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
Status of the Application
Claims 1-4 and 6-9 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendments to the Claims and Remarks filed on 03/22/2026.
Claims 1 and 6 are currently amended.
Claims 5 and 10 remain canceled and not considered at this time.
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 1-4 and 6-9 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-4 fall within the statutory category of a process. Claims 6-9 fall within the statutory category of an apparatus or system.
Step 2A, Prong One
As per Claims 1 and 6, the limitations of setting a plurality of disease types according to a target disease, wherein the target disease is dementia; setting a time interval, wherein the user does not suffer from the target disease, wherein the time interval is between a third time point and a fourth time point, the third time point is Z years ago before a time point, wherein the time point is when the medical history data is obtained, the fourth time point is X years ago before the third time point, and Z and X are positive numbers; performing a pre-processing operation on the at least one second disease according to the disease types to obtain processed data; determining whether the subject will be diagnosed with the target disease; wherein the step of performing the pre-processing operation on the at least one second disease according to the disease types to obtain the processed data comprises: encoding the at least one second disease in a disease sequence as one-dimensional or two-dimensional encoded data according to the disease types and using the encoded data as processed data, wherein a length of the encoded data is set by a sentence embedding method to generate a vector as a format of the encoded data, wherein the encoded data retains a sequence relationship between the at least one second disease; weighting each of the at least one second disease; and respectively converting the weighted at least one second disease into at least one piece of word frequency information and treating the word frequency information as the processed data, wherein the second disease is weighted based on one of below: whether the at least one second disease has been diagnosed, wherein in response to the at least one second disease is diagnosed, a weight of the at least one second disease is set to 1, the weight of the at least one second disease is set to a number of visits for the at least one second disease, other medical history information including individual disease dosages, surgery information, symbolic chronic disease and other treatments, the weight of the at least one second disease is set to a number of disease dosages, a disease importance sorted through a machine learning method, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The steps of setting disease types according to a target disease, setting a time interval where the user does not suffer from the target disease, performing a pre-processing operation on the second disease according to the disease types to obtain processed data which comprises encoding the second disease in a disease sequence as a one or two-dimensional encoded data and using the encoded data as processed data, weighting each second disease and converting the weighted second disease into a piece of word frequency information and treating the word frequency information as the processed data, are concepts performed including observation, evaluation, judgement and opinion in the human mind. The claim describes the encoded data to be in the format of a vector which retains a sequence relationship between the at least one second disease. The encoding in a disease sequence as a vector is not described in a manner beyond that which can be performed using human mental observation, evaluation, judgment, or opinion, because a human can mentally create vectors of data in a particular sequence. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The pre-processing operation which comprises weighting a disease and converting the weighted disease into word frequency information by using a TF-IDF algorithm and treating the word frequency information as the processed data encompasses mathematical concepts such as weighting and converting. The converting is performed using a specific mathematical calculation (TF-IDF algorithm)and therefore encompasses mathematical concepts. As per the October 2019 Update on Subject Matter Eligibility, a claim can recite more than one judicial exception and claims which recite a series of steps that recite mental steps which are also mathematical calculations are identified as both. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims (claim 6) recite the additional element – an electronic device comprising an input circuit and a flash memory. The electronic device in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite inputting the processed data to a neural network for the intended use of training the neural network. The step of inputting data into a neural network invokes the computer as a tool to perform in its ordinary capacity for tasks such as transmitting data which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f). The claims also recite the use of the trained neural network to carry out the concepts of the abstract idea which provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). The step of determining whether the subject will be diagnosed with the target disease wherein a prediction effect of the trained neural network is enhanced by training with the processed data is performed “by the trained neural network”. The trained neural network is used to generally apply the abstract idea without placing any limits on how the trained neural network functions. Rather these limitations only recite the outcome of the abstract idea and do not include any details of how the determining is accomplished. The recitation of “by the trained neural network” also merely indicates a field of use or technological environment in which the abstract idea is performed. This type of limitations merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). The claims also recites the additional elements of obtaining medical history data comprising at least one first disease suffered by a user, obtaining a second disease in the time interval from the medical history data, and receiving the medical history data of a subject which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the steps of obtaining medical history data and a second disease from the medical history data and receiving medical history data are mere data gathering in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea. Including that the second disease is weighted based on one of the 5 different types of weighting is descriptive of the analysis but does not provide a specific step and is therefore does not provide any functional element to the claim.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of an electronic device comprising an input circuit and flash memory to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system including the "electronic device” are recited at a high level of generality and are recited as generic computer components by reciting an input interface or circuit ([0010]) and a storage circuit which is embodied as a flash memory ([0012]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The steps of inputting the processed data to a neural network for the intended use of training the neural network and use of the trained neural network to carry out the concepts of the abstract idea are also found to be mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of obtaining medical history data comprising at least one first disease suffered by a user, obtaining a second disease in the time interval from the medical history data, and receiving the medical history data which are elements that are well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data as well as storing and retrieving information from memory, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added) and (iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims 2-4 and 7-9 add further limitations which are also directed to an abstract idea. For example, Claims 2 and 7 further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 6.
