Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Arguments
Applicant's arguments filed 02/20/2026, have been fully considered but they are not persuasive.
On pages 8-9 of the arguments Applicant argues that the rejection under 35 USC 101 is overcome due to amendment of a processor citing analogy to the suggestions. The rejection is maintained, as the amendments have simply recited extra solution activity without integrating a processor or significant hardware/software driven step into a significant limitation itself, nor has a significant step been added. From the cited examples, shown below both hardware/software AND significant steps are provided, not merely the recitation of extra solution activity via processor and memory, when the steps themselves can be performed by a person or manipulated on a device in a non-significant way.
Further, in light of the recent Memo regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, the rejection under 35 USC 101 has been updated. No evidence in the specification of the present invention points to an improvement of technology or practical application. If such text exists, please point out precisely where, wherein such evidence can be used to overcome said rejection in lieu of adding a significant step.
On page 15 of the arguments Applicant argues that the prior art fails to teach: wherein the reference value is computed based on how recently the conversation refers to information contained in the prompts and responses in the cluster.
Examiner does not concur, as a reference value or metric in some capacity is needed to utilize a threshold as taught by HATTANGADY 0083 “…older messages are removed from an extracted communication thread that have a date/timestamp past a recency threshold…”
Compared the amended claims per se:
“wherein the reference value is computed based on how recently the conversation refers to information contained in the prompts and responses in the cluster.”
HATTANGADY almost exclusively reads upon this semantically differing with “older” versus “how recent” as well as reference value which is otherwise an inherency to invoke a threshold.
Further amendment is suggested.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 3-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception such as a natural phenomenon, abstract idea, or law of nature, without significantly more and/or a practical application per se, specifically with one or more of:
1) Not integrating a judicial exception into a practical application (see explanation below), and
2) Not reciting elements that would amount to significantly more than the judicial exception (see explanation below).
Accordingly, claims 1 and 3-20 are directed towards patent ineligible subject matter under 35 U.S.C. 101.
The independent claims:
When taking the current claim limitations of the present invention, we see that they are directed to a process that a human can perform such as reading or listening or interacting directly with a dialogue/conversation and documenting or mentally classifying which parts are relevant and recent, a language model being analogous to a human’s mind as simply a construct to receive data i.e. listening or reading a conversation/transcript or any dialogue between people.
Regarding the claim limitations of claim(s) 1, 10, and 13 as recited:
1. (Currently Amended) A language model thread truncation system, comprising:a processor set;one or more computer-readable storage media; andprogram instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising:[[the]]a language model during a conversation[[,]];clusteringthe prompts and responses based on their topical representation to create a cluster around a topic, and;after a timing threshold for the cluster has been reached, truncating the thread by removing the cluster from the thread if [[the]]a current topic of the conversation differs from the topic of the cluster and if a reference value of the cluster is below a minimum value, otherwise retain the cluster in the thread, wherein the reference value is computed based on how recently the conversation refers to information contained in the prompts and responses in the cluster.
10. (Currently Amended) A language model thread truncation system, comprising: a processor set;one or more computer-readable storage media; andprogram instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising:obtaining prompts and responses from a thread of user interactions with the language model during a conversation[[,]];clusteringthe prompts and responses based on their topical representation to create a cluster around a topic[[,]];generating individual reference scores for the prompts and responses in the cluster, wherein the individual reference scores represent a last time the conversation has comes back to information contained in the prompts and responses[[,]]; and after a timing threshold for the cluster has been reached, truncating the thread by removing the cluster from the thread if [[the]]a current topic of the conversation differs from the topic of the cluster and if a reference value of the cluster is below a minimum reference value, otherwise retain the cluster in the thread, wherein the reference value of the cluster is determined based on the individual reference scores for the prompts and responses in the cluster, wherein the reference value is computed based on how recently the conversation refers to information contained in the prompts and responses in the cluster.
