DETAILED ACTION
Status
This communication is in response to the application filed on 5 September 2023. Claims 1-26 are pending and presented for examination.
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 .
Priority
This Application is a 371 National Stage Entry of PCT/JP2022/007706, filed on 24 February 2022, which claims benefit to JP2021-039643, filed on 11 March 2021. The claim to benefit is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 5 September 2023 and 13 September 2024 were filed after the mailing date of the application on 5 September 2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Note
The Examiner notes that “modal information” at the claims is indicated by Applicant's specification as being almost any medical information, such as images (scans, photographs, pictures, etc.) or measurements (see, e.g., Applicant ¶¶ 0017-0019, 0042, 0046-0047, as submitted, 0077-0079, 0105, 0109-0110 as published), where a/the device may be a variety of devices (at least as listed at Applicant ¶ 0022 as submitted, 0083 as published), and the type(s) of modal information may include at least “an electronic medical record, a diagnosis result, or a diagnosis estimation result regarding a patient” (as indicated at least at Applicant ¶ 0025 as submitted, 0085 as published).
Since “efficacy of … a health condition” (as claimed at the independent claims) does not appear to make sense (see the definitions at the pertinent prior art not relied on below) based on the meaning of “efficacy” (i.e., the “efficacy” of a health condition such as obesity would apparently be the health condition itself – the person is obese).The Examiner notes, however, that Applicant ¶ 0105 as published indicates that “the diagnosis assist system 1 is … to estimate, by machine learning, a diagnosis result of a disease such as ‘cancer’ (such as the health condition of a patient or a subject, a medicine effective for treatment or effectiveness of the medicine (also referred to as efficacy)) that is caused by a very wide variety of factors”. Therefore, “efficacy of a medicine or health condition for the patient or the subject” is interpreted as any diagnosis, health condition, medicine, medication, treatment, etc. for a patient or subject.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a setting unit” at least at claims 1, 7, and 18, “an output unit” at least at claims 1 and 18, “an information acquiring unit” at least at claims 5, 6, and 7, “a generation unit” at least at claims 8, 9, 10, 11, 12, 17, and 18, “a calculation unit” at least at claims 8 and 18, “an influence degree presenting unit” at least at claims 13, 14, 15, 16, and 17, and “a format conversion unit” at least at claims 19 and 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claim 18 is objected to because of the following informalities: claim 18 recites “included in the learning data on an estimation result”, which should apparently be “included in the learning data based on an estimation result”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), fourth paragraph:
Subject to the [fifth paragraph of 35 U.S.C. 112 (pre-AIA )], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 1-23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1-23 recite (either directly, or through parent claims) various “unit[s]” as indicated above at Claim Interpretation (i.e., “a setting unit”, “an output unit”, “an information acquiring unit”, “a generation unit”, “a calculation unit”, “an influence degree presenting unit”, and “a format conversion unit”); however, the Examiner has searched for an indication of what a “unit” would encompass or comprise of, and if/when there is an indication of what this may mean (such as a computer), what algorithm may transform a “unit” from a general purpose to a special purpose computer or machine. The Examiner does not find an indication of what a “unit” would be, and finds no indication of an algorithm that would indicate how such a “unit” would perform the functions indicated by the claims – i.e., an algorithm.
Therefore, claims 1-23 are lacking written description support for what “unit” performs the functions indicated.
Although various “unit[s]” are indicated at many claims, and particularly at independent claims 1, 8, and 18, dependent claims 2-7, 9-17, and 19-23 do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 2-7, 9-17, and 19-23 are also lacking written support for the reasons indicated for the independent claims.
The Examiner suggests amending the claims to recite a hardware computer as performing the functions indicated at the claims.
Claims 1-26 are 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 pre-AIA the applicant regards as the invention.
