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 information disclosure statement filed 11/20/2025 fails to comply with 37 CFR 1.98(a)(3)(i) because it does not include a concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information, of each reference listed that is not in the English language. It has been placed in the application file, but the information referred to therein has not been considered.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/02/2026 has been entered.
Claims 1-5, 8-9, 11-15, 18-19 and 21-38 remain pending in this application.
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 1-5, 8-9, 11-15, 18-19 and 21-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-5, 8-9, 21, 23-30 are drawn to a system which is within the four statutory categories (i.e. machine). Claims 11-15, 18-19, 22, 31-38 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claims 1 and 11 recite:
“receive, from a user device, an input corresponding with access to a digital therapeutic application;
responsive to the input being validated, establish a connection between the system and the user device to allow temporal access to the digital therapeutic application;
provide, via the connection, to a user the digital therapeutic application to the user device and present at least one prompt element via the user device;
obtain, via the at least one prompt element of the digital therapeutic application, first data from the user device associated with the user provided with a digital therapeutic via the user device for addressing a condition of the user, the first data including free text generated by the user in response to the at least one prompt element presented via the user device;
generate first vectorized data using the free text of the first data;
determine, using a machine learning model, whether the first vectorized data satisfies relevancy criteria corresponding to (i) a relevancy score of the free text data with the at least one prompt element (ii) a determination that the free text data comprises content, or (iii) historical event data;
generate a likelihood of an event associated with the condition in the user using the machine learning model based on the first vectorized data;
perform at least one operation of a plurality of operations comprising:
adding the first data to the database responsive to determining that the first vectorized data satisfies the relevancy criteria;
refraining from adding the first data to the database, responsive to determining that the first vectorized data does not satisfy the relevancy criteria; or
transmitting, to the user device via the connection, information associated with the event, responsive to the likelihood satisfying a threshold”
The steps of “determine, …, whether the first vectorized data satisfies relevancy criteria corresponding to (i) a relevancy score of the free text data with the at least one prompt element (ii) a determination that the free text data comprises content, or (iii) historical event data”, “generate a likelihood of an event associated with the condition in the user …based on the first vectorized data …”, “adding the first data to the database responsive to determining that the first vectorized data satisfies the predetermined relevancy criteria”, “refraining from adding the first data to the database, responsive to determining that the first vectorized data does not satisfy the predetermined relevancy criteria” correspond to “certain methods of organizing human activity” (e.g. This is a method of managing interactions between people, such as user following rules or instructions). The mere nominal recitation of a generic processor does not take the claim out of the methods of organizing human interactions grouping. The processor described in the current specification as a generic purpose computing device (as described in par. 83).
The steps of “generate first vectorized data using the free text of the first data” and “generate a likelihood of an event associated with the condition in the user using the machine leaning model based on the first vectorized data” correspond to a mathematical relationships/calculations, which falls within the “mathematical concepts” of abstract ideas.
The steps of “obtain…data…” and “transmit…information…” correspond to mere data gathering and output, which recited at high level of generality and thus are insignificant extra solution activities.
Claims 23 and 31 recite:
“receive, from a user device, an input corresponding with access to a digital therapeutic application;
responsive to the input being validated, establish a connection between the system and the user device to allow temporal access to the digital therapeutic application;
provide, via the connection, to a user the digital therapeutic application to the user device and present at least one prompt element via the user device;
obtain, via the at least one prompt element of the digital therapeutic application, first data from the user device associated with the user provided with a digital therapeutic via the user device for addressing a condition of the user, the first data including free text generated by the user in response to the at least one prompt element presented via the user device;
generate first vectorized data using the free text of the first data;
determine whether the first vectorized data shares similar characteristics with second vectorized data corresponding to second data on a database, based on comparing the first vectorized data with the second vectorized data using a machine learning model;
generate a likelihood of an event associated with the user using the machine learning model based on the first vectorized data; and
perform at least one operation of a plurality of operations comprising:
associating the first data with the second data on the database, responsive to determining that the first vectorized data shares similar characteristics with the second vectorized data;
refraining from associating the first data to the database, responsive to determining that the first vectorized data does not share similar characteristics the second vectorized data; or
transmitting, to the user device via the connection, information associated with the event, responsive to the likelihood satisfying a threshold”
The steps of “determine whether the first vectorized data shares similar characteristics with second vectorized data corresponding to second data on a database,…”, “generate a likelihood of an event associated with the user …based on the first vectorized data …”, “associating the first data with the second data on the database, responsive to determining that the first vectorized data shares similar characteristics with the second vectorized data”, “refraining from associating the first data to the database, responsive to determining that the first vectorized data does not share similar characteristics the second vectorized data” correspond to “certain methods of organizing human activity” (e.g. This is a method of managing interactions between people, such as user following rules or instructions). The mere nominal recitation of a generic processor does not take the claim out of the methods of organizing human interactions grouping. The processor described in the current specification as a generic purpose computing device (as described in par. 83).