13. (Currently Amended) A method for language model thread truncation, comprising:obtaining prompts and responses from a thread of user interactions with a language model during a conversation;clustering the prompts and responses based on their topical representation to create a cluster around a topic;generating individual reference scores for the prompts and responses in the cluster,wherein the reference scores represent a last time the conversation has come back to information contained in the prompts and responses; andafter a timing threshold for the cluster has been reached, truncating the thread by removing the cluster from the thread if [[the]]a current topic of the conversation differs from the topic of the cluster and if a reference value of the cluster is below a minimum value, otherwiseretaining the cluster in the thread, wherein the reference value of the cluster is determined based on the individual reference scores for the prompts and responses in the cluster, wherein the reference value is computed based on how recently the conversation refers to information contained in the prompts and responses in the cluster.
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
Yes
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
No. Regarding the independent claims. No, analogous to Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 2019 USPQ2d 281076 (Fed. Cir. 2019), the claims are directed to a process that a human can perform such as reading or listening or interacting directly with a dialogue/conversation and documenting or mentally classifying which parts are relevant and recent, a language model being analogous to a human’s mind as simply a construct to receive data i.e. listening or reading a conversation/transcript or any dialogue between people such as lacking a clear improvement of function/technology or practical application, appearing at the present time, to be unsupported in the specification of the present invention.
Further as demonstrated in Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 2019 USPQ2d 281076 (Fed. Cir. 2019), the claims were to methods for electronically processing paper checks, all of which contained limitations setting forth receiving merchant transaction data from a merchant, crediting a merchant’s account, and receiving and scanning paper checks after the merchant’s account is credited. In part one of the Alice/Mayo test, the Federal Circuit determined that the claims were directed to the abstract idea of crediting the merchant’s account before the paper check is scanned. The court first determined that the recited limitations of “crediting a merchant’s account as early as possible while electronically processing a check” is a “long-standing commercial practice” like in Alice and Bilski. 931 F.3d at 1167, 2019 USPQ2d 281076, at *5 (Fed. Cir. 2019). The Federal Circuit then continued with its analysis under part one of the Alice/Mayo test finding that the claims are not directed to an improvement in the functioning of a computer or an improvement to another technology. In particular, the court determined that the claims “did not improve the technical capture of information from a check to create a digital file or the technical step of electronically crediting a bank account” nor did the claims “improve how a check is scanned.” Id.
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole.
The claim which demonstrated improvements to technology and/or function recites: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task.".
The decision recites that “We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.”
When considering the limitation decided upon, there are clear improvements to machine learning that are not rudimentary or a long-standing practice, for instance adjusting for optimization and protection of performance, as claimed, are improvements to a machine learning models operations, not simply a general mathematical or generic recitation, but rather an improvement to function.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further, specifically:
“Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.”
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
The second paragraph of MPEP § 2106.05(a), subsection I, is revised to add new examples xiii and xiv to the list of examples that may show an improvement in computer functionality:
xiii. An improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to effectively learn new tasks; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential); and
xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential).
Step 2B: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT AMOUNT TO SIGNIFICANTLY MORE THAN THE JUDICIAL EXCEPTION?
No, the claims amount to a process that a human can perform such as reading or listening or interacting directly with a dialogue/conversation and documenting or mentally classifying which parts are relevant and recent, a language model being analogous to a human’s mind as simply a construct to receive data i.e. listening or reading a conversation/transcript or any dialogue between people.
