DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
This communication is in response to communications received on 3/27/25. Claim(s) none is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification. Therefore, Claims 1-20 is/are pending and have been addressed below.
Response to Arguments
There are no arguments.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 1, 11, and 16 that, under its broadest reasonable interpretation, is directed to automated customer care.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 1-10 recites a series of steps) and system (claim(s) 11-20 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) (emphasis added):
Claim 1: providing, by a computing system, to a first machine learning model, one or more customer care conversations;
mapping, by the computing system using the first machine learning model, the one or more customer care conversations to one or more clusters;
providing, by the computing system, to a second machine learning model, said one or more clustered customer care conversations; and
generating, by the computing system using the second machine learning model, one or more playbooks based on said one or more clustered customer care conversations.
Claim(s) 11 and 16: same analysis as claim(s) 1.
Dependent claims 2-10, 12-15 and 17-20 recite the same or similar abstract idea(s) as independent claim(s) 1, 11, and 16 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with automated customer care.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include machine learning model (claim(s) 1, 12, 17), computer (claim(s) 1), system, at least one processor, memory (claim(s) 11), a non-transitory computer-readable storage medium, at least one processor of a computing system (claim(s) 16), machine learning model(s) (claim 2, 4-6, 8-10, 12-15, 17-20).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using 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 - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [pg 20 para 2]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [pg 20 para 2]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-4, 10, 11-12, and 16-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Soiaporn et al. (US 2021/0399999 A1).
Regarding claim 1, 11, and 16, Soiaporn teaches a computer-implemented method, comprising:
{a system, comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: - claim 11}
{a non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method, comprising: - claim 16}
providing, by a computing system, to a first machine learning model, one or more customer care conversations;
mapping, by the computing system using the first machine learning model, the one or more customer care conversations to one or more clusters;
providing, by the computing system, to a second machine learning model, said one or more clustered customer care conversations; and
generating, by the computing system using the second machine learning model, one or more playbooks based on said one or more clustered customer care conversations [for the limitations above, see at least Fig. 2 and [0019, 0025] “As shown in FIG. 2, system 200 may include mobile device 222 and user terminal 224. … FIG. 2 also includes cloud components 210. …
Cloud components 210 includes machine learning model 202.”
[0042, 0035] “At step 402, process 400 (e.g., using one or more components in system 200 (FIG. 2)) receives a user action. …
In some embodiments, the information (e.g., a user action) may include conversation details”;
[0043-0044] “At step 404, process 400 (e.g., using one or more components in system 200 (FIG. 2)) determines an intent of a user based on a two-tier machine learning model. For example, the system may first use a first tier of a model (e.g., model 320 (FIG. 3)) to determine an intent cluster of the user's intent. The system may then determine a second tier of a model (e.g., model 330 (FIG. 3)) to determine a specific intent of the user's intent.
For example, the first machine learning model (or first tier) may be selected based on its attributes to generate results with sparse amounts of training data and/or in a supervised manner. For example, the first tier of the machine learning model may comprise a factorization machine model. … For example, the first machine learning model may group the feature input into one of a plurality of categories of specific intents. The second machine learning model may then determine a specific intent based on the output from the first machine learning model. Given the two-tiered structure, the second machine learning model may be individually trained and/or trained on training data specific to the second machine learning model.”;
[0045] “At step 406, process 400 (e.g., using one or more components in system 200 (FIG. 2)) generates a dynamic conversational response based on the intent of the user. For example, by using the two-tier machine learning model, the system may ensure that at least a conversational response is generated based on an intent in the correct cluster.”;
[0046] “For example, the system may generate a dynamic conversational response (e.g., response 102 (FIG. 1)) and present the response in a user interface (e.g., user interface 100 (FIG. 1)). The response may appear with one or more likely responses (e.g., as shown in FIG. 1)). In some embodiments, the system may receive a user action selecting (or not selecting) a response (e.g., response 102 (FIG. 1)) from a user interface.”;
[0047] “It is contemplated that the steps or descriptions of FIG. 4 may be used with any other embodiment of this disclosure. In addition, the steps and descriptions described in relation to FIG. 4 may be done in alternative orders or in parallel to further the purposes of this disclosure. For example, each of these steps may be performed in any order, in parallel, or simultaneously to reduce lag or increase the speed of the system or method.”;
[0008] further define second machine learning model output of step 404 to be a subset of a plurality of options (playbook) that the system chooses from “The system may receive a second output from the second machine learning model. The system may select a dynamic conversational response from a plurality of dynamic conversational responses based on the second output.”].