Claims 1-23 recite (either directly, or through parent claims) various “unit[s]” as indicated above at Claim Interpretation (i.e., “a setting unit”, “an output unit”, “an information acquiring unit”, “a generation unit”, “a calculation unit”, “an influence degree presenting unit”, and “a format conversion unit”); however, the Examiner has searched for an indication of what a “unit” would encompass or comprise of – i.e., what the scope or bounds of a “unit” may be. The Examiner does not find an indication of what a “unit” would be – the “unit[s]” may be hardware such as a device and/or computer, or software (i.e., merely data) such as would execute on a computer.
Therefore, claims 1-23 are indefinite regarding what scope, meets and bounds, or limit would or should be applied to the term “unit”.
Although various “unit[s]” are indicated at many claims, and particularly at independent claims 1, 8, and 18, dependent claims 2-7, 9-17, and 19-23 do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 2-7, 9-17, and 19-23 are also indefinite for the reasons indicated for the independent claims.
The Examiner suggests amending the claims to recite a hardware computer as performing the functions indicated at the claims.
Claim 1 recites a setting unit that sets input data from the learning data, an output unit that outputs an estimation result, and indicates that the input data is set based on a degree of influence of each piece of modal information based on an/the estimation result at a time of learning the machine learning model. This appears to be impossible. In order to set the input data according to a degree of influence, the degree of influence must be known; however, the degree of influence can apparently (per the claim) only be known by running the machine learning model to see what influence each piece of modal information has on the estimation result. This is to say, the input data is required to train (i.e., learn) the model but the model must be established beforehand in order to determine the input data according to its respective degree of influence on the result.
Even if or when this is considered as part of the retraining or updating of a machine learning model, the model and respective degree of influence for each of the data pieces used to retrain or update the model changes with every iteration. For instance, if or when a previously used piece of data is determined to not have an adequate or sufficient degree of influence such that that particular piece of data is left out of the model, the model itself changes and the degree of influence for each of the other pieces of data also changes.
Claims 2-7 and 19-23 depend from claim 1, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 2-7 and 19-23 are also indefinite.
See below for an indication of how the claims are being interpreted.
Similar to claim 1 immediately above, independent claim 8 recites a generation unit that generates a machine learning model that outputs an estimation result, and a calculation unit that calculates a degree of influence each piece of input information has on the estimation result at the time of generating the machine learning model, where the generation unit changes the number of types of information based on the degree of influence. This also appears impossible – in order to calculate the degree of influence, the model must be run, but the model cannot be run since the input data is determined based on the result of the model.
As above, even when considered as retraining or updating a model, the model and degrees of influence for each piece of data used changes with each iteration.
Claims 9-17 depend from claim 8, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 9-17 are also indefinite.
See below for an indication of how the claims are being interpreted.
Similar to claims 1 and 8 immediately above, claim 18 recites the same generation unit, calculation unit, setting unit, and output unit activities as indicated at claims 1 and 8. Therefore, claim 18 suffers the same issues as indicated for claims 1 and 8 above, and is therefore also indefinite for the same reasons as indicated above.
See below for an indication of how the claims are being interpreted.
Claims 24, 25, and 26 are method claims that recite the same respective activities as indicated at claims 1, 8, and 18 above, and therefore suffer the same issues as indicated for claims 1, 8, and 18 above. As such, claims 24-26 are also indefinite for the same reasons as indicated above.
Claims 1-26 are being interpreted, as much as possible, retraining a machine learning model, even though the claims recite excluding or eliminating some of the data so as to arrive at an entirely new model.
Claim 4 recites “wherein the input data comprises modal information of a type different from the types of the modal information included in the learning data” – i.e., that the model is trained on one type of data, but the input to that model is of a different type. For example, the model may be trained on image data such as “a pathological image”, or on “gene expression information” (at claim 22), but somehow the user or inventor expects to be able to input “lifestyle information” such as “hours of sleep”, “heart rate”, or “number of steps” and still expect the image or gene expression model to actually be able to output any form or type of reasonable indication of the efficacy of medicine or a health condition. The entire basis of training a model is that the model reflects relevance to the data that will be used by the model. It would appear impossible, or virtually impossible, to be able to estimate efficacy when the data being used as input is not of the same type as the data the model has been trained to analyze or assess. Therefore, claim 4 appears indefinite as to whether it is even possible for the invention claimed at claim 4 to function or work.