• Collecting and comparing known information (Classen)
• Collecting, displaying, and manipulating data (Int. Ventures v. Cap One Financial)
• Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group; West View†)
• Delivering user‐selected media content to portable devices (Affinity Labs v. Amazon.com)
Assistance for Applicant in amending to overcome 101:
Limitations that the courts have found to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));
ii. Improvements to any other technology or technical field, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art, as discussed in Diamond v. Diehr, 450 U.S. 175, 191-92, 209 USPQ 1, 10 (1981) (see MPEP § 2106.05(a));
iii. Applying the judicial exception with, or by use of, a particular machine, e.g., a Fourdrinier machine (which is understood in the art to have a specific structure comprising a headbox, a paper-making wire, and a series of rolls) that is arranged in a particular way to optimize the speed of the machine while maintaining quality of the formed paper web, as discussed in Eibel Process Co. v. Minn. & Ont. Paper Co., 261 U.S. 45, 64-65 (1923) (see MPEP § 2106.05(b));
iv. Effecting a transformation or reduction of a particular article to a different state or thing, e.g., a process that transforms raw, uncured synthetic rubber into precision-molded synthetic rubber products, as discussed in Diehr, 450 U.S. at 184, 209 USPQ at 21 (see MPEP § 2106.05(c));
v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); or
vi. Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, e.g., an immunization step that integrates an abstract idea of data comparison into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases, as discussed in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057, 1066-68, 100 USPQ2d 1492, 1499-1502 (Fed. Cir. 2011) (see MPEP § 2106.05(e)).
To help in amending the claims and for analysis purposes, example claims 3 and 4 are listed below from the courts, however such example amendment potentials are not limited to the provided examples and alternative amendments are possible using i-vi from the courts. The example below show differences between eligible claims (court claim 4) and ineligible claims (court claim 3), which thus illustrates significantly more which is tied to hardware that is not generally recited in the art. In this case general changing of font size in claim 3 versus a significant step of conditionally changing font size tied to hardware in claim 4.
See below examples based on MPEP and not on the current claim set, to help amend to overcome 101 rejections:
Regarding independent claim examples:
For instance in the example claims, for example claims 3 and 4 below:
Ineligible
3. A computer‐implemented method of resizing textual information within a window displayed in a graphical user interface, the method comprising:
(not significant) generating first data for describing the area of a first graphical element;
(not significant) generating second data for describing the area of a second graphical element containing textual information;
(not significant) calculating, by the computer, a scaling factor for the textual information which is proportional to the difference between the first data and second data.
The claim recites that the step of calculating a scaling factor is performed by “the computer” (referencing the computer recited in the preamble). Such a limitation gives “life, meaning and vitality” to the preamble and, therefore, the preamble is construed to further limit the claim. (See MPEP 2111.02.)
However, the mere recitation of “computer‐implemented” is akin to adding the words “apply it” in conjunction with the abstract idea. Such a limitation is not enough to qualify as significantly more. With regards to the graphical user interface limitation, the courts have found that simply limiting the use of the abstract idea to a particular technological environment is not significantly more. (See, e.g., Flook.)
Whereas in similar claim 4:
Eligible
4. A computer‐implemented method for dynamically relocating textual information within an underlying window displayed in a graphical user interface, the method comprising:
displaying a first window containing textual information in a first format within a graphical user interface on a computer screen;
displaying a second window within the graphical user interface;
constantly monitoring the boundaries of the first window and the second window to detect an overlap condition where the second window overlaps the first window such that the textual information in the first window is obscured from a user’s view;
determining the textual information would not be completely viewable if relocated to an unobstructed portion of the first window;
calculating a first measure of the area of the first window and a second measure of the area of the unobstructed portion of the first window;
calculating a scaling factor which is proportional to the difference between the first measure and the second measure;
scaling the textual information based upon the scaling factor;
(significant step) automatically relocating the scaled textual information, by a processor, to the unobscured portion of the first window in a second format during an overlap condition so that the entire scaled textual information is viewable on the computer screen by the user;
(significant step) automatically returning the relocated scaled textual information, by the processor, to the first format within the first window when the overlap condition no longer exists.
These limitations are not merely attempting to limit the mathematical algorithm to a particular technological environment. Instead, these claim limitations recite a specific application of the mathematical algorithm that improves the functioning of the basic display function of the computer itself. As discussed above, the scaling and relocating the textual information in overlapping windows improves the ability of the computer to display information and interact with the user.
The dependent claims are rejected as follows, for the same reasoning as being directed towards patent ineligible subject matter under 35 U.S.C. 101, and not adding eligible subject matter to the respective parent claim.