Regarding claim 3, Soiaporn teaches the computer-implemented method of claim 1, wherein the playbooks provide one or more of:
enforcement of brand-specific criteria, enforcement of brand leadership and/or agent manager goal alignment, or assurance of customer care conversation quality [see at least [0005-0008] “Accordingly, determining a specific intent of a user, with a high level of precision is difficult, even when using a machine learning model.
To overcome these technical challenges, the methods and systems disclosed herein are powered through a two-tier machine learning model. …
The system may receive a second output from the second machine learning model. The system may select a dynamic conversational response from a plurality of dynamic conversational responses based on the second output.”].
Regarding claim 4, 12, and 17, Soiaporn teaches the computer-implemented method of claim 1, further comprising:
generating, by the computing system, a record of one or more conversation clusters to which said one or more generated playbooks correspond [see at least [0025] “In some embodiments, outputs 206 may be fed back to machine learning model 202 as input to train machine learning model 202 (e.g., alone or in conjunction with user indications of the accuracy of outputs 206, labels associated with the inputs, or with other reference feedback information). In another embodiment, machine learning model 202 may update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction (e.g., outputs 206) and reference feedback information (e.g., user indication of accuracy, reference labels, or other information). In another embodiment, where machine learning model 202 is a neural network, connection weights may be adjusted to reconcile differences between the neural network's prediction and the reference feedback. In a further use case, one or more neurons (or nodes) of the neural network may require that their respective errors are sent backward through the neural network to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model 202 may be trained to generate better predictions.”];
mapping, by the computing system using the first machine learning model, a new customer care conversation to a cluster; and
determining, by the computing system using the record, a playbook generated for said cluster to which the new customer care conversation has been mapped [for the limitations above, see at least [0017] “In order to maintain the conversational interaction, the system may need to generate response (e.g., conversational response) dynamically and/or in substantially real-time. For example, the system may generate responses within the normal cadence of a conversation. In some embodiments, the system may continually determine a likely intent of the user in order to generate responses (e.g., in the form of prompts, notifications, and/or other communications) to the user. It should be noted that a response may include any step or action (or inaction) taken by the system, including computer processes, which may or may not be perceivable to a user.”;
[0043] “At step 404, process 400 (e.g., using one or more components in system 200 (FIG. 2)) determines an intent of a user based on a two-tier machine learning model. For example, the system may first use a first tier of a model (e.g., model 320 (FIG. 3)) to determine an intent cluster of the user's intent. The system may then determine a second tier of a model (e.g., model 330 (FIG. 3)) to determine a specific intent of the user's intent.”
[0045] “At step 406, process 400 (e.g., using one or more components in system 200 (FIG. 2)) generates a dynamic conversational response based on the intent of the user. For example, by using the two-tier machine learning model, the system may ensure that at least a conversational response is generated based on an intent in the correct cluster.”;
[0008] further define second machine learning model output of step 404 to be a subset of a plurality of options (playbook) that the system chooses from “The system may receive a second output from the second machine learning model. The system may select a dynamic conversational response from a plurality of dynamic conversational responses based on the second output.”].
