comparing day of week, holidays, etc.; see also Figure 17 and para. 384-398)
Gratton/Subramanian/Embree does not teach, but Liu teaches:
by comparing text of the new incident information with text of the one or more pieces of related incident information (para. 45 training AI model by converting textural representation of historical incidents into vector representations; para. 49-50 receives an incident and parses the incident to identify the textural data; para. 51 the received incident is converted into a textural representation and then the vector representation of the received incident is compared with representations of historical incidents based on similarity measurements such as cosine similarity; para. 52 for example; “similarity threshold” in para. 51 K nearest neighbors of the newly occurred incident based on the similarity measurements and para. 52 relevant incidents are the top K incidents with the highest cosine similarity scores)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Liu with the motivation of increasing accuracy.
See para. 3-4 and para. 51.
As per dependent Claim 3 and Claim 13,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 1 and the electronic device of claim 11.
Gratton teaches:
selecting the one or more pieces of related incident information using incident classification, report date and time, and a report location of the new incident information; and determining the similarity (Para. 386 comparing characteristics of the event to characteristics of a plurality of prior events specifically comparing event category, event time, event location, etc.; para. 391 comparing day of week, holidays, etc.; see also Figure 17 and para. 384-398)
Gratton/Subramanian/Embree does not teach, but Liu teaches:
using a text similarity algorithm (para. 45 training AI model by converting textural representation of historical incidents into vector representations; para. 49-50 receives an incident and parses the incident to identify the textural data; para. 51 the received incident is converted into a textural representation and then the vector representation of the received incident is compared with representations of historical incidents based on similarity measurements such as cosine similarity; para. 52 for example; “similarity threshold” in para. 51 K nearest neighbors of the newly occurred incident based on the similarity measurements and para. 52 relevant incidents are the top K incidents with the highest cosine similarity scores)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Liu with the motivation of increasing accuracy.
See para. 3-4 and para. 51.
As per dependent Claim 4 and Claim 14,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 1 and the electronic device of claim 11.
Gratton teaches:
determining whether the new incident information is obtained by the false or mistaken report (para. 196-197 event detection infrastructure can determine event truthfulness (how likely an event is actually an event versus a hoax, fake, misinterpreted, etc.); para. 206 determine truthfulness based on source, type, age, and content of normalized signals; see also para. 215-230 for entire truthfulness module description)
Gratton suggests the limitation in para. 623 where the power utility is deemed a reliable source of information and a social media source is not. Similarly, para. 206 teaches the reliability of sources.
Gratton/Subramanian does not teach, but Embree teaches:
using a judgment weight based on the past incident information in the database (para. 54 user issue reporting performance table with records of past performance of a user in reporting issues – false positive rate, historical accuracy or correctness in reporting uses; para. 59 issue table with record for each issue including identity of the reporting entity and reported entity; para. 67-68, 74 update performance data of reporting entities; figure 9 and para. 72 if reporting entity has been highly reliable and accurate in reporting issues, the module will assign a higher performance priority to the issue; para. 73 “the user performance module 138 may factor in both the past performance of a reporting entity, and a reported entity when calculating the performance priority 172. The module 138 can also attribute different weights to information concerning the reporting entity and the reported entity. For example, a higher weighting may be attributed to the past performance of the reporting entity”; see also para. 75-78)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton/Subramanian invention with Embree with the motivation of increasing the accuracy of the determination by accounting for the reliability of the source.
See para. 2-4 “The above issues pertaining to the processing of issue reports are amplified by a number of factors, such as an increase in the complexity or rules pertaining to the operation of a system (e.g., an online resource of forum), and an increase in the number of sources from which issue reports may originate” and para. 97 “This has the effect of allowing the historical accuracy (or other performance metrics) associated with a reporting entity (e.g., a human reporting user) to be factored into the prioritization of response activities to an issue.”
As per dependent Claim 5 and Claim 15,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 4 and the electronic device of claim 14.
