Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Application
Claims 1-19 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Response to Restriction Requirement filed on 07/21/2026 and the Claims filed on 12/12/2025.
Claims 1-18 have been elected.
Claim 19 is not elected and therefore is withdrawn and currently not considered.
Claim Objections
Claim 9 is objected to because of the following informalities: The claim recites “wherein the at least one computing device configured to” which is not grammatically correct. This should read “computing device is configured to”. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: The claim recites “weighing at least one segment of the conversation data”. However, Examiner interprets this to be “weighting”. Appropriate correction is required.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-18 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-18 fall within the statutory category of an apparatus or system.
Step 2A, Prong One
As per Claim 1, the limitations of process the conversation data to generate a language model output and/or acoustic model output; apply weights to the language model output and/or acoustic model output, wherein the language model output and acoustic model output each comprise a plurality of outputs corresponding to a plurality of time segments of the conversation data, and wherein the weights are optionally temporally-based; and generate an electronic report, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The steps of processing the conversation data, applying weights to the output, and generating an electronic report are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the 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.
As per Claim 18, the limitations of process the conversation data to generate a language model output, wherein the language model output comprises an identification of at least one query based on conversation data from an agent and at least one response to the query based on the conversation data from a patient, wherein the at least one query is mapped onto a predefined query and the at least one response to the at least one query is mapped onto a predefined response to the predefined query; and generate an electronic report, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The steps of processing the conversation data and generating an electronic report are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the 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.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – an input device, an output device, and a computing device. The input, output, and computing devices in these steps are recited at a high-level of generality, such that they amount to 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 also recite the input device is used for receiving conversation data from a user, the output device is used for outputting an electronic report, and the computing device is in communication with the input and output devices. As per MPEP 2106.05(f)(2), the use of computers in their ordinary capacity for tasks such as receiving or transmitting data amounts to mere instructions to apply the exception. The claims also recite receiving the conversation data from the at least one input device and transmitting the electronic report to the output device, which as described above, describe the computing device receiving and transmitting data. As per MPEP 2106.05(f)(2), this amounts to mere instructions to apply the exception. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of an input device, an output device, and a computing device to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system including the input device, output device, and computing device are recited at a high level of generality and are recited as generic computer components by reciting an input device as a voice recorder, passive listening device, or another capture device methodology which can include a microphone (Specification [00141]). The computing device is described as any type of general-purpose microprocessor (Specification [00155]). The output device is described as a display such as a display screen and speaker (Specification [00162]). These known general purpose computer components do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The claims also recite receiving the conversation data from the at least one input device and transmitting the electronic report to the output device, which describe the computing device receiving and transmitting data and amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-17 add further limitations which are also directed to an abstract idea. For example, Claim 2 includes fusing the weighted language and acoustic model output generating a composite output which is a mental process for the same reasons as the independent claims. Claims 3-4, 6-8, 13, 15-16 further limit or specify the limitations of Claim 1 and are therefore directed to the same abstract idea. Claim 5 includes the use of a language model neural network and an acoustic neural network trained on labelled conversation data collected from one or more other subjects to generate the output. The use of a neural network to execute the abstract idea amounts to mere instructions to apply the exception. As per MPEP 2106.05(f)(2), the use of a mathematical algorithm applied on a general purpose computer has been found by the courts to do no more than invoke computers as a tool and amount to mere instructions to apply the exception. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea. The training data is described as labelled conversation data labelled as having the behavioral or mental health condition and not having the behavioral or mental health condition, but this is merely descriptive and does not provide functionality to the claim. Claim 9 includes determining a role of a speaker which falls into the grouping of mental processes because it can be performed using human mental observation, evaluation, judgment, and opinion. The claim also further limits the elements of Claim 1. Claims 10-12 further limits or specifies the limitations of claim 9 and is therefore directed to the same abstract idea. Claim 14 includes processing conversation data to generate language model output which falls into the abstract grouping of a mental process for the same reasons as Claim 1. The claim additionally recites using more computationally robust models to generate the model output. The use of a model to execute the abstract idea amounts to mere instructions to apply the exception. As per MPEP 2106.05(f)(2), the use of a mathematical algorithm applied on a general purpose computer has been found by the courts to do no more than invoke computers as a tool and amount to mere instructions to apply the exception. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea. Claim 17 includes prior to processing the conversation data to generate the language model output, pre-processing the conversation data by performing at least one of: weighing at least one segment of the conversation data based on a relation between the at least one segment of the conversation data and the behavioral or mental health condition, summarizing the at least one segment of the conversation data, providing analytics on the at least one segment of the conversation data, summarizing at least one aspect of the behavioral or mental health condition, and providing analytics on the at least one aspect of the behavioral or mental health condition which falls into the abstract grouping of mental processes. Pre-processing conversation data can be performed using human mental processing including any of the alternatives such as weighing, summarizing, and providing analytics. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “more computationally robust” in claim 14 is a relative term which renders the claim indefinite. The term “more computationally robust” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-9, 11-13, and 15-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shriberg et al. (US 2020/0118458 A1), hereinafter Shriberg.
