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 .
DETAILED CORRESPONDENCE
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 17, 2026 has been entered.
Status of Claims
Claims 1, 10, 19 have been amended.
Claims 2, 3, 7, 11, 12, 16 have been cancelled.
No claims have been added.
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, 4 – 6, 8 – 10, 13 – 15, 18 – 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
formulate replies to incoming user messages;
transmit a prompt instructing to identify a target object for fraud analysis according to the conversation,
determine an indicator of the target object according to a replay to the prompt
transmit the indicator of the target object to the threat analyzer, and
formulate a reply to the user according to an output of the threat analyzer, the reply to the user indicating whether the user is a victim of online fraud; and
in response to receiving the indicator of the target object from the chatbot agent:
extract a set of features characterizing the target object according to the indicator of the target object,
carry out a fraud analysis according to the set of features to determine whether the target object is indicative of fraud, and
output a result of the fraud analysis
The invention is directed towards the abstract idea of fraud detection, which is further based on the collection and comparison of information and, based on a rule(s), identify options, which corresponds to “Mental Processes” and “Certain Methods of Organizing Human Activities”, as it is directed towards steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper, e.g., a human agent speaking with a human customer, the human customer conveying (verbally, writing, or etc.) their concerns, the human agent comparing what was conveyed with known information, and, based on the comparison and any associated rule(s), identify whether fraudulent activity has occurred, which is further directed to risk mitigation.
The limitations of:
formulate replies to incoming user messages;
transmit a prompt instructing to identify a target object for fraud analysis according to the conversation,
determine an indicator of the target object according to a replay to the prompt
transmit the indicator of the target object to the threat analyzer, and
formulate a reply to the user according to an output of the threat analyzer, the reply to the user indicating whether the user is a victim of online fraud; and
in response to receiving the indicator of the target object form the chatbot agent:
extract a set of features characterizing the target object according to the indicator of the target object,
carry out a fraud analysis according to the set of features to determine whether the target object is indicative of fraud, and
output a result of the fraud analysis
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer (the specification has defined the generic chatbot, generic language model, and generic threat analyzer as components of generic machine learning, see, at least, ¶ 23, 24, 56, 75 – 79 of the applicant’s specification). That is, other than reciting a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer in the context of this claim encompasses, for example, two human users communicating with one another where a first user conveys their concerns to a second user and the second user comparing what was conveyed against known information and determining if fraud has occurred. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer, then it falls within the “Mental Processes” and “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer to communicate information, as well as performing operations that a human can perform in their mind and/or pen and paper, i.e. comparing information and, based on a rule(s), identify options, in this case, whether fraud has occurred based on the comparison of transmitted and retrieved information. The generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer in the steps are recited at a high-level of generality (i.e., as a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer can perform the insignificant extra solution steps of communicating information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer are merely being applied to perform the steps that can be performed by a human, in the human mind, and/or with the aid of pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology and directed to an “idea of a solution or outcome” (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer.
Although the claim recites “a chatbot”, “a language model”, and a “threat analyzer,” the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying a machine learning model is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a machine learning model is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards real estate property evaluation and the data associated with real estate properties that can affect a property’s value. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2. Moreover, as evidenced at ¶ 23, 24, 75 – 79, the claimed invention is not improving upon the technology or resolving an issue that arose in the technology, but utilizing generic and known technology (ChatGPT® from OpenAI, Inc. or BARD® from Google, Inc.) and applying it to the abstract idea, wherein the abstract idea, as discussed above, can be performed by one or more humans.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
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 element of using a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer to perform the steps of:
formulate replies to incoming user messages;
transmit a prompt instructing to identify a target object for fraud analysis according to the conversation,
determine an indicator of the target object according to a replay to the prompt
transmit the indicator of the target object to the threat analyzer, and
formulate a reply to the user according to an output of the threat analyzer, the reply to the user indicating whether the user is a victim of online fraud; and
in response to receiving the indicator of the target object form the chatbot agent:
extract a set of features characterizing the target object according to the indicator of the target object,
carry out a fraud analysis according to the set of features to determine whether the target object is indicative of fraud, and
output a result of the fraud analysis
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claim 4 is, as discussed above and supported by, at least, ¶ 23, 24, 75 – 79 of the applicant’s specification, directed the recitation of generic technology at a high level of generality and applying it to the abstract idea, as was also discussed above.
Claim 5 is directed towards descriptive subject matter, in this case, describing what the target object could consist of.
Claim 6 is directed towards descriptive subject matter, in this case, describing what the target object includes.
