Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is in reference to the communication filed on 8 SEPT 2025.
Amendments to claims 2-15 entered and considered, as is the addition of claim 19, 20.
Claims 1-20 are present and have been examined.
Claim Objections
Claims 19 objected to because of the following informalities: Claim 19 appears to depend upon itself. Examiner believes this to be a typo and the intention is for claim 19 to depend on claim 18. This assumption is reflected in the rejection of claim 19 under 35 USC 112b below. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 3, 19, (and by dependency, claims 4-8, 20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 3, 19 both recite wherein…”the disease predicting information of the person comprises disease prediction items that comprise…. b) second most important disease prediction item… c) third most important disease prediction item… that is information regarding a weight of a person…” i.e. the second and third most important prediction items are to be weight of the person. This renders the scope of the claim indefinite at the very least, as it is unclear if the intent of the claim is to consider the weight of the person as being of importance enough to have two separate factors, and/or if not, what is the intended important prediction item in place of which factor in which order of importance. Correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-20 the independent claim(s) 1, 18 recite(s) a method and a non-transitory computer readable medium, i.e. a process and product, both of which are a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-20 the independent claim(s) (claims 1, 18) is/are directed, in part, as shown in exemplary claim 1:
A method for predictive diagnosis of a disease in a person, the method comprising:
obtaining, by a machine learning process hosted by a
applying by the
storing the vector in a
obtaining the vector by a classifier model hosted
applying,
storing an outcome of the applying of the classifier model in a
These claim elements are considered to be abstract ideas because they are directed to a mental process, in that the claims ensconce concepts performed in the human mind including observation, evaluation, judgment, and opinion functions. Obtaining information, applying a process, storing the vector, applying the model, and storing an outcome are all evidence of the above mentioned functions. If a claim limitation, under its broadest reasonable interpretation, covers a concept performed in the human mind, then it/they falls/ fall into the “mental processes” category.
The claims are further directed to mathematical concepts – i.e. mathematical relationships, formulas, equations, and/or calculations. The elements pertaining to a machine learning process, and the application of the machine learning into a vector, and obtaining information through the classifier model are all examples of mathematical relationships as identified above. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, formulas, equations, and/or calculations, then it/they falls/ fall into the “mathematical processes” category.
Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements – a first and second ”processing circuit,” as well as a first, second and third “data structure” common to both claims, as well as a “non-transitory computer readable medium that stores instructions that once executed by a computer system cause a system to…” as in claim 18, to perform the claim steps. The processing circuit and the data structures are recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Examiner further notes that storage of data is generally found to be adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g). Examiner finds similarity with regard to the non-transitory computer readable medium in claim 18 – this is at best, using the computer as a tool to execute the abstract idea(s) identified above. Examiner finds evidence of improvement to the functioning of the computer or any other technology or technical field in the processing circuit, data structure, and/or non-transitory computer readable medium as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e).
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea with no practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: a first and second ”processing circuit,” as well as a first, second and third “data structure” common to both claims, as well as a “non-transitory computer readable medium that stores instructions that once executed by a computer system cause a system to…” as in claim 18. When considered individually, these claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0082] Processor 1079 is configured to execute and/or control an execution of at least some (of all) step of method 1000 and/or method 1100. Processor 1079 includes multiple processing circuits.
[0083] First memory unit 1078 stores instructions and/or information for the execution of method 1000 and/or method 1100. FIG. 10C illustrates the first memory unit as storing normalizing software 1083 for executing step 1030, access control software 104 for controlling access of users to any of the storage units, operating system 1085, training software 1086 for determining using training the statistical normalizing function, regression software 1087 for determining using regression the statistical normalizing function and a machine learning module (referred to as Artificial Intelligence/machine learning/deep learning) 1088 for applying any operation by either one of artificial intelligence, machine learning or deep learning.
[0085] Processor 1079 includes a plurality of processing units. Any reference to one unit or item should be applied mutatis mutandis to multiple units or items. For example—any reference to processor should be applied mutatis mutandis to multiple processors, any reference to communication system should be applied mutatis mutandis to multiple communication systems.
[0086] According to an embodiment, first memory unit 1078 includes one or more memory unit, each memory unit may include one or more memory banks.
