DETAILED ACTION
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 .
Priority
Receipt is acknowledged that application claims priority to foreign application with application number GB 2109185.5 dated 6/25/2021. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Receipt is acknowledged that application is a National Stage application of PCT /GB2022/051630. Priority to GB2109185.5 with a priority date of 6/24/2022 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS dated 5/20/2024 has been considered and placed in the application file.
Response to Amendment and Arguments.
Applicant’s arguments, see page 14-16, filed 3/17/2026, with respect to claims 26-37 rejection have been fully considered and are persuasive. The 101 rejection of claims 26-37 has been withdrawn.
Applicant’s arguments, see page 14-16, filed 3/17/2026, with respect to claims 38-45 rejection have been fully considered and are not persuasive. Applicant argues that “See Office Action, page 11. Applicant respectfully disagrees with the Office Action's arguments. MPEP § 2106.04(a)(2)(III)(A) explicitly states that "a claim with limitation(s) that cannot practically be performed in the human mind does not recite a mental process." Here, Claim 26 recites "a plurality of sub-dialogue units, each configured to deliver an element of the care protocol," which are discrete computational components that exceed the capacity of human cognition. Claim 26 also recites "an orchestrator configured to present the sub-dialogue units to the user, sequentially, wherein each sub-dialogue unit and the orchestrator comprises: a natural language understanding module configured to receive at least one of an input and reply and determine at least one intent and, where present, at least one slot within the at least one of input14 of and reply; a dialogue planning module configured to determine an output based, at least in part, on the at least one intent and, where present, slot associated within the at least one of input and reply, and a natural language generation module configured to provide the output to the user," which similarly cannot be performed mentally. Just as the human mind is not equipped with a monitor to detect "suspicous activity via a network", the human mind is also not equipped with a plurality of sub-dialogue units and an orchestrator. See MPEP 2106.04(a)(2) citing SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019). Applicant respectfully submits that Claim 26 is not directed to mental process.”. The Examiner respectfully disagrees claim 38 does not mention orchestrator and the sub-dialogue units. Hence being a mental process of a user going through a therapy session and the therapist is conducting it. Where the therapist can ask questions or hear the users’ issues and provide a response on said issues. Furthermore, detecting “suspicious activity via a network” isn’t mentioned in the claim. Therefor being a mental process. The claims are not patent eligible.
Applicant further argues that “Applicant respectfully disagrees with the Office Action's arguments. In Ex Parte Desjardins, the Patent Trial and Appeal Board held that 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 and that the claim as a whole integrated the judicial exception into a practice application. Appeal 2024-000567 (PTAB September 26, 2025). Here, claim 26 recites a concrete architecture comprising a plurality of sub-dialogue units and an orchestrator, providing technical benefits such as, but not limited to, enabling the system to handle complex interactions.”, Furthermore, applicant cites “Ex parte Desjardins et al.” The Examiner respectfully disagrees claim 38 does not mention the architecture comprising a plurality of sub-dialogues units and an orchestrator, nor any training/adapting a model like in Desjardins which provides a technical improvement, therefor claim 38 does not have an improvement of a computer. The claims are not patent eligible.
Therefore, the 101 rejection of claims 38-45 are maintained.
Applicant’s arguments, see page 10-12, filed 3/17/2026, with respect to claims 26-45 rejection have been fully considered and are persuasive there for a new reference has been applied, hence a non-final has been presented below. The 102/103 rejection of claims 26-45 has been withdrawn.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“sub-dialogue units” in claim 26,28,29,30,31-36;
“understanding module ” in claim 26, 30, 34, 36; and
“planning module” in claim 32, 36, ; and
“generation module” in claim 26, 32, 36, .
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 26-27, 29-45 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6, 8-16 of U.S. Patent No. 12,293,819 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the issued patent are narrower in scope than that of the instant application and therefore anticipates the claims of the instant application. Please see the Claim Mapping in the table below.
Each of the claims of the instant application map to the issued patent, where Claim (I) maps to Claim (P).
Claim 26 (I): Claim 1 (P); Claim 27 (I): Claim 2 (P); Claim 29 (I): Claim 3 (P); Claim 30 (I): Claim 1 (P); Claim 31 (I): Claim 4 (P); Claim 32 (I): Claim 1&4 (P); Claim 33 (I): Claim 1&6&10 (P); Claim 34 (I): Claim 5 (P); Claim 35 (I): Claim 6 (P); Claim 36 (I): Claim 1 (P); Claim 37 (I): Claim 8+9 (P); Claim 38 (I): Claim 10 (P); Claim 39 (I): Claim 11 (P); Claim 40 (I): Claim 11 (P); Claim 41 (I): Claim 12 (P); Claim 42 (I): Claim 13 (P); Claim 43 (I): Claim 14 (P); Claim 44 (I): Claim 15 (P); Claim 45 (I): Claim 16 (P).
Instant Application: 18/572902
Issued Patent: 12,293,819 B2
26. (New) A conversational agent for maintaining or improving the wellbeing of a user presenting with diabetes and mental health needs by the delivery of a care protocol, the conversational agent comprising:
a plurality of sub-dialogue units, each configured to deliver an element of the care protocol; and
an orchestrator configured to present the sub-dialogue units to the user, sequentially,
wherein each sub-dialogue unit and the orchestrator comprises:
a natural language understanding module configured to receive at least one of an input and reply and determine at least one intent and, where present, at least one slot within the at least one of input and reply;
a dialogue planning module configured to determine an output based, at least in part, on the at least one intent and, where present, slot associated within the at least one of input and reply, and
a natural language generation module configured to provide the output to the user.
1. A computer-based system comprising a conversational agent for maintaining or improving the wellbeing of a user presenting with mental health needs by the delivery of a psychotherapy protocol, the conversational agent comprising:
a plurality of treatment sub-dialogue units, each configured to deliver an element of the psychotherapy protocol;
an orchestrator configured to present the treatment sub-dialogue units to the user, sequentially;
and a risk assessment sub-dialogue unit configured to run in parallel to an active sub-dialogue unit with which the user is engaged;
wherein each sub-dialogue unit and the orchestrator comprises:
a natural language understanding module configured to receive at least one of an input from the user and a reply from the user and determine an intent and, where present, a slot within the at least one of an input and a reply;
a dialogue planning module configured to determine an output based, at least in part, on the at least one of the intent and the slot associated within the at least one of an input and a reply,
and an output generation module configured to provide the output to the user;
wherein the dialogue planning module of each treatment sub-dialogue unit is further configured to determine its output based, at least in part, on the element of the psychotherapy protocol being delivered to the user; and wherein the risk assessment sub-dialogue unit comprises a natural language understanding module configured to receive the at least one input and reply from the user, and analyse the at least one input and reply to determine, if present within the at least one input and reply, at least one intent indicating a risk and, where present, at least one slot within the at least one input and reply; and wherein the risk assessment sub-dialogue unit is configured to, when the at least one intent indicating the risk is present, take an action based, at least on part, on the identified risk.
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 38-45 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 38 recites A computer-implemented method for maintaining or improving the wellbeing of a user presenting with chronic physical health needs and mental health needs, the method comprising: receiving an input from a user;
analysing the input using a natural language understanding module configured to determine at least one intent and, where present, at least one slot within the input;
determining an output using a dialogue planning module, wherein the output is based, at least in part, on the at least one intent and, where present, slot associated with the input;
providing the output to the user using a natural language generation module; and
receiving, in response to the output, a reply from the user.
The limitation of “analyzing…”, “…receiving…”, “…determining…”, and “….providing…” , as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a user going through a therapy session and the therapist is conducting it. Where the therapist can ask questions or hear the users’ issues and provide a response on said issues.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements that are computer components “sub-dialogue units” (page 20 lines 20-30) recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the computer components amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claim 39 additionally recites the method according to claim 38, further comprising: determining a user engagement level, wherein the output is based, at least in part, on the user engagement level. However, these limitations encompass a therapist having sessions with a patient where they have conversations. Where the therapist can see how engaged the patient is. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 40 additionally recites the method according to claim 39, further comprising: determining if the user engagement level is below a predetermined threshold; and sending an alert to a second user upon determining a user engagement level below the predetermined threshold. However, these limitations encompass a therapist having sessions with a patient where they have conversations. Where the therapist can see how engaged the patient is. If their engagement is below a certain threshold they can call the patients parents. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 41 additionally recites the method according to claim 40, wherein after having received the alert, the second user at least one of: provides a second output to the user and amends the output determined by the dialogue planning module. However, these limitations encompass a therapist having sessions with a patient where they have conversations. Where the therapist can see how engaged the patient is. If their engagement is below a certain threshold they can call the patients parents. Further the parents can help the patient alongside the therapist or give advice. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 42 additionally recites the method according to claim 41, further comprising: reviewing, using the natural language understanding module, the at least one of second output and amended output and providing, using the dialogue planning module, further amendments to the at least one of second output and amended output where needed. However, these limitations encompass a therapist having sessions with a patient where they have conversations. Where the therapist can see how engaged the patient is. If their engagement is below a certain threshold they can call the patients parents. Further the parents can help the patient alongside the therapist or give advice. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 43 additionally recites the method according to claim 38, further comprising: reviewing a memory configured to store at least one event, wherein each event comprises: an intent and, where present, at least one slot corresponding to a previous input; a previous output corresponding to the previous input, and where present, a previous reply received in response to the previous output, wherein the output is based, at least in part, on an event stored within the memory. However, these limitations encompass , a therapist remembering events that the patient had said or did in a session to help build rapport with the patient. Where this would build connect for the patient. In particular, the claim only recites additional elements that are computer components “memory” (well known ) recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Claim 44 additionally recites The method according to claim 41, wherein the output is based, at least in part, on at least one of: the next stage in a psychotherapy treatment model for the user; the need to obtain a piece of information from the user; the piece of information required next from the user; a question contained within the input; the frequency of questions contained within the input; the frequency of questions generated by the natural language generation module; the amount of repetition within an input compared to a previous input or reply; and the amount of repetition within an output compared to a previous output. However, these limitations encompass a therapist asking a variety/multiple questions to the patient and the patient responding to them. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 45 additionally recites the method according to claim 41, wherein the method further comprises: alerting a second user in response to: determining when the at least one of input and reply is outside of a predetermined treatment model; or determining when the natural language understanding module is unable to determine the intent or, where present, slot associated with the input. However, these limitations encompass a therapist alerting parents or another therapist for assistance in the situation their patient is in. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 26,27, 28, 29, 36, 37, 38, and 43 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20170324868 A1, (Tamblyn; Eric.) in view of US Patent US 20220068462 A1, (DOLAN; Eric William.)