Claims 3 and 8 include obtaining a disease sequence formed by the second disease from the first disease according to an earliest occurrence time of each of the first disease, wherein the at least one second disease in the disease sequence is sorted according to the earliest occurrence time, a number of the second disease is less than or equal to a predetermined number, and each second disease only occurs once which merely further specifies details of the obtaining data step of the independent claims and thus is directed to mere data gathering which is insignificant extra-solution activity which is found to be well-understood, routine, and convention for that same reasons as the independent claims.
Claims 4 and 9 include deleting the third disease in the medical history data to obtain a disease sequence formed by the at least one second disease, wherein an occurrence time of the third disease is earlier than an occurrence time of the second disease, the second disease in the disease sequence is sorted according to an earliest occurrence time, and a number of the at least one second disease is less than or equal to a predetermined number which amounts to a mental process. The sorting of diseases in the sequence can be performed by using human mental observation, evaluation, judgment, and opinion and is therefore directed to an abstract idea.
Response to Arguments
Applicant’s arguments, see Pages 8-10, “Discussion of Claim Rejections under 35 U.S.C. 101”, filed 03/22/2026 with respect to claims 1-4, and 6-9 have been fully considered but they are not persuasive.
Applicant argues that the claims provide a specific improvement in how a machine learning model is trained – including specific data processing and parameter adjustments, similar to that in Desjardins. Examiner respectfully disagrees. In Desjardins, the claims were directed to a method of training a machine learning model on a series of tasks which provided benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements which are disclosed in the application specification. The specification identified the improvements of how the machine learning model itself operates which protects knowledge about previous tasks to overcome the problem of “catastrophic forgetting” in continual learning systems. This specific improvement in the training of a machine learning model was found to integrate the abstract idea into a practical application. The present invention is not analogous to the technical improvement to the technical problem of Desjardins. Providing specific data processing and parameter adjustments to train a machine learning model does not provide similar technical improvements over prior systems and does not protect knowledge about previous tasks to overcome the problem of “catastrophic forgetting” in continual learning systems in the same manner as Desjardins. Applicant has not provided a technical improvement to the technical problem over prior systems which is overcome by the claims and has not provided any specific description of this from the instant specification.
Applicant argues that the claims recite a specific technical process of converting raw medical history into a specific computer-readable format which creates a specific data structure that enables the neural network to process sequential medical events effectively. Examiner is not persuaded that this is a technical improvement to a technical problem. The claims recite pre-processing data which includes encoding data to generate a vector which retains a sequence of the data. However, this is directed to the abstract idea and therefore is not an additional element which is analyzed in Step 2A, prong two or Step 2B. Any improvement to the process of encoding data to generate a vector is an improvement to the abstract idea itself which does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself.
Applicant argues that the amended claims are not directed to mental processes or mathematical concepts because the concept of a length of the encoded data is set by a sentence embedding method is not a mental process or mathematical concept and also converting weighted disease into at least one piece of word frequency information by using TF-IDF algorithm is also not a mental process or mathematical concept. Examiner respectfully disagrees. Looking first at the concept of setting a length of the encoded data by a sentence embedding method, the concept can be performed using human mental observation, evaluation, judgment, and/or opinion. The BRI of sentence embedding is a representation of a sentence such as a vector of numbers which encodes meaningful semantic information. Sentence embedding methods convert sentences into fixed-length numerical vectors that capture the semantic meaning. Methods could include similarity detection or clustering which can be performed using human mental processing. The claim does not specify a particular sentence embedding method which would limit to a process that is beyond human mental processing. Therefore, the claims are directed to a mental process. With regard to converting weighted disease into a piece of word frequency information by using TF-IDF algorithm, this is directed to a mathematical concept because Term Frequency-Inverse Document Frequency is a known statistical analysis which is a mathematical analysis or calculation. Applicant additionally argues that the claim recites the use of physical “flash memory” to execute the steps of the claim. The flash memory is recited at a high-level of generality such that it merely applies the abstract idea by use of general computing components. This amounts to mere instructions to apply the exception which does not integrate the abstract idea into a practical application. Therefore, the claims are directed to an abstract idea.
Applicant argues that the claims integrate the abstract idea into a practical application of training data processing for dementia processing because the claims recite “wherein the target disease is dementia”. Examiner respectfully disagrees. The claims recite that the disease type is set as dementia. This only limits the claims to determining whether the subject will be diagnosed with the target disease of dementia, which merely gives parameters to the data analysis including setting the plurality of disease types. This does not provide a technical improvement to a technical problem. This is more similar to merely indicating a field of use in which to apply the judicial exception, as per MPEP 2106.05(h). Similar to example vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment (Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)), the present claims are limiting the abstract idea to the determination of whether the subject will be diagnosed with dementia instead of any other disease. Therefore, this does not integrate into a practical application.
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 Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/EVANGELINE BARR/Primary Examiner, Art Unit 3682