DETAILED ACTION
This communication is a Final Office Action on the merits in response to communications received on 03/26/2026. Claims 1, 3, 6, 8, 10, 13, 15-16, and 19 have been amended. Therefore, claims 1-20 are pending and have been addressed below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
1. 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.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract without significantly more.
3. Under Step 1 of the two-part analysis from Alice Corp, claim 1 recites a process (i.e., an act or step, or a series of acts or steps), claim 8 recites a manufacture (i.e., "an article that is given a new form, quality, property, or combination through man-made or artificial means."), claim 15 recites a machine (i.e., concrete thing, consisting of parts, or of certain devices and combination of devices). Thus, each of the claims fall within one of the four statutory categories.
4. Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
5. Claim 1 which is representative of claims 8 and 15 recites:
“generating…chat summaries for communications between customers and service agents;”, “creating a chat summary modification history for each of the chat summaries, wherein the chat summary modification history includes an initial version of a chat summary of a communication between a customer and a service agent and multiple modified versions of the chat summary including a final version and one or more intermediate versions, wherein each of the multiple modified versions is modified by the service agent on some aspects of the communication;”, “receiving a request from an inquiring customer;”, “responding to the request based on a chat summary modification history created with respect to a chat summary on a previous communication involving the inquiring customer;”, “generating feedback data based on: a discrepancy between the chat summary on the previous communication involving the inquiry customer and the final version of the chat summary;” and “incremental discrepancies of adjacent versions starting from the initial machine generated chat summaries and ending at the final version;”
The limitations under the broadest reasonable interpretation recite the abstract idea of managing customer services requests by collecting and analyzing chats/feedback that take place between a customer and service agent which encompasses commercial interactions (i.e. marketing or sales activities or behaviors, business relations) and mental processes, (i.e., observations, evaluations, judgments, and opinions). As such, the limitations cover concepts that fall within the mental processes and certain methods of organizing human activity groupings enumerated in MPEP 2106.04 II
The Applicant’s Specification in at least [0001] Customer services are often provided via telephonic conversations between customers and service representatives. During such communications, customers may ask questions and service representatives may address and provide answers or resolutions to address issues raised by the customers. In recent years, it has becoming increasingly popular to provide customer service via online chat conversations between customers and chat agents made available by a service provider on its website..
Consistent with the disclosure, the limitations of “generating”, “creating”, “receiving”, “responding” recite commercial interactions because they carry-out customer service tasks related to generating chat summaries, allowing a customer to input a request for service, providing a response to the request according to a previous communication involving the customer which are activities that managed or handled by operators working in a contact/call center. The step of “generating” also recites mental processes because the limitation pertains to collecting information and recognizing certain information from within the collected information,[ i.e., discrepancy, discrepancies between versions], which are evaluations or observations that can be performed by a human using the human mind and/or by a human using pen or paper. The limitations in the claim(s) recite an abstract idea.
7. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “automatically”, “by an engine”, “based on one or more previously machine-trained models”, “automatically generated using the machine-trained models”, “adapting via continued training of the machine trained models based on the feedback data in an iterative process to minimize discrepancies between chat summaries of communications between customers and service agents and corresponding modified versions of the chat summaries”, - see claim 1, “a non-transitory machine-readable medium having information recorded thereon”, - see claim 8, “a system”, “an automated chat summary generator”, “a processor”, “a chat summary modification unit implemented by a processor”, “a user service module implemented by a processor”, “a chat summary quality assessment unit implemented by a processor” – see claim 15 is/are recited at a high-level of generality in light of the specification. Because the specification in [¶ 0017-0019, 0031, 0037] describes the additional elements in general terms without any of the particulars, the additional elements may be broadly construed as reciting generic computer components being used to perform the abstract idea. At best, the additional elements add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f).