Claim 26
Regarding Claim 26, Tamblyn teach the conversational agent comprising: a plurality of sub-dialogue units, each configured to deliver an element of the care protocol; and (doesn’t teach the bold)
(See fig 2a it shows the plurality of sub dialogues being chatbot 202a-202-c
Paragraph 90 "FIG. 2A is a more detailed schematic block diagram of the chat automation server 140 operating as part of the chat automation system 100, according to some embodiments of the present invention. The chat automation server 140 is coupled to (e.g., in electronic communication with) the end user device 108 operated by the customer 106 over the data communications network 110. As discussed above, the chat automation server 140 may be operated by the business 104 as part of the contact center system 102 (shown in FIG. 1), for engaging in automated chat conversations with the customers of the business. In this regard, the chat automation server 140 hosts one or more chat automation modules 202a-202c (collectively referenced as 202), configured with computer program instructions for engaging in the automated chat conversations. Chat automation modules 202 may also be referred to as chat robots, chat bots, chatterbots, dialog systems, conversational agents, automated chat resources, or the like."
Paragraph 91 "The chat bots 202 may operate, for example, as an executable program that can be launched according to demand for the particular chat bot. According to some embodiments, the IMR 122 may operate as an execution engine or environment for the chat bots 202, analogous to loading VoiceXML files to a media server for interactive voice response (IVR) functionality. Loading and unloading may be controlled by the chat automation server 140, analogous to how a VoiceXML script may be controlled in the context of an interactive voice response. The IMR 122 may provide a common means for capturing and passing collecting user data in a unified way, similar to user data capturing in the context of IVR. Such data can be stored (e.g., in the CMR database), shared, and utilized in follow-up conversation steps, whether with the same chat bot, a different chat bot, an agent chat, or even a different media type. According to one embodiment, the chat automation server 140 is configured to orchestrate the sharing of data among the various chat bots as interactions are transferred or transitioned over from one chat bot to another, or from one chat bot to a human agent. According to one embodiment, the data captured during interaction with a particular chat bot may be transferred along with a request to invoke a second chat bot or human agent."
Paragraph 92 "In one embodiment, the number of chat bots 202 may vary according to the design and function of the chat automation server 140, and is not limited to the number illustrated in FIG. 2A. For example, different chat bots may be created to have different profiles. The profile of a particular chat bot may be used to select a chat bot with expertise for helping a customer on a particular subject control, for example, how the chat bot communicates with a particular customer. Engaging chat bots with profiles that are catered to specific types of end users 106 may allow more effective communication with such users. For example, one chat bot may be designed or specialized to engage in a first topic of communication (e.g., opening a new account with the business 104), while another chat bot may be designed or specialized to engage in a second topic of communication (e.g., technical support for a product or service provided by the business 104), that is different from the first topic of communication. In another example, the chat bots may be configured to utilize different dialects or slang, or may have different personality traits or characteristics. For example, the vocabulary of the different chat bots may be tailored to use the slang or diction of young people, elder people, people in a certain region of the country, and/or people having a certain language or ethnic background.")
an orchestrator configured to present the sub-dialogue units to the user, sequentially,
(Paragraph 85 "The contact center system 102 may additionally include a chat automation server 140 for conducting and managing automated/electronic chat communications with end users 106 operating end user devices 108. The chat communications may be conducted in such a way that the end users are not aware that they are communicating with an automated system, as opposed to a human agent, although embodiments of the present invention are not limited thereto, and in some embodiments, end users may be aware that they are interacting with an automated system. According to some embodiments, the chat automation server 140 may operate as a chat orchestration server, dispatching actual chat conversations to various chat bots or agent chats. The processing logic of the chat automation server 140 may be rules driven, and may leverage, for example, intelligent workload distribution protocols and various business rules for routing communications."
Paragraph 91 "The chat bots 202 may operate, for example, as an executable program that can be launched according to demand for the particular chat bot. According to some embodiments, the IMR 122 may operate as an execution engine or environment for the chat bots 202, analogous to loading VoiceXML files to a media server for interactive voice response (IVR) functionality. Loading and unloading may be controlled by the chat automation server 140, analogous to how a VoiceXML script may be controlled in the context of an interactive voice response. The IMR 122 may provide a common means for capturing and passing collecting user data in a unified way, similar to user data capturing in the context of IVR. Such data can be stored (e.g., in the CMR database), shared, and utilized in follow-up conversation steps, whether with the same chat bot, a different chat bot, an agent chat, or even a different media type. According to one embodiment, the chat automation server 140 is configured to orchestrate the sharing of data among the various chat bots as interactions are transferred or transitioned over from one chat bot to another, or from one chat bot to a human agent. According to one embodiment, the data captured during interaction with a particular chat bot may be transferred along with a request to invoke a second chat bot or human agent.")
wherein each sub-dialogue unit and the orchestrator comprises:
a natural language understanding module configured to receive at least one of an input and reply and determine at least one intent and, where present, at least one slot within the at least one of input and reply;
(Paragraph 101 " FIG. 2B is a more detailed block diagram of an exemplary chat bot 202 according to one embodiment of the invention. In the exemplary embodiment of FIG. 2B, the chat bot 202 includes a text analytics module 250, dialog manager 252, and output generator 254. The text analytics module is configured to analyze and understand natural language. In this regard, the text analytics module may be configured with a lexicon of the language, a syntactic/semantic parser, and grammar rules for breaking a phrase provided by the end user device 108, into an internal syntactic and semantic representation. According to one embodiment, the configuration of the text analytics module depends on the particular profile associated with the chat bot. For example, certain slang words may be included in the lexicon for one chat bot, but excluded from another chat bot."
Paragraph 98 "Additionally, the end user interface 160 and the agent interface 170 may operate to facilitate or coordinate the exchange of text-based or chat communications between the end user device 108 and the agent device 130. That is, the end user interface 160 may operate to transmit and receive signals to and from the end user device 108 during a chat communication session. For example, the end user interface 160 may transmit signals to the end user device 108 for displaying a message or text-based communication on a display of the end user device 108 by way of the network 110, and may receive signals from the end user device 108 that include text based or chat communication messages from the end user 106."
Paragraph 109 " As illustrated in FIG. 3A, at the start 210 of a chat communication session, a default chat bot 202a is invoked (e.g., as an automated receptionist or concierge) for identifying, for example, a topic of the communication. For example, the default chat bot 202a may transmit, by way of the end user interface 160, a message to the end user device 108 a chat or text-based message inquiring as to the purpose/intent of the communication (e.g., “How can I help you today?”). Additionally, according to some embodiments, the default chat bot 202a may transmit a message to the end user device 108 a chat or text-based message asking the end user 106 to provide identifying information about the end user 106, or may determine profile information from a user account associated with the end user 106."
Paragraph 104 " According to some embodiments, every segment, step, or input in a chat communication may have a corresponding list of possible responses. Responses may be categorized based on topics (determined using a suitable text analytics and topic detection scheme), and suggested next actions are assigned. Actions may include, for example, responses with answers, additional questions, assignment for a human to assist (e.g., by disambiguating input from the end user), and the like."
Synthetic/semantic parser extracts semantic representation from the user phrase is being interpreted as a slot like information)
a dialogue planning module configured to determine an output based, at least in part, on the at least one intent and, where present, slot associated within the at least one of input and reply, and
(Paragraph 93 "The chat automation server 140 may also host a default chat bot that may be invoked at a beginning of a chat conversation if there is insufficient information about the customer to invoke a more specialized chat bot. For example, if a customer intent is unknown when the conversation initially ensues, the default chat bot may be invoked to ask questions about the customer intent."
paragraph 102 "The dialog manager 252 receives the syntactic and semantic representation from the text analytics module, and manages the general flow of the conversation based on a set of decision rules. In this regard, the dialog manager maintains history and state of the conversation, and generates an outbound communication based on the history and state. The communication may follow the script of a particular conversation path selected by the dialog manager. As described in further detail below, the conversation path may be selected based on an understanding of a particular purpose or topic of the conversation. The script for the conversation path may be generated using any of various languages and frameworks conventional in the art, such as, for example, Artificial Intelligence Markup Language (AIML), SCXML, or the like."
Paragraph 113 "According to one embodiment, the particular chat bot 202b selected by the default chat bot 202a may have various conversation paths or scripts that it may follow based on, for example, the customer intent. According to some embodiments of the present invention, the chat bot 202a or 202b1 may identify or calculate a confidence level of the purpose of the communication from among of plurality of different possible communications purposes or paths 212a-212c that are predetermined. The number of possible predetermined communication or conversation paths or purposes may vary according to the design and function of the chat automation system 100, and is not limited to the number illustrated in FIG. 3A. The chat automation system 100, however, may be designed such that all (or substantially all) communications conducted with an end user can be categorized into a finite number of categories (e.g., account support, product or service technical support, sales, billing, other, etc.).")
a natural language generation module configured to provide the output to the user.
(Paragraph 103 "The dialog manager 252 selects a response deemed to be appropriate at the particular point of the conversation flow/script, and outputs the response to the output generator 254. According to one embodiment, the dialog manager 252 may also be configured to compute a confidence level for the selected response, and provide the confidence level to the agent device 130.")
