DETAILED ACTION
1. The present application, filed on or after March 13, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a CONTINUATION application claims priority to the parent application 17/979,351 filed November 2, 2022, now U.S. Patent No. 12,260,387.
No IDS has been submitted in connection with this Application.
A preliminary amendment was filed October 6, 2025 cancelling Claim 1 and adding new claims 2 – 34.
Therefore, Claims 2 - 34 are pending and examined as follows:
NOTE: interviews are welcome at any stage of prosecution. Please use the AIR form, the link for which can be found at the end of this action, to schedule the interview.
Claim Rejections – 35 USC § 101
2. 35 USC § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture and composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
A. Rejection Based on Abstract Idea
Claims 2 - 34 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Furthermore, this rejection is based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
B. Statutory Categories
Claim 2 is a method Claim and therefore falls into the category of a process. Claim 18 is a system Claim and also recites a memory and a processor and therefore falls into the category of machine/manufacture. Claim 34 recites a non-transitory CRM and therefore falls into the category of machine/manufacture.
C. The Claim Recites an Abstract Idea
Claim 2 is illustrative of the rejection of all claims.
Claim 2 recites the limitation:
“5receiving a first input through an interactive user interface, wherein the first input is associated with a first user account; retrieving historical first user account information associated with the first user account from a data structure based on the first input; parsing a first string of text corresponding to the first input using a trained machine learning model to predict an intent associated with the first user account, wherein the trained machine learning model considers the historical first user account information as context for parsing the first string of text to predict the intent, wherein the trained machine learning model includes a plurality of nodes arranged in a plurality of layers, wherein the trained machine learning model includes a plurality of connections between nodes, and wherein the plurality of connections correspond to a plurality of memory elements that store numeric weights;”
This limitation, as drafted, is a process that, under its broadest reasonable interpretation, constitutes a method of organizing human activity, specifically, fundamental economic principles or practices. That is, analyzing this limitation in the context of the claim as a whole, it recites a process that falls within the grouping of abstract ideas comprising certain methods of organizing human activity. Fundamental economic principles or practices are examples of such methods. In this case, the fundamental economic principle or practice is the common practice of using neural networks to predict user or customer intent with interacting with a merchant or other service provider. Customer relations management systems of this type are ubiquitous. Machine learning in general, and neural networks in particular, are a very common and generic mode of implementing such services. This practice occurs literally billions of times every day as customers seek answers to their questions or help with their problems.
Furthermore, the mere nominal recitation of terms - such as “model” or “database” - does not remove the claim from the category of common or abstract methods of organizing human activity.
Thus, Claim 2 recites a judicial exception, namely, an abstract idea.
D. The Claim Does Not Integrate the Abstract Idea into a Practical Application
Moreover, this judicial exception is not integrated into a practical application. The possible “additional limitations” recited in the Claim that must be considered are as follows:
generating, based on a confidence level associated with the prediction of the intent being lower than a threshold, a question to clarify the intent;
receiving a second input through the interactive user interface in response to output of the question through the interactive user interface,
wherein the second input is associated with the first user account;
parsing a second string of text corresponding to the second input using the trained machine learning model to revise the intent and increase the confidence level; and
initiating an interaction between the first user account and a second user account based on the revised intent.
No additional computer components are mentioned in these limitations, and those quoted above are recited at a high level of generality. No other particular computer functions or computer component interactions within this system are recited. Generating a confidence level – or any type of metric or score – is commonplace for computers. This is what computers do. Trained machine learning models do this on a common and generic basis. Posing questions to the user to clarify intent or purpose or customer goals is also extremely common.
These limitations do not address “how” the ML model solves a technical problem, only the broad outcome or result that a second interaction is initiated. Analyzing these additional limitations individually, and taking the claim as a whole and as an ordered combination, it is clear that these additional limitations do not serve to integrate the abstract idea into a practical application. They do not recite a technological solution to a technological problem. They do not improve the functioning of the computer system itself. In fact, there are very few computerized system components or functions recited. Thus, these limitations fail to recite with specificity any technical function or any improvement to the functioning of the computer system itself – if any. Therefore, the claim lacks the specificity required to transform the claim from one claiming only an outcome or a result – a second interaction is initiated - to one claiming a specific way of achieving that outcome or result.