The steps of “generate first vectorized data using the free text of the first data”, “determine whether the first vectorized data shares similar characteristics with second vectorized data corresponding to second data on a database, based on comparing the first vectorized data with the second vectorized data using a machine learning model” and “generate a likelihood of an event associated with the user using the machine learning model based on the first vectorized data” correspond to a mathematical calculation, which falls within the “mathematical concepts” of abstract ideas.
The steps of “obtain…data…” and “transmit…information…” correspond to mere data gathering and output, which recited at high level of generality and thus are insignificant extra solution activity.
Dependent claims also are directed to an abstract idea of certain methods of organizing human activity. Such as, claims 2/12/25/33 recite “assigning… a risk-assessment value to the first data corresponding to the likelihood that the first data indicates the event has occurred or is about to occur”, and these limitations are directed to managing interactions between people, such as user following rules or instructions.
Dependent claims also are directed to an abstract idea of mathematical concepts. Such as, claims 8/18 recite “the machine learning model performs fuzzy matching processes to compare the first vectorized data to second vectorized data corresponding to second data on the database”, claims 9/19 recite “the fuzzy matching processes include a high sensitivity setting to identify the first vectorized data as probable matches with the second vectorized data to escalate the first data”, claims 21/22 recite “determine whether the first vectorized data is similar with second vectorized data corresponding to second data on the database, based on comparing the first vectorized data with the second vectorized data using one or more machine learning models”, claims 24/32 recite “determine, using one or more machine learning models, whether the first vectorized data satisfies relevancy criteria associated with the at least one prompt element”, claim 29 recites “the instructions that when executed on the data processing hardware cause the data processing hardware to generate the likelihood of the event associated with the event, responsive to detecting text associated with the medication in the free text of the first data using the machine learning model”, claim 30 recites “the instructions that when executed on the data processing hardware cause the data processing hardware to generate the first vectorized data by vectorizing the free text of the first data”, claim 37 recites “generating the likelihood further comprises generating the likelihood of the event associated with the event, responsive to detecting text associated with the medication in the free text of the first data using the machine learning model”, and claim 38 recites “generating the first vectorized
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Claims 3-5, 8-9, 13-15, 18-19, 21-22, 25-30, 33-38 are ultimately dependent from claims 1, 11, 23, 31 and include all the limitations of claims 1, 11, 23, 31. Therefore, claims 3-5, 8-9, 13-15, 18-19, 21-22, 25-30, 33-38 recite the same abstract idea. Claims 3-5, 8-9, 13-15, 18-19, 21-22, 25-30, 33-38 describe a further limitation regarding “generate a likelihood of an event associated with the user, based on determination of obtained and embedded data with the data in the database”. These are all just further describing the abstract idea recited in claims 1, 11, 23, 31, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “data processing hardware”, “memory hardware in communication with the data processing hardware, the memory hardware storing instructions”, “one or more processors”, “maintain, on a database, data…”, “receive, from a user device, an input corresponding with access to a digital therapeutic application; responsive to the input being validated, establish a connection between the system and the user device to allow temporal access to the digital therapeutic application;”, “provide, via the connection, to the user a digital therapeutic application to the user device…”, “using one or more processors to obtain data, generate first vectorized data using the free text, determining, using a machine learning model, predetermined relevancy criteria associated with the entry prompt/determine whether the first vectorized data shares similar characteristics with second vectorized data…, generating a likelihood of an event associated with the user using the machine learning model based on the first vectorized data, adding the first data to a database responsive to determining that the first vectorized data satisfies the relevancy criteria, refraining from adding the first data to the database, responsive to determining that the first vectorized data does not satisfy the relevancy criteria, and transmitting, to the user device via the connection, information associated with the event, responsive to the likelihood exceeding a threshold…”.
These additional elements are hardware and software elements, and the limitations are performed by one or more processors, which is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using generic computer. These limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
The machine learning model used to generally apply the abstract idea without limiting how the learning model functions, the machine learning model is described at a high level of generality such that it amounts to using a computer with a generic machine learning model to apply the abstract idea. These limitations only recite the outcomes of “determining whether the first vectorized data satisfies predetermined relevancy criteria associated with the entry prompt/determining whether the first vectorized data is similar to the second vectorized data” and without any details about how the outcomes are accomplished.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are 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. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform generating, determining and adding/refraining data steps 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.