Tamblyn do not explicitly teach all of 26. (Currently Amended) A conversational agent for maintaining or improving the wellbeing of a user presenting with diabetes chronic physical health needs and mental health needs by the delivery of a care protocol,
the conversational agent comprising: a plurality of sub-dialogue units, each configured to deliver an element of the care protocol; and (doesn’t teach the bold)
However, DOLAN teach
26. (Currently Amended) A conversational agent for maintaining or improving the wellbeing of a user presenting with diabetes chronic physical health needs and mental health needs by the delivery of a care protocol,
(Paragraph 30 "FIG. 1 includes the system 100. This paragraph names labeled parts of system 100. The figure includes a mental health chatbot 133, a user (or a patient) 194, a CBT patient therapy plan database 145, a high-level structure of a CBT session 111, an artificial memory database 159, and a relationship ontology database 187. The mental health chatbot 133 can comprise of a natural language processing (NLP) engine 143, artificial memory engine 163, and a tangential conversation engine 173. The artificial memory database can comprise of a relationship database 169 and a global knowledge database 179."
Paragraph 33 "The treatment in CBT is based on cognitive formulation, the beliefs and behavioral strategies that characterize a specific disorder (Alford & Beck, 1997). Treatment is also based on conceptualization and understanding of specific beliefs and patterns of behavior of individual patients. The therapist seeks in a variety of ways to produce cognitive change—modification in the patient's thinking and belief system—to bring about enduring emotional and behavioral change. The CBT is used to manage a variety of conditions in the categories of psychiatric disorders, psychological problems, and medical problems with psychological components. Examples of psychiatric disorders include depressive disorder, panic disorder, substance abuse, health anxiety, etc. Examples of psychological problems include couple problems, family problems, grief, anger and hostility, etc. Examples of medical problems with psychological components include insomnia, migraine headaches, chronic back pain, etc.")
the conversational agent comprising: a plurality of sub-dialogue units, each configured to deliver an element of the care protocol; and ( teaches the bold)
(Paragraph 30 "FIG. 1 includes the system 100. This paragraph names labeled parts of system 100. The figure includes a mental health chatbot 133, a user (or a patient) 194, a CBT patient therapy plan database 145, a high-level structure of a CBT session 111, an artificial memory database 159, and a relationship ontology database 187. The mental health chatbot 133 can comprise of a natural language processing (NLP) engine 143, artificial memory engine 163, and a tangential conversation engine 173. The artificial memory database can comprise of a relationship database 169 and a global knowledge database 179."
Paragraph 40 "The mental health chatbot 133 can build on what the chatbot can remember from the patient's ongoing description of relationships including resolution of hypernyms and memory of sentiments towards people, pets, places, and things. The chatbot uses this information to make multi-session chats feel like working with a CBT therapist. The mental health chatbot can use the logic implemented in the NLP engine 143, the artificial memory engine 163, and the tangential conversation engine 173 to provide CBT therapist-like treatment to patients. For example, when a patient utters “my cat just passed away”, the technology disclosed applies logic that detects the event in the utterance i.e., “death” of the cat. The technology disclosed further includes logic to detect the appropriate sentiment associated with the event. The sentiment can be positive, negative, neutral, etc. In this example, the sentiment is negative as the patient is “sad” due to her loss. An inappropriate prompt to the patient such as “cats are awesome”, or “cats are amazing” in response to the above utterance can destroy the therapeutic relationship between the patient and the chatbot. The chatbot includes the logic to form an appropriate response to the above utterance as explained below.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamblyn to incorporate the teachings of DOLAN to provide a “26. (Currently Amended) A conversational agent for maintaining or improving the wellbeing of a user presenting with diabetes chronic physical health needs and mental health needs by the delivery of a care protocol, the conversational agent comprising: a plurality of sub-dialogue units, each configured to deliver an element of the care protocol; and (doesn’t teach the bold)” Doing so would Detect that the user needs a different therapeutic intervention, as recognized by DOLAN. (Paragraph 24).
Claim 36
Regarding Claim 36, Tamblyn in view of DOLAN, further DOLAN teach
A computer-based system for maintaining or improving the wellbeing of a user presenting with mental health needs, the system comprising a conversational agent and a treatment model module configured to provide a computer-readable representation of a treatment protocol;
(Paragraph 30 "FIG. 1 includes the system 100. This paragraph names labeled parts of system 100. The figure includes a mental health chatbot 133, a user (or a patient) 194, a CBT patient therapy plan database 145, a high-level structure of a CBT session 111, an artificial memory database 159, and a relationship ontology database 187. The mental health chatbot 133 can comprise of a natural language processing (NLP) engine 143, artificial memory engine 163, and a tangential conversation engine 173. The artificial memory database can comprise of a relationship database 169 and a global knowledge database 179."
Paragraph 40 "The mental health chatbot 133 can build on what the chatbot can remember from the patient's ongoing description of relationships including resolution of hypernyms and memory of sentiments towards people, pets, places, and things. The chatbot uses this information to make multi-session chats feel like working with a CBT therapist. The mental health chatbot can use the logic implemented in the NLP engine 143, the artificial memory engine 163, and the tangential conversation engine 173 to provide CBT therapist-like treatment to patients. For example, when a patient utters “my cat just passed away”, the technology disclosed applies logic that detects the event in the utterance i.e., “death” of the cat. The technology disclosed further includes logic to detect the appropriate sentiment associated with the event. The sentiment can be positive, negative, neutral, etc. In this example, the sentiment is negative as the patient is “sad” due to her loss. An inappropriate prompt to the patient such as “cats are awesome”, or “cats are amazing” in response to the above utterance can destroy the therapeutic relationship between the patient and the chatbot. The chatbot includes the logic to form an appropriate response to the above utterance as explained below.")
See claim 26 for rationale.
Claim 36 contains limitations similar to those found in claims 26 and therefore are not patent eligible for the same reasons.
Claim 38
Regarding Claim 38 contains limitations similar to those found in claims 26 and therefore are not patent eligible for the same reasons. Furthermore, claim 38 is broader than claim 26 since it does not have “wherein each sub-dialogue unit and the orchestrator comprises:” and the limitations prior.
Claim 27
Regarding Claim 27, Tamblyn in view of DOLAN, further DOLAN teach
27. (New) The conversational agent according to claim 26, wherein the care protocol is at least one of: a clinical protocol; and
(Paragraph 29 "We describe a system for a mental health chatbot that can conduct cognitive behavioral therapy sessions with patients by acting as a therapist. Cognitive Behavioral Therapy (CBT) or Cognitive Therapy is a type of psychotherapy. In general, psychotherapy refers to treatment of mental disorders by psychological rather than medical means. When conducting CBT therapy sessions, the system includes natural language processing logic to remember content received from a patient for future chatbot therapy sessions. The system can make use of the remembered content during a therapy session from earlier interactions with the patient in a series of sessions. The system also includes logic to detect when to diverge to a tangential conversation from linearly structured steps of a CBT session and when to merge back to the flow of linearly structured steps of the session from the tangential conversation in the therapy session. The system is described with reference to FIG. 1 showing an architectural level schematic of a system in accordance with an implementation. Because FIG. 1 is an architectural diagram, certain details are intentionally omitted to improve the clarity of the description. The discussion of FIG. 1 is organized as follows. First, the elements of the figure are described, followed by their interconnection. Then, the use of the elements in the system is described in greater detail.")
a transdiagnostic CBT protocol.
(Paragraph 30 "FIG. 1 includes the system 100. This paragraph names labeled parts of system 100. The figure includes a mental health chatbot 133, a user (or a patient) 194, a CBT patient therapy plan database 145, a high-level structure of a CBT session 111, an artificial memory database 159, and a relationship ontology database 187. The mental health chatbot 133 can comprise of a natural language processing (NLP) engine 143, artificial memory engine 163, and a tangential conversation engine 173. The artificial memory database can comprise of a relationship database 169 and a global knowledge database 179.")
See claim 26 for rationale.
Claim 28
Regarding Claim 28, Tamblyn in view of DOLAN, further Tamblyn teaches
28. (New) The conversational agent according to claim 26, wherein the conversational agent comprises in excess of ten sub-dialogue units.
(Paragraph 92 " In one embodiment, the number of chat bots 202 may vary according to the design and function of the chat automation server 140, and is not limited to the number illustrated in FIG. 2A. For example, different chat bots may be created to have different profiles. The profile of a particular chat bot may be used to select a chat bot with expertise for helping a customer on a particular subject control, for example, how the chat bot communicates with a particular customer. Engaging chat bots with profiles that are catered to specific types of end users 106 may allow more effective communication with such users. For example, one chat bot may be designed or specialized to engage in a first topic of communication (e.g., opening a new account with the business 104), while another chat bot may be designed or specialized to engage in a second topic of communication (e.g., technical support for a product or service provided by the business 104), that is different from the first topic of communication. In another example, the chat bots may be configured to utilize different dialects or slang, or may have different personality traits or characteristics. For example, the vocabulary of the different chat bots may be tailored to use the slang or diction of young people, elder people, people in a certain region of the country, and/or people having a certain language or ethnic background." )
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamblyn in view of DOLAN to incorporate “The conversational agent according to claim 26, wherein the conversational agent comprises in excess of ten sub-dialogue units.” Doing so would make a chatbot have a particular subject control, as recognized by Tamblyn. (Paragraph 92). The number of chatbot to be over 10 would be a design choice as Tamblyn mentions in paragraph 92. Therefore, it would be obvious to include more than 10.
Claim 29
Regarding Claim 29, Tamblyn in view of DOLAN, further Tamblyn teaches 29. (New) The conversational agent according to claim 26, wherein the orchestrator is configured to select which sub-dialogue unit is presented to the user.
