DETAILED ACTION
The following NON-FINAL Office Action is in response to Applicant’s Remarks filed on 08/25/2026.
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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/25/2026 has been entered.
Status of Claims
Claims 1-20 were previously pending and subject to a final Office Action mailed 06/29/2026. Claims 1, 3-5, 9, 11, 13-15, and 19 were amended. Claims 1-20 are currently pending and are subject to the non-final Office Action below.
Priority
Examiner has noted that the Applicant has claimed priority from the foreign application KR10-2023-0157461 filed on 11/14/2023.
Response to Arguments
35 USC § 112
Applicant has amended the independent claims by removing the limitation which failed to comply with the written description requirement. Accordingly, the 35 U.S.C. 112(a) rejections of Claims 1-20 have been rendered moot and thus, have been withdrawn.
35 USC § 101
Applicant’s arguments, see pages 10-14, filed 08/25/2026, with respect to the 35 U.S.C. 101 rejections of Claims 1-20 have been fully considered and are not persuasive.
First, Examiner respectfully disagrees that the specification discloses a technical problem as the problem is within the realm of business relations or social activities since manpower/property loss may occur if the report is false/mistaken and the manager is not able to handle the incident in a timely manner or may inappropriately handle the incident.
Applicant argues that Claims 1-20 do not articulate any abstract idea. Examiner respectfully disagrees. Although the bolded limitations may be considered as additional elements, the features of receipt of information, determination, conditional similarity determination, and outputting are considered as part of the abstract idea. Specifically, commercial interactions or business relations in light of paragraph 4 of Applicant’s specification.
See also MPEP 2106.04(a)(2)(II) “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping.”
Applicant further argues that the claims improve the functioning or operation of the electronic device and the underlying incident-response technology. Examiner respectfully disagrees. Regarding the real-time false-report filtering via a hybrid approach, Examiner notes that the judgment weight is generated using non-text features derived from the reporter information, new incident information, and preprocessed past incident information.
It is noted that the features upon which applicant relies (i.e., past statistics/logistic regression, SoftMax score compared to a threshold) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The binary classification deep learning model is trained on preprocessed past incident information to generate a probability. Every machine learning model is trained on a data set in order to output a result. Accordingly, such a feature is generic “machine learning”.
Regarding the conditional, filtered similarity search, Examiner again notes that the features of vector embeddings, cosine similarity, within a predefined threshold are not recited in the claims. Additionally, the retrieval of similar incidents allows for the transmission of the appropriate response information to the on-site manager which is an improvement in the abstract idea itself. See below.
Regarding the output of similar incident information, Examiner notes that supporting situational understanding and decision-making is an improvement in the abstract idea as the on-site manager is equipped with additional data to perform their duties.
Examiner respectfully disagrees that the claims are similar to the eligible claims Applicant noted and that they recite a specific improvement in the technical field of automated emergency incident processing systems. The improvements are directed to determining whether the new incident information is obtained by false/mistaken report in order to prevent manpower and property loss and to help an on-site manager handle the incident by providing them with the appropriate on-site response measure. Such improvements are improvements in the abstract idea itself.
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, see pages 14-18 of Applicant’s Remarks, filed 08/25/2026, with respect to the 35 U.S.C. 103 rejection of Claims 1-20 have been fully considered and are persuasive. The 35 U.S.C. 103 rejection of Claims 1-20 has been withdrawn. See “Closest Prior Art” section below.
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, …, whether the new incident information is obtained by a false or mistaken report, based on the new incident information and the reporter information by generating a judgment weight using non-text features derived from the reporter information, the new incident information, and preprocessed past incident information … computing, … , a probability that the new incident information is obtained by the false or mistaken report based on text of the new incident information, and determining the false or mistaken report based on the judgment weight and the probability;
when the new incident information is determined … to be a false or mistaken report, transmitting the new incident report as false or mistaken report information;
when the new incident information is determined … to be true, selectively determining a similarity with one or more pieces of related incident information associated with the reported new incident that are filtered … by selecting the related incident information based on incident classification, report date and time, and report location of the new incident information and computing the similarity … that is applied to text of the new incident information and text of the related incident information;
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, “[determining] in real-time”, a processor of the electronic device, “using a binary classification deep learning model trained on the preprocessed past incident information”, a database/the 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, “[determine] in real-time”, “using a binary classification deep learning model trained on the preprocessed past incident information”, a database/the 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/the 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).
“[Determining] in real-time” and “[determine] in real-time” are merely indicating a field of use or technological environment in which to apply a judicial exception because limiting the use of the process/machine to real-time processing did no more than describe how/when the abstract idea of business relations or social activities could be used and further limiting the abstract idea to activities that are executed in a computer environment (i.e., execution in real time using the processor). See MPEP 2106.05(h).
The additional elements of “using a binary classification deep learning model trained on the 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 “using a binary classification deep learning model trained on the preprocessed past incident information” and further, specifying that the abstract idea of “when” 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.
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/the database that stores past incident information amount to no more than mere instructions to apply the exception using a generic computer component.
Again, the limitations of “[determining] in real-time using the processor of the electronic device” and “[determine] in real-time using the processor” are merely indicating a field of use or technological environment in which to apply a judicial exception because limiting the use of the process/machine to real-time processing did no more than describe how/when the abstract idea of business relations or social activities could be used and further limiting the abstract idea to activities that are executed in a computer environment (i.e., execution in real time using the processor).
The additional elements of “using a binary classification deep learning model trained on the preprocessed past incident information” and “a text similarity algorithm” are specifying that the abstract idea of “determining” was to be implemented by “using a binary classification deep learning model trained on the preprocessed past incident information” and further, specifying that the abstract idea of “when” 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 again recite the additional element of the 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 again recite the additional element of the 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 the 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.
Closest Prior Art
Examiner noting that Claims 1-20 are still rejected under 35 U.S.C. 101.
The following is a statement of reasons for the indication of closest prior art: available prior art, alone or in combination, fail to disclose every element of the independent claims.
Examiner noting the limitations of “determining, in real time using the 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 by generating a judgment weight using non-text features derived from the reporter information, the new incident information, and preprocessed past incident information stored in a database, and computing, using a binary classification deep learning model trained on the preprocessed past incident information, a probability that the new incident information is obtained by the false or mistaken report based on text of the new incident information, and determining the false or mistaken report based on the judgment weight and the probability” as the specific limitation not disclosed by available prior art.
The following are the closest prior art:
Gratton et al. (US2021/0081559) teaches “receiving”, “outputting”, determining 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)
Subramanian et al. (US2020/0074359) teaches extracting features from text, computing a probability score, and 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)
Embree et al. (US2005/0160330) teaches judgment weights 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)
Liu et al. (US2021/0398137) teaches a text similarity algorithm and 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)
Formhals et al. (US2016/0203817) teaches a user reporting incident details and immediate analysis of incident information to facilitate a rapid response to the incident.
Asano et al. (US2020/0250183) teaches police officers accessing databases which store crime information in order to obtain necessary information for an incident/accident that has occurred.
Mukund et al. (US2023/0077338) teaches processing of incident reports using natural language and machine learning and providing real-time feedback.
Galitsky (US2021/0165969) teaches using a machine learning model to generate a complexity score that is indicative of a level of deception in the text and further teaches sentiment detection using weights.
Conclusion
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/L.M./Examiner, Art Unit 3628
/RUPANGINI SINGH/Primary Examiner, Art Unit 3628
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