Claim 8 is, as discussed above and supported by, at least, ¶ 23, 24, 78 of the applicant’s specification, directed the recitation of generic technology at a high level of generality and applying it to the abstract idea, as was also discussed above, as well as the collection and organization of information.
Claim 9 is directed towards human activities, in this case, providing advice, recommendation, suggestion, or the like when fraud has been detected and advising how to address it.
Claim 20 is directed towards “Mental Processes” and “Certain Methods of Organizing Human Activities” and referring to a rule to determine whether a conversation should end based on the contents of the conversation (collecting and comparing information and, based on a rule(s), identify options).
Claim 22 is directed towards collecting and comparing information and, based on a rule(s), identify options, in this case, collecting information and comparing it to provided information and, based on a rule(s), determining that information should be parsed according to a rule, which further falls under “Mental Processes” and “Certain Methods of Organizing Human Activities”.
The remaining claims recite similar subject matter that has already been discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for identifying and managing detected fraud. Accordingly, the claims are not patent eligible.
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 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.
Claims 1, 4 – 6, 8 – 10, 13 – 15, 18 – 23 are rejected under 35 U.S.C. 103 as being unpatentable over Hazony et al. (US PGPub 2021/0240836 A1) in view of Jiron et al. (US PGPub 2019/0394333 A1).
In regards to claims 1, 10, 19, Hazony discloses (Claim 1) a computer system for detecting online fraud while countering adversarial attacks and maintaining separation between fraud detection heuristics and user interaction, the computer system comprising at least one hardware processor configured to execute a chatbot agent and a threat analyzer coupled to the chatbot agent, wherein; (Claim 10) a computer-implemented method of detecting online fraud while countering adversarial attacks and maintaining separation between fraud detection heuristics and user interaction, the method comprising employing at least one hardware processor of a computer system to execute a chatbot agent and a threat analyzer coupled to the chatbot agent (Fig. 1, 2, 3), wherein; (Claim 19) a non-transitory computer-readable medium storing instructions which, when executed by at least one hardware processor of a computer system, cause the computer system to execute a method of detecting online fraud while countering adversarial attacks and maintaining separation between fraud detection heuristics and user interaction, the method comprising executing a chatbot agent and a threat analyzer coupled to the chatbot agent, wherein (A preamble is generally not accorded any patentable weight where it merely recites the purpose of a process or the intended use of a structure, and where the body of the claim does not depend on the preamble for completeness but, instead, the process steps or structural limitations are able to stand alone. See In re Hirao, 535 F.2d 67, 190 USPQ 15 (CCPA 1976) and Kropa v. Robie, 187 F.2d 150, 152, 88 USPQ 478, 481 (CCPA 1951).):
the chatbot agent is configured to engage in a conversation with a user in a natural language (NL), comprising employing a language model (LM) to formulate replies to incoming user messages, and further configured to:
transmit a language model prompt to the LM, the LM prompt instructing the LM to identify a target object for fraud analysis according to the conversation,
determine an indicator of the target object according to a reply by the LM to the LM prompt,
transmit the indicator of the target object to the threat analyzer, and
formulate a reply to the user according to an output of the threat analyzer, the reply to the user indicating whether the user is a victim of online fraud; and
the threat analyzer is configured to, in response to receiving the indicator of the target object from the chatbot agent:
extract a set of features characterizing the target object according to the indicator of the target object,
carry out a fraud analysis according to the set of features to determine whether the target object is indicative of fraud, and
output a result of the fraud analysis to the chatbot agent
(¶ 145 wherein a user enters into a conversation with a chatbot (assistant unit (AU)) by asking questions and the chatbot providing answers, e.g., the user asking the chatbot about a suspicious email, as well as guiding the user through a particular issue. In other words, in response to the user submitting an NL question, the chatbot processes the question to determine how to respond, which is based on the results provided by a language model to allow it to extract the target of the question and message (e-mail), which allows the AU to respond to the user’s question in natural language;
¶ 26, 28, 35, 79, 80, 108, 110 wherein the chatbot (AU) and threat analyzer (CAU) include a natural language model that allows it to mimic a human and respond to input provided by users, e.g., questions, wherein the AU/CAU have been trained using data comprised of natural language in order to not only provide answers to user submitted questions, but to also analyze the content of messages received by the user, i.e. extract content from the conversation to identify features corresponding to a target so that the chatbot can provide a corresponding/appropriate response;
¶ 34, 35, 145, 146 wherein the system is receiving natural language information and processing the natural language information to identify specific natural language that are indications of fraudulent activity and formulating a prompt, i.e. the search/analysis, that is being applied to the received content and stored content to identify the word usage, writing style, grammar, form of speech, and etc. and determine whether fraudulent activity is present in the received content
¶ 33, 34, 35 wherein the system, which includes the CA and CAU, is trained to identify specific content within a target object (e.g., e-mail) that the user is inquiring about and wherein the identification is based on natural language processing (NLP);
¶ 33, 34, 35, 37, 38, 44, 56, 145 wherein the CAU (threat analyzer) is trained and re-trained to utilize, at least, NLP to perform a fraud analysis, e.g., phishing attempt, on the user’s e-mail to determine if there is an indication that the e-mail is a phishing attempt to allow the chatbot to retrieve the CAU’s results to present to the user. The system may monitor user behavior in order to identify an indication that the user may be a victim of fraudulent activity and take appropriate actions or may the user may request for the system to inform them if they are a victim of fraudulent activity and the system will take appropriate actions, i.e. the system may do this on its own or in response to a user’s request).