(Paragraph 90 "FIG. 2A is a more detailed schematic block diagram of the chat automation server 140 operating as part of the chat automation system 100, according to some embodiments of the present invention. The chat automation server 140 is coupled to (e.g., in electronic communication with) the end user device 108 operated by the customer 106 over the data communications network 110. As discussed above, the chat automation server 140 may be operated by the business 104 as part of the contact center system 102 (shown in FIG. 1), for engaging in automated chat conversations with the customers of the business. In this regard, the chat automation server 140 hosts one or more chat automation modules 202a-202c (collectively referenced as 202), configured with computer program instructions for engaging in the automated chat conversations. Chat automation modules 202 may also be referred to as chat robots, chat bots, chatterbots, dialog systems, conversational agents, automated chat resources, or the like."
Paragraph 91 "The chat bots 202 may operate, for example, as an executable program that can be launched according to demand for the particular chat bot. According to some embodiments, the IMR 122 may operate as an execution engine or environment for the chat bots 202, analogous to loading VoiceXML files to a media server for interactive voice response (IVR) functionality. Loading and unloading may be controlled by the chat automation server 140, analogous to how a VoiceXML script may be controlled in the context of an interactive voice response. The IMR 122 may provide a common means for capturing and passing collecting user data in a unified way, similar to user data capturing in the context of IVR. Such data can be stored (e.g., in the CMR database), shared, and utilized in follow-up conversation steps, whether with the same chat bot, a different chat bot, an agent chat, or even a different media type. According to one embodiment, the chat automation server 140 is configured to orchestrate the sharing of data among the various chat bots as interactions are transferred or transitioned over from one chat bot to another, or from one chat bot to a human agent. According to one embodiment, the data captured during interaction with a particular chat bot may be transferred along with a request to invoke a second chat bot or human agent."
Paragraph 92 "In one embodiment, the number of chat bots 202 may vary according to the design and function of the chat automation server 140, and is not limited to the number illustrated in FIG. 2A. For example, different chat bots may be created to have different profiles. The profile of a particular chat bot may be used to select a chat bot with expertise for helping a customer on a particular subject control, for example, how the chat bot communicates with a particular customer. Engaging chat bots with profiles that are catered to specific types of end users 106 may allow more effective communication with such users. For example, one chat bot may be designed or specialized to engage in a first topic of communication (e.g., opening a new account with the business 104), while another chat bot may be designed or specialized to engage in a second topic of communication (e.g., technical support for a product or service provided by the business 104), that is different from the first topic of communication. In another example, the chat bots may be configured to utilize different dialects or slang, or may have different personality traits or characteristics. For example, the vocabulary of the different chat bots may be tailored to use the slang or diction of young people, elder people, people in a certain region of the country, and/or people having a certain language or ethnic background.")
Claim 37
Regarding Claim 37, Tamblyn in view of DOLAN, further Tamblyn teaches 37. (New) The system according to claim 36, further comprising at least one of: a dialogue history module configured to store previous inputs, outputs and, where present, replies;
(Paragraph 102 "The dialog manager 252 receives the syntactic and semantic representation from the text analytics module, and manages the general flow of the conversation based on a set of decision rules. In this regard, the dialog manager maintains history and state of the conversation, and generates an outbound communication based on the history and state. The communication may follow the script of a particular conversation path selected by the dialog manager. As described in further detail below, the conversation path may be selected based on an understanding of a particular purpose or topic of the conversation. The script for the conversation path may be generated using any of various languages and frameworks conventional in the art, such as, for example, Artificial Intelligence Markup Language (AIML), SCXML, or the like.")
a user data module configured to store information about the user; and
(paragraph 81 "According to one exemplary embodiment of the invention, the mass storage device(s) 126 may store one or more databases relating to agent data (e.g. agent profiles, schedules, etc.), customer data (e.g. customer profiles), interaction data (e.g. details of each interaction with a customer, including reason for the interaction, disposition data, time on hold, handle time, etc.), and the like.")
a content module configured to store predefined data for providing to the user.
(paragraph 84 "The contact center system 102 may additionally include a knowledge management server 150 for facilitating interactions between customers operating the end user devices 108a-108c and a knowledge system 152 (which may be included as part of the contact center system 102, or may be operated remotely by a third party). The knowledge management server 152 is a computer system capable of receiving questions and providing answers as output. According to some example embodiments, the knowledge system may be embodied as IBM Watson®. Of course, any other knowledge system may be used as will be appreciated by a person having ordinary skill in the art. In some embodiments, the knowledge system 152 is an artificially intelligent computer system capable of answering questions posed in natural language by retrieving information from information sources such as encyclopedias, dictionaries, newswire articles, literary works, or other documents submitted to the knowledge system 152 as reference materials, as is well known in the art. Additional details of the knowledge management server is provided in U.S. application Ser. No. 14/449,018, filed on Jul. 31, 2014, entitled “System and Method for Controlled Knowledge System Management,” the content of which is incorporated herein by reference.")
Claim 43
Regarding Claim 43, Tamblyn in view of DOLAN, further DOLAN teaches 43. (New) The method according to claim 38, further comprising: reviewing a memory configured to store at least one event, wherein each event comprises:
(Paragraph 35 "For effective and successful treatment, the therapist builds a therapeutic relationship with the patient from session to session in a therapy module and across multiple therapy modules. The technology disclosed includes patient specific artificial memory (also referred to as Mercer memory) database 159 for storing information collected from patient's utterances. The system includes logic to resolve pronouns and hypernyms in a patient's utterances and remember the state information such as whether a pet (or another entity) mentioned in patient's utterance is dead or alive. The system also includes logic to remember sentiment information of the patient towards other entities such as her spouse, her boss, her sibling, etc. The system includes triggers to collect such information from utterances of the patient during a session. The system also includes logic to resolve the pronouns and ambiguities to link the information to the correct entity. For example, during a session, if the patient utters, “I had a long discussion with my daughter”. The system can query the artificial memory data structure for the patient's daughter and find out that the patient has two daughters, then the system can respond to the patient “Is this Mary or Jessica?” to disambiguate and link the information in the following conversation to the correct entity.")
an intent and, where present, at least one slot corresponding to a previous input; a previous output corresponding to the previous input, and
(Paragraph 35 "For effective and successful treatment, the therapist builds a therapeutic relationship with the patient from session to session in a therapy module and across multiple therapy modules. The technology disclosed includes patient specific artificial memory (also referred to as Mercer memory) database 159 for storing information collected from patient's utterances. The system includes logic to resolve pronouns and hypernyms in a patient's utterances and remember the state information such as whether a pet (or another entity) mentioned in patient's utterance is dead or alive. The system also includes logic to remember sentiment information of the patient towards other entities such as her spouse, her boss, her sibling, etc. The system includes triggers to collect such information from utterances of the patient during a session. The system also includes logic to resolve the pronouns and ambiguities to link the information to the correct entity. For example, during a session, if the patient utters, “I had a long discussion with my daughter”. The system can query the artificial memory data structure for the patient's daughter and find out that the patient has two daughters, then the system can respond to the patient “Is this Mary or Jessica?” to disambiguate and link the information in the following conversation to the correct entity."
Paragraph 41 "The technology disclosed includes logic to form an appropriate response that is not only sensitive to the patient's situation in the current utterance but also draws upon the information from the artificial memory to build a therapeutic relationship with the patient. For the above example, the system detects the state (i.e., dead) of the entity (cat) in the utterance. The system also detects a negative sentiment (i.e., loss or grief) associated with the event in the utterance. The system can access the artificial memory database for the patient to query the cat's name e.g., “Mittens”. The mental health chatbot can then respond to the patient by saying something like, “I am sorry to hear about Mittens”. The system also includes logic to present one or more appropriate therapy exercises or coping tools to the patient based on the detected sentiment of the patient towards the entity and the state of the entity. For example, the chatbot can say, “Here are some tools that you may find helpful in dealing with the loss”. The patient may accept to go through a suggested exercise, in which case, the chatbot can bookmark the last completed step in the structured steps of the CBT therapy session and start a tangential conversation. The system can also access the artificial memory database to query positive sentiments associated with the entity (in this case patient's cat) to say something like, “There is a lot you can look back and talk about when you shared ice cream with Mittens”. The technology disclosed can thus provide a high-quality therapy experience to patients by maintaining the patient specific artificial memory graph data structure and making use of remembered content by matching it with appropriate sentiment."
Paragraph 201 "The technology disclosed can handle digressions and tangential conversations as a result of unstructured conversations with patients. A therapist can handle such situations naturally however, it is a challenge for chatbot to detect patient's intent to shift topics and respond appropriately. The technology disclosed can handle unstructured conversations when chatting with the patients by firstly understanding the contents of the patient's utterance. Secondly, when the content is understood and co-references are resolved, the technology disclosed can understand the meaning and desire of the content which is referred to as intent detection. Thirdly, based on the detected intent, the chatbot presents appropriate responses or treatment options to the patient (fulfillment). The illustration 832 indicates the three steps presented above in the form of overlapping ovals. Understanding the utterance and intent detection can be considered as part of natural language processing (NLP). The fulfillment is based on the skills of the therapist and our technology enables the chatbot to provide high quality therapy experience to patients."
Paragraph 57 -58 "The intent detector 225 includes logic to classify a patient's conversational intent from a particular utterance. The system can use this classification as one of the inputs to the logic to detect a present intent of the patient to diverge from the flow of linearly structured steps of the CBT session and start a tangential conversation. In natural language processing, an intent categorizes an end-user's intention for one conversation turn. When a chatbot receives an utterance (in verbal or written form), the intent detection engine can match the utterance to one of the many pre-defined intents of the chatbot. The system can use intent detection Application Programming Interfaces (APIs) to detect a change of intent in the utterances of the patient. Examples of such APIs include Google™ DialogFlow.