Claim 9 recites “the generation unit sets, as the learning data, a predetermined number of pieces of modal information set in advance in descending order of values of the degrees of influence of the respective pieces of modal information”. It would be impossible to set “values of the degrees of influence” in advance since the degrees necessarily require the running of the machine learning model. Therefore, it is indefinite regarding what is meant by setting a descending order in advance of the model, when the model is required to set the order.
Claims 2 and 21 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 2 depends from claim 1 and recites “wherein the input data comprises at least one piece of modal information of a same type as a type of modal information included in the learning data”; however, parent claim 1 recites that the “setting unit … sets input data to be used as input to the machine learning model from the learning data”. If the input data is “from the learning data”, it must – by definition – “comprise[ ] at least one piece of modal information of a same type as a type of modal information included in the learning data”. Therefore, claim 2 fails to further limit the subject matter of the claim upon which it depends.
Parent independent claim 1 recites that the output unit outputs an “estimation result of the efficacy of the medicine or the health condition for the patient or the subject” and dependent claim 3 recites that this “includes at least one of a health condition of the patient or the subject or efficacy of a medicine prescribed for the patient or the subject”. The phrasing of the parent claim would appear to apply the “for the patient or the subject” phrase to both of “the medicine or the health condition” phrase. Therefore, the only change is the term “prescribed” in relation to medicine, but even generic or over-the-counter medicines (e.g., vitamins, lots of rest, etc.) can be and are included as “prescribed”. Therefore, there does not appear to be any further limitation to be found at claim 21, and claim 21 then is improper for failing to further limit the subject matter of the claim upon which it depends.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to systems (claims 1-23) and methods (claims 24-26), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites an information processing system including: a machine learning model learned using learning data including two or more types of modal information acquired from a patient or a subject; a setting unit that sets input data to be used as input to the machine learning model from the learning data; and an output unit that inputs the input data to the machine learning model and outputs an estimation result of efficacy of a medicine or a health condition for the patient or the subject, wherein the setting unit sets the input data on a basis of a degree of influence of each piece of the modal information included in the learning data on estimation of a diagnosis result at a time of learning of the machine learning model.
Independent claim 8 is analyzed similarly since it is directed to an information processing system including: a generation unit that generates a machine learning model that outputs an estimation result of efficacy of a medicine or a health condition for a patient or a subject by using learning data including two or more types of modal information acquired from the patient or the subject; and a calculation unit that calculates a degree of influence of each piece of the modal information included in the learning data on the estimation result of the efficacy of the medicine or the health condition at the time of generating the machine learning model, wherein the generation unit changes the number of types of modal information included in the learning data to be used for generation of the machine learning model on a basis of the degree of influence.
And independent claim 18 is also analyzed similarly since it is directed to an information processing system including: a generation unit that generates a machine learning model that outputs an estimation result of efficacy of a medicine or a health condition for a patient or a subject by using learning data including two or more types of modal information acquired from the patient or the subject; a calculation unit that calculates a degree of influence of each piece of the modal information included in the learning data on an estimation result of the efficacy of the medicine or the health condition at the time of generation of the machine learning model; a setting unit that sets input data as input to the machine learning model from the learning data on a basis of the degree of influence; and an output unit that inputs the input data to the machine learning model and outputs an estimation result of efficacy of a medicine or a health condition for the patient or the subject, wherein the generation unit changes the number of types of modal information included in the learning data to be used for generation of the machine learning model on a basis of the degree of influence, and the setting unit sets the input data on a basis of a degree of influence of each piece of the modal information included in the learning data on estimation of a diagnosis result at the time of learning of the machine learning model.