Accordingly, the recitation of these generic components amounts to no more than mere instructions “to apply” the abstract idea exception using generic computer components. That is, the additional elements recited in the claim beyond the judicial exception(s) have been evaluated to determine whether those additional elements, considered individually and in combination, integrate the judicial exception(s) into a practical application. They do not.
E. Step 2B: The Claim Does Not Recite Significantly More than the Abstract Idea
This step involves the search for an “inventive concept.” However, it is clear from the case law and the MPEP that the considerations at issue are the same as those considered above with respect to the analysis of a practical application. See MPEP 2106.05(a) – (c) and (e). In other words, these analyses sharply overlap.
Therefore, based on the above analysis, the identified additional limitations do not provide “significantly more” than the abstract idea. The claim is therefore ineligible under §101. The other independent claims are, likewise, ineligible for the same reasons as they are virtually identical to Claim 10.
F. The Dependent Claims Do Not Recite Meaningful Additional Limitations
Similarly, Claim 3 recites the same abstract idea as Claim 2 by virtue of its dependency on Claim 2. Like Claim 2, this claim does not recite sufficient additional elements to integrate the abstract idea into a practical application. Claim 3 merely recites the abstract concept of analyzing a string of text.
Claim 4 merely recites the abstract concept of a chatbot.
Claim 5 merely recites the abstract concept of parsing the text of a voice recording.
Claim 6 merely recites the abstract concept of a call center.
Claim 7 merely recites the abstract concept of an automated assistant such as a chatbot.
Claim 8 merely recites the abstract concept of posing a second question to clarify intent.
Claim 9 merely recites the abstract concept of an amount to be sent to a transferee.
Claim 10 merely recites the abstract concept of a conversation.
Claim 11 merely recites the abstract concept of suggesting a community to join.
Claim 12 merely recites the abstract concept of recommending a product.
Claim 13 merely recites the abstract concept of recommending a service.
Claim 14 merely recites the abstract concept of using historical content to predict intent.
Claim 15 merely recites the abstract concept of generating a question using information obtained
Claim 16 merely recites the abstract concept of generating a question for the customer using a machine learning model.
Claim 17 merely recites the abstract concept of updating the model.
Claims 18 - 34 are virtually identical to various of the aforementioned claims and are ineligible for the same reasons as set forth above.
None of these claims provide any additional meaningful limitations, non-generic computer components, or specific assignments of functionality among those components. Likewise, if at all, these claims recite only generic, computer-related limitations which are recited at such a high level of generality as to be devoid of any meaningful limitations. These limitations do not recite improvements in the functioning of the computer or to any other technology or technical field.
Therefore, these claims do not include additional elements that are sufficient to integrate the abstract idea into a practical application, nor do they amount to significantly more than the recited abstract idea because the additional elements, when considered both individually and as an ordered combination, constitute only a mere instruction to “apply” the abstract idea.
Thus, Claims 2 - 34 constitute ineligible subject matter under 35 USC § 101 as being directed to an abstract idea without more.
Claim Rejections - 35 USC § 103
4. 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 of this title, 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 2 - 34 are rejected under 35 U.S.C. §103 as being unpatentable over U.S. Patent Publication No. 2020/0274969 to Dunn et al. (hereinafter “Dunn”) in view of U.S. Patent Publication No. 2022/0050966 to Yang et al. (hereinafter “Yang”)
Dunn is directly on point with the claimed invention and in the same field of endeavor. Dunn is Applicant’s own publication and it should be very familiar.
The title of Dunn is: Intent-driven contact center
The Abstract reads as follows:
“The present disclosure relates generally to providing an intent-driven contact center. The contact center according to some embodiments analyzes intents to determine to which device or agent to route a communication. The analyzed intent information can also be used to formulate reports and analyze the accuracy of the identified intents with respect to the received communication..” (Emphasis Added)
Therefore, Dunn in view of Yang teaches:
2. (New) A method of intent clarification and intent-based interactivity, the method comprising: receiving a first input through an interactive user interface, wherein the first input is associated with a first user account;
Broadest reasonable interpretation:
At the outset, it is important to established the broadest reasonable interpretation relating to Claim 2. Here, the term “user” is used. From the specification, it is clear that this term refers to a customer or client of the entity which is implementing the claimed method. Thus, 0051 of the specification reads as follows:
“[0051] FIG. 2 is a block diagram 200 illustrating a system architecture of a system for intent-based recommendations. The system includes a user front-end 202, an agent back-end 210, an automation engine 216, historical information sources 228, a cloud computing engine 244, a user-agent communication engine 250, web server(s) 208, and a customer relationship management (CRM) engine 226.” (Emphasis Added)
With this interpretation in mind, it is clear that Dunn teaches a system for implementing virtually an identical platform for a CRM method, to assist “clients,” which is shown in Fig. 1 as follows:
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As to determining and clarifying the intent of the client or customer (i.e. user), please see Abstract quoted above.
Furthermore, the client computing device 130 illustrated above would certainly have a user interface, as described in more detail in Figs. 10A – 10G.
retrieving historical first user account information associated with the first user account from a data structure based on the first input; (See at least [0050] and [0090])
parsing a first string of text corresponding to the first input using a trained machine learning model to predict an intent associated with the first user account, wherein the trained machine learning model considers the historical first user account information as context for parsing the first string of text to predict the intent, (See at least [0100], wherein a person of ordinary skill in the art would readily understand that a “semantic analysis” of the “message” would be equivalent to parsing text to predict an intent. See also 0106 and 0117.)
wherein the trained machine learning model includes a plurality of nodes arranged in a plurality of layers, wherein the trained machine learning model includes a plurality of connections between nodes, and wherein the plurality of connections correspond to a plurality of memory elements that store numeric weights; (See at least [0100] referenced above. A person of ordinary skill in the art would understand that all neural networks are composed of nodes and connections and that biases or “weights” are used to implement the algorithms.)
generating, based on a confidence level associated with the prediction of the intent being lower than a threshold, a question to clarify the intent; (See at least [0023] and [0111]. As to generating a question to clarify the intent, please see 0046, 0056, and especially 0101.)
receiving a second input through the interactive user interface in response to output of the question through the interactive user interface, wherein the second input is associated with the first user account; (A person would readily understand from 0101 that a response would ordinarily be received to the clarifying question.)
parsing a second string of text corresponding to the second input using the trained machine learning model to revise the intent and increase the confidence level; (Based on 0101, it would be clear to a person of ordinary skill in the art that the response to the clarifying question would also have to be parsed and analyzed for semantics by the ML model. Thus, the “conversation” referenced in this paragraph is evidence that Dunn teaches using natural language processing or NLP to make such analysis. See also 0096 – 0097. See 0109 – 0111 for teachings related to increasing the confidence score.)
initiating an interaction between the first user account and a second user account based on the revised intent. (See at least Abstract with respect to routing a communication or interaction.)
Therefore, Dunn appears to teach the basic limitations of Claim 2. However, out of an abundance of caution, and subject to further consideration of the cited reference and subject to the broadest reasonable interpretation of the relevant limitation, Yang is cited for its teachings relating to the use of a neural network.