In general, conversations of users with chatbots can be categorized as structured or unstructured. A structured or linear conversation with a pre-set number of options and outcomes makes a conversation predictable. The conversations with patients in therapy sessions are not structured and therefore, the system includes implicit and explicit triggers to detect when the patient wants to start a tangential conversation. We present details of the implicit and explicit triggers to start and end tangential conversations later in this text. The technology disclosed can apply the implicit and explicit triggers in addition to the intent detection APIs to patient utterances to detect the intent of the patient to shift topics.")
where present, a previous reply received in response to the previous output,
(Paragraph 226 "The method can be extended by checking in with the patient, asking about sentiment of the patient regarding an entity from a previous conversation. This can include sending a check-in prompt to the patient in a conversation session following the current conversation session, the prompt including a request for status update for a particular entity related to the patient with a negative sentiment from the patient towards the particular entity. In a response utterance from the patient, it includes a new positive sentiment of the patient towards the particular entity, followed by updating, in the artificial memory, the positive sentiment of the patient towards the particular entity in the entity node representing the particular entity. The method advances the conversation using the distinguishing name and the positive sentiment of the patient towards the particular entity.")
wherein the output is based, at least in part, on an event stored within the memory.
(Paragraph 35 "For effective and successful treatment, the therapist builds a therapeutic relationship with the patient from session to session in a therapy module and across multiple therapy modules. The technology disclosed includes patient specific artificial memory (also referred to as Mercer memory) database 159 for storing information collected from patient's utterances. The system includes logic to resolve pronouns and hypernyms in a patient's utterances and remember the state information such as whether a pet (or another entity) mentioned in patient's utterance is dead or alive. The system also includes logic to remember sentiment information of the patient towards other entities such as her spouse, her boss, her sibling, etc. The system includes triggers to collect such information from utterances of the patient during a session. The system also includes logic to resolve the pronouns and ambiguities to link the information to the correct entity. For example, during a session, if the patient utters, “I had a long discussion with my daughter”. The system can query the artificial memory data structure for the patient's daughter and find out that the patient has two daughters, then the system can respond to the patient “Is this Mary or Jessica?” to disambiguate and link the information in the following conversation to the correct entity.")
See claim 26 for rationale.
Claims 30,31,32 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20170324868 A1, (Tamblyn; Eric.) in view of US Patent US 20220068462 A1, (DOLAN; Eric William.) in further view of US Patent US 20210098110 A1, (Periyasamy; Periyasamy.).
Claim 30
Regarding Claim 30, Tamblyn in view of DOLAN do not explicitly teach all of 30. (New) The conversational agent according to claim 26, further comprising a risk assessment sub-dialogue unit which comprises a natural language understanding module configured to
receive at least one of an input and reply for a user and analyze the input to determine, if present within the at least one of input and reply, at least one intent indicating a risk and, where present, at least one slot within the at least one of input and reply.
However, Periyasamy teaches
30. (New) The conversational agent according to claim 26, further comprising a risk assessment sub-dialogue unit which comprises a natural language understanding module configured to
(Paragraph 14 " In some examples, the virtual agent may be a chatbot utilizing at least one of an artificial intelligence (AI) based dialogue conversational engine, AI based mental health and wellbeing question engine, or AI based mental health and wellbeing response engine."
paragraph 18 "In some examples, determining the mental health condition of the user may include determining a level of severity of the mental health condition, where the level of severity comprises a low risk, a medium risk, or a high risk; and alerting at least one of the user or a mental health expert of the mental health condition based at least in part on a determination that the level of severity is at the high risk, immediately upon the determination."
Paragraph 27 " In some examples, the instructions to cause the processor to determine the mental health condition of the user comprises instructions to cause the processor to: determine a level of severity of the mental health condition, wherein the level of severity comprises a low risk, a medium risk, or a high risk; and alert at least one of the user or a mental health expert of the mental health condition based at least in part on a determination that the level of severity is at the high risk, immediately upon the determining.")
receive at least one of an input and reply for a user and analyse the input to determine, if present within the at least one of input and reply, at least one intent indicating a risk and, where present, at least one slot within the at least one of input and reply.
(Paragraph 14 " In some examples, the virtual agent may be a chatbot utilizing at least one of an artificial intelligence (AI) based dialogue conversational engine, AI based mental health and wellbeing question engine, or AI based mental health and wellbeing response engine."
paragraph 18 "In some examples, determining the mental health condition of the user may include determining a level of severity of the mental health condition, where the level of severity comprises a low risk, a medium risk, or a high risk; and alerting at least one of the user or a mental health expert of the mental health condition based at least in part on a determination that the level of severity is at the high risk, immediately upon the determination."
Paragraph 27 " In some examples, the instructions to cause the processor to determine the mental health condition of the user comprises instructions to cause the processor to: determine a level of severity of the mental health condition, wherein the level of severity comprises a low risk, a medium risk, or a high risk; and alert at least one of the user or a mental health expert of the mental health condition based at least in part on a determination that the level of severity is at the high risk, immediately upon the determining."
paragraph 55 "After displaying the subpages, the informative video may show the benefits and a variety of self-help available to the user via the mental health monitoring device 10. The benefits of the mental health monitoring device 10 may include continuous collection of the real-time user data while the user is performing his/her daily activities throughout the day without interrupting the user's daily activities. Thus, no important user data is missed or uncollected by the mental health monitoring device 10, thereby allowing the mental health monitoring device 10 to provide an accurate, reliable, and up-to-date identification of a symptom(s) and determination of a type and severity of a potential mental health condition. Further, with the 24/7 ready and available virtual agent, e.g., a chatbot 310, the user may enjoy timely, and comprehensive recommendation for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified, where the variety of self-help is available at the user's fingertip or voice. In addition, the chatbot 310 utilizes at least one of AI based dialogue conversational engine 12, AI based mental health and wellbeing question engine 14, or AI based mental health and wellbeing response engine 16, thereby capable of engaging with the user in a personalized, interactive one-on-one therapy and counseling session appropriate for the user's current or potential mental health condition. Moreover, the therapy and counseling session is available via text 300A, live audio and/or live video 300B directly interacting with the chatbot 310, thereby removing possible feelings of isolation or loneliness and providing the immediate and timely advice and counsel when the user needs it most. In some examples, the chatbot 310 may direct the user to a mental health expert for a granular diagnosis and therapy in an online platform via the mental health monitoring application, so that the user may immediately receive an even more detailed determination of the root causes and the mental health condition, cognitive behavioral therapy, self-help recommendations that are personalized and unique to the user at that very instance. In some examples, the chatbot 310 may alert the user or initiate the therapy and counseling session with the user if the severity of the current or potential mental health condition is determined to have a high risk to the user or others around the user—the severity may be categorized as a low risk, a medium risk, or a high risk. If the user is not reachable or responsive, the chatbot may alert a mental health expert (e.g., the user's regular therapist or psychologist, or any mental health expert near the user's current location) or even contact the police for help.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamblyn in view of DOLAN to incorporate the teachings of Periyasamy to provide a “The conversational agent according to claim 26, further comprising a risk assessment sub-dialogue unit which comprises a natural language understanding module configured to receive at least one of an input and reply for a user and analyse the input to determine, if present within the at least one of input and reply, at least one intent indicating a risk and, where present, at least one slot within the at least one of input and reply.” Doing so would provide an alert system in case of emergency, as recognized by Periyasamy. (Paragraph 45).
Claim 31
Furthermore, Periyasamy teaches 31. (New) The conversational agent according to claim 30, wherein the risk assessment sub- dialogue unit is configured to receive and analyse all inputs from the user.
(Fig 8 shows continuously collecting data from the user input (element 810)
paragraph 55 "After displaying the subpages, the informative video may show the benefits and a variety of self-help available to the user via the mental health monitoring device 10. The benefits of the mental health monitoring device 10 may include continuous collection of the real-time user data while the user is performing his/her daily activities throughout the day without interrupting the user's daily activities. Thus, no important user data is missed or uncollected by the mental health monitoring device 10, thereby allowing the mental health monitoring device 10 to provide an accurate, reliable, and up-to-date identification of a symptom(s) and determination of a type and severity of a potential mental health condition. Further, with the 24/7 ready and available virtual agent, e.g., a chatbot 310, the user may enjoy timely, and comprehensive recommendation for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified, where the variety of self-help is available at the user's fingertip or voice. In addition, the chatbot 310 utilizes at least one of AI based dialogue conversational engine 12, AI based mental health and wellbeing question engine 14, or AI based mental health and wellbeing response engine 16, thereby capable of engaging with the user in a personalized, interactive one-on-one therapy and counseling session appropriate for the user's current or potential mental health condition. Moreover, the therapy and counseling session is available via text 300A, live audio and/or live video 300B directly interacting with the chatbot 310, thereby removing possible feelings of isolation or loneliness and providing the immediate and timely advice and counsel when the user needs it most. In some examples, the chatbot 310 may direct the user to a mental health expert for a granular diagnosis and therapy in an online platform via the mental health monitoring application, so that the user may immediately receive an even more detailed determination of the root causes and the mental health condition, cognitive behavioral therapy, self-help recommendations that are personalized and unique to the user at that very instance. In some examples, the chatbot 310 may alert the user or initiate the therapy and counseling session with the user if the severity of the current or potential mental health condition is determined to have a high risk to the user or others around the user—the severity may be categorized as a low risk, a medium risk, or a high risk. If the user is not reachable or responsive, the chatbot may alert a mental health expert (e.g., the user's regular therapist or psychologist, or any mental health expert near the user's current location) or even contact the police for help.")
See claim 30 for rationale.