Independent claims 24, 25, and 26 are analyzed in the same manner as claims 1, 8, and 18 above since directed to methods of performing the same or similar activities as at claims 1, 8, and 18.
The dependent claims (claims 2-7, 9-17, and 19-23) appear to be encompassed by the abstract idea of the independent claims since they merely indicate acquiring (including via an information acquiring unit) and using the same and different types of information for learning and input (claims 2 and 4-6), the learning data having more types than the input data (claim 3), selecting data based on degree of influence (claims 7, 9-11), a management table of enabled and disabled data as estimating the accuracy of the machine learning model when learned using the data (claim 12), presenting the degree of influence for each piece of information (including via an influence degree presenting unit) (claims 13-14), presenting regions (on/in an image) of derivation having influence (claim 15), assigning meta-information and presenting it as affecting the derivation (claim 16), relearning the machine learning model using data selected by degree of influence (claim 17), converting a format of modal information (by a format conversion unit) (claim 19) into image data (claim 20), the efficacy being of medicine or a health condition of the patient or subject (claim 21, as at parent claim 1), and/or what the modal information includes (e.g., an image, information, test result, lifestyle information, etc.) (claim 22) and/or what the lifestyle information encompasses (e.g., one hour of sleep, heart rate, step count, blood oxygen or glucose readings) (claim 23).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of modeling patient/subject information to estimate efficacy of a medicine or condition; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) based on using a machine learning model and outputting the estimation result as well as estimating the degree of influence of the information;
Certain methods of organizing human activity (e.g. … commercial or legal interactions such as … business relations; and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) based on that people can and have used trained models (including machine learning models that have been trained), set input to use, and receive an output (that would estimate efficacy of a medicine or health condition when the model is trained for that output), including setting input according to degree of influence (i.e., this is apparently what doctors and other such healthcare personnel have done for a long time); and
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) based on the observation, evaluation, judgment and/or opinion in estimating the degree of influence and applicability of medicine and conditions to estimate efficacy as claimed.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are a system including a setting unit an output unit, a generation unit, a calculation unit, an information acquiring unit, an influence degree presenting unit, and a format conversion unit. These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. The only description of a computer that may be used for implementing the claimed functions is Applicant ¶¶ 0157-0163 as submitted (¶¶ 0231-0237 as published) and Fig. 28, but this merely indicates or describes a generic or general-purpose computer.
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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 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.
Examiner’s Note
The Examiner notes that since the claims appear impossible as currently phrased, the application of art below is essentially an approximation of what the claims may possibly mean.
Claim Rejections - 35 USC § 102
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.
Claims 1-3, 5-6, 8, 18, and 24-26 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hayward et al. (U.S. Patent Application Publication No. 2021/0256615, hereinafter Hayward) .
