Detailed Notice
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/28/2025 has been entered.
Status of Claims
Claims 1, 4-6, and 8-9 are currently pending.
Claims 2-3 and 7 are canceled.
Claims 1, 8, and 9 are amended.
Claims 1, 4-6, and 8-9 are rejected.
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, 4-6, and 8-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1 and 4-6 are directed toward a processing apparatus (i.e., machine), claim 8 is directed toward a processing method (i.e. a process), and claim 9 is directed toward a non-transitory computer readable medium (i.e., manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 8, and 9 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of mental process but for the recitation of generic computer components.
Claim 1 recites: “An information processing apparatus comprising: one or more storage devices configured to store a plurality of models including at least a first model that uses first data as input to carry out a first prediction task which is a mortality prediction task and a second model that uses second data as input to carry out a second prediction task which is a long-term hospitalization prediction task or a complication prediction task; and at least one processor, communicatively coupled to the one or more storage devices, wherein the processor is configured to: acquire information data representing information comprising a plurality of items related to a specific target; vectorize each item included in the information data; input the vectorized items to the first model to acquire an output result related to the mortality prediction task; select a selection model to be used for prediction as any one of the first model or the second model from among the plurality of models based on the output result; process the vectorized items with the selection model; derive a degree of importance to the specific target for each item included in the information data by using the selection model; and control a display unit to display items for which the degree of importance exceeds a predetermined value, in an order based on the degree of importance, wherein the mortality prediction task is for deriving a probability of a first state, wherein the long-term hospitalization prediction task or the complication prediction task is related to a second state different from the first state, wherein the mortality prediction task is more important to the specific target than the long-term hospitalization prediction task and the complication prediction task, wherein the first model is selected as the selection model in a case in which the probability of the first state exceeds a threshold value, wherein the second model is selected as the selection model in a case in which the probability of the first state is equal to or lower than the threshold value”.
The limitations of uses first data as input to carry out a first prediction task which is a mortality prediction task and… uses second data as input to carry out a second prediction task which is a long-term hospitalization prediction task or a complication prediction task; acquire information data representing information comprising a plurality of items related to a specific target; vectorize each item included in the information data; input the vectorized items.., to acquire an output result related to the mortality prediction task; process the vectorized items; derive a degree of importance to the specific target for each item included in the information data… display items for which the degree of importance exceeds a predetermined value, in an order based on the degree of importance, wherein the mortality prediction task is for deriving a probability of a first state, wherein the long-term hospitalization prediction task or the complication prediction task is related to a second state different from the first state, wherein the mortality prediction task is more important to the specific target than the long-term hospitalization prediction task and the complication prediction task, in a case in which the probability of the first state exceeds a threshold value, in a case in which the probability of the first state is equal to or lower than the threshold value, given the broadest reasonable interpretation, cover the abstract idea of a mental process that a person/doctor/nurse/etc. can perform mentally or via pen and paper, but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claims 8 and 9 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of mental process that a person/doctor/nurse/etc. can perform mentally or via pen and paper, but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2).
Step 2A—Prong 2:
Independent claims 1, 8, and 9 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea), do meet the criteria in MPEP 2106.04(d) (improvement in the function of a computer, or improvement to other technology, particular treatment or prophylaxis, particular machine, effecting a transformation or reduction, etc.), thus amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “storage device”, “models”, “processor”, “selection model”, “display unit”, “first model”, “second model”, and “non-transitory computer readable medium”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 7, FIG. 9, FIG. 10, FIG. 11, and [0027], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “one or more storage devices configured to store a plurality of models including at least a first model that”, “a second model that”, “and at least one processor, communicatively coupled to the one or more storage devices, wherein the processor is configured to”, “to the first model”, “select a selection model to be used for prediction as any one of the first model or the second model from among the plurality of models based on the output result”, “with the selection model”, “by using the selection model; and control a display unit to”, “wherein the first model is selected as the selection model”, “wherein the second model is selected as the selection model”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “acquire information data representing information comprising a plurality of items related to a specific target”, “acquire an output result related to the mortality prediction task”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Step 2B:
The claims do not include additional elements (i.e., “storage device”, “models”, “processor”, “selection model”, “display unit”, “first model”, “second model”, and “non-transitory computer readable medium”) that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “acquire information data representing information comprising a plurality of items related to a specific target”, “acquire an output result related to the mortality prediction task”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
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)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Dependent Claims Analysis:
Dependent claims 4-6 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 8, and 9. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Additionally, dependent claims 4-6 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 8, and 9, and hence also do not integrate the aforementioned abstract idea into a practical application.
Dependent claims 4-6 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 8, and 9, and hence do not amount to “significantly more” than the abstract idea.