Claims 3-9, 11, 12, and 14-20 contain mathematical extra solution activity as well as similar aspects of reading or listening or interacting directly with a dialogue/conversation and documenting or mentally classifying which parts are relevant and recent, a language model being analogous to a human’s mind as simply a construct to receive data i.e. listening or reading a conversation/transcript or any dialogue between people
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.
Claims 1, 3-10, 12-17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230023160 A1 Schemers; Roland et al. (hereinafter Schemers) in view of US 20250005295 A1 HATTANGADY; Poonam Ganesh et al. (hereinafter HATTANGADY).
Re claim 1, Schemers teaches
1. A language model thread truncation system, comprising: (removal of data in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score)
a processor set; (fig. 1)
one or more computer-readable storage media; and (fig. 1)
program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising: (fig. 1)
obtaining …prompts and responses from a thread of user interactions with the language model during a conversation (topic modeling 0080-0081)…
cluster the prompts and responses based on their topical representation to create a cluster around a topic, and after a timing threshold for the cluster has been reached, truncate the thread by removing the cluster from the thread if the current topic of the conversation differs from the topic of the cluster and if a reference value of the cluster is below a minimum value, otherwise retain the cluster in the thread. (in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score)
However, while the information is being sent in and out of the topic model including send and reply messages, Schemers does not necessarily teach a language model per se, and including the messages thereof, wherein HATTANGADY has been included for clarity to cover instances where a human is interacting with at least one AI agent including prompt and reply per se, and thus fails to teach:
prompts and responses from a thread of user interactions during a conversation (HATTANGADY in a conversation thread 0049-0050 and fig. 2b prompts and replies between humans or AI agents in a language model)
wherein the reference value is computed based on how recently the conversation refers to information contained in the prompts and responses in the cluster. (HATTANGADY a reference value or metric in some capacity is needed to utilize a threshold 0083 “…older messages are removed from an extracted communication thread that have a date/timestamp past a recency threshold…” recency threshold also in 0102 in a conversation thread with prompts and responses 0049-0050 and fig. 2b prompts and replies between humans or AI agents in a language model)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Schemers to incorporate the above claim limitations as taught by HATTANGADY to allow for use of a known technique such as language models to include AI responses to improve similar devices in the same way such as threads using language models, wherein the topic and language model both use user inputs, and Schemers is expressly clarified to now include AI inputs in additional to humans as part of a thread without changing that functioning of threads thereof, but improving the thread capabilities to include further topic refinement in the language model by using human to human and also human to ai, such as to not miss conversations with AI agents which are needed for rich content categorization in threads, and to allow for use of a known technique such as a recency threshold in thread classification to improve similar devices in the same way such as time period satisfaction in threads, thereby improving classification further to include most recent topic based messages to help uses recall a recent conversation but also have the option to still search entire threads.