Claim 1: Hayward discloses an information processing system including:
a machine learning model learned using learning data including two or more types of modal information acquired from a patient or a subject (see Hayward at least at, e.g., ¶¶ 0025, “training a machine-learning analytics model, algorithm, or module”, 0042, “the machine-learning analytics model (which may be additionally or alternatively included as part of the dynamic data set) may include health-related data received from fitness trackers, health-related software applications such as weight loss applications, activity loggers, physical sensors (e.g., heart rate monitors, blood pressure monitors, thermometers, weights scales, glucose monitors, baby monitors, pregnancy monitors, sleep monitors, etc.), social media, etc.”, 0098, “the examples shown in FIG. 3 associated with the dynamic data set and user data, such as the electronic medical records, demographic information, insurance records, lifestyle information, etc., are but some examples of the types of information that may be relevant to train and execute a machine-learning analytics model”; citation hereafter by number only);
a setting unit that sets input data to be used as input to the machine learning model from the learning data (0105, “the overall process of training the machine-learning analytics model may include defining the sample inputs, the importance (e.g., weighting) of these inputs, and defining one or more outputs that are determined using the weighted inputs”); and
an output unit that inputs the input data to the machine learning model and outputs an estimation result of efficacy of a medicine or a health condition for the patient or the subject (0056, “Once the machine-learning analytics model is trained in this way, the machine-learning analytics model may be applied to received user data to predict various medical-related conditions associated with a new or updated life or health insurance policy. Moreover, once such predictions are made, aspects include the machine-learning analytics engine 120.1 determining an initial level of risk associated with insuring the user based upon the one or more predicted medical-related conditions for a particular life or health insurance policy as part of an artificial intelligence (AI) driven underwriting process. The machine-learning analytics engine 120.1 may then identify one or more intervening actions that, when executed by the user within a future time period, reduce the initial level of risk associated with insuring the user to a second level of risk.”),
wherein the setting unit sets the input data on a basis of a degree of influence of each piece of the modal information included in the learning data on estimation of a diagnosis result at a time of learning of the machine learning model (0105, “the overall process of training the machine-learning analytics model may include defining the sample inputs, the importance (e.g., weighting) of these inputs, and defining one or more outputs that are determined using the weighted inputs”).
Claim 2: Hayward discloses the information processing system according to claim 1, wherein the input data comprises at least one piece of modal information of a same type as a type of modal information included in the learning data (0042, “the machine-learning analytics model (which may be additionally or alternatively included as part of the dynamic data set) may include health-related data received from fitness trackers, health-related software applications such as weight loss applications, activity loggers, physical sensors (e.g., heart rate monitors, blood pressure monitors, thermometers, weights scales, glucose monitors, baby monitors, pregnancy monitors, sleep monitors, etc.), social media, etc.”, 0098, “the examples shown in FIG. 3 associated with the dynamic data set and user data, such as the electronic medical records, demographic information, insurance records, lifestyle information, etc., are but some examples of the types of information that may be relevant to train and execute a machine-learning analytics model”).
Claim 3: Hayward discloses the information processing system according to claim 1, wherein the input data comprises a number of types of modal information, the number being smaller than a number of types of the modal information included in the learning data (0105, “the machine-learning process allows correlations to be made among different subsets of data within the dynamic data set”, where assessing subsets indicates a smaller number being used).
Claim 5: Hayward discloses the information processing system according to claim 1, further including: an information acquiring unit that acquires one or more pieces of modal information included in the input data (0026, “The computing device may include a communication unit configured to access a dynamic data set associated with one or more users including electronic medical records, demographic information, insurance records, and/or lifestyle information, and to receive user data associated with a user”).
Claim 6: Hayward discloses the information processing system according to claim 5, wherein the information acquiring unit acquires, as the input data, modal information acquired by a smaller number of types of information acquiring devices than two or more types of information acquiring devices used for acquisition of each piece of the modal information included in the learning data (0042, “the machine-learning analytics model (which may be additionally or alternatively included as part of the dynamic data set) may include health-related data received from fitness trackers, health-related software applications such as weight loss applications, activity loggers, physical sensors (e.g., heart rate monitors, blood pressure monitors, thermometers, weights scales, glucose monitors, baby monitors, pregnancy monitors, sleep monitors, etc.), social media, etc.”, 0098, “the examples shown in FIG. 3 associated with the dynamic data set and user data, such as the electronic medical records, demographic information, insurance records, lifestyle information, etc., are but some examples of the types of information that may be relevant to train and execute a machine-learning analytics model”).
Claims 8, 18, and 24-26 are rejected on the same basis as claim 1 above as indicating a generation unit and a calculation unit since the recitations reflect the generating and calculating indicated.