In summary, Hazony discloses a system and method for utilizing a chatbot and machine learning, which are configured to mimic humans and understand natural language, to assist a user with identifying fraudulent activity in response to a user submitting a query to the system asking about the content of their e-mail to determine if the e-mail is a phishing attempt or by the system monitoring user behavior.
However, the Examiner asserts that, as currently claimed, the claimed invention does not provide sufficient subject matter to describe an order of operations of when certain actions are taking place or, to put it another way, that the query submitted by the user is in regards to content not previously analyzed by the system, i.e. whether the fraud analysis is being performed in response to a user requesting the system to look into an issue that has already occurred (i.e. fraud has already been committed and the user is a targeted victim of fraud), whether the fraud analysis is being performed on the conversation itself, whether the fraud analysis is being performed on a current issue that has raised some concerns for the user (e.g., an e-mail received by the user seems suspicious and the user wants the system to look into it before the user responds to the user and the user is a targeted victim of fraud), or whether the fraud analysis is being performed on a new concern that the system has no experience on and is extrapolating an analysis using some level of known information (e.g., the issue is not a historical issue that the system has been trained on, but the system is able to extract certain information and compare it to known information to extrapolate that the user is a victim of fraud). As a result, in the interest of compact prosecution, the Examiner has provided Jiron to teach that it is well-known and obvious to one of ordinary skill in the art for a chatbot and machine learning model that has been trained using, at least, natural language processing, to process user submitted queries regarding content that it has not already been trained on as a means of covering alternate interpretations of the claimed invention.
With that said, Jiron, which is also directed towards a user communicating with an agent, further teaches that it is well-known and obvious to one of ordinary skill in the art for a system comprised of a chatbot and machine learning/artificial intelligence (ML/AI) to not only be trained to identify and respond to fraudulent activity, but to perform this process while being presented with fraudulent content at the time of the interaction. That is to say, Jiron teaches that it would have been obvious to one of ordinary skill in the art that such a system can also be programmed to identify fraudulent content at the time that it is being presented with the fraudulent content.
One of ordinary skill in the art looking upon Hazony would have found that such systems are used in order to prevent fraudulent activity from taking place, but would have questioned whether such prevention techniques can be extended to fraudulent activities that it has not already been decided upon. Accordingly, one of ordinary skill in the art looking upon the teachings of Jiron would have found that it is, indeed, obvious that fraud detection can also take place at the time that the fraudulent activity is being presented to the system. One of ordinary skill in the art would have found it beneficial to cover not only pre-existing fraud, but also newly presented fraud as this provides a more robust system that serves to further enhance the preventative fraud detection system of Hazony. That is to say, by incorporating the teachings of Jiron into Hazony one of ordinary skill in the art would have a more robust and reliable fraud detection system and method by covering more instances of when fraud can occur, thereby providing a better and more effective preventative fraud detection system.
(For support see: ¶ 3, 32, 33, 61)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the chatbot and ML/AI based fraud detection system of Hazony with the ability to utilize such chatbot and ML/AI based fraud detection system to also identify fraudulent activity as it is being presented to it, as taught by Jiron, as this would result in a more robust and effective fraud detection system that would further enhance security and preventative fraud detection.
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that applying the known technique of utilizing a chatbot and ML/AI based fraud detection system to detect fraudulent activity as it is presented with it (i.e., a determination was not made beforehand), as taught by Jiron, would have yielded predictable results and resulted in an improved system. It would have been obvious that applying the technique of Jiron to Hazony would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such fraud detection techniques utilized by fraud detection systems that utilize chatbots and ML/AI. Further, applying the fraud detection technique of Jiron to that of Hazony would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for better fraud detection by casting a wider net of when fraud can be detected.
