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 .
Notice to Applicant
This communication is in response to the amendment filed 12/31/2025. Claims 1 and 16 have been amended. Claims 1, 5-7, 9, 11, 12, and 15-17 remain pending and have been examined.
Response to Arguments
A. Applicant's arguments with respect to the rejection of claims 1, 5-7, 9, 11, 12, and 15-17 under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues starting on page 7 that claims 1 and 16 recite additional elements which integrate the abstract idea(s) into a practical application. Applicant asserts that “[t]he claim explicitly involves hardware feature registers and their concrete configuration and usage (i.e., an N-bit state register with mutually exclusive valid bits)” and that “[s]uch hardware elements and their operational constraints constitute specific technical features, rather than mental processes, mathematical concepts, or methods of organizing human activity.” Examiner respectfully disagrees.
Examiner notes that the recited N-bit state register is not limited to hardware feature registers as argued by Applicant. Paragraph 102 provides the only description of an “N-bit state register” in the disclosure, and does not describe it as limited to hardware. In the example provided of how the register maps a numerical value to categorical data, a Gestational Diabetes History feature is described having three possible categorical values of “never given birth,” “given birth without having gestational diabetes,” and “had gestational diabetes,” where are coded to numerical values of 1, 2, and 3 respectively. Paragraph 102 then states that “the category feature corresponding to the target user can be mapped,” where a patient value of “never given birth” would accordingly be assigned a value of “1.” State registers are not inherently and exclusively limited to hardware, and the example provided aligns with an interpretation of the N-bit state register being a mapping scheme of possible Boolean category values to numerical values of 1, 2, and 3. Examine additionally notes that a hardware state register does not associate states with values other than 0 and 1, and would not map possible states to values such as “2,” and “3.” Examiner additionally notes that, even if the N-bit state register were construed as limited to a hardware register, it would only amount to mere instructions to implement the data processing task of converting the categorical feature data into numerical values using computing elements. Merely reciting the use of a hardware register to perform the one-hot encoding and categorical feature data conversion would not be found to integrate the abstract idea into a practical application or to amount to significantly more.
Applicant further argues that “the claimed encoding operation goes well beyond a mere "manipulation of basic mathematical constructs",” that “[t]his operation achieves a data type transformation, namely converting boolean and categorical feature data into numerical feature data in a form suitable for subsequent processing by the disease risk prediction model,” and that the process recites effecting a transformation or reduction of a particular article to a different state or thing. Examiner respectfully disagrees.
As addressed above, the recited encoding operation only constitutes a conversion of one manner of representing an item of data, i.e. a Boolean value and category, to a different manner of representing that same data, i.e. a mapped numerical value. Examiner maintains that changing how an item of data is represented constitutes a form of manipulating basic mathematical constructs.
MPEP 2106.05(c) states that:
“An "article" includes a physical object or substance. The physical object or substance must be particular, meaning it can be specifically identified. "Transformation" of an article means that the "article" has changed to a different state or thing.”
“For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation” (emphasis added).
While Applicant asserts that the argued limitation does not fall into this exception, no argument or rationale for this assertion is provided. Examiner maintains that changing how an item of data is represented from a Boolean-type to a number constitutes “manipulation of basic mathematical constructs” as set out in MPEP 2106.05(c).
The rejection under 35 USC 101 is maintained.
Claim Objections
The previous objection to claims 1 and 16 is withdrawn based on the amendment filed 12/31/2025.
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, 5-7, 9, 11, 12, and 15-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 5-7, 9, and 11-12 are drawn to a method, claim 15 is drawn to a non-transitory computer readable medium, and claims 16 and 17 are drawn to a device, each of which is within the four statutory categories.
Step 2A(1)
Claim 1 recites, in part, performing the steps of:
obtaining risk feature data of a target user, wherein the risk feature data comprises features of boolean value type and category type;
encoding N states using an N-bit state register, to convert the features of the boolean value
type and category type into features of numerical type through One-Hot encoding, wherein each state has an independent register bit, and at any time, only one bit in the register is valid, and wherein N is an integer greater than 1;
determining, by a disease risk prediction model for gestational diabetes based on risk feature data, a disease risk value of the target user and a reliability score of the disease risk value,
wherein the determining, by the disease risk prediction model based on the risk feature data, the disease risk value of the target user comprises:
the disease risk prediction model comprising a first risk prediction parameter; and
obtaining the disease risk value of the target user based on the risk feature data and the first risk prediction parameter; and
training the disease risk prediction model to obtain the first risk prediction parameter, wherein the training the disease risk prediction model to obtain the first risk prediction parameter comprises:
inputting feature training data into the disease risk prediction model to determine a second risk prediction parameter;
determining a reliability score of the disease risk prediction model according to the second risk prediction parameter; and
training the disease risk prediction model based on the reliability score to obtain the first risk prediction parameter;
wherein the feature training data comprises risk feature training data and disease risk training data, and the inputting the feature training data into the disease risk prediction model to determine the second risk prediction parameter comprises:
determining a mapping relationship between the risk feature training data and the disease risk training data in a first part of the feature training data to establish the disease risk prediction model;
inputting the risk feature training data and disease risk training data in a second part of the feature training data into the disease risk prediction model, and constructing an objective function; and
determining the second risk prediction parameter according to the objective function,
wherein the determining the reliability score of the disease risk prediction model according to the second risk prediction parameter comprises:
determining a performance parameter corresponding to the second risk prediction parameter in the mapping relationship; and
calculating the performance parameter to obtain the reliability score of the disease risk prediction model, and
wherein the training the disease risk prediction model based on the reliability score to obtain the first risk prediction parameter comprises:
acquiring a third part of the feature training data when the reliability score is lower than a preset threshold; and
training the disease risk prediction model based on the third part of the feature training data, to update the first risk prediction parameter after the training is completed according to a risk prediction parameter obtained by the training based on the third part of the feature training data.