The Examiner notes that it does not appear reasonable to apply art to claims 4, 7, 9-17, and 19-23 based on the 112 rejections above. If or when the 112 rejection issues are resolved such that the claims can be understood as being possible to perform and the claim steps are not indefinite, the Examiner notes that prior art may be applied.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fang et al. (U.S. Patent Application Publication No. 2023/0245772, hereinafter Fang) discusses “ A machine learning system and method are disclosed that enable full automation of the process of analyzing retinal fundus images to predict Alzheimer's disease, thereby obviating the need for manual labeling of retinal features while also improving prediction accuracy. A machine learning system and method are disclosed that classify retinal features and predict, based on the classified retinal features, the onset or presence of Alzheimer's disease in a human subject. The system comprises a processor configured to perform one or more machine learning models and a memory device in communication with the processor. The machine learning model(s) is trained to process retinal fundus images acquired by an image acquisition system to classify retinal features contained in the images and to predict, based on the classified retinal features, whether the images are indicative of the presence or onset of Alzheimer's disease.” (Fang at Abstract).
Cambridge English Dictionary, Efficacy definition, downloaded 23 May 2025 from https://dictionary.cambridge.org/us/dictionary/english/efficacy, indicating “efficacy” is “the ability of something to produce the intended result” or “how well a particular treatment or drug works under carefully controlled scientific testing conditions” (at p. 1).
Merriam-Webster dictionary, Efficacy definition, downloaded 23 May 2025 from https://www.merriam-webster.com/dictionary/efficacy, indicating “efficacy” is “the power to produce an effect” (at p. 1).
National Cancer Institute, Efficacy definition, downloaded 23 May 2025 from https://www.cancer.gov/publications/dictionaries/cancer-terms/def/efficacy, indicates that “In medicine, [efficacy is] the ability of an intervention (for example, a drug or surgery) to produce the desired beneficial effect”.
Porzsolt et al., Efficacy and effectiveness trials have different goals, use different tools, and generate different messages. Pragmat Obs Res. 2015 Nov 4;6:47-54. doi: 10.2147/POR.S89946. Erratum in: Pragmat Obs Res. 2016 Jan 12;7:1. doi: 10.2147/POR.S100784. PMID: 27774032; PMCID: PMC5045025, downloaded 23 May 2025 from https://pubmed.ncbi.nlm.nih.gov/27774032/, indicating in part that “we conclude that scientists tell what they see, a continuum from clear explanatory to clear pragmatic trials. Clinicians tell what they want to see, a clear explanatory trial to describe the expected effects under ideal study conditions and a clear pragmatic trial to describe the observed effects under real-world conditions. Following this discussion, the solution was not too difficult. When we accept what we see, we will not get what we want. If we discuss a necessary change of management, we will end up with the conclusion that two types of studies are necessary to demonstrate efficacy and effectiveness. Efficacy can be demonstrated in an explanatory, ie, a randomized controlled trial (RCT) completed under ideal study conditions. Effectiveness can be demonstrated in an observational, ie, a pragmatic controlled trial (PCT) completed under real-world conditions. It is impossible to design a trial which can detect efficacy and effectiveness simultaneously. The RCTs describe what we may expect in health care, while the PCTs describe what we really observe” (at Abstract).
Nie et al. (U.S. Patent Application Publication No. 2024/0079138, hereinafter Nie) indicates “Systems and methods relate to predicting disease progression by processing digital pathology images using neural networks. A digital pathology image that depicts a specimen stained with one or more stains is accessed. The specimen may have been collected from a subject. A set of patches are defined for the digital pathology image. Each patch of the set of patches depicts a portion of the digital pathology image. For each patch of the set of patches and using an attention-score neural network, an attention score is generated. The attention-score neural network may have been trained using a loss function that penalized attention-score variability across patches in training digital pathology images labeled to indicate no or low subsequent disease progression. Using a result-prediction neural network and the attention scores, a result is generated that represents a prediction of whether or an extent to which a disease of the subject will progress.” (At Abstract).
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685