Thus, Yang is in the same field of endeavor as Dunn and the claimed invention –
The title is: Entity resolution for chatbot conversations
The Abstract reads as follows:
“A system performs conversations with users using chatbots customized for performing a set of tasks. The system may be a multi-tenant system that allows customization of the chatbots for each tenant. The system receives a task configuration that maps tasks to entity types and an entity configuration that specifies methods for determining entities of a particular entity type. The system receives a user utterance and determines the intent of the user using an intent detection model, for example, a neural network. The intent represents a task that the user is requesting. The system determines one or more entities corresponding to the task. The system performs tasks based on the determined intent and the entities and performs conversations with users based on the tasks..” (Emphasis Added)
Therefore, it would have been obvious to one of ordinary skill in the relevant art at the time of filing the claimed invention to have modified the intent-driven contact center teachings of Dunn to add the neural network teachings of Yang. The motivation to do so comes from Dunn. As quoted above, Dunn also teaches the use of a neural network machine learning model. It would greatly improve the accuracy of the intent-determining systems of Dunn to implement the neural network details of Yang.
With regard to Claims 3 - 17, Dunn in view of Yang teaches:
3. (New) The method of claim 2, wherein the first input includes the first string of text, and wherein the second input includes the second string of text. (See at least 0057, 0095-0097, and 0100)
4. (New) The method of claim 2, wherein the interactive user interface is a chat- based interactive user interface. (See at least 0095, 0100 and 0105)
5. (New) The method of claim 2, further comprising: parsing a first voice recording to generate the first string of text, wherein the first input includes the first voice recording; and parsing a second voice recording to generate the second string of text, wherein the second input includes the second voice recording. (See at least 0057, 0095, 0105)
6. (New) The method of claim 2, wherein the interactive user interface is a call- based interactive user interface. (See at least 0105)
7. (New) The method of claim 2, wherein the interactive user interface is an automated assistant-based interactive user interface. (See at least 0105-0106, wherein a chat application is considered to constitute the recited term “automated assistant.)
8. (New) The method of claim 2, further comprising: generating, based on a second confidence level associated with the revised intent being lower than the threshold, a second question to clarify the revised intent; receiving a third input through the interactive user interface in response to output of the second question through the interactive user interface, wherein the third input is associated with the first user account; and parsing a third string of text corresponding to the third input using the trained machine learning model to further revise the intent and increase the second confidence level. (See at least 0111 and 0132)
9. (New) The method of claim 2, wherein parsing the first string of text using the trained machine learning model to predict the intent includes identifying, based on the first string of text, an amount for a transaction and a transferee account for the transaction, wherein the second user account is the transferee account, and wherein the interaction between the first user account and the second user account includes the transaction between the first user account and the second user account. (See at least 0121 - 123)
10. (New) The method of claim 2, wherein parsing the first string of text using the trained machine learning model to predict the intent includes identifying, based on the first string of text, the second user account for a conversation with the first user account, and wherein the interaction between the first user account and the second user account includes the conversation between the first user account and the second user account. (See at least 0121 - 123)
11. (New) The method of claim 2, wherein parsing the first string of text using the trained machine learning model to predict the intent includes identifying, based on the first string of text, a community for the first user account to join, wherein the community includes the second user account, and wherein the interaction between the first user account and the second user account includes the first user account joining the community. (See at least 0048 wherein a sales associate would encourage a user to join the community of the entity offering goods or services for sale.)
12. (New) The method of claim 2, wherein parsing the first string of text using the trained machine learning model to predict the intent includes identifying, based on the first string of text, a product associated with the first user account, wherein the second user account is also associated with the product, and wherein and wherein the interaction between the first user account and the second user account is associated with the product. (See at least 0121 – 0123, wherein it would understood that the “bill” is associated with the product purchased.)
13. (New) The method of claim 2, wherein parsing the first string of text using the trained machine learning model to predict the intent includes identifying, based on the first string of text, a service associated with the first user account, wherein the second user account is also associated with the service, and wherein and wherein the interaction between the first user account and the second user account is associated with the service. (See at least 0121 – 0123, wherein it would understood that the “bill” is associated with the service product purchased.)