Gratton teaches:
determining a probability of whether the new incident information is obtained by the false or mistaken report, based on text of the new incident information (figure 1A and para. 141 (and para. 129) raw signals such as social posts, 911 calls, crowd sourced information where the content of raw signals can include images, video, audio, text, etc.; para. 196-197 event detection infrastructure can determine event truthfulness (how likely an event is actually an event versus a hoax, fake, misinterpreted, etc.); para. 206 determine truthfulness based on source, type, age, and content of normalized signals; see also para. 215-230 for entire truthfulness module description)
determining whether the new incident information is obtained by the false or mistaken report, based on the probability of whether the new incident information is obtained by the false or mistaken report (para. 215-230 where in para. 218 truth score or truthfulness is represented by a percentage indicating a probability that an associated event is actually true; para. 224 based on the truth score exceeding a threshold, event detection infrastructure can trigger an event detection for the event)
Gratton does not teach, but Subramanian teaches:
using a binary classification deep learning model that trains the past incident information (para. 12 determine whether the reports include an issue (are fraudulent, are inaccurate, etc.); para. 16-17 receive data related to reports in the form of text “text of a historical expense report input to an expense reporting system, text of a historical audit report, and/or the like”; para. 23 facilitate training of a model to identify issues in a report based on attributes included in the data; para. 28 prepare or pre-process the data; para. 30-35 different types of machine learning models specifically para. 32 binary classification of historical data to train the machine learning model; para. 43-54 and figure 1G specifically para. 48 input the report into the super model and para. 49-50 the model outputs a score; para. 51 determining whether the score satisfies a threshold where in para. 47 “the score may indicate a likelihood of the expense report including an issue. For example, the score may indicate a likelihood of the expense report including a fraudulent expense, a likelihood of the expense report failing an audit, a likelihood of the expense report including data that does not match the features of the super model, and/or the like”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Subramanian with the motivation of increasing efficiency and accuracy.
See para. 13-14 “Some implementations described herein provide a report analysis platform that is capable of processing reports (e.g., thousands, millions, or more reports) associated with an organization utilizing a machine learning model and detecting issues in the reports. In this way, the report analysis platform can process a significant majority (e.g., 90 percent or more), or all, of the reports generated by the organization in a quick and efficient manner. This improves an accuracy of processing reports to identify an issue relative to other techniques. In addition, this increases a throughput of an organization's capability to process reports associated with the organization, thereby reducing or eliminating a risk of missed reports that include an issue. Further, this conserves resources of the organization (e.g., monetary resources, time resources, computing resources, and/or the like) that would otherwise be consumed as a result of using other techniques for processing reports.”
Gratton/Subramanian does not teach, but Embree teaches:
based on the judgment weight (para. 54 user issue reporting performance table with records of past performance of a user in reporting issues – false positive rate, historical accuracy or correctness in reporting uses; para. 59 issue table with record for each issue including identity of the reporting entity and reported entity; para. 67-68, 74 update performance data of reporting entities; figure 9 and para. 72 if reporting entity has been highly reliable and accurate in reporting issues, the module will assign a higher performance priority to the issue; para. 73 “the user performance module 138 may factor in both the past performance of a reporting entity, and a reported entity when calculating the performance priority 172. The module 138 can also attribute different weights to information concerning the reporting entity and the reported entity. For example, a higher weighting may be attributed to the past performance of the reporting entity”; see also para. 75-78)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Embree with the motivation of increasing the accuracy of the determination by accounting for the reliability of the source. See para. 2-4 and para. 97.
As per dependent Claim 8 and Claim 18,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 1 and the electronic device of claim 11.
Gratton teaches:
storing the new incident information in the database by preprocessing the new incident information (figures 3A-C and para. 160-162 storage for signals in different stages of normalization; figure 4 and para. 168-169 storing the normalized signal; para. 380-397 where in para. 397 events can be stored as a prior event in event history database along with predicted impacts)
Claims 6-7, 9-10, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gratton et al. (US2021/0081559) in view of Subramanian et al. (US2020/0074359) in view of Embree et al. (US2005/0160330) in view of Liu et al. (US2021/0398137) as applied to claims 1 and 11, further in view of Shaffer et al. (US2008/0280637).
As per dependent Claim 6 and Claim 16,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 1 and the electronic device of claim 11.