These elements fall within the scope of abstract ideas in the form of a method of organizing human activity as well as mathematical concepts in the form of mathematical relationships, formulas or equations, or calculations as explained below.
The limitations reciting “obtaining risk feature data of a target user, wherein the risk feature data comprises features of boolean value type and category type, encoding N states using an N-bit state register, to convert the features of the boolean value type and category type into features of numerical type through One-Hot encoding, wherein each state has an independent register bit, and at any time, only one bit in the register is valid, and wherein N is an integer greater than 1,” “determining, by a disease risk prediction model for gestational diabetes based on risk feature data, a disease risk value of the target user and a reliability score of the disease risk value” and “wherein the determining, by the disease risk prediction model based on the risk feature data, the disease risk value of the target user comprises: the disease risk prediction model comprising a first risk prediction parameter; and obtaining the disease risk value of the target user based on the risk feature data and the first risk prediction parameter” amount to forms of managing personal behavior or relationships or interactions between people and therefore fall within the scope of an abstract idea in the form of a method of organizing human activity.
Examiner notes that the elements reciting “wherein the risk feature data comprises features of boolean value type and category type, encoding N states using an N-bit state register, to convert the features of the boolean value type and category type into features of numerical type through One-Hot encoding, wherein each state has an independent register bit, and at any time, only one bit in the register is valid, and wherein N is an integer greater than 1” set out a conversion of the Boolean and categorical data to numerical representation. For example, paragraph 102 of the specification as originally filed provides an example of a Gestational Diabetes History features having possible categorical values of “never given birth,” “given birth without having gestational diabetes,” and “had gestational diabetes” assigned to numerical values of 1, 2, and 3 respectively, and where a patient value of “never given birth” would accordingly be assigned a value of “1.” This falls within the scope of what a human could do as part of determining a disease risk value of an individual and a reliability score of the disease risk value.
The remaining elements set out a series of mathematical calculations for training a disease risk prediction model and fall within the scope of an abstract idea in the form of mathematical concepts.
Examiner notes that limitations falling within the scope of a mathematical concept are not limited strictly to numerical mathematical equations, and may express mathematical calculations or operations using words. See MPEP 2106.04(a)(2)(I). For example, MPEP 2106.04(a)(2)(I)(C) states that “[a] claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number.”
Independent claim 16 recites similar limitations and also recites an abstract idea under the same analysis.
Step 2A(2)
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claim 16 recites the additional elements of an electronic device comprising a processor and a non-transient memory, where the non-transient memory is recited as storing instructions executable by the processor and the processor is recited as executing the stored instructions to perform the subsequent functions.
Paragraphs 65, 68, and 71 describe an electronic device as including a processing device such as a CPU which executes programs stored on memory, such as RAM or ROM. Each of the devices is described at a high level of generality in terms of its respective function of storing instructions and executing those instructions. The electronic device, processor, and non-transient memory are each given their broadest reasonable construction as encompassing generic computing elements.
The above elements only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, each of the processor and non-transient memory are recited at a high level of generality as used to execute their corresponding functions. For example, the processor is only recited configured to execute the instructions and as “caused to” perform functions such as training the disease risk prediction model based on the reliability score, determining a mapping relationship between the risk feature training data and disease risk training data, and determining a performance parameter. The non-transient memory is likewise only broadly recited as “configured to” store the executable instructions. These elements are not sufficient to integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claim 16 only recites the electronic device comprising a processor and a non-transient memory as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f)
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Depending Claims
Claim 5 recites wherein the determining the mapping relationship between the risk feature training data and the disease risk training data in the first part of the feature training data comprises: obtaining a latent factor vector corresponding to the risk feature training data; obtaining a distribution of the risk feature training data and a distribution of the disease risk training data based on the latent factor vector; and establishing the mapping relationship between the risk feature training data and the disease risk training data according to the distribution of the risk feature training data and the distribution of the disease risk training data. These limitations amount to mathematical calculations and fall within the scope of an abstract idea in the form of a mathematical concept as set out above.
