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 .
Information Disclosure Statement
The IDS filed 4/17/2026 and 12/13/2023 have been considered by the Examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application 63/392451 filed 7/26/2022 is acknowledged.
Status of Claims
Claims 1-20 are under examination.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-6 are drawn to a method.
Claims 7-13 are drawn to a system with a processor, so a machine.
Claims 14-20 are drawn to non-transitory computer readable medium, so a manufacture.
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. Transcribing into one or more transcripts one or more recordings of a session related to an administered therapy for an individual, as in claims 1, 7, and 14.
This step can be performed by the human mind or with the help of paper/pen. The step is therefore an abstract idea.
2. parsing the one or more transcripts into utterances, as in claims 1, 7, and 14.
This step reads on process that can be performed by the human mind by segmenting or filtering text. The step is therefore an abstract idea.
3. determining an utterance sentiment for individual utterances, as in claims 1, 7, and 14.
This step reads on a process of assigning sentiment to utterances which can be performed by the human mind and is therefore an abstract idea.
4. predicting an outcome of the individual’s response to the administered therapy based upon the utterance sentiment or using machine learning to predict, as in claims 1, 7, and 14.
This step can be performed by the human mind through thinking about the sentiment of the utterances. The step is therefore an abstract idea. In claims 7 and 14, the machine learning is recited at a high level of generality and reads on matrix mathematics used to predict an outcome based on sentiment values. The step can be performed entirely by math and is therefore an abstract idea.
Claims 2-6, 8-13 and 15-20 are drawn to further limitations that detail the abstract idea steps and are therefore also judicial exceptions.
Step 2A Prong Two: Consideration of Practical Application
The claims result in predicting an outcome based on utterance sentiment which is an abstract idea. The claims do not recite any additional elements that integrate the abstract idea into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
A computer implemented method, as in claim 1.
A processor and memory, as in claim 7.
A non-transitory computer readable medium, as in claim14.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer, processor, and memories are a recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claim 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Shriberg et al. (US 2021/0110895; IDS 12/13/2023).
Shriberg et al. teach (Abstract) assessing a mental state of a subject in a single session or over multiple different sessions, where the mental step may be depression (par. 0009) and the method comprises assessing a mental state of a subject in a single session or over multiple different sessions by processing the data (par. 0032)
Shriberg et al. teach receiving response speech data from a subject (par. 0005); speech samples transcribed by a human or machine speech recognizer (par. 0341)(i.e. transcribing into a transcript recordings of a session related to an administered therapy for an individual), as in claims 1, 7 and 14.
Shriberg et al. teach segmenting the NLP output, the acoustic output (par. 0034) and performing a sentiment analysis of speech utterances (par. 0150)(i.e. parsing transcripts into utterances), as in claims 1, 7 and 14.
Shriberg et al. teach “features” can determine emotions and applying labels to speech utterances based on their features (par. 0290); Shriberg et al. teach an emotional effect model that scores matching to emotions (par. 0347), interpreting utterances as laughter, sighs, or deep breaths (par. 0348); Shriberg et al. teach an emotion analyzer (par. 0358)(i.e. Shriberg et al. therefore suggest determining an utterance sentiment for individual utterances), as in claims 1, 7 and 14.
Shriberg et al. teach tracking voice based biomarkers to track a response to medication and analyze scores indicative of depression (par. 0514-0515)(i.e. predicting an outcome to the individual’s response to administered therapy based on utterance sentiment), as in claims 1, 7 and14.
Shriberg et al. teach that the model may be a machine learning model (par. 505)(i.e. using a machine learning model to predict outcome of the individual’s response), a in claims 6, 7 and 14.
Shriberg et al. do not directly teach determining an utterance sentiment for individual utterances, as in claims 1, 7 and 14.
However Shriberg et al. teach “descriptive features” from transcribed speech can determine emotions (par. 290); Shriberg et al. also teach applying labels to speech utterances and suggest that those can be done based on features (par. 0290) which describe the sentiment (par. 0291). Shriberg et al. also teach an emotional effect model that scores matching to emotions (par. 0347), interpreting utterances as laughter, sighs, or deep breaths (par. 0348); Shriberg et al. also teach an emotion analyzer (par. 0358)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have combined the teachings of Shriberg et al. for determining features that describe emotions in speech and with the teaching that speech utterances can be labeled and scored based on features. The combination of elements taught in Shriberg et al. would equate to a predictable result of utterances labeled with sentiment based on the emotional features determined in the utterances. Shriberg et al. provide motivation by teaching that such labels allow speech to be analyzed by machine learning algorithms (par. 0290). One of skill in the art would have had a reasonable expectation of success at success of converting assigning labels of sentiment to speech utterances because Shriberg et al. teach that such analysis can be performed with language models of original text (par. 0341).
Regarding dependent claims 2-6, 8-13 and 15-20
Shriberg et al. teach providing a score over set of predetermined emotions (par. 0347), as in claims 2, 8 and 15.
Shriberg et al. teach whether a treatment is having an effect on a patient (par. 0516) including for depression (par. 0514), as in claims 3, 9, and 16.
Shriberg et al. teach recording a clinical session (par. 0074)(generating recordings); statistically correlating words with sentiment like depression (par. 0342) and providing a score correlating the sample speech to emotions (par. 0347)(i.e. assigning sentiment score); Shriberg et al. scaling the scores (par. 0422)(i.e. computing session averages of the sentiment score), as in claims 4, 10 and 17.
Shriberg et al. teach an arousal analyzer (par. 0358-059) which suggests there are scores associated it arousal and scores that measure positive and negative content of a patient’s speech (i.e. valence score), as in claims 5, 11, and 18.
Shriberg et al. teach that the model may be a machine learning model (par. 505)(i.e. using a machine learning model to predict outcome of the individual’s response), a in claims 6, 12 and 19.
Shriberg et al. teach a natural language processing (NPL) model (par. 0051) to perform semantic analysis of patient speech utterances (par. 0150)(i.e. a classifier built on a large language model), as in claims 13 and 20.
Shriberg et al. their method executed by a computer processor with the use of memory (par. 0030-0031), for claims 7-20.
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna Skibinsky whose telephone number is (571) 272-4373. The examiner can normally be reached on 12 pm - 8:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ram Shukla can be reached on (571) 272-7035. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights