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
The instant application having Application No. 18753083 has a total of 20 claims pending in the application, all of which are ready for examination by the examiner.
I. ACKNOWLEDGEMENT OF REFERENCES CITED BY APPLICANT
Information Disclosure Statement
As required by M.P.E.P 609(c), the applicant’s submissions of the Information Disclosure Statements dated 2/25/25 and 10/22/24 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
II. REJECTIONS NOT BASED ON PRIOR ART
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-20 are provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-20 of copending Application No. 19495736 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
As per claims 1-20, Each claim in the instant application is matched exactly with the corresponding numbered claim in the reference application, verbatim. This causes the claim to be rejected as statutory type double patenting.
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 an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Claim 8 is a machine type claim. Claim 15 is a manufacture type claim. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“Generating a set of features from each communication session of the set of communications sessions by processing communications of the communication session with a natural language model” The Doctor mentally or with pencil and paper listens to the communications and notes the important aspects of the conversations.
“defining a subset of the set of communication sessions by filtering one or more communication sessions from the set of communication sessions based on the set of features” The Doctor mentally or with pencil and paper removes some of the communications that he feels is not relevant or useful to the current concept.
“Generating a training dataset from the set of communication sessions” The Doctor mentally or with pencil and paper chooses the communication sessions he feels are most relevant to the current concept.
“… generate one or more contexts associated with a feature of the set of features” The Doctor mentally or with pencil and paper makes a decision based on the communications and the data within for treatment or diagnosis of the patient.
“generating … using a feature vector derived from the request, a context associated with the particular feature” The Doctor mentally or with pencil and paper makes a decision based on the communications and the data within for treatment or diagnosis of the patient.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Computer implemented (mere instructions to apply the exception using a generic computer component);
“training a machine learning model using the training dataset, wherein the machine learning model…”, “the machine learning model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing more than a generic machine learning model and training process, with no additional limitations or descriptions beyond a generic, off the shelf machine learning model.
“Receiving a set of communication sessions, wherein each communication session of the set of communication sessions includes communications between a doctor and a patient”, “Extracting the communications from the set of communication sessions”, “receiving a request associated with a particular feature” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Computer implemented (mere instructions to apply the exception using a generic computer component)
“training a machine learning model using the training dataset, wherein the machine learning model…”, “the machine learning model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing more than a generic machine learning model and training process, with no additional limitations or descriptions beyond a generic, off the shelf machine learning model.
“Receiving a set of communication sessions, wherein each communication session of the set of communication sessions includes communications between a doctor and a patient”, “Extracting the communications from the set of communication sessions”, “receiving a request associated with a particular feature”, “facilitating a presentation of the context” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed Receiving and Extracting steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 2-3, 5-7, these claims contain additional mental steps similar to claim 1, and are rejected for similar reasons.
As per claim 4, this claim contains similar generic machine learning models to claim 1 and are rejected for similar reasons.
As per claim 8,
2A Prong 1:
“Generating a set of features from each communication session of the set of communications sessions by processing communications of the communication session with a natural language model” The Doctor mentally or with pencil and paper listens to the communications and notes the important aspects of the conversations.
“defining a subset of the set of communication sessions by filtering one or more communication sessions from the set of communication sessions based on the set of features” The Doctor mentally or with pencil and paper removes some of the communications that he feels is not relevant or useful to the current concept.
“Generating a training dataset from the set of communication sessions” The Doctor mentally or with pencil and paper chooses the communication sessions he feels are most relevant to the current concept.
“… generate one or more contexts associated with a feature of the set of features” The Doctor mentally or with pencil and paper makes a decision based on the communications and the data within for treatment or diagnosis of the patient.
“generating … using a feature vector derived from the request, a context associated with the particular feature” The Doctor mentally or with pencil and paper makes a decision based on the communications and the data within for treatment or diagnosis of the patient.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
One or more processors, a non-transitory computer readable medium (mere instructions to apply the exception using a generic computer component);
“training a machine learning model using the training dataset, wherein the machine learning model…”, “the machine learning model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing more than a generic machine learning model and training process, with no additional limitations or descriptions beyond a generic, off the shelf machine learning model.
“Receiving a set of communication sessions, wherein each communication session of the set of communication sessions includes communications between a doctor and a patient”, “Extracting the communications from the set of communication sessions”, “receiving a request associated with a particular feature” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
One or more processors, a non-transitory computer readable medium (mere instructions to apply the exception using a generic computer component)
“training a machine learning model using the training dataset, wherein the machine learning model…”, “the machine learning model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing more than a generic machine learning model and training process, with no additional limitations or descriptions beyond a generic, off the shelf machine learning model.
