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 with respect to claims 1, 10, and 13 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; new prior art is provided below.
Re: The remarks pertinent to 35 USC 101, Examiner concurs that paragraph 0058, and also 0055-0057, of the present invention specification is sufficient in overcoming said rejection. See reasoning below.
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
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?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve scoring for topic classification while removing less pertinent parameters, and supported by the specification, and reflected by the claims e.g. in spec: 0055-0058.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
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:
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.
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 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.
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) and further in view of US 20230041272 A1 Ayllón Álvarez; David et al. (hereinafter Ayllón).
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 (topic modeling 0080-0081 topic model in a thread environment where messages are collected and classified 0046) …prompts and responses from a thread of user interactions with the language model during a conversation
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… supplementally, 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)
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.
However, while the combination teaches a recency threshold for identification of newer messages in a thread, it fails to teach this pertinent to topics per se, thus failing to teach:
computing a reference value of the cluster based on how recently the conversation refers to the topic of the cluster; (Ayllón exemplifies uses of a cosine similarity models as in fig. 12, for instance in the context of BERT which uses cosine similarity as a reference value with weighting, as is the nature of BERT itself, in which BERT by its own existence and purpose, uses internal self-attention to place weights on words in a vector which is used for comparison with cosine similarity of new information given the historical or conversational information already on record, wherein in the context of BERT in light of topic and recent entries, here we observe recency and topic correlation if a sentence-level embedding of the most recent turns of dialogue in the current conversation has a high cosine similarity with a piece of fact from the pre-selected fact collection FC, that piece of fact is likely related to the current topic of the conversation and would be an interesting addition to the conversation)
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 Ayllón to allow for combining prior art elements according to known methods to yield predictable results such as using the existing cosine similarity and recency threshold concepts in HATTANGADY now applied to topic with both BERT and cosine similarity thereby improving the combination to improve traditional recency-threshold cosine similarity by capturing semantic meaning rather than just matching raw keywords, by introducing BERT embeddings, cosine similarity, and hybrid weighting as part of BERT in combination with cosine similarity, you can build a system that understands the true tangible context of the conversation and dynamically adjusts how much weight to give to older versus newer information, e.g. which replaces or enhances operations with a decaying weight function that balances topic relevance against time.
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 value 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 (topic modeling 0080-0081 topic model in a thread environment where messages are collected and classified 0046) … prompts and responses from a thread of user interactions with the language model during a conversation
clustering the prompts and responses based on their topical representation to create a cluster around a topic;
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)
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, and in addition 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.
However, while the combination teaches a recency threshold for identification of newer messages in a thread, it fails to teach this pertinent to topics per se, thus failing to teach:
computing a reference value of the cluster based on how recently the conversation refers to the topic of the cluster using a weighted cosine similarity model (Ayllón exemplifies uses of a cosine similarity models as in fig. 12, for instance in the context of BERT which uses cosine similarity as a reference value with weighting, as is the nature of BERT itself, in which BERT by its own existence and purpose, uses internal self-attention to place weights on words in a vector which is used for comparison with cosine similarity of new information given the historical or conversational information already on record, wherein in the context of BERT in light of topic and recent entries, here we observe recency and topic correlation if a sentence-level embedding of the most recent turns of dialogue in the current conversation has a high cosine similarity with a piece of fact from the pre-selected fact collection FC, that piece of fact is likely related to the current topic of the conversation and would be an interesting addition to the conversation)
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 Ayllón to allow for combining prior art elements according to known methods to yield predictable results such as using the existing cosine similarity and recency threshold concepts in HATTANGADY now applied to topic with both BERT and cosine similarity thereby improving the combination to improve traditional recency-threshold cosine similarity by capturing semantic meaning rather than just matching raw keywords, by introducing BERT embeddings, cosine similarity, and hybrid weighting as part of BERT in combination with cosine similarity, you can build a system that understands the true tangible context of the conversation and dynamically adjusts how much weight to give to older versus newer information, e.g. which replaces or enhances operations with a decaying weight function that balances topic relevance against time.
Re claims 12 and 19, Schemers teaches
12. The language model thread truncation system of claim 10, wherein a highest reference 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 (topic modeling 0080-0081 topic model in a thread environment where messages are collected and classified 0046)… 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; (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)
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, and supplemental with 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.
However, while the combination teaches a recency threshold for identification of newer messages in a thread, it fails to teach this pertinent to topics per se, thus failing to teach:
computing a reference value of the cluster based on how recently the conversation refers to the topic of the cluster; (Ayllón exemplifies uses of a cosine similarity models as in fig. 12, for instance in the context of BERT which uses cosine similarity as a reference value with weighting, as is the nature of BERT itself, in which BERT by its own existence and purpose, uses internal self-attention to place weights on words in a vector which is used for comparison with cosine similarity of new information given the historical or conversational information already on record, wherein in the context of BERT in light of topic and recent entries, here we observe recency and topic correlation if a sentence-level embedding of the most recent turns of dialogue in the current conversation has a high cosine similarity with a piece of fact from the pre-selected fact collection FC, that piece of fact is likely related to the current topic of the conversation and would be an interesting addition to the conversation)
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 Ayllón to allow for combining prior art elements according to known methods to yield predictable results such as using the existing cosine similarity and recency threshold concepts in HATTANGADY now applied to topic with both BERT and cosine similarity thereby improving the combination to improve traditional recency-threshold cosine similarity by capturing semantic meaning rather than just matching raw keywords, by introducing BERT embeddings, cosine similarity, and hybrid weighting as part of BERT in combination with cosine similarity, you can build a system that understands the true tangible context of the conversation and dynamically adjusts how much weight to give to older versus newer information, e.g. which replaces or enhances operations with a decaying weight function that balances topic relevance against time.
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 US 20230041272 A1 Ayllón Álvarez; David et al. (hereinafter Ayllón) 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, the combination 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 values 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 and Ayllón 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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/23/2026 has been entered.
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
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov
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