Claim 32
Furthermore, Periyasamy teaches 32. (New) The conversational agent according to claim 30, wherein the risk assessment sub dialogue unit further comprises a dialogue planning module configured to determine an output based,
(Paragraph 55 "After displaying the subpages, the informative video may show the benefits and a variety of self-help available to the user via the mental health monitoring device 10. The benefits of the mental health monitoring device 10 may include continuous collection of the real-time user data while the user is performing his/her daily activities throughout the day without interrupting the user's daily activities. Thus, no important user data is missed or uncollected by the mental health monitoring device 10, thereby allowing the mental health monitoring device 10 to provide an accurate, reliable, and up-to-date identification of a symptom(s) and determination of a type and severity of a potential mental health condition. Further, with the 24/7 ready and available virtual agent, e.g., a chatbot 310, the user may enjoy timely, and comprehensive recommendation for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified, where the variety of self-help is available at the user's fingertip or voice. In addition, the chatbot 310 utilizes at least one of AI based dialogue conversational engine 12, AI based mental health and wellbeing question engine 14, or AI based mental health and wellbeing response engine 16, thereby capable of engaging with the user in a personalized, interactive one-on-one therapy and counseling session appropriate for the user's current or potential mental health condition. Moreover, the therapy and counseling session is available via text 300A, live audio and/or live video 300B directly interacting with the chatbot 310, thereby removing possible feelings of isolation or loneliness and providing the immediate and timely advice and counsel when the user needs it most. In some examples, the chatbot 310 may direct the user to a mental health expert for a granular diagnosis and therapy in an online platform via the mental health monitoring application, so that the user may immediately receive an even more detailed determination of the root causes and the mental health condition, cognitive behavioral therapy, self-help recommendations that are personalized and unique to the user at that very instance. In some examples, the chatbot 310 may alert the user or initiate the therapy and counseling session with the user if the severity of the current or potential mental health condition is determined to have a high risk to the user or others around the user—the severity may be categorized as a low risk, a medium risk, or a high risk. If the user is not reachable or responsive, the chatbot may alert a mental health expert (e.g., the user's regular therapist or psychologist, or any mental health expert near the user's current location) or even contact the police for help.")
at least in part, on the at least one intent and, where present, slot associated within the at least one of input and reply, and a natural language generation module configured to provide the output to the user.
(Fig 8 shows continuously collecting data from the user input (element 810) and the system having a reply (element 850)
Paragraph 55 "After displaying the subpages, the informative video may show the benefits and a variety of self-help available to the user via the mental health monitoring device 10. The benefits of the mental health monitoring device 10 may include continuous collection of the real-time user data while the user is performing his/her daily activities throughout the day without interrupting the user's daily activities. Thus, no important user data is missed or uncollected by the mental health monitoring device 10, thereby allowing the mental health monitoring device 10 to provide an accurate, reliable, and up-to-date identification of a symptom(s) and determination of a type and severity of a potential mental health condition. Further, with the 24/7 ready and available virtual agent, e.g., a chatbot 310, the user may enjoy timely, and comprehensive recommendation for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified, where the variety of self-help is available at the user's fingertip or voice. In addition, the chatbot 310 utilizes at least one of AI based dialogue conversational engine 12, AI based mental health and wellbeing question engine 14, or AI based mental health and wellbeing response engine 16, thereby capable of engaging with the user in a personalized, interactive one-on-one therapy and counseling session appropriate for the user's current or potential mental health condition. Moreover, the therapy and counseling session is available via text 300A, live audio and/or live video 300B directly interacting with the chatbot 310, thereby removing possible feelings of isolation or loneliness and providing the immediate and timely advice and counsel when the user needs it most. In some examples, the chatbot 310 may direct the user to a mental health expert for a granular diagnosis and therapy in an online platform via the mental health monitoring application, so that the user may immediately receive an even more detailed determination of the root causes and the mental health condition, cognitive behavioral therapy, self-help recommendations that are personalized and unique to the user at that very instance. In some examples, the chatbot 310 may alert the user or initiate the therapy and counseling session with the user if the severity of the current or potential mental health condition is determined to have a high risk to the user or others around the user—the severity may be categorized as a low risk, a medium risk, or a high risk. If the user is not reachable or responsive, the chatbot may alert a mental health expert (e.g., the user's regular therapist or psychologist, or any mental health expert near the user's current location) or even contact the police for help.")
See claim 30 for rationale.
Claims 33 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20170324868 A1, (Tamblyn; Eric.) in view of US Patent US 20220068462 A1, (DOLAN; Eric William.) in view of US Patent US 20210098110 A1, (Periyasamy; Periyasamy.) in further view of US Patent US 20220223259 A1, (Bergh; Cecilia.)
Claim 33
Regarding Claim 33, Tamblyn in view of DOLAN in view of Periyasamy do not explicitly teach all of the conversational agent according to claim 30, wherein the risk assessment sub dialogue unit is configured to identify whether one or more intents identified in each at least one of user input and reply correspond to a predetermined list of intents associated with risk.
However, Bergh teach 33. (New) The conversational agent according to claim 30, wherein the risk assessment sub dialogue unit is configured to identify whether one or more intents identified in each at least one of user input and reply correspond to a predetermined list of intents associated with risk.
(Paragraph 21 "The computing system might analyze at least one of the recorded interactions between the virtual clinician and the patient, the received first response, the received second response, the received third response, or the received food intake and satiety data associated with the patient, to determine likelihood of risk of suicide by the patient. Part of the analysis of the recorded interactions between the virtual clinician and the patient might be to identify flagged words or expressions (as described herein, and as shown in FIG. 2A, for example). Based on a determination that a likelihood of risk of suicide by the patient exceeds a first predetermined threshold value, the computing system might send a message to one or more healthcare professionals regarding the likelihood of risk of suicide by the patient. Based on a determination that a likelihood of risk of suicide by the patient is below the first predetermined threshold value but exceeds a second predetermined threshold value, sending, with the computing system, suggestions to the patient to change eating behavior of the patient toward at least one of eating rates, food amounts, and mealtime durations that correspond to levels designed to stimulate physiological responses that evoke positive feelings for the patient."
Paragraph 75 "During or after each session, the computing system might identify one or more flagged words or expressions spoken and/or typed by the patient during the interaction. According to some embodiments, identifying one or more flagged words or expressions spoken and/or typed by the patient during the interaction might comprise determining, with the computing system, whether words or expressions spoken or typed by the patient match predetermined flagged words and expressions that are indicative of suicide risk (such as the words and expressions depicted in FIG. 2A, or the like). The computing system might also record, to a datastore (e.g., database(s) 110a, 110b, and/or 155, or the like), the interactions between the virtual clinician and the patient.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have Tamblyn in view of DOLAN in view of Periyasamy to incorporate the teachings of Bergh to provide a “The conversational agent according to claim 30, wherein the risk assessment sub dialogue unit is configured to identify whether one or more intents identified in each at least one of user input and reply correspond to a predetermined list of intents associated with risk.” Doing so would provide an alert message to a provider on the chance of a suicide to provide treatment , as recognized by Bergh. (Paragraph 9& 100).
Claims 34 and 35 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20170324868 A1, (Tamblyn; Eric.) in view of US Patent US 20220068462 A1, (DOLAN; Eric William.) in view of US Patent US 20210098110 A1, (Periyasamy; Periyasamy.) in further view of US Patent US 20230353512 A1, (GOLLAREDDY; Jaya Kishore Reddy)
Claim 34
Regarding Claim 34, Tamblyn in view of DOLAN in view of Periyasamy do not explicitly teach all of The conversational agent according to claim 30, further comprising an adjudicator configured to identify each sub-dialogue unit comprising a natural language understanding module that identifies and intent; determine which of the identified sub-dialogue units meets a predetermined criterion; and select the sub-dialogue unit that meets the predetermined criterion such that only the selected sub-dialogue unit determines and provides an output to the user in response to each input.
However, GOLLAREDDY teach
34. (New) The conversational agent according to claim 30, further comprising an adjudicator configured to identify each sub-dialogue unit comprising a natural language understanding module that identifies and intent;
(paragraph 27 " Initially, when a user query is received from the user device 105, the bot assistance system 101 may process the user query to identify a context associated with the user query and intent using Natural Language Processing (NLP) technique. The context is with respect to specific department associated with the entity. For instance, when the entity is bank, the context may be with respect to home loan, personal loan, account detail, saving information and the like. The content and intent identification may include spell correction, synonym replacement and the like. In an embodiment, the context and intent of the user query may be identified using known processing techniques.")
determine which of the identified sub-dialogue units meets a predetermined criterion;
(Paragraph 28 "The bot assistance system may identify at least one virtual assistant bot from the plurality of virtual assistant bots 103 for responding to the user query using a pretrained model. In an embodiment, the pretrained model is a machine learning model. The bot assistance system 101 compares a confidence score associated with the intent of each virtual assistant bot with a threshold confidence score. Based on the comparison, if the confidence score for a virtual assistant bot is greater than the threshold confidence score, the bot assistance system 101 may identify an ideal virtual assistant bot which may be relevant to the user query. However, when the confidence score is below the threshold confidence score, the bot assistance system 101 may provide a list of virtual assistant bot closely matching context to the user query using an unsupervised fallback model. Particularly, the bot assistance system 101 determines the ideal virtual assistant bot based on the context of the user query using predefined governance rules. In an embodiment, the predefined governance rules may be business logic depending on the entity. That is, the predefined governance rules may include information on context of each virtual assistant bot. In case of determining the ideal virtual assistant bot of the plurality of virtual assistant bots 103, the bot assistance system 101 may pass control to the ideal virtual assistant bot for providing response to the user query.")
and select the sub-dialogue unit that meets the predetermined criterion such that only the selected sub-dialogue unit determines and provides an output to the user in response to each input.