Re claims 3 and 14, Schemers teaches
3. The language model thread truncation system of claim 1, wherein the thread truncation module is further configured to obtain new prompts and responses as the conversation continues, and add the new prompts and response to the cluster or to another cluster. (new messages in real time… in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score)
Re claims 4 and 15, Schemers teaches
4. The language model thread truncation system of claim 1, wherein the prompts and responses are clustered using cosine similarity or latent Dirichlet allocation. (LDA 0080)
Re claims 5 and 16, Schemers teaches
5. The language model thread truncation system of claim 1, wherein reference is made in the conversation to the information contained in a given prompt or response, and wherein the thread truncation module is further configured to increase the individual reference score for the given prompt or response. (the highest scores are used thus increasing the total final group of scores thereof 0146… in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score)
Re claim 6, Schemers teaches
6. The language model thread truncation system of claim 1, wherein the timing threshold comprises a passage of more than a certain amount of time since the cluster was created. (using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score)
Re claim 7, Schemers teaches
7. The language model thread truncation system of claim 1, wherein the prompts and responses comprise messages in the thread, and wherein the timing threshold comprises an exchange of more than a certain number of messages since the cluster was created. (in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
Re claim 8, Schemers teaches
8. The language model thread truncation system of claim 1, wherein to remove the cluster from the thread, the thread truncation module is configured to remove the prompts and responses from the thread that were used to create the cluster. (remove/filtering… in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
Re claim 9, Schemers teaches
9. The language model thread truncation system of claim 1, wherein to retain the cluster in the thread, the thread truncation module is configured to retain the prompts and responses in the thread that were used to create the cluster. (filtering to retain as well as omit… in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
Re claim 10, Schemers teaches
10. A language model thread truncation system, comprising (0046):
a processor set; (fig. 1)
one or more computer-readable storage media; and (fig. 1)
program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising: (fig. 1)
obtaining …prompts and responses from a thread of user interactions with the language model during a conversation (topic modeling 0080-0081)…
cluster the prompts and responses based on their topical representation to create a cluster around a topic, generate individual reference scores for the prompts and responses in the cluster, wherein the individual reference scores represent a last time the conversation has comes back to information contained in the prompts and responses, and after a timing threshold for the cluster has been reached, truncate the thread by removing the cluster from the thread if the current topic of the conversation differs from the topic of the cluster and if a reference value of the cluster is below a minimum reference value, otherwise retain the cluster in the thread, (in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
wherein the reference value of the cluster is determined based on the individual reference scores for the prompts and responses in the cluster. (utilizing a threshold and scores including object/message scores, to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
However, while the information is being sent in and out of the topic model including send and reply messages, Schemers does not necessarily teach a language model per se, and including the messages thereof, wherein HATTANGADY has been included for clarity to cover instances where a human is interacting with at least one AI agent including prompt and reply per se, and thus fails to teach:
prompts and responses from a thread of user interactions during a conversation (HATTANGADY in a conversation thread 0049-0050 and fig. 2b prompts and replies between humans or AI agents in a language model)
wherein the reference value is computed based on how recently the conversation refers to information contained in the prompts and responses in the cluster. (HATTANGADY a reference value or metric in some capacity is needed to utilize a threshold 0083 “…older messages are removed from an extracted communication thread that have a date/timestamp past a recency threshold…” recency threshold also in 0102 in a conversation thread with prompts and responses 0049-0050 and fig. 2b prompts and replies between humans or AI agents in a language model)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Schemers to incorporate the above claim limitations as taught by HATTANGADY to allow for use of a known technique such as language models to include AI responses to improve similar devices in the same way such as threads using language models, wherein the topic and language model both use user inputs, and Schemers is expressly clarified to now include AI inputs in additional to humans as part of a thread without changing that functioning of threads thereof, but improving the thread capabilities to include further topic refinement in the language model by using human to human and also human to ai, such as to not miss conversations with AI agents which are needed for rich content categorization in threads, and to allow for use of a known technique such as a recency threshold in thread classification to improve similar devices in the same way such as time period satisfaction in threads, thereby improving classification further to include most recent topic based messages to help uses recall a recent conversation but also have the option to still search entire threads.