Regarding claim 10, Soiaporn teaches the computer-implemented method of claim 1, further comprising:
providing, by the computing system to the second machine learning model as fine-tuning training inputs, one or more customer care conversations; and
providing, by the computing system to the second machine learning model as fine- tuning training outputs, one or more playbooks [for the limitations above, see at least [0025] “In some embodiments, outputs 206 may be fed back to machine learning model 202 as input to train machine learning model 202 (e.g., alone or in conjunction with user indications of the accuracy of outputs 206, labels associated with the inputs, or with other reference feedback information). In another embodiment, machine learning model 202 may update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction (e.g., outputs 206) and reference feedback information (e.g., user indication of accuracy, reference labels, or other information). In another embodiment, where machine learning model 202 is a neural network, connection weights may be adjusted to reconcile differences between the neural network's prediction and the reference feedback. In a further use case, one or more neurons (or nodes) of the neural network may require that their respective errors are sent backward through the neural network to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model 202 may be trained to generate better predictions.”].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 2, 5-9, 13-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soiaporn et al. (US 2021/0399999 A1) in view of Vasylyev (US 2024/0412720 A1).
Regarding claim 2, Soiaporn teaches the computer-implemented method of claim 1,
(original vs citation) further comprising: wherein the first machine learning model is a transformer encoder machine learning model, and/or wherein the second machine learning model is a transformer decoder machine learning model [see at least [0044] “For example, the first machine learning model (or first tier) may be selected based on its attributes to generate results with sparse amounts of training data and/or in a supervised manner. For example, the first tier of the machine learning model may comprise a factorization machine model. … For example, the first machine learning model may group the feature input into one of a plurality of categories of specific intents. The second machine learning model may then determine a specific intent based on the output from the first machine learning model. Given the two-tiered structure, the second machine learning model may be individually trained and/or trained on training data specific to the second machine learning model.”].
Soiaporn doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as context based response generation, Vasylyev discloses
(original vs citation) further comprising: wherein the first machine learning model is a transformer encoder machine learning model, and/or wherein the second machine learning model is a transformer decoder machine learning model [see at least [0033] “Various embodiments of the invention are directed to an Artificial Intelligence (AI) Assistant, which may also be hereinafter referred to as “AI Assistant” or simply “Assistant”, comprise both hardware and software components working synergistically to provide a personalized, contextual conversation experience. … The Assistant may be equipped with the ability to perform complex tasks like voice recognition, tokenization, encoding, decoding, and detokenization using various Natural Language Processing (NLP) models. Useful examples of such NLPs include but are not limited to advanced Transformer-Based Models (TBMs), Large Language Models (LLMs), and/or other known forms or combinations of generative AI technology. The LLMs may be trained on a large corpus of text and utilize a neural network with a transformer-based architecture, such as a Generative Pretrained Transformer (GPT) style model that uses self-attention mechanisms.”;
[0602] “It employs state-of-the-art NLP techniques, such as transformer-based language models (e.g., BERT, GPT), which are pre-trained on large corpora of text data and fine-tuned for the specific domains and tasks of the Assistant system 2.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Soiaporn with Vasylyev to include the limitation(s) above as disclosed by Vasylyev. Doing so would improve Soiaporn’s (Soiaporn) content generation by using a specific method of analysis and/or specific goal of analysis [see at least Vasylyev [0003-0009] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Soiaporn and b) Vasylyev and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 5-9, 13-15, and 18-20 (5, 13, 18; 6, 14, 19; 8, 15, 20; 7-9), Soiaporn teaches the computer-implemented method of claim 1,
(original vs citation) further comprising: providing, by the computing system, to a further machine learning model, one or more customer care conversations and one or more playbooks; {further comprising: providing, by the computing system, to a further machine learning model, one or more customer care conversations - claim 8-9, 15, 20} [see at least [0042, 0035] “At step 402, process 400 (e.g., using one or more components in system 200 (FIG. 2)) receives a user action. …
In some embodiments, the information (e.g., a user action) may include conversation details”;
[0043] “At step 404, process 400 (e.g., using one or more components in system 200 (FIG. 2)) determines an intent of a user based on a two-tier machine learning model. For example, the system may first use a first tier of a model (e.g., model 320 (FIG. 3)) to determine an intent cluster of the user's intent. The system may then determine a second tier of a model (e.g., model 330 (FIG. 3)) to determine a specific intent of the user's intent.”;
[0045] “At step 406, process 400 (e.g., using one or more components in system 200 (FIG. 2)) generates a dynamic conversational response based on the intent of the user. For example, by using the two-tier machine learning model, the system may ensure that at least a conversational response is generated based on an intent in the correct cluster.”;
[0008] further define second machine learning model output of step 404 to be a subset of a plurality of options (playbook) that the system chooses from “The system may receive a second output from the second machine learning model. The system may select a dynamic conversational response from a plurality of dynamic conversational responses based on the second output.”].
