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 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.
The rejection under 35 USC 101 has been overcome via amendment.
NOTE: the terms “object”, “attached”, and “updating” are minimally supported or unsupported in the present invention specification, let alone when claimed precisely together. For purposes of prior art, the concepts are given their most ordinary meaning under BRI such as attaching a file which is an update in itself. Please provide support and clarity as to such terms.
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 chat flow supported by the specification, and reflected by the claims e.g. in spec: 0066 In other words, the claims enable the invention to improve efficiency when processing calls/chat to identity relationships in the conversation.
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, 2, 6-9, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10515156 B2 Wooters; Charles C. (hereinafter Wooters) in view of US 20210182497 A1 Mullins; Christopher Lee et al. (hereinafter Mullins).
Re claim 1, Wooters teaches
1. (Currently Amended) An information processing apparatus comprising a processor configured to execute operations comprising: (fig 1 processors)
specifying, based on a character string representing an utterance during a conversation between two or more persons, a scene of the conversation between the two or more persons, wherein the scene represents a topical representation of a subset of [[a]] the conversation between the two or more persons: the scene comprises at least an opening of the conversation; (as in col 3 lines 25-36 multiple humans, system extracts the intent or topic (flight booking) and analyzes the initial inquiry can I book etc. … removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
creating, based on relationship definition information, visualization information, wherein (the chat is visualized per se as user types or speaks, system extracts the intent or topic (flight booking) and analyzes the initial inquiry can I book etc. … removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
the relationship definition information describes a relationship between at least the scene and other scenes of the conversation, (different contexts as conversation emerges, each scene or interaction evolves… removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
the relationship definition information further comprises a hierarchical structure definition and an association definition of relationships, (hierarchical as scope or topic intent evolves and based on time stamps per se, question-answer and the content in the question thereof, also as in fig. 3 definitions<DATARANGE> <CCARD> and the like … removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
the hierarchical structure definition describes a scene and one or more scenes of the other scenes in a parent-child relationship, (hierarchical as scope or topic intent evolves and based on time stamps per se, question-answer and the content in the question thereof, also as in fig. 3 definitions<DATARANGE> <CCARD> and the like … removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
the association definition describes a scene having a dependency relationship with one or more scenes of the other scenes of the conversation, (the antecedent of future inputs with past requests e.g. flight to destination to arrival to number of people and payment etc. as in fig. 3 definitions<DATARANGE> <CCARD> and the like … removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
the visualization information visualizes a first time series of character strings in the conversation[[,]]and a second time series of the scene[[s]] in the conversation, [[and]] (time based chat entries, N and N + m, removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
the visualization information further visualizes the [[a]] relationship between the scene[[s]] and the other scenes of the conversation, and when the one or more scenes of the other scenes are immediately subsequent to the scene in the second time series and the scene is a parent of the one or more scenes, the visualization information further comprises displaying a presentation of object of the scene while suppressing display of one or more presentation objects of the one or more scenes, and further dynamically updating, based on an interactive selection received for presenting the one or more scenes in the parent-child relationship of the scene… (dependency expressly under BRI as a relationship with the previous or parent input including answers thereof, the object” is not defined in the present invention spec, and is merely an input block with text, suppressed data is redacted as in fig. 4… hierarchical as scope or topic intent evolves and based on time stamps per se, question-answer and the content in the question thereof, also as in fig. 3 definitions<DATARANGE> <CCARD> and the like … removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
However, while Wooters teaches redacted and tagged or description definitions in a chat where a user can create an object by typing i.e. chat message, it fails to teach:
…display of the one or more presentation objects of the one or more scenes attached to the presentation object of the scene. (Mullins user can attach multimedia or press buttons to interact with the other senders message e.g. like 0051 0053 and fig. 3, 5)
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 Wooters to incorporate the above claim limitations as taught by Mullins to allow for combining prior art elements according to known methods to yield predictable results such as using chat interface features of attaching an object other than text or interacting with another message not sent by the user, to improve clarity and allow a user to attach files that the agent can process to avoid communication typos, such as for bill correction or flight booking customer service, e.g. using documentation.
Re claim 13, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
For instance, see fig. 3 which contains the processor or necessitated equivalent thereof.
Re claim 2, Wooters teaches
2. (Currently Amended) The information processing apparatus according to The information processing apparatus according to the processor further configured to execute operations comprising: transmitting terminal connected to the information processing apparatus via a communication network. (any network to connect two people and displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
Re claim 6, Wooters teaches
6. (Currently Amended) The information processing apparatus according to claim 4,wherein the creating further comprises creating scenes, the scenes have respective scenes of the scenes with a line. (Lines in a GUI to isolate who is talking and aligned vertically per box, displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
Re claim 7, Wooters teaches
7. (Currently Amended) The information processing apparatus according to The information processing apparatus according to wherein the creating further comprises creating second time series of scenes. (a warning when sentiment indicates a mismatch per se as in 304 in fig. 3 col 15 lines 12-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
Re claim 8, Wooters teaches
8. (Currently Amended) The information processing apparatus according to claim 4,wherein when second time series of scenes include identical scenes, the creating further comprises creating
Re claim 9, Wooters teaches
9. (Currently Amended) The information processing apparatus according to claim 1,wherein the creating further comprises creating second time series of scenes. (an agent has a script col 13 lines 47-51…displaying a chat or conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
Re claim 14, Wooters teaches
14. (Previously Presented) A computer-readable non-transitory recording medium storing computer-executable program instructions that when executed by a processor of a computer, execute the information processing method according to claim 13. (scope or topic intent evolves and based on time stamps per se, question-answer and the content in the question thereof, also as in fig. 3 definitions<DATARANGE> <CCARD> and the like … removing sensitive information and replacing it col 13 lines 17-36 in conversation with dependency upon previous inputs e.g. in fig. 3 sequential dependence live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
Claim 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10515156 B2 Wooters; Charles C. (hereinafter Wooters) in view of US 20210182497 A1 Mullins; Christopher Lee et al. (hereinafter Mullins) and further in view of US 20190058793 A1 Konig; Yochai et al. (hereinafter Konig).
Re claim 10, Wooters teaches
for a scene that transitions from a current scene and a transition probability, based on the current scene in the second time series of scenes and previous conversation history information. (dependency upon previous inputs live and also from past conversation col 6 lines 13-29…displaying a chat with different time series such as an agent and customer or each segment of an agent or customer per se, in conversation using time stamps otherwise inherently in a time series col 23 lines 59-67, exemplified as in fig. 1 and fig. 3 using keyword identification col 24 lines 1-16 via speech to text col 18 lines 30-53)
However, while the combination teaches object to attach, conversations displayed, and probability scores, it fails to teach:
10. (Currently Amended) The information processing apparatus according to claim 1,wherein the creating further comprises creating
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 Wooters in view of Mullins to incorporate the above claim limitations as taught by Konig to allow for combining prior art elements according to known methods to yield predictable results such as using the agent metrics in Wooters improved with selectable candidates in Konig, to tie in the existing probability of Wooters as a selectable response to minimize agent error and speed up response time by a human and alternatively a bot with human supervision.
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 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10984794 B1 Kaneko; Yuki et al.
Concierge and conversation scenes or context
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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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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