Claim 6 recites wherein the mapping relationship between the risk feature training data and the disease risk training data is:
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Claim 7 recites wherein the objective function is max lnp(Y|X), and wherein Y is the disease risk training data, X is the risk feature training data, and wherein the determining the second risk prediction parameter according to the objective function comprises: training the risk feature training data and the disease risk training data in the second part of the feature training data using a maximum likelihood estimation algorithm, and obtaining the second risk prediction parameter at a maximum probability value of the objective function. These limitations amount to mathematical calculations and mathematical formulas/equations and fall within the scope of an abstract idea in the form of a mathematical concept as set out above.
Claim 9 recites wherein the performance parameter is
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Claim 11 recites wherein the determining reliability score of the disease risk value by the disease risk prediction model comprises: determining the performance parameter corresponding to the first risk prediction parameter in the mapping relationship; and calculating the performance parameter to obtain the reliability score for the disease risk value. These limitations amount to mathematical calculations and fall within the scope of an abstract idea in the form of a mathematical concept as set out above.
Claim 12 recites wherein the obtaining the disease risk value of the target user based on the risk feature data and the first risk prediction parameter comprises: determining the disease risk value of the target user according to a relationship between the risk feature data and the first risk prediction parameter:
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The limitation reciting obtaining the disease risk value of the target user based on the risk feature data and the first risk prediction parameter falls within the scope of an abstract idea in the form of a method of organizing human activity as set out above with respect to claim 1.
The remaining limitations amount to mathematical calculations and mathematical formulas/equations and fall within the scope of an abstract idea in the form of a mathematical concept as set out above.
Claim 15 recites the additional elements of a) a non-transient computer-readable storage medium used to store a computer program and b) a processor recited as executing the computer program to implement the method according to claim 1.
Paragraphs 65, 68, and 71 describe an electronic device as including a processing device such as a CPU which executes programs stored on memory, such as RAM or ROM. Each of the devices is described at a high level of generality in terms of its respective function of storing a computer program and executing that computer program. The non-transient computer-readable storage medium and processor are each given their broadest reasonable construction as encompassing generic computing elements.
The above elements only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, each of the processor and non-transient computer-readable storage medium are recited at a high level of generality as used to execute their corresponding functions. For example, the processor is only recited configured to execute the computer program “to implement” the method according to claim 1. The non-transient computer-readable storage medium is likewise only broadly recited as “stored thereon with” a computer program. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 17 recites the additional elements of a) outputting the disease risk value of the target user and the reliability score of the disease risk value and presenting the disease risk value of the target user and the reliability score of the disease risk value to the user, b) the processor recited as configured to perform the subsequent outputting and presenting functions, and c) a terminal device recited as receiving the outputted disease risk value and reliability score.
With respect to elements (b) and (c), paragraphs 65, 68, and 71 describe a processing device such as a CPU which executes programs stored on memory, such as RAM or ROM. The processor is accordingly given its broadest reasonable construction as encompassing a generic computing element. Paragraph 64 states that terminal devices “may be various electronic devices, including but not limited to desktop computers, portable computers, smart phones, and tablet computers.” The terminal device is accordingly given its broadest reasonable construction as encompassing a generic computing element.
The above elements only amount to mere instructions to implement the abstract idea using computing elements as tools. Specifically, each of the processor and terminal device are recited at a high level of generality as used to execute their corresponding functions. For example, the processor is only recited configured to perform the subsequent outputting and providing functions, while the terminal device is only recited at a high level of generality as receiving the output.
With respect to element (a), the functions of outputting the disease risk value of the target user and the reliability score of the disease risk value and presenting the disease risk value of the target user and the reliability score of the disease risk value to the user only amount to insignificant extra-solution activity in the form of insignificant data output following performance of the abstract idea. The above function is only recited at a high level of generality as providing an output of the values generated during performance of the abstract idea.
In addition to constituting insignificant extra-solution activity, these functions also amount to well-understood routine and conventional activity. For example, the outputting and presenting functions may be construed as a type of receiving or transmitting data over a network. As noted above, the outputting and presentation of the disease risk value of the target user and the reliability score of the disease risk value are only recited at a high level of generality following performance of the abstract idea.
These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claims 1, 5-7, 9, 11, 12, and 15-17 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112(a)
The previous rejection of claims 1, 5-7, 9, 11, 12, and 15-17 under 35 USC 112(a) is withdrawn based on the amendment filed 12/31/2025.
Claim Rejections - 35 USC § 112(b)
The previous rejection of claims 1, 5-7, 9, 11, 12, and 15-17 under 35 USC 112(b) is withdrawn based on the amendment filed 12/31/2025.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM G LULTSCHIK whose telephone number is (571)272-3780. The examiner can normally be reached 9am - 5pm.
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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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/Gregory Lultschik/Examiner, Art Unit 3682
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