In regards to claims 4, 13, the combination of Hazony and Jiron discloses the computer system of claim 1 (the method of claim 10), wherein the chatbot agent is configured to:
formulate the LM prompt to cause the LM to include a conversation summary in the LM reply, the conversation summary comprising a summary of the conversation; and
determine the indicator of the target object according to the conversation summary
(Hazony – ¶ 56, 57, 91; Jiron – ¶ 34, 37, 54 wherein the chatbot and ML/AI are trained and retrained to provide a self-learning system and method that utilizes and improves upon its training and trained data, wherein the trained data is comprised of utilizing historical information, i.e. conversations that have previously occurred (conversation summary), for future conversations, thereby providing improved responses and fraud detection capabilities.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the chatbot and ML/AI based fraud detection system of Hazony with the ability to utilize such chatbot and ML/AI based fraud detection system to also identify fraudulent activity as it is being presented to it, as taught by Jiron, as this would result in a more robust and effective fraud detection system that would further enhance security and preventative fraud detection.
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that applying the known technique of utilizing a chatbot and ML/AI based fraud detection system to detect fraudulent activity as it is presented with it (i.e., a determination was not made beforehand), as taught by Jiron, would have yielded predictable results and resulted in an improved system. It would have been obvious that applying the technique of Jiron to Hazony would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such fraud detection techniques utilized by fraud detection systems that utilize chatbots and ML/AI. Further, applying the fraud detection technique of Jiron to that of Hazony would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for better fraud detection by casting a wider net of when fraud can be detected.).
In regards to claims 5, 14, the combination of Hazony and Jiron discloses the computer system of claim 1 (the method of claim 10), wherein the target object includes an item selected from a group consisting of a screenshot received from the user and a uniform resource identifier (URI) of an Internet resource, the URI included in the incoming message (Hazony – ¶ 36, 37, 65, 88 wherein the fraud detection system is configured to identify a wide range of content, such as, but not limited to, a URL, link, images, and etc.).
In regards to claims 6, 15, the combination of Hazony and Jiron discloses the computer system of claim 1 (the method of claim 10), wherein the target object includes a text determined according to the conversation (Hazony – ¶ 34, 35, 146 wherein the target content includes, at least, text according to the conversation between the chatbot and user and wherein the conversation includes the NL message (question)).
In regards to claims 8, 17, the combination of Hazony and Jiron discloses the computer system of claim 1 (the method of claim 10), wherein:
the fraud analysis comprises classifying the target object into a selected category of a plurality of categories, each category of the plurality of categories indicative of a distinct type of online fraud; and
the result of the fraud analysis includes an indicator of the selected category
(Hazony – ¶ 34, 37, 38, 40, 41, 43, 60, 146 wherein, as part of the fraud analysis, the system classifies the target object into a particular category with each category corresponding to a particular type of online fraud and the results of the fraud analysis includes an indicator of the category).
In regards to claims 9, 18, the combination of Hazony and Jiron discloses the computer system of claim 8 (the method of claim 17), wherein the reply to the user includes fraud protection advice formulated according to the selected category (¶ 146 wherein the system provides the user with guidance/instructions/actions, i.e. advice, they can follow when fraudulent activity has been detected by the system).
In regards to claims 20, 21, the combination of Hazony and Jiron discloses the computer system of claim 1 (the method of claim 10), wherein the chatbot agent is further configured to:
in preparation for transmitting the LM prompt to the LM, determine whether the incoming message is indicative of an attack on the LM; and
in response, if yes, end the conversation
(Jiron – ¶ 61, 62 wherein the system determines that the user is not a human, but a robot and, in response, the chatbot ends the conversation with the robot by sending it to a fraud prevention system in the contact center.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the chatbot and ML/AI based fraud detection system of Hazony with the ability to utilize such chatbot and ML/AI based fraud detection system to also identify fraudulent activity as it is being presented to it, as taught by Jiron, as this would result in a more robust and effective fraud detection system that would further enhance security and preventative fraud detection.
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that applying the known technique of utilizing a chatbot and ML/AI based fraud detection system to detect fraudulent activity as it is presented with it (i.e., a determination was not made beforehand), as taught by Jiron, would have yielded predictable results and resulted in an improved system. It would have been obvious that applying the technique of Jiron to Hazony would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such fraud detection techniques utilized by fraud detection systems that utilize chatbots and ML/AI. Further, applying the fraud detection technique of Jiron to that of Hazony would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for better fraud detection by casting a wider net of when fraud can be detected.).