Re claims 12 and 19, Schemers teaches
12. The language model thread truncation system of claim 10, wherein a highest reference score value amongst the individual reference scores for the prompts and responses in the cluster is used as the reference value of the cluster. (the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144, in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075)
Re claim 13, Schemers teaches
13. A method for language model thread truncation, comprising (0046 and thread and topic model 0080-0081):
obtaining prompts and responses from a thread of user interactions with a language model during a conversation; (0080 and 0081 topic model in a thread environment where messages are collected and classified 0046)
clustering the prompts and responses based on their topical representation to create a cluster around a topic; (in a thread environment where messages are collected and classified 0046, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
generating individual reference scores for the prompts and responses in the cluster, wherein the reference scores represent a last time the conversation has come back to information contained in the prompts and responses; and (utilizing a threshold and scores including object/message scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
after a timing threshold for the cluster has been reached, truncating the thread by removing the cluster from the thread if the current topic of the conversation differs from the topic of the cluster and if a reference value of the cluster is below a minimum value, otherwise retaining the cluster in the thread, wherein the reference value of the cluster is determined based on the individual reference scores for the prompts and responses in the cluster. (using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
However, while the information is being sent in and out of the topic model including send and reply messages, Schemers does not necessarily teach a language model per se, and including the messages thereof, wherein HATTANGADY has been included for clarity to cover instances where a human is interacting with at least one AI agent including prompt and reply per se, and thus fails to teach:
prompts and responses from a thread of user interactions during a conversation (HATTANGADY in a conversation thread 0049-0050 and fig. 2b prompts and replies between humans or AI agents in a language model)
wherein the reference value is computed based on how recently the conversation refers to information contained in the prompts and responses in the cluster. (HATTANGADY a reference value or metric in some capacity is needed to utilize a threshold 0083 “…older messages are removed from an extracted communication thread that have a date/timestamp past a recency threshold…” recency threshold also in 0102 in a conversation thread with prompts and responses 0049-0050 and fig. 2b prompts and replies between humans or AI agents in a language model)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Schemers to incorporate the above claim limitations as taught by HATTANGADY to allow for use of a known technique such as language models to include AI responses to improve similar devices in the same way such as threads using language models, wherein the topic and language model both use user inputs, and Schemers is expressly clarified to now include AI inputs in additional to humans as part of a thread without changing that functioning of threads thereof, but improving the thread capabilities to include further topic refinement in the language model by using human to human and also human to ai, such as to not miss conversations with AI agents which are needed for rich content categorization in threads, and to allow for use of a known technique such as a recency threshold in thread classification to improve similar devices in the same way such as time period satisfaction in threads, thereby improving classification further to include most recent topic based messages to help uses recall a recent conversation but also have the option to still search entire threads.
Re claim 17, Schemers teaches
17. The method of claim 13, wherein the prompts and responses comprise messages in the thread, and wherein the timing threshold is selected from the group consisting of: a passage of more than a certain amount of time since the cluster was created, an exchange of more than a certain number of messages since the cluster was created, or combinations thereof. (using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
Re claim 20, Schemers teaches
20. The method of claim 13, wherein removing the cluster from the thread comprises: removing the prompts and responses from the thread that were used to create the cluster. (filtering, omitting, or retaining thread messages via filter based on time and score, using a time limit/period to filter selection 0151, specifically utilizing a threshold and scores to remove non-relevant topics 0011, wherein such conversations comprise send and reply messages 0143 including real-time conversations updated as received in the aggregated thread 0075, the highest scores are used thus increasing the total final group of scores thereof 0146 and using more than or a minimum number of messages as part of a score 0144)
Claims 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230023160 A1 Schemers; Roland et al. (hereinafter Schemers) in view of US 20250005295 A1 HATTANGADY; Poonam Ganesh et al. (hereinafter HATTANGADY) and further in view of US 10229205 B1 Grant; Myles et al. (hereinafter Grant).
Re claims 11 and 18, while Schemers teaches tread filtering and scoring based on send and receive messages in a conversation for topic grouping, it fails teach
11. The language model thread truncation system of claim 10, wherein the reference value of the cluster is determined as an average value of the individual reference scores for the prompts and responses in the cluster. (Grant abstract and col 14 lines 1-51)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Schemers in view of HATTANGADY to incorporate the above claim limitations as taught by Grant to allow for use of a known technique of averaging scores to improve similar devices in the same way such as aggregate scoring of multiple messages, wherein the concept of averaging scores results in improved predictive performance, reduced bias and variance, and enhanced model robustness for thread classification such as not to miss borderline outlier data.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190182382 A1 Mazza; Arnon et al.
Removing edge topics
US 20250005288 A1 Amatriain-Rubio; Xavier et al.
Thread classification
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
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Michael.Colucci@uspto.gov
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