(Paragraph 28 "The bot assistance system may identify at least one virtual assistant bot from the plurality of virtual assistant bots 103 for responding to the user query using a pretrained model. In an embodiment, the pretrained model is a machine learning model. The bot assistance system 101 compares a confidence score associated with the intent of each virtual assistant bot with a threshold confidence score. Based on the comparison, if the confidence score for a virtual assistant bot is greater than the threshold confidence score, the bot assistance system 101 may identify an ideal virtual assistant bot which may be relevant to the user query. However, when the confidence score is below the threshold confidence score, the bot assistance system 101 may provide a list of virtual assistant bot closely matching context to the user query using an unsupervised fallback model. Particularly, the bot assistance system 101 determines the ideal virtual assistant bot based on the context of the user query using predefined governance rules. In an embodiment, the predefined governance rules may be business logic depending on the entity. That is, the predefined governance rules may include information on context of each virtual assistant bot. In case of determining the ideal virtual assistant bot of the plurality of virtual assistant bots 103, the bot assistance system 101 may pass control to the ideal virtual assistant bot for providing response to the user query.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamblyn in view of DOLAN in view of Periyasamy to incorporate the teachings of GOLLAREDDY to provide a “The conversational agent according to claim 30, further comprising an adjudicator configured to identify each sub-dialogue unit comprising a natural language understanding module that identifies and intent; determine which of the identified sub-dialogue units meets a predetermined criterion; and select the sub-dialogue unit that meets the predetermined criterion such that only the selected sub-dialogue unit determines and provides an output to the user in response to each input.” Doing so would increase productivity and enhance user experience, as recognized by GOLLAREDDY. (Paragraph 100).
Claim 35
Regarding Claim 35, DOLAN in view of Periyasamy in further view of GOLLAREDDY, Periyasamy further teaches the conversational agent according to claim 34, wherein the adjudicator is configured to enable the risk assessment sub-dialogue unit to provide an output to the user, where an intent relating to risk is identified.
(Paragraph 14 " In some examples, the virtual agent may be a chatbot utilizing at least one of an artificial intelligence (AI) based dialogue conversational engine, AI based mental health and wellbeing question engine, or AI based mental health and wellbeing response engine."
paragraph 18 "In some examples, determining the mental health condition of the user may include determining a level of severity of the mental health condition, where the level of severity comprises a low risk, a medium risk, or a high risk; and alerting at least one of the user or a mental health expert of the mental health condition based at least in part on a determination that the level of severity is at the high risk, immediately upon the determination."
Paragraph 27 " In some examples, the instructions to cause the processor to determine the mental health condition of the user comprises instructions to cause the processor to: determine a level of severity of the mental health condition, wherein the level of severity comprises a low risk, a medium risk, or a high risk; and alert at least one of the user or a mental health expert of the mental health condition based at least in part on a determination that the level of severity is at the high risk, immediately upon the determining.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamblyn in view of DOLAN in view of GOLLAREDDY to incorporate the teachings of Periyasamy to provide a “The conversational agent according to claim 34, wherein the adjudicator is configured to enable the risk assessment sub-dialogue unit to provide an output to the user, where an intent relating to risk is identified.” Doing so would provide an alert system in case of emergency, as recognized by Periyasamy. (Paragraph 45).
Claims 39, 40, 41, 42, 44, and 45,are rejected under 35 U.S.C. 103 as obvious over US Patent US 20170324868 A1, (Tamblyn; Eric.) in view of US Patent US 20220068462 A1, (DOLAN; Eric William.) in further view of US Patent US 20160140320 A1, (Moturu; Sai.).
Claim 39
Regarding Claim 40, Tamblyn in view of DOLAN do not explicitly teach all of the method according to claim 38, further comprising: determining a user engagement level, wherein the output is based, at least in part, on the user engagement level.
However, Moturu teach 39. (New) The method according to claim 38, further comprising: determining a user engagement level, wherein the output is based, at least in part, on the user engagement level.
(paragraph 86 " In variations, an individualized report can include information related to one or more of: contextual information of the coaching entity (e.g., coach name, coach identification code); a summary of contextual information of the individual (e.g., individual name, individual identification code, demographic information, diagnoses, medications, and patient notes); relevant metrics from the survey dataset (e.g., PHQ-9 scores, PHQ-9 score trends over time); relevant outputs derived from passive data and/or predictive models; goals of the individual and indications of progress in achieving such goals; tasks (e.g., assigned tasks, unassigned tasks) intended to be performed by the individual and indications of progress in achieving such tasks; status(es) of the symptom(s) of the individual in relation to health state (e.g., indications of suicidal ideation, etc.); decision criteria (e.g., including level of engagement of the individual) for reaching out to the individual; notes (e.g., a list of notes that the coaching can update for use in delivering follow up care); treatment plan information (e.g., therapists, medications) associated with the individual; and any other suitable information pertaining to the individual."
Paragraph 104 " In relation to escalation of care, the method 100 can include blocks for generation of reports for licensed therapist and/or patient care providers analogous to those for generation of a report for a coaching entity. In a first example of a report for a licensed therapist, as shown in FIG. 12A, a report can include: an indication of overall patient state (e.g., improving state, stable state, worsening state, variable state, etc.); a number of touch points (e.g., for the past 60 days) with the patient in terms of number of sessions or check-ins with the patient and level of engagement with a mobile application associated with the method 100 (e.g., low level of engagement, medium level of engagement, high level of engagement, etc.); a timeline of patient events in different categories associated with the health condition of the patient (e.g., PHQ-9 scores, sleep symptoms, changes in physical activity, changes in appetite, changes in occupational function, changes in communication behavior, etc.); and notes and goals pertaining to the patient."
paragraph 97 "As such, in relation to the above described coaching entity functions of supporting individuals, reaching out to individuals, and triaging individuals, and triaging individuals, the reports generated using instances of Block S310 of the method 100 and/or processing of data acquired from Blocks S110-S140 of the method 100 can be used to: provide context for supporting individuals (e.g., in terms of recalling past interactions with an individual, in terms of progress of an individual in reaching a goal, in terms of progress of an individual in improving mental health state); increase efficiency in providing support for individuals (e.g., by notifying the coaching individual of any mental health-related statuses of the individuals pertaining to exercise, sleep, relationships, symptoms, and any other suitable factor); facilitate making of decisions by the coaching entity; automate reaching out to individuals by the coaching entity in times of need (e.g., by automatically establishing a communication between the coaching entity and an individual with a messaging or phone calling client); automate reaching out to individuals for positive reinforcement of behaviors; automate reaching out to individuals for re-engagement with system components associated with the method 100; automate adjustment to proposed treatment plans for individuals; provide decision making support or automation in relation to triaging individuals according to criticality of state; increase efficiency in triaging individuals; and perform any other suitable function."
Paragraph 62 "Furthermore, in extensions of the method 100 to a population of patients, the predictive model can be used to identify differences in passive data and/or active data, as associated with identified mental health states, between different demographics of individuals. For instance, the predictive model can be used to identify sets of feature vectors and/or subsets of features (e.g., related to communication behavior, related to survey responses, related to mobility behavior, etc.) that have high efficacy in determining risk/severity for one or more of: different genders, different age groups, different employment statuses, different ethnicities, different nationalities, different socioeconomic classes, and any other suitable demographic difference.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamblyn in view of DOLAN to incorporate the teachings of Moturu to provide a “The method according to claim 38, further comprising: determining a user engagement level, wherein the output is based, at least in part, on the user engagement level.” Doing so would guide individuals to the necessary help they need, as recognized by Moturu. (Paragraph 101).
Claim 40
Regarding Claim 40, Tamblyn in view of DOLAN in view of Moturu, furthermore Moturu teach
40. (New) The method according to claim 39, further comprising: determining if the user engagement level is below a predetermined threshold; and
(Paragraph 96 "In the variation of FIG. 10C shown in FIG. 10D, individuals associated with the coaching entity can be grouped according priority. For instance, individuals who have not communicated with the coaching entity for above a threshold duration of time (e.g., 10 days), with recent PHQ-9 scores may grouped according to a first level of priority for outreach, individuals who have not responded to recent requests to take a PHQ-9 assessment, but who have engaged with the coaching entity within a threshold duration of time (e.g., last 30 days) can be grouped according to a second level of priority for outreach, and individuals who have communicated with the coaching entity within a threshold duration of time (e.g., 10 days) and who have recently taken a PHQ-9 assessment can be grouped according to a third level of priority for outreach.")
sending an alert to a second user upon determining a user engagement level below the predetermined threshold.
(paragraph 97 "As such, in relation to the above described coaching entity functions of supporting individuals, reaching out to individuals, and triaging individuals, and triaging individuals, the reports generated using instances of Block S310 of the method 100 and/or processing of data acquired from Blocks S110-S140 of the method 100 can be used to: provide context for supporting individuals (e.g., in terms of recalling past interactions with an individual, in terms of progress of an individual in reaching a goal, in terms of progress of an individual in improving mental health state); increase efficiency in providing support for individuals (e.g., by notifying the coaching individual of any mental health-related statuses of the individuals pertaining to exercise, sleep, relationships, symptoms, and any other suitable factor); facilitate making of decisions by the coaching entity; automate reaching out to individuals by the coaching entity in times of need (e.g., by automatically establishing a communication between the coaching entity and an individual with a messaging or phone calling client); automate reaching out to individuals for positive reinforcement of behaviors; automate reaching out to individuals for re-engagement with system components associated with the method 100; automate adjustment to proposed treatment plans for individuals; provide decision making support or automation in relation to triaging individuals according to criticality of state; increase efficiency in triaging individuals; and perform any other suitable function.")
See claim 39 for rationale.
Claim 41
Regarding Claim 41, Tamblyn in view of DOLAN in view of Moturu, furthermore Moturu teach
41. (New) The method according to claim 40, wherein after having received the alert, the second user at least one of:
provides a second output to the user and amends the output determined by the dialogue planning module.