In regards to claims 22, 23, the combination of Hazony and Jiron discloses the computer system of claim 1 (the method of claim 10), wherein:
the LM is configured to include a pre-determined action indicator within the reply by the LM to the LM prompt, the action indicator indicative of the target object; and
(Claim 22) determining the indicator of the target object comprises the chatbot agent parsing the reply by the LM to the LM prompt for the action indicator
(Claim 23) determining the indicator of the target object comprises parsing the reply by the LM to the LM prompt for the action indicator
(Hazony – ¶ 35, 36, 37, 38, 40, 42, 43, 48 wherein the language model utilized by the system includes predetermined actions, i.e. rules, that dictate how information within a message, conversation, e-mail, or etc. that it has been provided should be analyzed in order to provide an assessment of the information (spelling/grammar mistakes, writing styles, and etc. that may be known to be used by attackers and having the system associate a score to the message), which, in turn, provides its results to the AU and CAU to assess the risk level of the message (or the like) by parsing/analyzing the results, e.g., identify the offensive content within the message, and take an appropriate actions, e.g., hiding/not providing information to the user, presenting information to the user, determine the source of the information to determine the appropriate risk level, when a message was transmitted, track correspondences to avoid false alarms, and etc.).
Response to Arguments
Applicant's arguments filed 3/17/2026 have been fully considered but they are not persuasive.
Declaration under 37 CFR 1.132
The declaration received on March 17, 2026 is unpersuasive.
Points 1 – 3 are establishing the background of the individual and claimed subject matter.
Point 4: The Examiner asserts that the statement is an opinion and not a statement of fact because it is directed to what the Mr. Cernat believes cannot be performed in the human mind because of the volume of computation required for carrying out the steps and because fraud is constantly evolving. However, no evidence has been provided in this statement. As was stated in the rejection, the claimed invention is directed towards the abstract idea of fraud detection, which is further based on the collection and comparison of information and, based on a rule(s), identify options, which corresponds to “Mental Processes” and “Certain Methods of Organizing Human Activities”, as it is directed towards steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper, e.g., a human agent speaking with a human customer, the human customer conveying (verbally, writing, or etc.) their concerns, the human agent comparing what was conveyed with known information, and, based on the comparison and any associated rule(s), identify whether fraudulent activity has occurred. In other words, the Examiner has provided an example demonstrating that the claimed invention can, indeed, be performed in the human mind and directed towards risk mitigation.
As was also stated in the rejection, although the additional elements of a generic processor executing computer code stored on a computer medium, generic chatbot, generic language model, and generic threat analyzer have been recited, the Examiner asserts that the specification explicitly states that the technology is generic. The specification states that the claimed invention is not improving upon the technology, but reciting it at a high level of generality and applying it to the abstract idea, as well as relying on established technology rather than improving the technology (¶ 23, 24, 56, 75 – 79 of the applicant’s specification). Moreover, the Examiner asserts that point 4 has only established that the generic technology is being utilized for the advantages that the technology provides, i.e. faster, more efficient, and etc.
The Examiner asserts that the specification lacks any disclosure of evidence to demonstrate that the invention is seeking to improve upon the technology or, more specifically, that the claimed invention is directed towards addressing and improving upon an issue that arose from the technology, but merely demonstrating that the claimed invention is directed towards the abstract idea and merely applying or utilizing generic computing devices performing their generic functions to carry out activities in the technical field of fraud detection, which is further based on the collection and comparison of information and, based on a rule(s), identify options due to the benefits that computing devices provided, i.e. faster, more efficient, and etc.. The courts further stated:
“The Supreme Court has not established a definitive rule to determine what constitutes an “abstract idea” sufficient to satisfy the first step of the Mayo/Alice inquiry. See id. at 2357. Rather, both this court and the Supreme Court have found it sufficient to compare claims at issue to those claims already found to be directed to an abstract idea in previous cases. “[The Court] need not labor to delimit the precise contours of the ‘abstract ideas’ category in this case. It is enough to recognize that there is no meaningful distinction between the concept of risk hedging in Bilski and the concept of intermediated settlement at issue here.” Alice, 134 S. Ct. at 2357; see also OIP Techs., 788 F.3d at 1362. For instance, fundamental economic and conventional business practices are often found to be abstract ideas, even if performed on a computer. See, e.g., OIP Techs., 788 F.3d at 1362–63.”