[0087] According to an embodiment, first memory unit 1078 includes a volatile memory and/or a non-volatile memory. The first memory unit 1078 may be a random access memory (RAM) and/or a read only memory (ROM).
[0088] According to an embodiment, the non-volatile memory unit and/or any of the storage units of FIG. 10C is a mass storage device, which can provide non-volatile storage of computer code, computer readable instructions, data structures, program modules, and other data for the processor or any other unit of vehicle. For example and not meant to be limiting, a mass storage device can be a hard disk, a removable magnetic disk, a removable optical disk, magnetic cassettes or other magnetic storage devices, flash memory cards, CD-ROM, digital versatile disks (DVD) or other optical storage, random access memories (RAM), read only memories (ROM), electrically erasable programmable read-only memory (EEPROM), and the like.
[0089] Any content may be stored in any part or any type of the memory unit.
[0090] According to an embodiment, the at least one memory unit stores at least one database—such as any database known in the art—such as DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL, PostgreSQL, and the like.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility. The claims are directed to an abstract idea without significantly more.
As per dependent claims 2-17, 19,20:
Dependent claims 2-17, 19, 20 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as the types of information considered in the mental processes(es) outlined above, including prior treatments or coexisting conditions/predictions made, specific functions or ratios of these treatments/conditions as well as additional description of the mathematical functions identified with respect to claims 1, 18. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Steinberg-Koch et al (WO2020245727, hereinafter Steinberg Koch).
In reference to claim 1, 18:
Steinberg-Koch teaches: A method for predictive diagnosis of a disease in a person, the method comprising (at least abstract) (claim 1); and
A non-transitory computer readable medium that stores instructions for predictive diagnosis of a disease in a person, the non-transitory computer readable medium stores instructions that once executed by computerized system cause the computer system to (at least [page 32] “ The term
computer-readable medium does not encompass transitory electrical and electromagnetic signals
propagating through a medium, and may therefore be considered tangible and non-transitory. Non-
limiting examples of a non-transitory tangible computer readable medium include nonvolatile memory, volatile memory, magnetic storage, and optical storage.”) (claim 18):
obtaining, by a machine learning process hosted by a first processing circuit, a health related data of the person that is stored in a first data structure (at least [page 14/14] “aggregating health related individual data sets of the subject, into a personal data store associated with the subject…” see also fig 7 and related text patient feature vectors 706);
applying by the first processing circuit, the machine learning process, on the health related data to convert the health related data into a vector that provides a compact representation of the health related data, the vector comprises disease predicting information of the health related data (at least [page 14] “applying to the aggregated health related data of the subject, a machine learning method
for converting parameters of the health related data, some of which may be indicative of a diagnosis of an autoimmune disease, into a vector that provides a compact representation of the health related data that reflects a medical condition of the subject….applying a classifier model to the vector to calculate the probability of the subject having an autoimmune disease or developing an autoimmune disease,”);
storing the vector in a second data structure (at least [fig 7 and related text] patient diagnosis vectors 707 );
obtaining the vector by a classifier model hosted by a second processing circuit (at least [fig 7 and related text] model parameters 715/716);
applying, by the second processing circuit, the classifier model to the vector to identify whether there is a likelihood of the person having or developing the disease (at least [page 14] “applying a classifier model to the vector to calculate the probability of the subject having an autoimmune disease or developing an autoimmune disease, wherein: (iv) if the probability exceeds a predefined threshold, inputting the vector and the health related data of the subject into an interventional recommendation model for outputting initial recommendations for an intervention or a treatment option selected from a group of potential interventions or treatments…”); and
storing an outcome of the applying of the classifier model in a third data structure; wherein the storing of the outcome makes available the outcome to one or more authorized users (at least [page 12] “Furthermore, the health related data of the subject may be tagged and added to the database comprising records of health related data of the large population. In that case, feedback from the health practitioner may be appended into the expert medical logic to improve accuracy of the predictive diagnostic method.” See also [fig 4 and related text] for discussion of intervention modeling) see [fig 7 and related text] for storage database of additional structures; “The memory 701 may comprise data relating to patient feature vectors 706 (Fig. 1, steps 106a, 106b ), patient diagnosis probability vectors 707 (Fig. 1, step 109), and expert medical logic 708 (Fig. 1, step 102).”)).