“Receiving a set of communication sessions, wherein each communication session of the set of communication sessions includes communications between a doctor and a patient”, “Extracting the communications from the set of communication sessions”, “receiving a request associated with a particular feature”, “facilitating a presentation of the context” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed Receiving and Extracting steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 9-10, 12-14, these claims contain additional mental steps similar to claim 1, and are rejected for similar reasons.
As per claim 11, this claim contains similar generic machine learning models to claim 1 and are rejected for similar reasons.
As per claim 15,
2A Prong 1:
“Generating a set of features from each communication session of the set of communications sessions by processing communications of the communication session with a natural language model” The Doctor mentally or with pencil and paper listens to the communications and notes the important aspects of the conversations.
“defining a subset of the set of communication sessions by filtering one or more communication sessions from the set of communication sessions based on the set of features” The Doctor mentally or with pencil and paper removes some of the communications that he feels is not relevant or useful to the current concept.
“Generating a training dataset from the set of communication sessions” The Doctor mentally or with pencil and paper chooses the communication sessions he feels are most relevant to the current concept.
“… generate one or more contexts associated with a feature of the set of features” The Doctor mentally or with pencil and paper makes a decision based on the communications and the data within for treatment or diagnosis of the patient.
“generating … using a feature vector derived from the request, a context associated with the particular feature” The Doctor mentally or with pencil and paper makes a decision based on the communications and the data within for treatment or diagnosis of the patient.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory computer readable medium, one or more processors, (mere instructions to apply the exception using a generic computer component);
“training a machine learning model using the training dataset, wherein the machine learning model…”, “the machine learning model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing more than a generic machine learning model and training process, with no additional limitations or descriptions beyond a generic, off the shelf machine learning model.
“Receiving a set of communication sessions, wherein each communication session of the set of communication sessions includes communications between a doctor and a patient”, “Extracting the communications from the set of communication sessions”, “receiving a request associated with a particular feature” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer readable medium, one or more processors (mere instructions to apply the exception using a generic computer component)
“training a machine learning model using the training dataset, wherein the machine learning model…”, “the machine learning model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain nothing more than a generic machine learning model and training process, with no additional limitations or descriptions beyond a generic, off the shelf machine learning model.
“Receiving a set of communication sessions, wherein each communication session of the set of communication sessions includes communications between a doctor and a patient”, “Extracting the communications from the set of communication sessions”, “receiving a request associated with a particular feature”, “facilitating a presentation of the context” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed Receiving and Extracting steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 16, 18-20, these claims contain additional mental steps similar to claim 1, and are rejected for similar reasons.
As per claim 17, this claim contains similar generic machine learning models to claim 1 and are rejected for similar reasons.
III. REJECTIONS BASED ON PRIOR ART
Examiners Note: Some rejections will be followed by an ‘EN’ that will denote an examiners note. This will be placed to further explain a rejection.
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.
Claims 1-2, 5-9, 12-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Talmor et al (US 20200335208 A1) in view of Chen et al (“ALPAGASUS: Training a Better Alpaca with Fewer Data”).
As per claims 1, 8, and 15, Talmor discloses, “A computer implemented method” (Pg.4, particularly paragraph 0059; EN: this denotes the hardware for the system).
“Receiving a set of communication sessions” (Pg.9, particularly paragraph 0129; EN: this denotes training data including audio feeds). “wherein each communication session of the set of communication sessions includes communication” (Pg.7, particularly paragraph 0094; EN: this denotes detecting speech). “between a doctor and a patient” (Pg.8, particularly paragraph 0108; EN: This denotes monitoring interactions with the doctor and patient. However, the role of individuals involved in the conversations is non-functional descriptive material, as the operation of the device does not know or change based upon the speakers real life role or credentials).
“Extracting the communications from the set of communications sessions” (Pg.9, particularly paragraph 0123; EN: this denotes using the audio sets for training).
“Generating a set of features from each communication session of the set of communications session by processing communications of the communication session with a natural language model” (Pg.8, particularly paragraph 0109; EN: this denotes pulling features out of the audio data).
“generating a training dataset from the set of communication sessions” (Pg.9, particularly paragraph 0129; EN: this denotes training data including audio feeds).
“Training a machine learning model using the training dataset, wherein the machine learning model is configured to generate one or more contexts associated with a feature of the subset” (Pg.9, particularly paragraph 0129; EN: This denotes providing better diagnosis/treatment for the user based on the training, with the context being the particular situation that patient is in which is determined based on the particular features of that patients situation).
“receiving a request associated with a particular feature” (Pg.9, particularly paragraph 0130; EN: this denotes inputting data with a request for treatment/diagnosis recommendations).