Therefore, whether taken individually or as an ordered combination, claims 1, 4-6, and 8-9 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-6, and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Bruin et al. (US 20140279746 A1), hereinafter Bruin, in view of Neuman et al. (US 20210098099 A1), hereinafter Neuman, Sirin et al. (US 20190384863 A1), hereinafter Sirin, and Neeland et al. (“The obesity paradox, extreme obesity, and long-term outcomes in older adults with ST-segment elevation myocardial infarction: results from the NCDR”), hereinafter Neeland
Regarding claim 1, 8, and 9 Bruin teaches an information processing apparatus comprising: one or more storage devices configured to store a plurality of models including at least a first model that uses first data as input to carry out a first prediction task… a second model that uses second data as input to carry out a second prediction task (Bruin, [0019]); and at least one processor, communicatively coupled to the one or more storage devices, wherein the processor is configured to/An information processing method executed by a processor of an information processing apparatus including a plurality of models including at least a first model that uses first data as input to carry out a first prediction task and a second model that uses second data as input to carry out a second prediction task different from the first prediction task, and at least one processor, the information processing method comprising/A non-transitory computer readable medium storing an information processing program causing a processor of an information processing apparatus including a plurality of models including at least a first model that uses first data as input to carry out a first prediction task and a second model that uses second data as input to carry out a second prediction task different from the first prediction task, and at least one processor, to execute a process comprising (Bruin, [0019]): acquire information data representing information comprising a plurality of items related to a specific target (Bruin, [0020], [0045]: “neuro-psycho-biological information obtained from the patient before starting the treatment, before treatment planning or before making clinical/medical diagnosis makes a multidimensional observation vector. For example, in experiments conducted during the development of the invention (these experiments being discussed in detail hereinafter), pre-treatment EEG signals and clinical attributes are collected. The EEG data includes the signals collected by many sensors placed on the scalp, which are then pre-processed to obtain some meaningful raw features that might be relevant to predict the treatment efficacy”, [0074]: “these include various features obtained from the EEG, functional magnetic resonance imaging (fMRI), personality traits, economic and social status, previous psychiatric history, sleep patterns, etc.”, and [0139]); vectorize each item included in the information data (Bruin, [0045]: “The neuro-psycho-biological information obtained from the patient before starting the treatment, before treatment planning or before making clinical/medical diagnosis makes a multidimensional observation vector”); input the vectorized items to the first model to acquire an output result related to the mortality prediction task (Bruin, [0016], [0019], [0022]-[0023], [0045], and [0068]); select a selection model to be used for prediction as any one of the first model or the second model from among the plurality of models based on the output result (Bruin, [0083], [0088], and [0107]-[0108]).
Bruin does not teach process the vectorized items with the selection model; derive a degree of importance to the specific target for each item included in the information data by using the selection model.
However, Neuman teaches process the vectorized items with the selection model (Neuman, [0050]); derive a degree of importance to the specific target for each item included in the information data by using the selection model (Neuman, [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Bruin to incorporate the teachings of Neuman and account for degree of importance for a target for the information using a model (Neuman, Abstract and [0050]).
Bruin and Neuman do not teach control a display unit to display items for which the degree of importance exceeds a predetermined value, wherein the prediction task is for deriving a probability of a first state, wherein the complication prediction task is related to a second state different from the first state, wherein the first model is selected as the selection model in a case in which the probability of the first state exceeds a threshold value, wherein the second model is selected as the selection model in a case in which the probability of the first state is equal to or lower than the threshold value.
However, Sirin teaches control a display unit to display items for which the degree of importance exceeds a predetermined value (Sirin, [0030]: “In some embodiments, each individual neural unit may have a summation function that combines the values of all its inputs together. In some embodiments, each connection (or the neural unit itself) may have a threshold function such that the signal must surpass the threshold before it is allowed to propagate to other neural units”, [0072]-[0075], and [0098]), wherein the prediction task is for deriving a probability of a first state (Sirin, [0072]-[0075]), wherein the complication prediction task is related to a second state different from the first state (Sirin, [0026] and [0050]), wherein the first model is selected as the selection model in a case in which the probability of the first state exceeds a threshold value, wherein the second model is selected as the selection model in a case in which the probability of the first state is equal to or lower than the threshold value (Sirin, [0041], [0045], [0049], [0074], and [0082]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Bruin and Neuman to incorporate the teachings of Sirin and account for additional templates may be provided to the neural network as reference feedback for the neural networks prediction of the additional templates to train the neural network (Sirin, Abstract and [0003]).
Bruin, Neuman, and Sirin do not teach the first prediction task is a mortality prediction task, the second prediction task is a long-term hospitalization prediction task or a complication prediction task, and wherein the mortality prediction task is more important to the specific target than the long-term hospitalization prediction task and the complication prediction task.
However, Neeland teaches a long-term hospitalization prediction task or a complication prediction task, and wherein the mortality prediction task is more important to the specific target than the long-term hospitalization prediction task and the complication prediction task (Neeland, pages 185-187: “It has a number of advantages over time-to-event measures including accounting for both number and duration of multiple hospitalizations, more clinical relevance by assigning greater weight to mortality, more patient-centric accounting for time spent outside the hospital with potential for better quality of life, and because it is not affected by issues of adjudication required for cause-specific outcomes”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Bruin, Neuman, and Sirin to incorporate the teachings of Neeland and account for long-term mortality or hospitalization outcomes (Neeland, pages 184).