Examiner submits that the search of the prior art shows that feature of “obtain,… first data from a user device associated with a user, the first data including free text generated by the user in response to at least one prompt element presented via the user device” and “generate first vectorized data using the free text of the first data” is well-understood, routine and conventional activity in the field, as evidenced by Davis et al. (US 2021/0345925 A1). In particular, Davis teaches “…a computing device (or plurality of computing devices) for receiving or collecting the input data and converting the input data signal into a representation for processing by the data processing device… text data can be input by a patient using a touchscreen or keyboard responsive to prompts on a user interface…” in [0033] and “…the input data 205 are transformed into features of a feature vector 220 by feature vector generation engine 210 (such as using the NLP models described in relation to FIG. 3A). The feature vector 220 concisely represents the characteristics of the input data for the patient. …” in [0047].
The claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Argument 1: Claim 1 is not directed to mathematical concepts:
Applicant argues that claims are not directed to an abstract idea of mathematical concepts, since the claim limitations of “generate first vectorized data using the free text of the first data” and “generate a likelihood of an event associated with the condition in the user using the machine learning model based on the first vectorized data” do not describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols (based on USPTO’s August Memorandum and Example 36 in the USPTO Eligibility Guidance).
In response, Examiner submits that the current specification recites “According to one example, the free text data 234 may be vectorized and compared to corresponding vector data associated with the pre-defined entries 238 in the clinically curated database 220 to generate the comparison data. According to this example, the add entry module 262 may determine that a given free text data 234 entry is sufficiently different from a given pre-defined entry 238 in the clinically curated database 220 if the vectors for the respective entries are outside of a predefined threshold. Such a determination may be made by artificial intelligence and/or machine learning (supervised or unsupervised).” in [0054], and “The merge entry module 264 is configured to determine whether the free text data 234 is closely related to any pre-defined entries 238 in the clinically curated database 220. According to one example, the free text data 234 may be vectorized and compared to corresponding vector data associated with the pre-defined entries 238 in the clinically curated database 220 to generate the comparison data.” in [0055]. Therefore, the features of “generating vectorized data and generating a likelihood of an event associated with the condition” correspond to mathematical relationships/calculations.
Argument 2: Claim 1 is not directed to a certain method of organizing human activity:
Applicant argues that claims are not directed to a method of organizing human activity, and are directed to a computer-implemented system for processing user-provided free-text data, generating vectorized representations, using machine learning model to determine whether the vectorized data satisfies relevancy criteria, and performing curation operations based on that determination.
In response, Examiner submits that the limitations of “determine, …, whether the first vectorized data satisfies relevancy criteria corresponding to (i) a relevancy score of the free text data with the at least one prompt element (ii) a determination that the free text data comprises content, or (iii) historical event data”, “generate a likelihood of an event associated with the condition in the user …based on the first vectorized data …”, “adding the first data to the database responsive to determining that the first vectorized data satisfies the predetermined relevancy criteria”, “refraining from adding the first data to the database, responsive to determining that the first vectorized data does not satisfy the predetermined relevancy criteria” correspond to certain methods of organizing human activity (a method of managing interactions between people, such as user following rules or instructions) with a recitation of a generic processor.
MPEP recites “…the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping.” in § 2106.04(a)(2)II. The current claims are directed to user activity that determines whether the first vectorized data satisfies relevancy criteria corresponding to (i) a relevancy score of the free text data with the at least one prompt element (ii) a determination that the free text data comprises content, or (iii) historical event data”, “generate a likelihood of an event associated with the condition in the user …based on the first vectorized data …” using a generic computing device and using a machine learning model. The feature of using a generic computing device and a machine learning model to perform the determining and generating steps correspond to additional elements that are directed to hardware and software elements, and the limitations are performed by one or more processors, which is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using generic computer.
Argument 3: Claim 1 integrates any alleged abstract idea into a practical application:
Applicant argues that claim 1 covers a technical solution to a technical problem, and as a whole integrates the recited judicial exception into a practical application. Applicant argues that the claims provide a technical solution to the technical problem of maintaining data quality and database size.
In response, Examiner submits that claim limitations of using a processor to perform generating, determining and adding/refraining data steps 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. Examiner submits that there is no indication in the current claims how the maintaining data quality and database size being accomplished. Claim limitations recite “obtaining data form the user, generating a vectorized data, determining the vectorized data satisfies a relevancy criteria, generate a likelihood of an event associated with the user, and either adding or refraining from adding the data to the database”. The claims do not recite how the system improves the database size or quality of data of the database. The claims recite adding the relevant data into the database and refrain to add the non-relevant data into the database, which is not directed to a technological improvement.
Arguments 4-6, about 35 USC 101 rejection of claim 23: Examiner submits that the same reasoning and response to the arguments given for claim 1, as addressed above, are incorporated herein.
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Argument 7: the USC 103 rejection of the claims:
Applicant’s arguments, see Remarks, filed 03/02/2026, with respect to 35 USC 103 rejection of the claims have been fully considered and are persuasive. The 35 USC 103 rejection of the claims has been withdrawn.
Conclusion
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/DILEK B COBANOGLU/ Primary Examiner, Art Unit 3687
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