The other additional element of: “a method for machine training, comprising:” is merely an attempt to limit the claimed invention to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
8. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “automatically”, “by an engine”, “based on one or more previously machine-trained models”, “automatically generated using the machine-trained models”, “adapting via continued training of the machine trained models based on the feedback data in an iterative process to minimize discrepancies between chat summaries of communications between customers and service agents and corresponding modified versions of the chat summaries”, - see claim 1, “a non-transitory machine-readable medium having information recorded thereon”, - see claim 8, “a system”, “an automated chat summary generator”, “a processor”, “a chat summary modification unit implemented by a processor”, “a user service module implemented by a processor”, “a chat summary quality assessment unit implemented by a processor” – see claim 15 amount to no more than mere instructions in which to apply the judicial exception and do not provide an inventive concept at Step 2B.
8. Claims 2-7, 9-14, and 16-20 are dependents of claims 1, 8 and 15.
Claims 2 and 9 recite “wherein the communications are conducted as online chats between customers and chat agents” serve to further describe the data or information being used in the abstract idea but does not make the claim any less abstract, Claims 3, 10, and 16 recite “wherein each of the chat summaries for a communication is generated by: obtaining a transcript of the communication; extracting textual features from the transcript based on a feature extraction model; and automatically generating the initial version of the chat summary for the communication based on the textual features in accordance with a summary generation model, wherein the initial version of the chat summary characterizes the transcript in accordance with one or more categories.” which further narrow how the abstract idea may be performed but does not make the claim any less abstract. Claims 4, 11, and 17 recite “wherein the creating the chat summary modification history for a chat summary comprises: receiving information specifying a sequence of modifications to be applied to the chat summary generated based on a communication involving a customer and a service agent; applying each of the modifications in the sequence to generate a corresponding updated version of the chat summary; indexing the chat summary modification history based on information related to the customer and/or the service agent; generating the chat summary modification history based on the chat summary, the updated versions of the chat summary with the index” which further narrows how the abstract idea may be performed but does not make the claim any less abstract. Claims 5, 12, and 18 recite “further comprising assessing the models by: with respect to each of the chat summary modification histories associated with a chat summary, obtaining at least one discrepancy between the chat summary and at least one of the updated versions of the chat summary in the chat summary modification history, determining a metric based on the at least one discrepancy; and obtaining an evaluation based on the metrics obtained for the respective chat summary modification histories to derive an assessment of the performance of the models.” serves to further narrow the abstract idea but does not make the claim any less abstract, Claims 6, 13, and 19 recite “wherein the feedback data is generated as training data with each training sample corresponding to a pair including content of a communication and the final version of the chat summary for the communication, wherein the content of the communication serves as an input for training, and the final version of the chat summary for the communication serves as a ground truth chat summary.” serves to further narrow the abstract idea but does not make the claim any less abstract. Claims 7, 14, and 20 recite “wherein the updating the models based on the feedback data comprises: based on each training sample in the feedback data, generating, based on the models, a predicted chat summary based on the input content of a communication, computing a loss based on the predicted chat summary and the ground truth chat summary, and determining, if the loss satisfies a pre-determined criterion, an adjustment to be made to parameters of the models in order to minimize the loss.” serves to further narrow the abstract idea but does not make the claim any less abstract. Accordingly, when considered individually and as a whole the dependent claims do not add any additional elements that integrate the abstract idea into a practical application or provide an inventive concept.
Claim Rejections - 35 USC § 103
9. 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.