Regarding claim 4 Bruin teaches the information data is a document data group including a plurality of document data (Bruin, [0042], [0045], [0186], and [0186]).
Bruin does not teach the degree of importance to the specific target is derived for each document data included in the document data group by using the selection model.
However, Neuman teaches the degree of importance to the specific target is derived for each document data included in the document data group by using the selection model (Neuman, [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Bruin to incorporate the teachings of Neuman and account for a degree of importance for a target for the information using a model (Neuman, Abstract and [0050]).
Regarding claim 5 Bruin further teaches the first data is a plurality of first medical information data associated with each of a plurality of patients (Bruin, [0022], [0052], and [0092]), and the specific target is a specific patient, the information data is a plurality of second medical information data associated with the specific patient (Bruin, [0069], [0074], and [0185]), and the item is each of the plurality of second medical information data (Bruin, [0185]-[0186]).
Regarding claim 6 Bruin further teaches each of the first prediction task and the second prediction task is a medical-related task (Bruin, [0185]-[0186]).
Response to Arguments
Applicant's arguments filed 11/28/2025 have been fully considered but they are not persuasive. Regarding the 35 U.S.C. 101 Rejection, Applicant argues the claims do not recite an abstract idea because the steps require machine-executed data transformation and multi-model computational processing that cannot be carried out in the human mind or via pen and paper. Examiner respectfully disagrees. The steps of “uses, acquire, vectorize, input, and derive” are the abstract idea and the multi-model computation processing is part of the additional element, which the abstract idea is being applied to (see MPEP 2106.04(a)(2) states “An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504” and “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”)”).
Applicant argues the claimed invention is integrated to a practical application because the current invention recites specific technical operations that enable the system to adaptively selected the appropriate model based on the target’s current condition. Examiner respectfully disagrees. Selecting an appropriate model based on a person/targets current condition is not a technological improvement, but an improvement to the abstract idea (see MPEP 2106.05(a) states “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107” and “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below”. Therefore, the 35 U.S.C 101 Rejection is maintained.
Regarding the 35 U.S.C. 103 Rejection, Applicant’s arguments with respect to amendments to claims 1, 8, and 9 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant also argues the prior art, Bruin, Neuman, and Sirin, does not teach “select a selection model to be used for prediction…”. Examiner respectfully disagrees. Under broadest reasonable interpretation Bruin teaches “select a selection model to be used for prediction as any one of the first model or the second model from among the plurality of models based on the output result”. Paragraphs [0083] discloses “Machine learning techniques and information processing methods are applied to the medical diagnosis and treatment databases, and all computational parts of the system including pre-processing, estimation, detection, and prediction models are revised and re-trained when the size of the these databases are increased. The improvement and adaptation process can be done automatically with the adaptive and automatic training and update method, but to further enhance performance, a team of expert professionals can administer the process (151), and add (if needed) revised methodology or new machine learning and signal/information processing methods to the system”, [0107] discloses “The classification/prediction/estimation and decision/inference techniques and models employed are derived from the computational signal/information processing and machine learning and inference methods and procedures which include, but are not limited to, parameter and model estimation methods, Bayesian learning and decision techniques, methods based on Bayesian estimation principles, kernel-based learning and classification methods, regularized prediction and regression methods, support vector regression and classification machines, dynamic decision-making methods, hidden Markov modeling methods, rule-based and fuzzy-learning techniques, fuzzy decision and estimation networks, structural, systematic, multi-level and hierarchical learning and inference methods, artificial neural networks, artificial intelligence, methods based on information theory, reinforcement learning, transductive learning and induction methods, and combinations”, and [0108] discloses “In semi-supervised machine learning and inference method, we have some labeled and some unlabeled training data. Because it is easier to collect unlabeled data, that shares the same characteristics and statistical, dynamic and geometric features as the labeled data, the performance of the detection, estimation and prediction methods and models are improved when semi supervised learning and inference methods are used. Various implementations of the technique include graph-based methods, generative models, probabilistic or statistical models, and combinations and extensions thereof”. See also, paragraphs [0138] discloses “The constraints define the numerical properties and complexity of the model, and also define the relationship between training data (measured input data versus outputs or target values). In the data fusion setup, we use a number of models and processors, and some of the optimality criteria that are used to build these models include: minimum number of errors, minimum regression error, minimum classification error, minimum model complexity, minimum of combination of model complexity and modeling error which is also called as minimum regularized error, minimum probability of error, minimum prediction error, minimum false alarm, minimum estimation error, minimum decision cost, minimum stochastic complexity, maximum pattern-separation margins, maximum likelihood, or combinations thereof” and [0186] discloses “The outputs or high-level results that the ‘medical expert system’ reports are numerical values for optimum treatment selection, treatment-efficacy prediction, and optionally, for medical diagnosis. Another less optimal option to perform the data fusion is to run the complete course of processing/analysis for each source of information separately, and then combine these high-level results afterwards”. Therefore, the 35 U.S.C. 103 Rejection is maintained.
Conclusion
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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