(Page 10)
“Moreover, we are not persuaded that the invention’s ability to run on a general-purpose computer dooms the claims. Unlike the claims at issue in Alice or, more recently in Versata Development Group v. SAP America, Inc., 793 F.3d 1306 (Fed. Cir. 2015), which Microsoft alleges to be especially similar to the present case, Appellee’s Br. 18, see also Oral Argument at 15:40–18:15, the claims here are directed to an improvement in the functioning of a computer. In contrast, the claims at issue in Alice and Versata can readily be understood as simply adding conventional computer components to well-known business practices. See Alice, 134 S. Ct. at 2358–60; Versata Dev. Grp., 793 F.3d at 1333–34 (computer performed “purely conventional” steps to carry out claims directed to the “abstract idea of determining a price using organization and product group hierarchies”); see also Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016) (claims attaching generic computer components to perform “anonymous loan shopping” not patent eligible); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367–69 (Fed. Cir. 2015) (claims adding generic computer components to financial budgeting); OIP Techs., 788 F.3d at 1362–64 (claims implementing offer-based price optimization using conventional computer activities); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714–17 (Fed. Cir. 2014) (claims applying an exchange of advertising for copyrighted content to the Internet); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1354–55 (Fed. Cir. 2014) (claims adding generic computer functionality to the formation of guaranteed contractual relationships). And unlike the claims here that are directed to a specific improvement to computer functionality, the patent ineligible claims at issue in other cases recited use of an abstract mathematical formula on any general purpose computer, see Gottschalk v. Benson, 409 U.S. 63, 93 (1972), see also Alice, 134 S. Ct. at 2357–58, or recited a purely conventional computer implementation of a mathematical formula, see Parker v. Flook, 437 U.S. 584, 594 (1978); see also Alice, 134 S. Ct. at 2358, or recited generalized steps to be performed on a computer using conventional computer activity, see Internet Patents, 790 F.3d 1348–49 (claims directed to abstract idea of maintaining computer state without recitation of specific activity used to generate that result), Digitech Image Techs., LLC v. Electrs. For Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014) (claims directed to abstract idea of “organizing information through mathematical correlations” with recitation of only generic gathering and processing activities).”
(Pages 16 – 17)
“In sum, the self-referential table recited in the claims on appeal is a specific type of data structure designed to improve the way a computer stores and retrieves data in memory. The specification’s disparagement of conventional data structures, combined with language describing the “present invention” as including the features that make up a self-referential table, confirm that our characterization of the “invention” for purposes of the § 101 analysis has not been deceived by the “draftsman’s art.” Cf. Alice, 134 S. Ct. at 2360. In other words, we are not faced with a situation where general-purpose computer components are added post-hoc to a fundamental economic practice or mathematical equation. Rather, the claims are directed to a specific implementation of a solution to a problem in the software arts. Accordingly, we find the claims at issue are not directed to an abstract idea.”
(Page 18)
As a result, the Examiner asserts that, in light of the applicant’s specification (see Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015); see Genetic Techs. Ltd. v. Merial L.L.C., 2016 WL 1393573, at *5 (Fed. Cir. 2016) (inquiring into “the focus of the claimed advance over the prior art”)), the claimed invention does not lie with the improvement of a technology, identifying and resolving an issue that arose from the technology, or that the claimed invention is “deeply rooted in the technology”, but that the claimed invention is directed towards the abstract idea of fraud detection and merely utilizing generic computing devices (see applicant specification at ¶ 23, 24, 56, 75 – 79) in order to perform activities in the field of fraud detection. As was found in Alice Corp v CLS Bank, the claims in Alice Corp v CLS Bank also required a computer that processed streams of data, but nonetheless were found to be abstract. There is no “inventive concept” in the claimed invention's use of a generic technology to perform activities used in the technical field, in this case, fraud detection.
Consequently, the Examiner asserts that the claimed invention is, in fact, more closely directed related to the decision of, inter alia, TLI Communications, LLC v AV Automotive, LLC, in that the claimed invention is merely relying on the use of a generic computing device to perform the abstract idea of fraud detection. As was done in TLI Communications, the Examiner refers to the specification to determine whether the claimed invention amounts to “significantly more” or whether the claimed invention is directed towards the improvement of the technological arts.
Point 5 is a general conclusionary statement based on opinion rather than fact.
Point 6 supports the rejection provided because it is a statement that is directed towards reciting a high-level explanation of the goals and objectives of the invention rather than how the technology is being improved upon or what issues arose in the technology and how the claimed invention seeks to improve upon the technology. The Examiner asserts that point 6 is directed towards reciting an idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished.