As per Claim 1, Shriberg discloses a system for a behavioral or mental health condition of a subject, the system comprising:
at least one input device for receiving conversation data from at least one user ([0031] receive speech data from a subject, [0165] clinician device receives data from the patient device including data of assessment, [0298] input/output module for enabling collection of response data);
at least one output device for outputting an electronic report ([0054-0055] computing device with display such as a dashboard for displaying scores indicating mental health disorder; [0168] clinician is provided with summary of dialogue and a report for the patient including a score indicating mental state prediction);
at least one computing device in communication with the at least one input device and the at least one output device ([0530-0531] system includes microprocessors to execute the steps of the method and communicate with input output devices), the at least one computing device configured to:
receive the conversation data from the at least one input device ([0031] receive speech data from a subject, [0165] clinician device receives data from the patient device including data of assessment, [0298] input/output module for enabling collection of response data);
process the conversation data to generate a language model output and/or an acoustic model output ([0031] receive speech data and process the speech data using models including NLP, acoustic to generate an output, [0277-0278] conversation is processed by language models and/or acoustic models to output distinction between patient and clinician voices and output mental state of patient, see also [0281] use language/acoustic models to produce results from audiovisual signal, [0283]);
apply weights to the language model output and/or the acoustic model output, wherein the language model output and the acoustic model output each comprise a plurality of outputs corresponding to a plurality of time segments of the conversation data, and wherein the weights are optionally temporally-based ([0031] use output to generate a score and a confidence level of the score, [0033] output corresponds to a time range of the data; segment the output into time segments and assign a weight to each time segment; [0282] identify where in the signal each word appears and assign degree of confidence);
generate an electronic report ([0068] outputting a report indicative of the mental state of the subject); and
transmit the electronic report to the output device ([0055] transmit the plurality of scores to a computing device and graphically displaying on the device; [0068] transmit report to user).
As per Claim 2, Shriberg discloses the system of Claim 1. Shriberg also teaches wherein the at least one computing device is further configured to: fuse the weighted language model output and the acoustic model output generating a composite output ([0035] generating the score comprises fusing the NLP model output and acoustic model output).
As per Claim 3, Shriberg discloses the system of Claim 1. Shriberg also teaches wherein the electronic report identifies a severity of at least one symptom of the behavioral or mental health condition based on the composite output ([0329] classify the score with a label based on the value of the score, labels include severe; [0421-0422] refine the scores to determine the severity of the mental state of the user such as mild, moderate, severe level of mental health condition, the score is a composite output of a weighted average of the individual scores).
As per Claim 5, Shriberg discloses the system of Claim 1. Shriberg also teaches wherein processing the conversation data to generate the language model output and the acoustic model output comprises using a language model neural network and an acoustic neural network trained on labelled conversation data collected from one or more other subjects ([0286] models use machine learning approaches including neural networks to generate output, see Fig. 2, 2212, 2216 Language model and acoustic model training; [0042] models trained on speech data from plurality of test subjects, i.e. other subjects, [0076] train the models using labelled speech data from a plurality of other subjects), wherein the labelled conversation data comprises is labelled as (i) having, to some level, the behavioral or mental health condition and (ii) not having the behavioral or mental health condition ([0076] training data from other subjects labelled as having a clinical determination of the mental health condition, [0169] training data is from data of depressed and non-depressed people).