(Paragraph 96 "In the variation of FIG. 10C shown in FIG. 10D, individuals associated with the coaching entity can be grouped according priority. For instance, individuals who have not communicated with the coaching entity for above a threshold duration of time (e.g., 10 days), with recent PHQ-9 scores may grouped according to a first level of priority for outreach, individuals who have not responded to recent requests to take a PHQ-9 assessment, but who have engaged with the coaching entity within a threshold duration of time (e.g., last 30 days) can be grouped according to a second level of priority for outreach, and individuals who have communicated with the coaching entity within a threshold duration of time (e.g., 10 days) and who have recently taken a PHQ-9 assessment can be grouped according to a third level of priority for outreach.")
paragraph 97 "As such, in relation to the above described coaching entity functions of supporting individuals, reaching out to individuals, and triaging individuals, and triaging individuals, the reports generated using instances of Block S310 of the method 100 and/or processing of data acquired from Blocks S110-S140 of the method 100 can be used to: provide context for supporting individuals (e.g., in terms of recalling past interactions with an individual, in terms of progress of an individual in reaching a goal, in terms of progress of an individual in improving mental health state); increase efficiency in providing support for individuals (e.g., by notifying the coaching individual of any mental health-related statuses of the individuals pertaining to exercise, sleep, relationships, symptoms, and any other suitable factor); facilitate making of decisions by the coaching entity; automate reaching out to individuals by the coaching entity in times of need (e.g., by automatically establishing a communication between the coaching entity and an individual with a messaging or phone calling client); automate reaching out to individuals for positive reinforcement of behaviors; automate reaching out to individuals for re-engagement with system components associated with the method 100; automate adjustment to proposed treatment plans for individuals; provide decision making support or automation in relation to triaging individuals according to criticality of state; increase efficiency in triaging individuals; and perform any other suitable function.")
See claim 39 for rationale.
Claim 42
Regarding Claim 42 Tamblyn in view of DOLAN in view of Moturu, furthermore DOLAN teach
42. (New) The method according to claim 41, further comprising: reviewing, using the natural language understanding module, the at least one of second output and amended output and providing, using the dialogue planning module,
(Paragraph 41 "The technology disclosed includes logic to form an appropriate response that is not only sensitive to the patient's situation in the current utterance but also draws upon the information from the artificial memory to build a therapeutic relationship with the patient. For the above example, the system detects the state (i.e., dead) of the entity (cat) in the utterance. The system also detects a negative sentiment (i.e., loss or grief) associated with the event in the utterance. The system can access the artificial memory database for the patient to query the cat's name e.g., “Mittens”. The mental health chatbot can then respond to the patient by saying something like, “I am sorry to hear about Mittens”. The system also includes logic to present one or more appropriate therapy exercises or coping tools to the patient based on the detected sentiment of the patient towards the entity and the state of the entity. For example, the chatbot can say, “Here are some tools that you may find helpful in dealing with the loss”. The patient may accept to go through a suggested exercise, in which case, the chatbot can bookmark the last completed step in the structured steps of the CBT therapy session and start a tangential conversation. The system can also access the artificial memory database to query positive sentiments associated with the entity (in this case patient's cat) to say something like, “There is a lot you can look back and talk about when you shared ice cream with Mittens”. The technology disclosed can thus provide a high-quality therapy experience to patients by maintaining the patient specific artificial memory graph data structure and making use of remembered content by matching it with appropriate sentiment."
paragraph 128 "We can see in the above example that length of patient's responses is short and is decreasing. The conversation has continued for several iterations without an intense response from the patient. The sentiment scores for the patient's response are neutral. The patient is not bringing up any other entity or herself in her utterances. Decision score calculator or implicit triggers model predicts that Serenity chatbot should send a prompt to end the conversation or suggest a new topic for conversation such as presented below:
TABLE-US-00003 Serenity: “Hey, do you want to talk about something else?” Patient: “Yes! Let's talk about my upcoming vacation!” [Tangential path ends]")
further amendments to the at least one of second output and amended output where needed.
(Paragraph 128 "We can see in the above example that length of patient's responses is short and is decreasing. The conversation has continued for several iterations without an intense response from the patient. The sentiment scores for the patient's response are neutral. The patient is not bringing up any other entity or herself in her utterances. Decision score calculator or implicit triggers model predicts that Serenity chatbot should send a prompt to end the conversation or suggest a new topic for conversation such as presented below:
TABLE-US-00003 Serenity: “Hey, do you want to talk about something else?” Patient: “Yes! Let's talk about my upcoming vacation!” [Tangential path ends]")
See claim 26 for rationale.
Claim 44
Regarding Claim 44, Tamblyn in view of DOLAN in view of Moturu, furthermore DOLAN teach
44. (New) The method according to claim 41, wherein the output is based, at least in part, on at least one of: the next stage in a psychotherapy treatment model for the user;
the need to obtain a piece of information from the user;
(Paragraph 226 "The method can be extended by checking in with the patient, asking about sentiment of the patient regarding an entity from a previous conversation. This can include sending a check-in prompt to the patient in a conversation session following the current conversation session, the prompt including a request for status update for a particular entity related to the patient with a negative sentiment from the patient towards the particular entity. In a response utterance from the patient, it includes a new positive sentiment of the patient towards the particular entity, followed by updating, in the artificial memory, the positive sentiment of the patient towards the particular entity in the entity node representing the particular entity. The method advances the conversation using the distinguishing name and the positive sentiment of the patient towards the particular entity." )
the piece of information required next from the user;
(Paragraph 226 "The method can be extended by checking in with the patient, asking about sentiment of the patient regarding an entity from a previous conversation. This can include sending a check-in prompt to the patient in a conversation session following the current conversation session, the prompt including a request for status update for a particular entity related to the patient with a negative sentiment from the patient towards the particular entity. In a response utterance from the patient, it includes a new positive sentiment of the patient towards the particular entity, followed by updating, in the artificial memory, the positive sentiment of the patient towards the particular entity in the entity node representing the particular entity. The method advances the conversation using the distinguishing name and the positive sentiment of the patient towards the particular entity." )
a question contained within the input;
the frequency of questions contained within the input;
the frequency of questions generated by the natural language generation module;
the amount of repetition within an input compared to a previous input or reply; and the amount of repetition within an output compared to a previous output.)
see claim 26 for rationale.
Claim 45
Regarding Claim 45, Tamblyn in view of DOLAN in view of Moturu, furthermore Tamblyn teach
45. (New) The method according to claim 41, wherein the method further comprises: alerting a second user in response to:
(paragraph 140 " In some instances, the chat automation server 140 may get stuck or may not understand the cause of the confusion or frustration on the part of the end user 106, and may not be able to determine whether or not a different communication path (or which different communication path) or a different chat automation profile should be selected. In such cases, the chat automation server 140 may route the chat communication to a live agent to continue with the chat communication session, if a live agent is available. According to some embodiments, the chat automation server 140 may route the chat communication to a live agent that has a corresponding (e.g., the same or similar) profile as the chat bot profile selected to handle the automated chat communication. Accordingly, the customer may be unaware of the transition from an automated chat communication to a communication with a live human agent, and the transfer may be relatively seamless or transparent to the customer.")
determining when the at least one of input and reply is outside of a predetermined treatment model; or determining when the natural language understanding module is unable to determine the intent or, where present, slot associated with the input.(paragraph 110 " In response, the default chat bot 202a may receive one or more chat or text-based communications from the end user device 108. The default chat bot 202a may then analyze the text-based communications, as discussed above, to identify one or more potential purposes or topics for the communication and/or one or more pieces of information for identifying the end user 106. In some instances, the user may remain anonymous initially, or throughout an entire communication session. Additionally, according to some embodiments, in circumstances in which the default chat bot 202a is unable to identify a topic or purpose, the chat bot 202a may politely terminate the communication or trigger transfer of the communication to a live agent."
Paragraph 111 "According to some embodiments of the present invention, based on identification information of the end user 106, previous communication history (e.g., with the business 104 or on a third party platform such as a social networking website) of the end user 106, and/or the purpose or topic for the communication, the default chat bot 202a may select a particular chat bot 202b having a profile that is deemed to be compatible with the profile of the end user 106. For example, the default chat bot 202a may select a particular chat bot 202b having a suitable vocabulary or diction based on the background or age of the end user 106, and a suitable specialization to appropriately or effectively handle the purpose of the communication. Profile and/or personality matching may also be subject to learning by the chat automation system 100, for example, by analyzing input from end users. When a selected profile or personality is determined to not be a good match for a particular customer or particular type of customer, communications may be redirected to a different chat bot with a different personality or profile, and the chat automation system 100 may learn not to make such matches in the future with customers having the same or similar characteristics. When a communication session is transitioned from one chat bot to another chat bot, context data about the communication session with the first chat bot may be stored and made available to the next chat bot (e.g., in the form of a transcript or aggregated/summarized data) and leveraged for avoiding repeating the same dialog."
Paragraph 112 “According to some embodiments, if the chat automation system 100 is unable to determine the proper chat bot personality or profile, multiple chat bots 202 may be executed concurrently. In such instances, according to some embodiments, only one chat bot may have an active response channel to the end user, and the other chat bot(s) may execute in silent mode, receiving customer requests but making their responses visible to other chat bots and/or contact center agents. Accordingly, a potential backup chat bot may be enabled to remain synchronized with the communication session in case a transition is made to the backup chat bot.”
Paragraph 140 “In some instances, the chat automation server 140 may get stuck or may not understand the cause of the confusion or frustration on the part of the end user 106, and may not be able to determine whether or not a different communication path (or which different communication path) or a different chat automation profile should be selected. In such cases, the chat automation server 140 may route the chat communication to a live agent to continue with the chat communication session, if a live agent is available. According to some embodiments, the chat automation server 140 may route the chat communication to a live agent that has a corresponding (e.g., the same or similar) profile as the chat bot profile selected to handle the automated chat communication. Accordingly, the customer may be unaware of the transition from an automated chat communication to a communication with a live human agent, and the transfer may be relatively seamless or transparent to the customer.”)
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 20190140986 A1 to Anderson; Ryan discloses a master and moderator chatbot.
CN Patent CN 112905770 A to ZHU, Ding-ju discloses an artificial intelligent chatting robot that helps in psychological health.
Conclusion
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/ALI M HASSAN/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
06/27/2026
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