10. The factual inquiries 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.
11. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mohammed (US 11,709,989 B1) in view of Teitelman (US 2013/0129071 A1) in view of Cardona De Leon (US 2024/0161123 A1) in further view of Byrd (US 2010/0104087 A1)
With respect to claims 1, 8, and 15, Mohammed discloses a method, a machine readable and non-transitory medium, and system (Fig. 8, abstract: discloses methods and system for generating and using a conversation summary) for machine training, comprising:
automatically generating, by an engine, chat summaries for communications between customers and service agents based on one or more previously machine-trained models (abstract, col. 5:63-67, col. 9:61-67: discloses the conversation summary generator method includes generated conversation summary model may be used to generate conversation summaries for chat conversations. The pre-trained model and the classifier are trained to generate the conversation summary. Col. 6:15-22: discloses the conversation summary model is configured to generate conversation summaries having text segments meeting a particular criterion.);
receiving a request from an inquiring customer (Fig. 8, col. 7:61-67: discloses the chatbot system 800 receives a query from a user through the input data module 802.);
The Mohammed reference does not explicitly disclose the following limitations. In the same field of endeavor, the Teitelman reference is related to a system and method for handling customer-agent interactions (¶ 0004) and teaches:
creating a chat summary modification history for each of the chat summaries (¶ 0015, 0022, 0024, 0060: discloses the successful agent’s solution may be distilled into a written summary report.), wherein the chat summary modification history an initial version of a chat summary of a communication between a customer and a service agent automatically generated using the machine-trained models and multiple modified versions of the chat summary including a final version and one or more intermediate versions (¶ 0015, 0022, 0024, 0060: discloses training module 110 keeps logs of or indexes each interaction. Each interaction may include audio, messaging, or written recordings of the customer and agents’ communication. The summary report may be generated for only successful interactions while in other embodiments the summary report may be generated for both successful and unsuccessful interactions.),
responding to the request based on a chat summary modification history created with respect to a chat summary on a previous communication involving the inquiring customer (¶ 0029-0030, 0033, 0066-0067, 0075, 0117: discloses the supports center 124 may receive a connection request from the user device. When a customer repeatedly contacts a support center with the same issue or problem the training module 110 may locate and retrieve the actual successful interaction recordings according to the interaction information. The training module may connect to the database and use summary ID 320 to retrieve the summary report, agent ID, and/or customer ID from the database. The training module may send the summary file and/or the original interaction file for the solution to the relevant agents involved in the interaction. For example, sending a report summarizing a successful interaction in real-time may include sending the summary report while the successful agent is conversing with the customer, i.e., via telephone, on-line text chat);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system and methods of Mohammed to include creating a chat summary modification history for each of the chat summaries, wherein the chat summary modification history incudes a chat summary of a communication between a customer and a service agent automatically generated using the machine-trained models and one or more updated modified versions of the chat summary, responding to the request based on a chat summary modification history created with respect to a chat summary on a previous communication involving the inquiring customer, as disclosed by Teitelman to achieve the claimed invention. As disclosed by Teitelman, the motivation for the combination would have been to benefits that allow company agents to reduce long wait times and repeated unnecessary transferring of requests between departments. (¶ 0002-0003)
The combination of Mohammed and Teitelman do not explicitly disclose the following limitations. In the same field of endeavor, the Cardona De Leon reference is related to data analysis and classification of user feedback data using trained machine learning models (¶ 0001) and teaches:
wherein each of the multiple modified versions is modified by the service agent on some aspects of the communication (¶ 0033-0034, 0039: discloses the classification of user 215’s communications by agent 235 and similar classification of other users communications by other agents of the service provider.);
generating feedback data based on: a discrepancy between the chat summary on the previous communication involving the inquiry customer and the final version of the chat summary (¶ 0014, 0051, 0054: discloses user communications may have been misclassified due to agent error or predefined categories that do not accurately reflect the user’s actual comments or reasons for initiating the communications with the service provider. User communications in the training data set and/or other data sets may be flagged using machine learning techniques to identify different categories of relevant feedback.); and