14. (New) The method of claim 2, wherein the trained machine learning model considers the historical first user account information as context for parsing the second string of text to revise the intent and increase the confidence level. (See at least 0050 and 0090)
15. (New) The method of claim 2, wherein generating the question to clarify the intent includes calling an application programming interface (API) to generate the question. (See at least 0130 wherein a person of ordinary skill in the art would understand that such algorithms can be “called” as through an API)
16. (New) The method of claim 2, wherein generating the question to clarify the intent includes generating the question using the trained machine learning model. (See at least 0019)
17. (New) The method of claim 2, further comprising: updating the trained machine learning model based on the second input and a difference between the intent and the revised intent, wherein updating the trained machine learning model includes adjusting at least one of the numeric weights as stored in at least one of the plurality of memory elements. (See at least 0100 – 0113)
With regard to Claims 18 – 34, these claims are essentially identical to Claims 2 – 17 and correspond exactly to those Claims. Therefore, Claims 18 – 34 are obvious for the same reasons as set forth above with respect to Claims 2 - 17.
Conclusion
5. Applicant should carefully consider the following in connection with this Office Action:
A. Search and Prior Art
The search conducted in connection with this Office Action, as well as any previous Actions, encompassed the inventive concepts as defined in the Applicant’s specification. That is, the search(es) included concepts and features which are defined by the pending claims but also pertinent to significant although unclaimed subject matter. Accordingly, such search(es) were directed to the defined invention as well as the general state of the art, including references which are in the same field of endeavor as the present application as well as related fields (e.g. the use of neural networks to predict user or customer intent in interactions with customer service or customer relations platforms.) Indeed, there is a plethora of prior art in these fields.
Therefore, in addition to prior art references cited and applied in connection with this and any previous Office Actions, the following prior art is also made of record but not relied upon in the current rejection:
U.S. Patent Publication No. 2020/0184307 to Lipka et al. This reference relates to the concept of using neural networks to determine intent.
U.S. Patent Publication No. 2020/0012954 to Botea et al. This reference relates to the concept of a chatbot for solving customer problems.
U.S. Patent Publication No. 2018/0322403 to Ron et al. This reference relates to the concept of predicting an intent in a chatbot.
U.S. Patent Publication No. 2023/0036167 to Yusuf et al. This reference relates to the concept of customer questionnaires.
Chinese Patent Publication No. CN 111639162 to Chen. This reference relates to the concept of generating questions.
B. Responding to this Office Action
In view of the foregoing explanation of the scope of searches conducted in connection with the examination of this application, in preparing any response to this Action, Applicant is encouraged to carefully review the entire disclosures of the above-cited, unapplied references, as well as any previously cited references. It is likely that one or more such references disclose or suggest features which Applicant may seek to claim. Moreover, for the same reasons, Applicant is encouraged to review the entire disclosures of the references applied in the foregoing rejections and not just the sections mentioned.
C. Interviews and Compact Prosecution
The Office strongly encourages interviews as an important aspect of compact prosecution. Statistics and studies have shown that prosecution can be greatly advanced by way of interviews. Indeed, in many instances, during the course of one or more interviews, the Examiner and Applicant may reach an agreement on eligible and allowable subject matter that is supported by the specification.
Interviews are especially welcomed by this examiner at any stage of the prosecution process. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool (e.g. TEAMS).
To facilitate the scheduling of an interview, the Examiner requests the use of the AIR form as follows:
USPTO Automated Interview Request http://www.uspto.gov/interviewpractice.
Other forms of interview requests filed in this application may result in a delay in scheduling the interview because of the time required to appear on the Examiner's docket. Thus, the use of the AIR form is strongly encouraged.
D. Communicating with the Office
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM BUNKER whose telephone number is (571)272-0017. The examiner can normally be reached on M - F 8:30AM - 5:30PM, Pacific.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas, can be reached at 571-270-1836. Information regarding the status of an application, whether published or unpublished, may be obtained from the “Patent Center” system. For more information about the Patent Center system, https://patentcenter.uspto.gov/
/William (Bill) Bunker/
U.S. Patent Examiner
AU 3691
william.bunker@uspto.gov
(571) 272-0017
May 2, 2026
/ABHISHEK VYAS/Supervisory Patent Examiner, Art Unit 3691
Prosecution Insights