Gratton/Subramanian/Embree/Liu does not teach, but Shaffer teaches:
wherein the outputting of the similar incident information further comprises, based on regulation information associated with laws, systems, or guidelines to respond to the reported new incident, outputting on-site response information changed according to the regulation information (para. 17 standard operating procedures (SOP) followed in the event of an incident and SOPs may be adapted to deviations that arise and the system may log the events and actions that occur so that “the basis of a database which may then be used to suggest actions to take should a similar deviation arise during a different incident”; para. 36 policy engine; para. 49-56 where in para. 54 the SOPs may be updated/revised based on how the deviations were handled in response to other incidents and para. 55 the system may be able to modify prior deviations to adapt them to a current incident; figure 4 and para. 57-69 where in para. 61 determining if the deviation event is similar to a previous event and para. 62 modifies the first policy based on deviation event)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Shaffer with the motivation of improving a user’s ability to respond to incidents.
See Para. 9 “Technical advantages of particular embodiments include methods and systems for handling dynamic incidents. Accordingly, an interoperability system may be able to adjust the actions of a policy or log the actions performed by a user in case an incident does not unfold exactly as the events of a standard operating procedure predicted it would unfold. Another technical advantage of particular embodiments is to allow deviations from a policy to be monitored and logged. Accordingly, the deviations may later be analyzed to determine if the policy needs to be updated or revised. The log of the deviations may also be stored in a database that may be used in creating or revising policies for different incidents.”
As per dependent Claim 7 and Claim 17,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 1 and the electronic device of claim 11.
Gratton/Subramanian/Embree/Liu does not teach, but Shaffer teaches:
wherein the outputting of the similar incident information further comprises outputting textual on-site response information to respond to the reported new incident, based on the new incident information and the similar incident information (para. 17 standard operating procedures (SOP) followed in the event of an incident and SOPs may be adapted to deviations that arise and the system may log the events and actions that occur so that “the basis of a database which may then be used to suggest actions to take should a similar deviation arise during a different incident”; para. 36 policy engine; para. 49-56 where in para. 54 the SOPs may be updated/revised based on how the deviations were handled in response to other incidents and para. 55 the system may be able to modify prior deviations to adapt them to a current incident; figure 4 and para. 57-69 where in in para. 60 bank robbery is occurring during middle of day and lasted longer than traditional bank robbery, system suggests a hostage negotiator be called in, para. 61 determining if the deviation event is similar to a previous event and para. 62 modifies the first policy based on deviation event or generating a suggested modification that is then presented to a user such as a dispatcher who may confirm/approve the modification; para. 23-24, 50 system communicates with various users through endpoints)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Shaffer with the motivation of improving a user’s ability to respond to incidents.
See Para. 9 “Technical advantages of particular embodiments include methods and systems for handling dynamic incidents. Accordingly, an interoperability system may be able to adjust the actions of a policy or log the actions performed by a user in case an incident does not unfold exactly as the events of a standard operating procedure predicted it would unfold. Another technical advantage of particular embodiments is to allow deviations from a policy to be monitored and logged. Accordingly, the deviations may later be analyzed to determine if the policy needs to be updated or revised. The log of the deviations may also be stored in a database that may be used in creating or revising policies for different incidents.”
As per dependent Claim 9 and Claim 19,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 1 and the electronic device of claim 11.
Gratton teaches:
when the new incident information is determined to be true, the reported new incident that is processed (para. 224 based on the truth score exceeding a threshold, event detection infrastructure can trigger an event detection for the event (“determined to be true” – see para. 217-218); Figure 17 and para. 384-391 where in para. 385 receiving an event feed of events detected from one or more normalized signals and specifically para. 386-388 where characteristics of the event are compared to characteristics of prior events; see also Figure 16 and para. 380-383; para. 375-379 “The impact prediction module can maintain an event history database of prior events and corresponding impacts. As new events are detected, the impact prediction module can refer to the event history database and compare the new events to prior events”)
Gratton/Subramanian does not teach, but Embree teaches:
when the new incident information is determined to be not true, storing the new incident information in the database by preprocessing the new incident information (para. 54 user issue reporting performance table with records of past performance of a user in reporting issues – false positive rate, historical accuracy or correctness in reporting uses; para. 59 issue table with record for each issue including identity of the reporting entity and reported entity; para. 67-68, 74 update performance data of reporting entities; figure 9 and para. 72-74 where in para. 74 update write process where if a particular issue is a false positive, records are updated to indicate the outcome; see also para. 100-105)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Embree with the motivation of increasing efficiency by storing information which is not true as it improves future determinations of whether information is true or not.