As per Claim 6, Shriberg discloses the system of Claim 1. Shriberg also teaches wherein the conversation data is processed based in part on at least one of a patient profile and an agent profile, wherein the profile comprises at least one of historical, biographical, demographic, and longitudinal data ([0005]/[0032] demographic, medical history data of the subject is used to determine mental state of subject using models; [0166] patient enters profile data such as demographics/history/etc. and screening process is based on the profile data, [0349] confidence based on metadata including demographic data).
As per Claim 7, Shriberg discloses the system of Claim 1. Shriberg also teaches wherein the conversation data comprises at least one of speech data and text-based data ([0004]/[0025] data received comprises speech data, [0031] monitoring a subject including receiving speech data, also see Fig. 6 capture speech data of conversation between patient and provider).
As per Claim 8, Shriberg discloses the system of Claim 1. Shriberg also teaches wherein the at least one computing device is configured to run a model based on human-interpretable features ([0287],[0289] models such as neural networks use metadata which include features and feature representations, [0290] where the descriptive model features are interpretable descriptions that convey information about speech patterns and these features are human interpretable).
As per Claim 9, Shriberg discloses the system of Claim 1. Shriberg also teaches determine at least one role of at least one speaker (see Fig. 25 speaker ID/personality model; [0277] listen and distinguish voices, determine a patient voice and clinician voice based on louder voice or other factors, where the patient/clinician are roles of the speakers; see also [0443-0445]); and
wherein the weights are based in part on the at least one role of the at least one speaker during each time segment ([0390-0391] weighting each model for each time segment where the weighting is based on the speaker identified during that time period; [0424] weights are based on features from the signal).
As per Claim 11, Shriberg discloses the system of Claim 9. Shriberg also teaches wherein the at least one role of the at least one speaker comprises at least one of a patient, an agent, an interactive voice response, and bot or AI speaker ([0277] listen and distinguish voices, determine a patient voice and clinician voice based on louder voice or other factors, where the patient/clinician are roles of the speakers).
As per Claim 12, Shriberg discloses the system of Claim 9. Shriberg also teaches wherein the weights applied to the language model output and the acoustic model output are based in part on determining that a number of the at least one speaker matches an expected number of speakers ([0424] weights are based on features from the signal including voice-based biomarkers within the speech sample at a point in time within the sample; [0445] using a single listening device and determining expected patient and clinician based on conventional techniques).
As per Claim 13, Shriberg discloses the system of Claim 1. Shriberg also teaches the language model output comprises one or more topics corresponding to one or more time ranges ([0043-0044] extracting from speech data topics over time ranges during the speech data using the topic model), and the weights are based in part on the one or more topics during each time segment ([0168] weighting time segments of the speech data based on how important that segment is to the output of the model; [0424] weights are based on features from the signal including voice-based biomarkers within the speech sample at a point in time within the sample).
As per Claim 15, Shriberg discloses the system of Claim 1. Shriberg also teaches wherein the language model output comprises an identification of at least one query based on conversation data from an agent and at least one response to the at least one query based on the conversation data from a patient ([0430] labels of the training data, i.e. metadata from the conversation data, are based on the questionnaires completed by the patient), wherein the at least one query is mapped onto a predefined query and the at least one response to the at least one query is mapped onto a predefined response to the predefined query ([0448-0452] identify the query based on matching question to those stored in the bank and identify corresponding answer from the patient).
As per Claim 16, Shriberg discloses the system of Claim 15. Shriberg also teaches wherein the weights are based in part on the at least one query ([0424] weights are based on features from the signal including voice-based biomarkers within the speech sample at a point in time within the sample; [0430] labels of the training data, i.e. metadata from the conversation data, are based on the questionnaires completed by the patient).
As per Claim 17, Shriberg discloses the system of Claim 1. Shriberg also teaches receive in-context learning comprising a description of the behavioral or mental health condition ([0031] receive speech data from a subject, [0165] clinician device receives data from the patient device including data of assessment, [0455-0456] screening conversation of a patient to learn health state, [0501] record conversations of patient to indicate patients’ feelings and determine mental health status, i.e. depression);
prior to processing the conversation data to generate the language model output and/or the acoustic model output, pre-processing the conversation data (see Fig. 21, pre-processor) by performing at least one of:
weighing at least one segment of the conversation data based on a relation between the at least one segment of the conversation data and the behavioral or mental health condition;
summarizing the at least one segment of the conversation data ([0168] provide a summary of the dialogue between system and patient for segments of speech);
providing analytics on the at least one segment of the conversation data ([0169] provide a word cloud or topic cloud extracted from a text transcript of the speech segments);
summarizing at least one aspect of the behavioral or mental health condition; and
providing analytics on the at least one aspect of the behavioral or mental health condition ([0170] quantify the risk of mental condition); and
transmit the pre-processed conversation data to one or more models to predict the behavioral or mental health condition of the subject.