Soiaporn doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as context based response generation, Vasylyev discloses
(original vs citation vs clarification: strikethrough of italicized original followed by plain lettering clarification in bold) further comprising: providing, by the computing system, to a further machine learning model, data; {further comprising: providing, by the computing system, to a further machine learning model, data - claim 8-9, 15, 20} and
generating, by the computing system using the further machine learning model, one or more overlooked tasks {generating, by the computing system using the further machine learning model, one or more next task suggestions – claim 6, 14, 19} {generating, by the computing system using the further machine learning model, one or more knowledge base entries – claim 8, 15} {generating, by the computing system using the further machine learning model, one or more questions and answers – claim 9}
{wherein the next task suggestions comprise one or more of nudges or stories – claim 7} [for the limitations above, see at least [0244] “Assistant system 2 may even further employ machine learning models with predictive caching and prefetching to predict which parts of the context are likely to be needed in the near future based on the conversation's trajectory and the user's behavior. It can then proactively cache or prefetch this information into the context window, improving response latency and reducing the need for frequent resizing.”;
[0456] further define which parts of the context are likely to be needed in the near future (of [0244]) as items forgotten “Moreover, assistant system 2 can be configured with the capacity to autonomously recognize and verbally highlight points of importance that have not been addressed during the discussion. This ensures that critical agenda items receive the necessary attention before the conference concludes. For example, if the subject of security measures for the new technology upgrade has been overlooked, the AI Assistant may intervene by posing a question or prompting the participants to discuss this particular aspect.”;
[0179] further define which parts of the context are likely to be needed in the near future (of [0244]) to include information from the updated knowledge base “As assistant system 2 continues to operate and collect new data from customer interactions, training module 162 can use this data to continuously update and refine the models. For example, it can use the new data to fine-tune the intent recognition and sentiment analysis models, adapting them to changes in customer behavior or language use. It can also use reinforcement learning techniques to optimize the dialogue management and response generation models based on user feedback and satisfaction scores. Furthermore, training module 162 may be configured to use unsupervised learning techniques to identify new patterns and trends in the customer conversations, such as emerging topics or common issues. This information can be used to update the knowledge bases and ontologies used by assistant system 2, as well as to generate insights and recommendations for a customer support team, for example.”;
[0242] further define which parts of the context are likely to be needed in the near future (of [0244]) to include context-aware question answering “Some tasks, such as document summarization or context-aware question answering”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Soiaporn with Vasylyev to include the limitation(s) above as disclosed by Vasylyev. Doing so would improve Soiaporn’s (Soiaporn) content generation by using a specific method of analysis and/or specific goal of analysis [see at least Vasylyev [0003-0009] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Soiaporn and b) Vasylyev and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Le – GB 2602699 A (relevant because it teaches same as US 2021/0399999)
Moradizeyveh –Intent Recognition in Conversational Recommender Systems (relevant because it teaches Intent Recognition in Conversational Recommender Systems)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES WEBB/Examiner, Art Unit 3624
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