adapting via continued training of the machine trained models based on the feedback data in an iterative process to minimize discrepancies between chat summaries of communications between customers and service agents and corresponding modified versions of the chat summaries. (¶ 0051, 0040, 0054: discloses the ML engine 328 may be adapted to train each of the ML models. The ML engine 328 may utilize feedback and annotations or labeling from agent device to iteratively train the model. The identification of such misclassified communications may be used to retrain the ML model in a continuous or iterative training process so that incorrect classifications may be reduced and/or eliminated and the ML model may more accurately classify user communications. Thus, the ML model may be trained for classification of new user communication as well as previous classifications performed by customer service agents or existing ML models.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the combination of Mohammed and Teitelman to include the data analysis and classification techniques using trained machine learning models, as disclosed by Cardona De Leon to achieve the claimed invention. As disclosed by Cardona De Leon, the motivation for the combination would have been to allow a service provider to gain valuable insight into underlying issues that concern its users and make any necessary changes to products and services by providing a more accurate representation of the user communications. (¶ 0002-0003, 0014)
The combination of the combination of Mohammed, Teitelman, Cardona De Leon does not explicitly disclose the following limitations. In the same field of endeavor, the Byrd reference is related to a system and method for producing contact center logs or summaries of a communication in a contact center environment which includes a feedback system that uses a computer generated log that has been edited to update one or more models in an iterative fashion (¶ 0023) and teaches:
incremental discrepancies of adjacent versions starting from the initial machine generated chat summaries and ending at the final version (¶ 0023-0025, 0031, 0036-0039: discloses utilizes a store of human-generated call logs, i.e., good quality logs and the computer generated call transcripts corresponding to these logs to learn rules and/or features that the humans used to create the human generated call logs. Feedback if provided by the call center agents who handled the call and edit the logs. Examines all available feedback and selects the feedback which will be used to modify the global model 750. The feedback is compared to a stored rejection list);
As can be seen from the teachings of Byrd, techniques for automatic adaptive creation of logs of customer/agent interactions were known in the state of the art and there was a need for to improve the quality of logs and upon inefficiencies.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the combination of Mohammed, Teitelman, and Cardona De Leon, to include incremental discrepancies of adjacent versions starting from the initial machine generated chat summaries and ending at the final version, as disclosed by Byrd to achieve the claimed invention. As disclosed by Byrd, the motivation for the combination would have been to provide the ability to improve the quality of the logs by creating less error-prone and more consistent document versions that can be used to support later continuation of the same customer interaction by different agents as well as the same agent. (¶ 0002-0008)
With respect to claims 2 and 9, the combination of Mohammed, Teitelman, Cardona De Leon, and Byrd discloses the method, medium, and system,
wherein the communications are conducted as online chats between customers and chat agents. (Fig. 8, col. 16:40-55, cols. 17:65-67 – col. 18:1-5: Mohammed discloses the chatbot system receives a query from a user which represents a customer. The query may be text typed directly into the graphical user interface. The input data may include a plurality of words representing the user question in the query, for example, “Has may package been shipped yet?”, “When will my package arrive”, etc. The chatbot system 800 can implement the conversation summary generator system to generate conversation summaries for the chat.)
With respect to claims 3, 10, and 16, the combination of Mohammed, Teitelman, Cardona De Leon, and Byrd discloses the method, medium, and system,
wherein each of the chat summaries for a communication is generated by:
obtaining a transcript of the communication (col. 6:5-22: Mohammed discloses receives a chat communication.); extracting textual features from the transcript based on a feature extraction model (Fig. 8: Mohammed discloses character frequency extractor module receives input data and may represent how frequently each word in the input data and character sequence appear.); and
automatically generating the initial version of the chat summary for the communication based on the textual features in accordance with a summary generation model (col. 6:5-22: Mohammed discloses the conversation summary model is used to generate a conversation summary for the chat conversation.),
wherein the initial version of the chat summary characterizes the transcript in accordance with one or more categories. (col. 6:5-22: Mohammed discloses the conversation summary model is configured to generate conversation summaries having text segments meeting a particular criterion.)