Points 7, 8, 9 are similar to point 4 and 6 and, accordingly, the Examiner refers to and incorporates the analysis provided above. Additionally, although one may argue that the human mind is unable to process and recognize the electronic stream of data that is being received, transmitted, stored, and etc. by the computing device, the Examiner asserts that this is insufficient to overcoming the rejection under 35 USC 101 (see Content Extraction and Transmission LLC v Wells Fargo Bank, National Association and Cyberfone where the system uses categories to organize, store, and transmit information, which was considered by the courts to be an abstract idea). The claims in Alice Corp v CLS Bank also required a computer that processed streams of data, but nonetheless were found to be abstract. There is no “inventive concept” in the claimed invention's use of the aforementioned generic technology to perform activities used in the technical field, in this case, fraud detection or, more specifically, collection and comparison of information and, based on a rule(s), identify options (identify whether fraudulent activity has occurred). (Content Extraction and Transmission LLC v Wells Fargo Bank, National Association) At most, the claims attempt to limit the abstract idea of recognizing and storing information using the devices to a particular environment. Such a limitation has been held insufficient to save a claim in this context.
Moreover, Mr. Cernat has stated that fraud constantly evolves to avoid detection and that “…form and content of fraudulent messages changes within time period on the order of hours. Therefore, the heuristics and algorithms involved in carrying out fraud analysis are updated with responsiveness matching evolving threats…” However, as was discussed above, this is unpersuasive because it is directed towards an idea of a solution or outcome rather than how or, more specifically, what the heuristics and algorithms are, how or what specificities within the claimed invention are able to react as quickly and effectively as Mr. Cernat is stating, and how the generic technology that the specification explicitly states it is relying on is being modified, improved, or etc. to allow for the idea of a solution or outcome to be received.
Point 10 is a conclusionary statement.
With regards to point 11 and 12, the Examiner asserts that 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). In this case, the claimed invention does not positively recite “keeping the threat analyzer separate from the components that engage in conversation with the user.” A preamble is generally not accorded any patentable weight where it merely recites the purpose of a process or the intended use of a structure, and where the body of the claim does not depend on the preamble for completeness but, instead, the process steps or structural limitations are able to stand alone. See In re Hirao, 535 F.2d 67, 190 USPQ 15 (CCPA 1976) and Kropa v. Robie, 187 F.2d 150, 152, 88 USPQ 478, 481 (CCPA 1951).
The Examiner also refers to ¶ 77 – 79 as the statements are supported by these portions of the applicant’s specification. However, similar to what has been discussed above, the statements and specification are conclusionary statements that fail to show the particular algorithms and heuristics that were previously discussed. Moreover, the specification fails to define what is considered “separate from the user interaction”. Specifically, is the threat analyzer and interface that a user is utilizing to interact with the invention at different locations? Are they different modules of a software package? Are they different software programs? If these components are separate from one another, how are they communicating with one another? If the components are communicating with one another then they are coupled with each and not separate? Is the statement referring to a user not visually seeing the backend processing that is occurring and that is why it is “separate from the user interaction” (For example, pressing the letter “h” in Microsoft Word would satisfy this interpretation because the interface is separate from the background processing that is occurring and the only thing the user will see is the letter “h” appearing in the interface and not the background processing)?
Finally, the Examiner asserts that upon review of the specification and analyses provided above, the claimed invention is reciting generic technology and applying it to the abstract idea while reciting, inter alia, and idea of a solution or outcome, e.g., utilizing ChatGPT that has only been programmed to allow for a user to converse with a computing device and utilizing ChatGPT in a particular environment (fraud detection) by coupling ChatGPT with a software program, module, or the like to perform the backend processing, passing its results to ChatGPT, and ChatGPT displaying the results to the user while failing to show the “heuristics and algorithms” of the backend process, i.e. threat analyzer. In addition to what has been discussed above, the Examiner asserts that the invention is an idea of a solution because it is directed towards reciting generic technology, e.g., ChatGPT, Bard, and etc., and applying it to a particular environment of use, i.e. fraud detection, with the intent of providing an all encompassing software package that is intended to be trained for use in the particular environment, but failing to recite the training, processes, algorithms, heuristics, and etc. that are necessary to achieve the idea or outcome, i.e. the algorithms and heuristics that comprise the threat analyzer.