As per Claim 18, Shriberg discloses a system for scoring surveys based on a conversation, the system comprising:
at least one input device for receiving conversation data from at least one user ([0031] receive speech data from a subject, [0165] clinician device receives data from the patient device including data of assessment, [0298] input/output module for enabling collection of response data);
at least one output device for outputting an electronic report ([0054-0055] computing device with display such as a dashboard for displaying scores indicating mental health disorder; [0168] clinician is provided with summary of dialogue and a report for the patient including a score indicating mental state prediction);
at least one computing device in communication with the at least one input device and the at least one output device ([0530-0531] system includes microprocessors to execute the steps of the method and communicate with input output devices), the at least one computing device configured to:
receive the conversation data from the at least one input device ([0031] receive speech data from a subject, [0165] clinician device receives data from the patient device including data of assessment, [0298] input/output module for enabling collection of response data);
process the conversation data to generate a language model output ([0031] receive speech data and process the speech data using models including NLP, acoustic to generate an output, [0277-0278] conversation is processed by language models and/or acoustic models to output distinction between patient and clinician voices and output mental state of patient, see also [0281] use language/acoustic models to produce results from audiovisual signal, [0283]), wherein the language model output comprises an identification of at least one query based on conversation data from an agent and at least one response to the at least one query based on the conversation data from a patient ([0430] labels of the training data, i.e. metadata from the conversation data, are based on the questionnaires completed by the patient), wherein the at least one query is mapped onto a predefined query and the at least one response to the at least one query is mapped onto a predefined response to the predefined query ([0448-0452] identify the query based on matching question to those stored in the bank and identify corresponding answer from the patient), and
generate an electronic report ([0068] outputting a report indicative of the mental state of the subject); and
transmit the electronic report to the output device ([0055] transmit the plurality of scores to a computing device and graphically displaying on the device; [0068] transmit report to user).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 10 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shriberg (US 2020/0118458 A1).
As per Claim 10, Shriberg discloses the system of Claim 9. Shriberg also teaches wherein the at least one speaker comprises an agent and applying the weights to the language model output and the acoustic model output comprises applying a zero weight to acoustic model output corresponding to the agent ([0277] distinguish the patient’s voice from the clinician’s voice and segment out the clinician’s voice from acoustic model analysis). Examiner interprets this to be analogous to applying a zero weight to the output corresponding to the agent/clinician because applying a zero weight would result in no value being assigned to the time segment data.
As per Claim 14, Shriberg discloses the system of Claim 13. Shriberg also teaches wherein time ranges of the conversation data corresponding to a predefined topic are processed to generate the language model output using more computationally robust models than those used for time ranges of the conversation data not corresponding to the predefined topic ([0284-0285] models include factors that correlate to the health state including topic based features and the model correlates topic information to corresponding portions of the signal). Examiner interprets that a model using topic-based features would be more computationally robust than a model which is not using topic based features because there is more data to be included in the model.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Shriberg (US 2020/0118458 A1) in view of Lin (US 2023/0320642 A1), hereinafter Lin.
As per Claim 4, Shriberg discloses the system of Claim 1. However, Shriberg may not explicitly disclose the following which is taught by Lin: wherein the electronic report comprises an annotation of the language model output indicating salience of at least one of the one or more time segments ([0153] app display which displays an annotated panel which provides a transcript with model output/scores in real-time along a timeline to assist therapist in determining importance of topics).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present invention to combine the known concept of an electronic report comprising annotated output indicating importance of speech segments from Lin with the system of processing conversation data to determine mental health condition of a subject from Shriberg in order to reduce the reliance on subjective understanding of a patient’s state (Lin [0003]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jonnalagadda et al. (US 2021/0201144 A1) teaches generating client intents using AI conversation system which uses natural language processing.
Knoth et al. (US 2018/0214061 A1) teaches determining mental health state of a patient based on collection and analysis of speech.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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