With respect to claims 4, 11, and 17, the combination of Mohammed, Teitelman, Cardona De Leon, and Byrd discloses the method, medium, and system,
wherein the creating the chat summary modification history for a chat summary comprises: receiving information specifying a sequence of modifications to be applied to the chat summary generated based on a communication involving a customer and a service agent (¶ 0015-0016: Teitelman discloses the successful agent’s solution may be distilled manually by an agent into a written summary report.);
applying each of the modifications in the sequence to generate a corresponding updated version of the chat summary (¶ 0021: Teitelman discloses agent successes and failures may be automatically determined by the training module.);
indexing the chat summary modification history based on information related to the customer and/or the service agent (¶ 0022: Teitelman discloses training module indexes each interaction from customer and agent communications.);
generating the chat summary modification history based on the chat summary (¶ 0024, 0060: Teitelman discloses training module generates a report of the successful interactions.), the updated versions of the chat summary with the index. (¶ 0022, 0060-0066 – See Teitelman)
With respect to claims 5, 12, and 18, the combination of Mohammed, Teitelman, Cardona De Leon, and Byrd discloses the method, medium, and system, further comprising
assessing the models by:
with respect to each of the chat summary modification histories associated with a chat summary, obtaining at least one discrepancy between the chat summary and at least one of the updated versions of the chat summary in the chat summary modification history (¶ 0054: Cardona De Leon discloses user communications in the training data may be flagged using machine learning techniques to identify different categories of relevant feedback data.),
determining a metric based on the at least one discrepancy (¶ 0053: Cardona De Leon discloses the performance metrics may include confidence scores computed by ML engine 328 for the user communications generated by the plurality of trained ML models.); and
obtaining an evaluation based on the metrics obtained for the respective chat summary modification histories to derive an assessment of the performance of the models. (¶ 0053, 0055-0056: Cardona De Leon discloses report generator 330 may be used to generate a report that can include statistics on the number of user communications that were classified differently.)
With respect to claims 6, 13, and 19, the combination of Mohammed, Teitelman, Cardona De Leon, and Byrd discloses the method, medium, and system,
wherein the feedback data is generated as training data with each training sample corresponding to a pair including content of a communication and the final version of the chat summary for the communication (col. 5:27-53: Mohammed discloses pooling data samples from chat conversations. Each data sample has a text and may have text segments summarizing the text.),
wherein the content of the communication serves as an input for training (col 8:14-27: discloses the training dataset serves as input into the training module 102), and the final version of the chat summary for the communication serves as a ground truth chat summary. (col. 10:43-67: Mohammed discloses after completing the fine tuning the conversation summary is generated. The fine tuning is performed using data samples which are considered ground truth obtained from the dataset.)
With respect to claims 7, 14, and 20, the combination of Mohammed, Teitelman, Cardona De Leon, and Byrd discloses the method, medium, and system,
wherein the updating the models based on the feedback data comprises:
based on each training sample in the feedback data, generating, based on the models, a predicted chat summary based on the input content of a communication (col. 5:54-65, col. 10:24-67: Mohammed discloses the conversation summary generator method includes fine is capable of predicting the text segments summarizing the text of the training datasets.), computing a loss based on the predicted chat summary and the ground truth chat summary (col. 10:43-67: Mohammed discloses the training module 102 computes a fine-tuning loss. The fine-tuning loss is calculated by comparing the predicated text segments of data samples with respective text segments of data samples which are considered as ground truth obtained from the dataset.), and
determining, if the loss satisfies a pre-determined criterion, an adjustment to be made to parameters of the models in order to minimize the loss. (col. 10:43-67: Mohammed discloses the fine-tuning loss is back propagated to adjust values of learnable parameters and to reduce the fine-tuning loss.)
Response to Arguments
Applicant's arguments filed 03/26/2026 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “The Office Action alleges that the claims fall under the "Mental processes" and "Certain methods of organizing human activity" groupings of abstract ideas. See, Office Action at pages 3-4. Applicant respectfully disagrees for at least the following reasons. Initially, claim 1 as a whole is related to machine training, which does not fall within "Mental Processes" and "Certain methods of organizing human activity" groupings of abstract ideas.”
“Particularly, claim 1 recites a specific machine training method including "adapting via continued training of the machine trained models based on" "a discrepancy between the chat summary on the previous communication involving the inquiry customer and the final version of the chat summary, and incremental discrepancies of adjacent versions starting from the initial machine generated chat summaries and ending at the final version" "in an iterative process to minimize discrepancies between chat summaries of communications between customers and service agents and corresponding modified versions of the chat summaries."