For these reasons, the declaration is unpersuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
In addition to the analysis provided with respect to the Declaration that was received on March 17, 2026, which is incorporated herein, the Examiner asserts that the claimed invention is not improving technology or resolving an issue that arose in technology, which is further evidenced by ¶ 23, 24, 56, 75 – 79 of the applicant’s specification. The applicant’s reference to Research Corp. Techs v. Microsoft Corp. is unpersuasive because the claimed invention does not rise to the level of demonstrating a technology improvement or resolving an issue that arose in technology, but an idea of a solution or outcome. The claimed invention is reciting generic technology at a high level of generality and applying it to the abstract idea while failing to demonstrate the particulars that comprise the technology, i.e. algorithms and heuristics, to achieve the solution or outcome. As discussed above, the claimed invention is directed towards the abstract idea of fraud detection, which is further based on the collection and comparison of information and, based on a rule(s), identify options, which corresponds to “Mental Processes” and “Certain Methods of Organizing Human Activities”, as it is directed towards steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper, e.g., a human agent speaking with a human customer, the human customer conveying (verbally, writing, or etc.) their concerns, the human agent comparing what was conveyed with known information, and, based on the comparison and any associated rule(s), identify whether fraudulent activity has occurred, which is further directed to risk mitigation, while reciting and applying generic technology for the advantages that it provides, i.e. faster, more efficient, and etc. Desjardins does not apply because, unlike Desjardins and as was discussed above, provide how the idea or outcome are achieved as it fails to disclose the algorithms and heuristics of the threat analyzer or define what is meant by “separate from a user interaction” in order to demonstrate technological improvement or resolution that arose in the technology. The Examiner asserts that simply stating a program and stating a solution without demonstrating how a solution is carried out or what comprises the solution is insufficient to overcoming the rejection and demonstrating an invention’s similarity to Desjardins.
Rejection under 35 USC 103
The Examiner asserts that the applicant’s arguments are directed towards newly amended limitations and are, therefore, considered moot. However, the Examiner has responded to the newly submitted amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments.
Pertinent Arguments
The applicant argues:
“Neither Hazony nor Jiron shows a chatbot agent transmitting an LM prompt to an LM, the LM prompt instructing the LM to identify a target object for fraud analysis according to the conversation, and determining an indicator of the target object according to a reply by the LM to the LM prompt.”
However, the Examiner respectfully disagrees.
As discussed in the rejection, Hazony discloses a system and method where a chatbot (AU) (see also provided NPL demonstrating that a chatbot that is having a conversation with a human includes a language model) is in communication with a threat analyzer (CAU) and language model to analyze communications to determine if there is an indication of fraudulent activity. Language models are, indeed, disclosed because this is how the system is able to communicate with a user and analyze the contents of a message, such as, but not limited to, grammar, spelling, order of word and sentences, writing styles, and etc. (see at least ¶ 35)
With regards to LM prompt, as was discussed in the rejection, the AU monitors and/or receives content that it then passes onto the language model for the language model to assess. In other words, the AU is in communication with the CAU and provides a reply conveying to the AU of its assessment of a message’s content, i.e. spelling, grammar, writing style, and etc., and transmitting its results to the AU so that the user can read the results (see ¶ 26, 28, 33, 34, 35, 37, 38, 44, 56, 79, 80, 108, 110, 145, 146).
The applicant argues:
“The reasoning/motivation provided by the Office Action for combining the teachings of Hazony and Jiron does not support the specific combination recited in the amended claims..”
However, the Examiner respectfully disagrees.
The Examiner asserts that a motivation/reasoning was, indeed, provided and that the motivation/reasoning is supported by the prior art and the requirements of 35 USC 103 (1. that art is in the same field of the inventor’s endeavor, regardless of the problem addressed; 2. If the reference is not within the field of the inventor’s endeavor, whether the reference still is reasonably pertinent to the particular).
Moreover, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Additionally, in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The fact that applicant has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
Finally, in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the chatbot and ML/AI based fraud detection system of Hazony with the ability to utilize such chatbot and ML/AI based fraud detection system to also identify fraudulent activity as it is being presented to it, as taught by Jiron, as this would result in a more robust and effective fraud detection system that would further enhance security and preventative fraud detection.
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that applying the known technique of utilizing a chatbot and ML/AI based fraud detection system to detect fraudulent activity as it is presented with it (i.e., a determination was not made beforehand), as taught by Jiron, would have yielded predictable results and resulted in an improved system. It would have been obvious that applying the technique of Jiron to Hazony would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such fraud detection techniques utilized by fraud detection systems that utilize chatbots and ML/AI. Further, applying the fraud detection technique of Jiron to that of Hazony would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for better fraud detection by casting a wider net of when fraud can be detected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Atef (How does ChatGPT work?); Budiu (The User Experience of Chatbots); Gidron - Language Models Conversational AI and Chatbots Explained); Iuchanka (How do chatbots work? Often with a little help from AI); Team Capacity (Everything you need to know about an NLP AI Chatbot) – which discuss how chatbots and large language models work
Kops et al. (US PGPub 2026/0080181 A1); Singh et al. (US Patent 12,626,247 B2); Martin et al. (US Patent 12,592,904 B2); Gils et al. (US Patent 12,555,399 B2); Tobb (US Patent 12,555,118 B2); Jiron et al. (CA 3037129 C) – which disclose systems and methods for fraud detection using machine learning, artificial intelligence, and/or chatbots
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 5/21/2026
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