See para. 2-4 “The above issues pertaining to the processing of issue reports are amplified by a number of factors, such as an increase in the complexity or rules pertaining to the operation of a system (e.g., an online resource of forum), and an increase in the number of sources from which issue reports may originate” and para. 97 “This has the effect of allowing the historical accuracy (or other performance metrics) associated with a reporting entity (e.g., a human reporting user) to be factored into the prioritization of response activities to an issue.”
Gratton/Subramanian/Embree/Liu does not teach, but Shaffer teaches
storing on-site processing information for the new incident information (para. 17 standard operating procedures (SOP) followed in the event of an incident and SOPs may be adapted to deviations that arise and the system may log the events and actions that occur so that “the basis of a database which may then be used to suggest actions to take should a similar deviation arise during a different incident”; para. 36 policy engine)
and the reported new incident that is processed in the database by preprocessing the on-site processing information (para. 49-56 where in para. 54 the SOPs may be updated/revised based on how the deviations were handled in response to other incidents and para. 55 the system may be able to modify prior deviations to adapt them to a current incident; figure 4 and para. 57-69 where in para. 61 determining if the deviation event is similar to a previous event and para. 62 modifies the first policy based on deviation event)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Shaffer with the motivation of improving a user’s ability to respond to incidents. See Para. 9.
As per dependent Claim 10 and Claim 20,
Gratton/Subramanian/Embree/Liu teaches the operating method of claim 1 and the electronic device of claim 11.
Gratton teaches:
wherein the new incident information comprises text information associated with a reporting situation of the reported new incident wherein the text information comprises incident classification of the reported new incident and content of the reported new incident (figure 1A and para. 141 (and para. 129) raw signals such as social posts, 911 calls, crowd sourced information where the content of raw signals can include images, video, audio, text, etc.; para. 142-151 where in para. 151 the raw signal is processed and the normalized signal (time, location, context, content (“content”), type (“classification”), and source) is sent to event detection infrastructure)
Gratton/Subramanian/Embree/Liu does not teach, but Shaffer teaches:
extraneous information wherein the extraneous information comprises an anomaly associated with the reporting situation (para. 6 deviation event may comprise an unexpected event; para. 39 detecting events that occur during the course of an incident; figure 4 and para. 57-69 where in in para. 60 system detects a deviation event, para. 61 determining if the deviation event is similar to a previous event and para. 62 modifies the first policy based on deviation event or generating a suggested modification that is then presented to a user such as a dispatcher who may confirm/approve the modification)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Gratton invention with Shaffer with the motivation of improving a user’s ability to respond to incidents.
See Para. 9 “Technical advantages of particular embodiments include methods and systems for handling dynamic incidents. Accordingly, an interoperability system may be able to adjust the actions of a policy or log the actions performed by a user in case an incident does not unfold exactly as the events of a standard operating procedure predicted it would unfold. Another technical advantage of particular embodiments is to allow deviations from a policy to be monitored and logged. Accordingly, the deviations may later be analyzed to determine if the policy needs to be updated or revised. The log of the deviations may also be stored in a database that may be used in creating or revising policies for different incidents.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Formhals et al. (US2016/0203817)
Asano et al. (US2020/0250183)
Mukund et al. (US2023/0077338)
Galitsky (US2021/0165969)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lisa Ma whose telephone number is (571)272-2495. The examiner can normally be reached Monday to Thursday 7 AM - 5 PM.
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/L.M./Examiner, Art Unit 3628
/RUPANGINI SINGH/Primary Examiner, Art Unit 3628
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