The above-quoted claim features cannot be performed in the human mind at least because the human mind cannot perform the recited machine training method, and thus the claims do not fall within the "mental processes" grouping of abstract ideas.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. It is important note performance of claim limitations using generic computer components, i.e., machine training in the claim does not preclude the limitations from being in the certain methods of organizing human activity or mental processes grouping. Claims can be directed to an abstract idea even if the claims require generic computer components or require operations that a human could not perform as quickly as a computer. See MPEP 2106.04(a)(2)(III)(c) The adapting step was previously considered under Prong Two and does not remove the claim from the abstract idea realm. The reply does not change the previous analysis or make the claimed invention any less abstract. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Also, these claim features extend far beyond fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), and thus do not fall within the enumerated group of Certain methods of organizing human activity. Accordingly, the claims do not fall into any of the abstract ideas exceptions provided by the Guidance, and thus the claims are patent eligible under Prong One of the Step 2A Analysis of the Guidance.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In the instant case, the Applicant’s remarks only focus on a subset of steps recited within the claim and do not address clearly discuss the claimed invention as a whole. Turning to the disclosure for support of the abstract idea, the Specification makes clear the claims are related to commercial interactions and managing personal behavior and/or interactions:
[¶ 0001] Customer services are often provided via telephonic conversations between customers
and service representatives. During such communications, customers may ask questions and
service representatives may address and provide answers or resolutions to address issues raised
by the customers. In recent years, it has becoming increasingly popular to provide customer
service via online chat conversations between customers and chat agents made available by a
service provider on its website.
Turning to the claimed invention, the series of steps in the claim also recite tasks for receiving a request from a customer and responding to the customer’s request which are commercial interactions and managing personal behavior and/or interactions that fall within the certain methods of organizing human activity grouping. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Moreover, claim 1 is patent eligible because the claimed concepts are integrated into a practical application. MPEP 2106.04(d) states: "after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two." MPEP 2106.04(d) also states "Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: - An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP S4 2106.04(d)(1) and 2106.05(a)."
Initially, as mentioned previously, the Office Action has improperly analyzed claim 1
when determining whether claim 1 recites a judicial exception because claim 1 does not fall into any of the abstract idea exceptions - mathematical concepts, certain methods of organizing human activity, or mental processes.”
“Even assuming, for the sake of argument, that claim 1 does recite an abstract idea (which the Applicant disagrees), Applicant respectfully submits that claim 1 is patent eligible under Prong Two of the Step 2A Analysis. The recited claim features are tied to a practical application, i.e., automatically generating chat summaries based on machine-learned models and adapting via continued training of machine trained models based on feedback data in an iterative process to minimize discrepancies between chat summaries of communications between customers and service agents and corresponding modified versions of the chat summaries.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The Examiner asserts the previous analysis under 101 was indeed proper as the rejection identified the limitations in the claim and explained why they fall within the certain methods of organizing human activity and mental processes groupings. Here, the features relied upon by Applicant (such as with respect to automatically generating chat summaries based on machine-learned models and adapting via continued training of machine trained models based on feedback data in an iterative process to minimize discrepancies between chat summaries of communications between customers and service agents and corresponding modified versions of the chat summaries) do not make the claimed invention eligible under Prong Two of the analysis. See Customedia, 951 F. 3d at 1365 (generic speed or efficiency increases that result from applying a computer to a task do not improve computer functioning); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)("But relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible."); Cellspin Soft, Inc. v. Fitbit, Inc., 927 F.3d 1306, 1316(Fed. Cir. 2019)("But the need to perform tasks automatically is not a unique technical problem."); Credit Acceptance Corp. v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (automating processes using generic computers does not improve computer technology); BancorpServs., L.L.C. v. Sun LifeAssur. Co. of Can. (US.), 687 F.3d 1266, 1279 (Fed. Cir. 2012) ("Using a computer to accelerate an ineligible mental process does not make that process patent-eligible.") Thus, the Examiner asserts merely automating one or more processes in the claim as argued does not automatically make the claim eligible. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Applicant's claimed concept provides "a service framework that simultaneous enables customer services via multiple channels." "The customer services via multiple channels may be facilitated by automatically generating, based on machine learned models, summaries of online chat communications between customers and chat agents. In operation, such automatically generated summaries may aim to capture the content of a communication in terms of some information categories such as the issue/situation, actions to be taken, and the resolution to the issue.”