“Generating, by the machine learning model using a feature vector derived form the request, a context associated with the particular feature” (Pg.9, particularly paragraph 0130; EN: this denotes inputting data with a request for treatment/diagnosis recommendations).
“facilitating a presentation of the context” (Pg.10, particularly paragraph 0140; EN: this denotes displaying notification and recommendations to the users).
However, Talmor fails to explicitly disclose, “Defining a subset of the set of communication sessions by filtering one or more communication session from the set of communication sessions based on the set of features.”
Chen discloses, “Defining a subset of the set of communication sessions by filtering one or more communication session from the set of communication sessions based on the set of features” (Abstract; EN: this denotes filtering out data that will be used to train an algorithm in order to improve that training).
Talmor and Chen are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Talmor and Chen in order to filter training data in order to improve the model.
The motivation for doing so would be to “propose a simple and effective data selection strategy that automatically identifies and filters out low-quality data” (Chen, Abstract) or in the case of Talmor, allow the system to review the training data and filter out data that will not be useful for the training process.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Talmor and Chen in order to filter training data in order to improve the model.
As per claims 2 and 9, Talmor discloses, “wherein processing communications of the communication session with natural language model includes classifying communications according to a contextual hierarchy” (Pg.6, particularly paragraph 0082; EN :this denotes the system organizing data by type of procedure, with each procedure having its own audio information. Pg.8, particularly paragraph 0108-109; EN: this denotes the different categories that the audio data can be put into in the procedures, such as type, role, action, activity, with the audio assigned to subgroups such as identifying individual speakers, actions, specific equipment usage, medical tools, and chemicals/medicines/drugs, this denotes the hierarchy).
As per claims 5, 12, and 18, Talmor discloses, “wherein the context identifies a relationship between patients and the particular feature” (Pg.9, particularly paragraph 0130; EN: this denotes inputting data with a request for treatment/diagnosis recommendations, the relationship is the treatment/diagnosis for this particular patient).
As per claims 6, 13, and 19, Talmor discloses, “Wherein the context includes a representation of the particular feature that is customized for a portion of patients” (Pg.9, particularly paragraph 0130; EN: this denotes the customization of the various features to the diagnosis/treatment of this particular patient).
As per claims 7, 14, and 20, Talmor disclose, “wherein the context indicates an efficacy of a treatment associated with the particular feature” (Pg.9, particualrly7 paragraph 0128; EN: this denotes providing improved recommendations and treatment to the patient, which denotes increased efficacy).
Claim Rejections - 35 USC § 103
Claims 3-4, 10-11, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Talmor et al (US 20200335208 A1) in view of Chen et al (“ALPAGASUS: Training a Better Alpaca with Fewer Data”) and further in view of Smith et al (“Identifying and Mitigating Privacy Risks Stemming from Language Models”).
As per claims 3, 10 and 16, Talmor and Chen fails to explicitly disclose, “wherein processing communications of the communication session with a natural language model includes filtering personal identifiable information from the communications.”
Smith discloses, “wherein processing communications of the communication session with a natural language model includes filtering personal identifiable information from the communications” (pg.9, particularly the sanitizing the training data section; EN: this denotes sanitizing the training data by removing identifying information before training).
Smith and Talmor modified by Chen are analogous art because both involve optimizing training data.
Before the effective filing date it would have been obvious to one skilled in the art of optimizing training data to combine the work of Smith and Talmor modified by Chen in order to privatize the training data.
The motivation for doing so would be to “remove all sensitive information from data before model training … to remove personally identifiable information (PII) by numerous companies across sectors” (Smith, Pg.9, Sanitizing training data section) or in the case of Talmor modified by Chen, allow the system to sanitize the information being used to train to prevent identification of people and their medical information.
Therefore before the effective filing date it would have been obvious to one skilled in the art of optimizing training data to combine the work of Smith and Talmor modified by Chen in order to privatize the training data.
As per claims 4, 11, and 17, Talmor fails to explicitly disclose, “wherein the machine learning model is generative transformer model.”
Smith discloses, “wherein the machine learning model is generative transformer model” (Pg.1, particularly the introduction section;; EN: this denotes using LLMs for healthcare data When combined with the Talmor reference, this denotes using the LLM for the machine learning of the Talmor reference).
Talmor and Smith are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Talmor and Smith in order to use an LLM for medical data.
The motivation for doing so would be to use LLMs to “bring real-world benefits to many a wide range of fields, including those that rely on highly sensitive datasets, such as law and healthcare” (Smith, Pg.1, introduction section first paragraph) or in the case of Talmor, allow the use of powerful LLM models to perform the actions of the Talmor reference.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Talmor and Smith in order to use an LLM for medical data.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 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, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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