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
Status of Claims
Claims 1, 4, 5, 6, 8, 9, 10, 13, 14, 15, 17, 18, 19 have been amended.
Claims 2, 3, 7, 11, 12, 16 have been cancelled.
Claims 20 – 23 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
extract a set of features characterizing 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.
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
extract a set of features characterizing 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
extract a set of features characterizing 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 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 preventing online fraud 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 form a chatbot agent and a threat analyzer coupled to the chatbot agent, wherein:
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:
extract a set of features characterizing 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, 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 9/22/2025 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
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 Examiner has provided an analysis with examples of how the claimed invention is, indeed, directed towards Mental Processes” and “Certain Methods of Organizing Human Activities” and how the claimed invention 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.
The specific communication that the applicant is referring to amounts to the extra-solution activity of transmitting and receiving information and “applying” generic technology that has been recited at a high level of generality. 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, and further demonstrates 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.
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:
“Stated otherwise, neither Hazony nor Jiron shows using a language model to automatically identify an object for analysis according to a conversation between the chatbot and the user. In contrast, Hazony teaches analyzing messages of a user's inbox, and engaging the user in conversation in response to the analysis. Furthermore, neither Hazony nor Jiron discloses an LM prompt as claimed.”
However, the Examiner respectfully disagrees.
As discussed in the rejection, Hazony discloses a system and method where a chatbot (AU) is in communication with a threat analyzer (CAU) and language model to analyze communications to determine if there is an indication of fraudulent activity. A language model is, indeed, disclosed because this is how the system is able to 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 transmits a prompt to the LM and the LM provides a reply conveying to the AU of its assessment of a message’s content, i.e. spelling, grammar, writing style, and et. (see ¶ 26, 28, 33, 34, 35, 37, 38, 44, 56, 79, 80, 108, 110, 145, 146)
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.
Frendo et al. (US Patent 12,438,980 B2); Bowers et al. (US Patent 12,321,910 B1) – which discloses fraud detection and user verification
Abdelrahman et al. (US Patent 12,333,523 B2); Cidon et al. (US PGPub 2019/0026461 A1); Cidon et al. (US PGPub 2019/0028499 A1); Cidon et al. (US PGPub 2019/0028509 A1); Benishti (US PGPub 2021/0234891 A1) – which discloses utilizing machine learning to detect fraudulent activity
Shraim et al. (US PGPub 2007/0299915 A1); Shraim et al. (US PGPub 2006/0068755 A1); Shraim et al. (US Patent 7,457,823 B2) – which is directed towards detecting fraudulent activity
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 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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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 11/13/2025
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