“To ensure such automatically generated chat summaries capture the information in such categories, an automatically generated chat summary may be modified by, e.g., the agent engaged in the communication to ensure to capture different aspects of the conversation between a customer and a chat agent." (Para. [0017]) "A chat summary, including its modified version(s) of the initial automatically generated chat summary, may be used as the basis or record to facilitate subsequent communications with the same customer in future services." (Para. [0018]) "Histories of modifying machine generated chat summaries may be utilized to adapt the models for automatically generating chat summaries. Such histories may be dynamically assessed as to discrepancies between a machine generated chat summaries and modified chat summaries to generate assessment feedback, which may then be used as updated training data for machine learning to update the models in accordance with the assessment so that the quality of subsequently generated chat summaries using such adapted models may possess improved qualities." (Para. [0019]) "In some embodiments, the discrepancies between the initial and final versions as well as incremental discrepancies may both be used to generate the feedback." (Para. [0025]) "With the training data 360 available, whether it is before or after it incorporates the feedbacks, the model training engine 370 carries out training at 365 via, e.g., machine learning. In some embodiments, the training may be performed as an iterative process and at each iteration of the learning process, parameters of models (320 and 340) may be adjusted to minimize some pre-configured loss function so that discrepancies between machine generated chat summaries from the parameterized models and the ground truth chat summaries provided in the training data may be minimized." (Para. [0037])”
“As discussed above, the claims are necessarily rooted in computer technology and the claimed concept provides improvements in the technical field of machine learning, improvements including minimizing some pre-configured loss function so that discrepancies between machine generated chat summaries and the ground truth chat summaries provided in training data may be minimized.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The passages provided from the Specification [¶ 0017-0019, 0025, 0037] fail to provide any technical details for improving the models, but instead predominately describe how the system and methods update the models in purely functional terms. At best, the remarks are relying upon particular technological environment or field of use in which the claimed invention may be used but does not describe improvements to machine learning technology. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The Office Action on page 22 alleges that "the Specification's disclosure [0017-0019, 0037] with respect to machine learning is nothing more than a high-level general explanation of generic machine learning technology and applying it to the abstract idea. The additional element of: "adapting" (i.e., updating) is merely being used to facilitate the tasks of the abstract idea which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words "apply it, per MPEP 2106.05(f)." Applicant respectfully submits that claim 1 is not directed to a high-level general explanation of generic machine learning technology, rather, it recites a specific machine training method including "adapting via continued training of the machine trained models based on" "a discrepancy between the chat summary on the previous communication involving the inquiry customer and the final version of the chat summary, and incremental discrepancies of adjacent versions starting from the initial machine generated chat summaries and ending at the final version" "in an iterative process to minimize discrepancies between chat summaries of communications between customers and service agents and corresponding modified versions of the chat summaries." "The discrepancies among different versions of the chat summary due to incremental modifications may also be utilized in evaluating the quality of the machine generated chat summary. The evaluation may be carried out on all chat summary modification histories and can be integrated to estimate an overall assessment on the quality of the machine generated chat summaries." (Para. [0038]) Thus, Applicant respectfully submits that, under the Prong Two of the Step 2A Analysis from the Guidance, the claimed concept is integrated into a practical application and therefore is not directed to a judicial exception. Therefore, Applicant respectfully submits that the claims are directed to patent eligible subject matter.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The specificity of the presently recited techniques does not automatically make the claim eligible. As previously explained, the passages provided from the Specification [¶ 0017-0019, 0025, 0037-0038] fail to provide any technical details for improving the models, but instead predominately describe how the system and methods update the models in purely functional terms. At best, the remarks are relying upon particular technological environment or field of use in which the claimed invention may be used but does not describe improvements to machine learning technology. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Further, claim 1 amounts to significantly more than the judicial exception. The Berkheimer v. HP Inc, No. 2017-1437 (Fed. Cir. Feb. 8, 2018) ("Berkheimer") decision re-emphasized that, "[a]t step two, we consider the elements of each claim both individually and 'as an ordered combination' to determine whether the additional elements 'transform the nature of the claim' into a patent eligible application." Berkheimer, pages 11 and 12. Berkheimer resolved that the "inventive concept" is not restricted to only the additional elements, but may include one or more allegedly abstract elements that, in combination with the additional elements, form the claim's inventive concept. See Id., page 12 (stating, without reference to an "additional" element, that "[t]he question of whether a claim element or combination of elements is well-understood, routine and conventional to a skilled artisan in the relevant field is a question of fact"). For example, while the Berkheimer Court held independent claim 1 to be directed to "the abstract idea of parsing and comparing data with conventional computer components, the Berkheimer Court nevertheless concluded that dependent claim 4 (which depended on claim 1).”
“In concluding that claim 4 could be patent-eligible, the Berkheimer Court did not narrow the inventive concept to merely the additional limitation of "storing a reconciled object structure in the archive without substantial redundancy." Indeed, the general operation of storing data/object structures in some archive without substantial redundancy by itself would clearly have been found to be well-understood, routine, and conventional. Despite this, however, the Berkheimer Court concluded that the claimed invention of claim 4 could be patent-eligible. In the instant application, the Office Action appears to allege that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Applicant respectfully disagrees with the contentions, and further submits that claim 1 is patent eligible under Step 2B Analysis from the Guidance. Berkheimer showed that it is not merely the additional elements that are to be viewed for eligibility, but the claimed concept described by the additional elements in conjunction with the non-additional elements.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In the instant case, the remarks do not explain how the cited guidance from the Berkheimer court decision is applicable to the claimed invention. The reply does not point to any unconventional features in the claims and the Applicant does not argue the other non-abstract features of the claimed invention alone or in combination are not well-understood, routine and conventional machine learning technology. The claims lack an inventive concept. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Furthermore, even assuming arguendo that each of the claim limitations individually is abstract, or is performed by or is a generic computer, so too are the BASCOM claim limitations (e.g., BASCOM Global Internet v. AT&T Mobility LLC, No. 2015-1763 (Fed. Cir. Jun. 27, 2016)2 ("BASCOM')). In addition to failing to consider Applicant's claims as an ordered combination and as a whole, the Office Action has improperly analyzed the claims without considering the "additional element(s)" in combination with the non-additional elements. As a result, the Office Action has also incorrectly and improperly identified that the alleged "additional elements" do not amount to significantly more than the alleged judicial exception. Accordingly, Applicant respectfully submits that claim 1 is patent eligible under the Step 2B Analysis of the Guidance. Applicant respectfully submits that claim 1 is directed to patent eligible subject matter. Accordingly, no further analysis is necessary to find claim 1 patent eligible under 35 U.S.C. § 101.” The Examiner respectfully disagrees.
The Applicant arguments are not persuasive. In the instant case, claim 1 is distinguishable from the claims those in BASCOM, which allegedly improved an existing technological process by describing "how a particular arrangement of elements is a technical improvement over prior art ways of filtering Internet content," i.e., "a filter implementation versatile enough that it could be adapted to many different users' preferences while also installed remotely in a single location." In regards to the ordered combination, there are no specific or limiting recitations of…improved technology explained by Applicant because the limitations in claim 1 merely describe generic computer components and machine learning models to implement the abstract idea. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Claims 8 and 15 recite features similar to the features of claim 1 discussed above and thus are directed to patent eligible subject matter for the same reasons as discussed above with respect to claim 1. Claims 2-7, 9-14, and 16-20 depend on claims 1, 8, and 15 and thus are directed to patent eligible subject matter for the same reasons as discussed above with respect to claims 1, 8, and 15.” The Examiner respectfully disagrees.
Applicant's arguments with respect to claims 2-8, 9-15, and 16-20 fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims integrates the judicial exception into a practical application or provide an inventive concept.
With Respect to Rejections Under 35 USC 103
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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