In reference to claim 16:
Steinberg-Koch further teaches: wherein the machine learning process was trained using multiple datasets that are associated with different diseases (at least [pages 11, 12] “Additionally, the classifier model may be trained to predict a diagnosis of either a
specific autoimmune disease or any autoimmune disease. The multi-class classifier model may also be developed using supervised learning, in which case, the supervised learning uses a form of artificial intelligence. In such methods, the machine learning method may be developed using self-supervised representation learning. In such a case, the self-supervised representation learning may use a form of artificial intelligence.” At [page 15] “In any of the last described methods for providing recommendations, the classifier model may be trained to predict a diagnosis of either a specific autoimmune disease or any
autoimmune disease. The database may comprise historical data on a subpopulation of subjects having a diagnosis of an autoimmune disease, or it may comprise records of health related data of a large population is used to generate the machine learning method.” See also claims 35, 38 in the publication.)
In reference to claim 17:
Steinberg-Koch further teaches: wherein the machine learning process was trained using different datasets that comprise disease predicting information associated with different diseases (at least [pages 11, 12] “Additionally, the classifier model may be trained to predict a diagnosis of either a
specific autoimmune disease or any autoimmune disease. The multi-class classifier model may also be developed using supervised learning, in which case, the supervised learning uses a form of artificial intelligence. In such methods, the machine learning method may be developed using self-supervised representation learning. In such a case, the self-supervised representation learning may use a form of artificial intelligence.” At [page 15] “In any of the last described methods for providing recommendations, the classifier model may be trained to predict a diagnosis of either a specific autoimmune disease or any
autoimmune disease. The database may comprise historical data on a subpopulation of subjects having a diagnosis of an autoimmune disease, or it may comprise records of health related data of a large population is used to generate the machine learning method.” See also claims 35, 38 in the publication.)
Response to Arguments
Applicant’s remarks as filed on 8 SEPT 2025 have been fully considered.
Applicant’s amendments have been found persuasive regarding claims 2, 12 and as such the objections/rejections are withdrawn.
Applicant begins a discussion of the rejection under 25 USC 101 on page 8 of the remarks with a discussion of exemplary claim 1. Examiner respectfully notes that the purported improvements identified by Applicant do not appear in the claims themselves, instead independent claim 1 remains as originally filed. Any improvements as in the currently claimed language does not recite an improvement to a technical field or technology. Applicant appears to argue various other improvements into page 9, however Examiner finds that these are either not reflected in the claims or are instead improvements to a non-technical aspect of the invention (for the purposes of subject matter eligibility). Training a model is not itself indicative of eligible subject matter.
Applicant makes reference to an ASIC on page 9 – Examiner notes again that this does not appear in the claim language, and further, these additional elements as noted are at best analogous to adding “apply it” to the computing environment to execute the abstract idea(s) identified. Examiner respectfully disagrees with Applicant’s conclusion regarding an improvement on page 10 at least for the reasons above, and as Examiner finds that the claim language does not recite any improvement nor any other evidence of a practical application/significantly more.
As per Applicant’s remarks regarding Example 41, Examiner does not dispute that well understood, routine and conventional subject can integrate a practical application of an abstract idea. However, Examiner does not find an analogy between example 41 and the present claims. As such the rejection remains.
Applicant turns to a discussion of the prior art on page 11, with a summary of exemplary claim 1. Examiner has added citations to the reference above to highlight the different storage elements as claimed. Examiner finds fig 7 discloses various data structures storing different elements.
Examiner finds that Applicant’s remarks regarding the storing of outcome makes available the outcome to one or more authorized users to be unpersuasive, the reference discloses that the database is available to other diagnostic practitioners, as well as validated by a health practitioner, i.e. authorized users. Applicant’s specification does not limit an authorized user by role.
Applicant’s amendments to claims 2-17, 19, 20 have overcome the prior art of record.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20230411018, to Lee, discloses a means of modeling predictions regarding diseases including variations of arthritis.
US 20230408493, to Hoffman, discloses modeling disease risk of an autoimmune disease.
Conclusion
THIS ACTION IS MADE FINAL. 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 KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at 571-270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687